[2024-11-25 16:42:30,000 INFO train.py line 128 2586773] => Loading config ... [2024-11-25 16:42:30,000 INFO train.py line 130 2586773] Save path: exp/nuscenes/train_highbay_07 [2024-11-25 16:42:30,199 INFO train.py line 131 2586773] Config: weight = 'model_best.pth' resume = False evaluate = True test_only = False seed = 28024989 save_path = 'exp/nuscenes/train_highbay_07' num_worker = 32 batch_size = 4 batch_size_val = None batch_size_test = None epoch = 50 eval_epoch = 50 sync_bn = False enable_amp = True empty_cache = False find_unused_parameters = False mix_prob = 0.8 param_dicts = [dict(keyword='block', lr=0.004)] hooks = [ dict( type='CheckpointLoader', keywords='module.seg_head.', replacement='module.seg_head_duplicate.'), dict(type='IterationTimer', warmup_iter=2), dict(type='InformationWriter'), dict(type='SemSegEvaluator'), dict(type='CheckpointSaver', save_freq=None), dict(type='PreciseEvaluator', test_last=False) ] train = dict(type='DefaultTrainer') test = dict(type='SemSegTester', verbose=True) model = dict( type='DefaultSegmentorV2', num_classes=2, backbone_out_channels=64, backbone=dict( type='PT-v3m1', in_channels=4, order=['z', 'z-trans', 'hilbert', 'hilbert-trans'], stride=(2, 2, 2, 2), enc_depths=(2, 2, 2, 6, 2), enc_channels=(32, 64, 128, 256, 512), enc_num_head=(2, 4, 8, 16, 32), enc_patch_size=(64, 64, 64, 64, 64), dec_depths=(2, 2, 2, 2), dec_channels=(64, 64, 128, 256), dec_num_head=(4, 4, 8, 16), dec_patch_size=(64, 64, 64, 64), mlp_ratio=4, qkv_bias=True, qk_scale=None, attn_drop=0.0, proj_drop=0.0, drop_path=0.3, shuffle_orders=True, pre_norm=True, enable_rpe=True, enable_flash=False, upcast_attention=True, upcast_softmax=True, cls_mode=False, pdnorm_bn=False, pdnorm_ln=False, pdnorm_decouple=True, pdnorm_adaptive=False, pdnorm_affine=True, pdnorm_conditions=('nuScenes', 'SemanticKITTI', 'Waymo')), criteria=[ dict(type='CrossEntropyLoss', loss_weight=1.0, ignore_index=-1), dict( type='LovaszLoss', mode='multiclass', loss_weight=1.0, ignore_index=-1) ]) optimizer = dict(type='AdamW', lr=0.004, weight_decay=0.005) scheduler = dict( type='OneCycleLR', max_lr=[0.004, 0.0002], pct_start=0.04, anneal_strategy='cos', div_factor=10.0, final_div_factor=100.0) data_root = '' ignore_index = -1 names = ['background', 'lane'] data = dict( num_classes=2, ignore_index=-1, names=['background', 'lane'], train=dict( type='SemanticKITTIDataset', split='train', data_root='', transform=[ dict( type='RandomRotate', angle=[-1, 1], axis='z', center=[0, 0, 0], p=0.5), dict(type='RandomScale', scale=[0.9, 1.1]), dict(type='RandomFlip', p=0.5), dict(type='RandomJitter', sigma=0.005, clip=0.02), dict( type='GridSample', grid_size=0.05, hash_type='fnv', mode='train', keys=('coord', 'strength', 'segment'), return_grid_coord=True), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment'), feat_keys=('coord', 'strength')) ], test_mode=False, ignore_index=-1, loop=1), val=dict( type='SemanticKITTIDataset', split='val', data_root='', transform=[ dict( type='GridSample', grid_size=0.05, hash_type='fnv', mode='train', keys=('coord', 'strength', 'segment'), return_grid_coord=True), dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'segment'), feat_keys=('coord', 'strength')) ], test_mode=False, ignore_index=-1), test=dict( type='SemanticKITTIDataset', split='test', data_root='', transform=[ dict(type='Copy', keys_dict=dict(segment='origin_segment')), dict( type='GridSample', grid_size=0.05, hash_type='fnv', mode='train', keys=('coord', 'strength', 'segment'), return_inverse=True) ], test_mode=True, test_cfg=dict( voxelize=dict( type='GridSample', grid_size=0.05, hash_type='fnv', mode='test', return_grid_coord=True, keys=('coord', 'strength')), crop=None, post_transform=[ dict(type='ToTensor'), dict( type='Collect', keys=('coord', 'grid_coord', 'index'), feat_keys=('coord', 'strength')) ], aug_transform=[[{ 'type': 'RandomRotateTargetAngle', 'angle': [0], 'axis': 'z', 'center': [0, 0, 0], 'p': 1 }]]), ignore_index=-1)) num_worker_per_gpu = 8 batch_size_per_gpu = 1 batch_size_val_per_gpu = 1 batch_size_test_per_gpu = 1 [2024-11-25 16:42:30,200 INFO train.py line 132 2586773] => Building model ... [2024-11-25 16:42:30,555 INFO train.py line 209 2586773] Num params: 46177302 [2024-11-25 16:42:30,662 INFO train.py line 134 2586773] => Building writer ... [2024-11-25 16:42:30,663 INFO train.py line 219 2586773] Tensorboard writer logging dir: exp/nuscenes/train_highbay_07 [2024-11-25 16:42:30,663 INFO train.py line 136 2586773] => Building train dataset & dataloader ... [2024-11-25 16:42:30,666 INFO defaults.py line 60 2586773] Totally 1503 x 1 samples in train set. [2024-11-25 16:42:30,667 INFO train.py line 138 2586773] => Building val dataset & dataloader ... [2024-11-25 16:42:30,668 INFO defaults.py line 60 2586773] Totally 527 x 1 samples in val set. [2024-11-25 16:42:30,668 INFO train.py line 140 2586773] => Building optimize, scheduler, scaler(amp) ... [2024-11-25 16:42:30,670 INFO optimizer.py line 54 2586773] Params Group 1 - lr: 0.004; Params: ['module.seg_head.weight', 'module.seg_head.bias', 'module.backbone.embedding.stem.conv.weight', 'module.backbone.embedding.stem.norm.weight', 'module.backbone.embedding.stem.norm.bias', 'module.backbone.enc.enc1.down.proj.weight', 'module.backbone.enc.enc1.down.proj.bias', 'module.backbone.enc.enc1.down.norm.0.weight', 'module.backbone.enc.enc1.down.norm.0.bias', 'module.backbone.enc.enc2.down.proj.weight', 'module.backbone.enc.enc2.down.proj.bias', 'module.backbone.enc.enc2.down.norm.0.weight', 'module.backbone.enc.enc2.down.norm.0.bias', 'module.backbone.enc.enc3.down.proj.weight', 'module.backbone.enc.enc3.down.proj.bias', 'module.backbone.enc.enc3.down.norm.0.weight', 'module.backbone.enc.enc3.down.norm.0.bias', 'module.backbone.enc.enc4.down.proj.weight', 'module.backbone.enc.enc4.down.proj.bias', 'module.backbone.enc.enc4.down.norm.0.weight', 'module.backbone.enc.enc4.down.norm.0.bias', 'module.backbone.dec.dec3.up.proj.0.weight', 'module.backbone.dec.dec3.up.proj.0.bias', 'module.backbone.dec.dec3.up.proj.1.weight', 'module.backbone.dec.dec3.up.proj.1.bias', 'module.backbone.dec.dec3.up.proj_skip.0.weight', 'module.backbone.dec.dec3.up.proj_skip.0.bias', 'module.backbone.dec.dec3.up.proj_skip.1.weight', 'module.backbone.dec.dec3.up.proj_skip.1.bias', 'module.backbone.dec.dec2.up.proj.0.weight', 'module.backbone.dec.dec2.up.proj.0.bias', 'module.backbone.dec.dec2.up.proj.1.weight', 'module.backbone.dec.dec2.up.proj.1.bias', 'module.backbone.dec.dec2.up.proj_skip.0.weight', 'module.backbone.dec.dec2.up.proj_skip.0.bias', 'module.backbone.dec.dec2.up.proj_skip.1.weight', 'module.backbone.dec.dec2.up.proj_skip.1.bias', 'module.backbone.dec.dec1.up.proj.0.weight', 'module.backbone.dec.dec1.up.proj.0.bias', 'module.backbone.dec.dec1.up.proj.1.weight', 'module.backbone.dec.dec1.up.proj.1.bias', 'module.backbone.dec.dec1.up.proj_skip.0.weight', 'module.backbone.dec.dec1.up.proj_skip.0.bias', 'module.backbone.dec.dec1.up.proj_skip.1.weight', 'module.backbone.dec.dec1.up.proj_skip.1.bias', 'module.backbone.dec.dec0.up.proj.0.weight', 'module.backbone.dec.dec0.up.proj.0.bias', 'module.backbone.dec.dec0.up.proj.1.weight', 'module.backbone.dec.dec0.up.proj.1.bias', 'module.backbone.dec.dec0.up.proj_skip.0.weight', 'module.backbone.dec.dec0.up.proj_skip.0.bias', 'module.backbone.dec.dec0.up.proj_skip.1.weight', 'module.backbone.dec.dec0.up.proj_skip.1.bias']. [2024-11-25 16:42:30,671 INFO optimizer.py line 54 2586773] Params Group 2 - lr: 0.004; Params: ['module.backbone.enc.enc0.block0.cpe.0.weight', 'module.backbone.enc.enc0.block0.cpe.0.bias', 'module.backbone.enc.enc0.block0.cpe.1.weight', 'module.backbone.enc.enc0.block0.cpe.1.bias', 'module.backbone.enc.enc0.block0.cpe.2.weight', 'module.backbone.enc.enc0.block0.cpe.2.bias', 'module.backbone.enc.enc0.block0.norm1.0.weight', 'module.backbone.enc.enc0.block0.norm1.0.bias', 'module.backbone.enc.enc0.block0.attn.qkv.weight', 'module.backbone.enc.enc0.block0.attn.qkv.bias', 'module.backbone.enc.enc0.block0.attn.proj.weight', 'module.backbone.enc.enc0.block0.attn.proj.bias', 'module.backbone.enc.enc0.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc0.block0.norm2.0.weight', 'module.backbone.enc.enc0.block0.norm2.0.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc1.weight', 'module.backbone.enc.enc0.block0.mlp.0.fc1.bias', 'module.backbone.enc.enc0.block0.mlp.0.fc2.weight', 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[2024-11-25 16:42:30,671 INFO train.py line 144 2586773] => Building hooks ... [2024-11-25 16:42:30,672 INFO misc.py line 215 2586773] => Loading checkpoint & weight ... [2024-11-25 16:42:30,672 INFO misc.py line 217 2586773] Loading weight at: model_best.pth [2024-11-25 16:42:31,228 INFO misc.py line 222 2586773] Loading layer weights with keyword: module.seg_head., replace keyword with: module.seg_head_duplicate. [2024-11-25 16:42:31,271 INFO misc.py line 240 2586773] Missing keys: ['module.seg_head.weight', 'module.seg_head.bias', 'module.backbone.enc.enc0.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc0.block1.attn.rpe.rpe_table', 'module.backbone.enc.enc1.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc1.block1.attn.rpe.rpe_table', 'module.backbone.enc.enc2.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc2.block1.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block1.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block2.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block3.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block4.attn.rpe.rpe_table', 'module.backbone.enc.enc3.block5.attn.rpe.rpe_table', 'module.backbone.enc.enc4.block0.attn.rpe.rpe_table', 'module.backbone.enc.enc4.block1.attn.rpe.rpe_table', 'module.backbone.dec.dec3.block0.attn.rpe.rpe_table', 'module.backbone.dec.dec3.block1.attn.rpe.rpe_table', 'module.backbone.dec.dec2.block0.attn.rpe.rpe_table', 'module.backbone.dec.dec2.block1.attn.rpe.rpe_table', 'module.backbone.dec.dec1.block0.attn.rpe.rpe_table', 'module.backbone.dec.dec1.block1.attn.rpe.rpe_table', 'module.backbone.dec.dec0.block0.attn.rpe.rpe_table', 'module.backbone.dec.dec0.block1.attn.rpe.rpe_table'] [2024-11-25 16:42:31,274 INFO train.py line 151 2586773] >>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>> [2024-11-25 16:42:44,081 INFO misc.py line 119 2586773] Train: [1/50][1/376] Data 9.333 (9.333) Batch 11.833 (11.833) Remain 61:47:24 loss: 0.8496 Lr: 0.00040 [2024-11-25 16:42:44,565 INFO misc.py line 119 2586773] Train: [1/50][2/376] Data 0.002 (0.002) Batch 0.483 (0.483) Remain 02:31:27 loss: 0.8123 Lr: 0.00040 [2024-11-25 16:42:45,121 INFO misc.py line 119 2586773] Train: [1/50][3/376] Data 0.002 (0.002) Batch 0.555 (0.555) Remain 02:54:00 loss: 0.8393 Lr: 0.00040 [2024-11-25 16:42:45,629 INFO misc.py line 119 2586773] Train: [1/50][4/376] Data 0.003 (0.003) Batch 0.507 (0.507) Remain 02:38:57 loss: 0.7708 Lr: 0.00040 [2024-11-25 16:42:46,111 INFO misc.py line 119 2586773] Train: [1/50][5/376] Data 0.003 (0.003) Batch 0.482 (0.495) Remain 02:35:00 loss: 0.7397 Lr: 0.00040 [2024-11-25 16:42:46,626 INFO misc.py line 119 2586773] Train: [1/50][6/376] Data 0.003 (0.003) Batch 0.515 (0.502) Remain 02:37:06 loss: 0.7494 Lr: 0.00040 [2024-11-25 16:42:47,181 INFO misc.py line 119 2586773] Train: [1/50][7/376] Data 0.003 (0.003) Batch 0.556 (0.515) Remain 02:41:20 loss: 0.7097 Lr: 0.00040 [2024-11-25 16:42:47,734 INFO misc.py line 119 2586773] Train: [1/50][8/376] Data 0.002 (0.003) Batch 0.552 (0.523) Remain 02:43:39 loss: 0.6994 Lr: 0.00040 [2024-11-25 16:42:48,272 INFO misc.py line 119 2586773] Train: [1/50][9/376] Data 0.002 (0.003) Batch 0.538 (0.525) Remain 02:44:26 loss: 0.6742 Lr: 0.00040 [2024-11-25 16:42:48,779 INFO misc.py line 119 2586773] Train: [1/50][10/376] Data 0.002 (0.003) Batch 0.507 (0.522) Remain 02:43:37 loss: 0.6734 Lr: 0.00040 [2024-11-25 16:42:49,306 INFO misc.py line 119 2586773] Train: [1/50][11/376] Data 0.002 (0.003) Batch 0.528 (0.523) Remain 02:43:49 loss: 0.6286 Lr: 0.00040 [2024-11-25 16:42:49,842 INFO misc.py line 119 2586773] Train: [1/50][12/376] Data 0.002 (0.003) Batch 0.536 (0.525) Remain 02:44:15 loss: 0.6159 Lr: 0.00040 [2024-11-25 16:42:50,317 INFO misc.py line 119 2586773] Train: [1/50][13/376] Data 0.002 (0.002) Batch 0.475 (0.520) Remain 02:42:42 loss: 0.6021 Lr: 0.00040 [2024-11-25 16:42:50,829 INFO misc.py line 119 2586773] Train: [1/50][14/376] Data 0.002 (0.002) Batch 0.512 (0.519) Remain 02:42:28 loss: 0.5973 Lr: 0.00040 [2024-11-25 16:42:51,359 INFO misc.py line 119 2586773] Train: [1/50][15/376] Data 0.002 (0.002) Batch 0.530 (0.520) Remain 02:42:45 loss: 0.6065 Lr: 0.00040 [2024-11-25 16:42:51,849 INFO misc.py line 119 2586773] Train: [1/50][16/376] Data 0.002 (0.002) Batch 0.490 (0.518) Remain 02:42:01 loss: 0.5827 Lr: 0.00040 [2024-11-25 16:42:52,397 INFO misc.py line 119 2586773] Train: [1/50][17/376] Data 0.002 (0.002) Batch 0.548 (0.520) Remain 02:42:42 loss: 0.5809 Lr: 0.00040 [2024-11-25 16:42:52,886 INFO misc.py line 119 2586773] Train: [1/50][18/376] Data 0.002 (0.002) Batch 0.489 (0.518) Remain 02:42:02 loss: 0.5667 Lr: 0.00040 [2024-11-25 16:42:53,377 INFO misc.py line 119 2586773] Train: [1/50][19/376] Data 0.003 (0.002) Batch 0.492 (0.516) Remain 02:41:31 loss: 0.5704 Lr: 0.00041 [2024-11-25 16:42:53,889 INFO misc.py line 119 2586773] Train: [1/50][20/376] Data 0.002 (0.002) Batch 0.512 (0.516) Remain 02:41:26 loss: 0.5392 Lr: 0.00041 [2024-11-25 16:42:54,408 INFO misc.py line 119 2586773] Train: [1/50][21/376] Data 0.002 (0.002) Batch 0.517 (0.516) Remain 02:41:27 loss: 0.5774 Lr: 0.00041 [2024-11-25 16:42:54,919 INFO misc.py line 119 2586773] Train: [1/50][22/376] Data 0.006 (0.003) Batch 0.512 (0.516) Remain 02:41:22 loss: 0.5580 Lr: 0.00041 [2024-11-25 16:42:55,400 INFO misc.py line 119 2586773] Train: [1/50][23/376] Data 0.004 (0.003) Batch 0.481 (0.514) Remain 02:40:49 loss: 0.5530 Lr: 0.00041 [2024-11-25 16:42:55,938 INFO misc.py line 119 2586773] Train: [1/50][24/376] Data 0.003 (0.003) Batch 0.538 (0.515) Remain 02:41:11 loss: 0.5624 Lr: 0.00041 [2024-11-25 16:42:56,459 INFO misc.py line 119 2586773] Train: [1/50][25/376] Data 0.003 (0.003) Batch 0.521 (0.515) Remain 02:41:15 loss: 0.5417 Lr: 0.00041 [2024-11-25 16:42:56,963 INFO misc.py line 119 2586773] Train: [1/50][26/376] Data 0.003 (0.003) Batch 0.504 (0.515) Remain 02:41:05 loss: 0.5398 Lr: 0.00041 [2024-11-25 16:42:57,533 INFO misc.py line 119 2586773] Train: [1/50][27/376] Data 0.003 (0.003) Batch 0.570 (0.517) Remain 02:41:48 loss: 0.5416 Lr: 0.00041 [2024-11-25 16:42:58,022 INFO misc.py line 119 2586773] Train: [1/50][28/376] Data 0.003 (0.003) Batch 0.489 (0.516) Remain 02:41:26 loss: 0.5407 Lr: 0.00041 [2024-11-25 16:42:58,548 INFO misc.py line 119 2586773] Train: [1/50][29/376] Data 0.004 (0.003) Batch 0.525 (0.516) Remain 02:41:32 loss: 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Train: [1/50][193/376] Data 0.003 (0.003) Batch 0.513 (0.511) Remain 02:38:27 loss: 0.5049 Lr: 0.00095 [2024-11-25 16:44:22,709 INFO misc.py line 119 2586773] Train: [1/50][194/376] Data 0.003 (0.003) Batch 0.500 (0.511) Remain 02:38:26 loss: 0.4737 Lr: 0.00096 [2024-11-25 16:44:23,208 INFO misc.py line 119 2586773] Train: [1/50][195/376] Data 0.003 (0.003) Batch 0.499 (0.511) Remain 02:38:24 loss: 0.4806 Lr: 0.00096 [2024-11-25 16:44:23,731 INFO misc.py line 119 2586773] Train: [1/50][196/376] Data 0.003 (0.003) Batch 0.522 (0.511) Remain 02:38:25 loss: 0.4719 Lr: 0.00097 [2024-11-25 16:44:24,256 INFO misc.py line 119 2586773] Train: [1/50][197/376] Data 0.002 (0.003) Batch 0.525 (0.511) Remain 02:38:26 loss: 0.4721 Lr: 0.00097 [2024-11-25 16:44:24,789 INFO misc.py line 119 2586773] Train: [1/50][198/376] Data 0.002 (0.003) Batch 0.533 (0.511) Remain 02:38:27 loss: 0.5064 Lr: 0.00098 [2024-11-25 16:44:25,327 INFO misc.py line 119 2586773] Train: [1/50][199/376] Data 0.002 (0.003) 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Train: [1/50][262/376] Data 0.002 (0.003) Batch 0.550 (0.512) Remain 02:38:02 loss: 0.3963 Lr: 0.00136 [2024-11-25 16:44:58,132 INFO misc.py line 119 2586773] Train: [1/50][263/376] Data 0.002 (0.003) Batch 0.526 (0.512) Remain 02:38:03 loss: 0.4045 Lr: 0.00137 [2024-11-25 16:44:58,598 INFO misc.py line 119 2586773] Train: [1/50][264/376] Data 0.002 (0.003) Batch 0.467 (0.511) Remain 02:37:59 loss: 0.4572 Lr: 0.00138 [2024-11-25 16:44:59,105 INFO misc.py line 119 2586773] Train: [1/50][265/376] Data 0.003 (0.003) Batch 0.507 (0.511) Remain 02:37:58 loss: 0.3709 Lr: 0.00138 [2024-11-25 16:44:59,624 INFO misc.py line 119 2586773] Train: [1/50][266/376] Data 0.003 (0.003) Batch 0.518 (0.511) Remain 02:37:58 loss: 0.4794 Lr: 0.00139 [2024-11-25 16:45:00,134 INFO misc.py line 119 2586773] Train: [1/50][267/376] Data 0.002 (0.003) Batch 0.511 (0.511) Remain 02:37:57 loss: 0.4318 Lr: 0.00140 [2024-11-25 16:45:00,646 INFO misc.py line 119 2586773] Train: [1/50][268/376] Data 0.002 (0.003) 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line 119 2586773] Train: [1/50][306/376] Data 0.003 (0.003) Batch 0.501 (0.511) Remain 02:37:27 loss: 0.3932 Lr: 0.00167 [2024-11-25 16:45:20,414 INFO misc.py line 119 2586773] Train: [1/50][307/376] Data 0.003 (0.003) Batch 0.509 (0.511) Remain 02:37:26 loss: 0.3837 Lr: 0.00168 [2024-11-25 16:45:20,888 INFO misc.py line 119 2586773] Train: [1/50][308/376] Data 0.002 (0.003) Batch 0.473 (0.511) Remain 02:37:24 loss: 0.4014 Lr: 0.00168 [2024-11-25 16:45:21,393 INFO misc.py line 119 2586773] Train: [1/50][309/376] Data 0.003 (0.003) Batch 0.506 (0.511) Remain 02:37:23 loss: 0.3853 Lr: 0.00169 [2024-11-25 16:45:21,868 INFO misc.py line 119 2586773] Train: [1/50][310/376] Data 0.002 (0.003) Batch 0.474 (0.511) Remain 02:37:20 loss: 0.3849 Lr: 0.00170 [2024-11-25 16:45:22,383 INFO misc.py line 119 2586773] Train: [1/50][311/376] Data 0.003 (0.003) Batch 0.515 (0.511) Remain 02:37:20 loss: 0.4587 Lr: 0.00171 [2024-11-25 16:45:22,861 INFO misc.py line 119 2586773] Train: [1/50][312/376] Data 0.003 (0.003) Batch 0.479 (0.510) Remain 02:37:17 loss: 0.4173 Lr: 0.00171 [2024-11-25 16:45:23,337 INFO misc.py line 119 2586773] Train: [1/50][313/376] Data 0.003 (0.003) Batch 0.476 (0.510) Remain 02:37:15 loss: 0.3974 Lr: 0.00172 [2024-11-25 16:45:23,845 INFO misc.py line 119 2586773] Train: [1/50][314/376] Data 0.002 (0.003) Batch 0.508 (0.510) Remain 02:37:14 loss: 0.3811 Lr: 0.00173 [2024-11-25 16:45:24,464 INFO misc.py line 119 2586773] Train: [1/50][315/376] Data 0.003 (0.003) Batch 0.620 (0.511) Remain 02:37:20 loss: 0.4300 Lr: 0.00173 [2024-11-25 16:45:25,013 INFO misc.py line 119 2586773] Train: [1/50][316/376] Data 0.002 (0.003) Batch 0.548 (0.511) Remain 02:37:22 loss: 0.3914 Lr: 0.00174 [2024-11-25 16:45:25,552 INFO misc.py line 119 2586773] Train: [1/50][317/376] Data 0.003 (0.003) Batch 0.540 (0.511) Remain 02:37:23 loss: 0.3977 Lr: 0.00175 [2024-11-25 16:45:26,022 INFO misc.py line 119 2586773] Train: [1/50][318/376] Data 0.002 (0.003) Batch 0.470 (0.511) Remain 02:37:20 loss: 0.4029 Lr: 0.00176 [2024-11-25 16:45:26,521 INFO misc.py line 119 2586773] Train: [1/50][319/376] Data 0.003 (0.003) Batch 0.499 (0.511) Remain 02:37:19 loss: 0.4006 Lr: 0.00176 [2024-11-25 16:45:27,016 INFO misc.py line 119 2586773] Train: [1/50][320/376] Data 0.002 (0.003) Batch 0.495 (0.511) Remain 02:37:17 loss: 0.4120 Lr: 0.00177 [2024-11-25 16:45:27,545 INFO misc.py line 119 2586773] Train: [1/50][321/376] Data 0.002 (0.003) Batch 0.529 (0.511) Remain 02:37:18 loss: 0.4159 Lr: 0.00178 [2024-11-25 16:45:28,091 INFO misc.py line 119 2586773] Train: [1/50][322/376] Data 0.003 (0.003) Batch 0.546 (0.511) Remain 02:37:19 loss: 0.3646 Lr: 0.00179 [2024-11-25 16:45:28,613 INFO misc.py line 119 2586773] Train: [1/50][323/376] Data 0.003 (0.003) Batch 0.522 (0.511) Remain 02:37:20 loss: 0.3478 Lr: 0.00179 [2024-11-25 16:45:29,144 INFO misc.py line 119 2586773] Train: [1/50][324/376] Data 0.002 (0.003) Batch 0.532 (0.511) Remain 02:37:20 loss: 0.4126 Lr: 0.00180 [2024-11-25 16:45:29,687 INFO misc.py line 119 2586773] Train: [1/50][325/376] Data 0.003 (0.003) Batch 0.543 (0.511) Remain 02:37:22 loss: 0.3482 Lr: 0.00181 [2024-11-25 16:45:30,222 INFO misc.py line 119 2586773] Train: [1/50][326/376] Data 0.003 (0.003) Batch 0.534 (0.511) Remain 02:37:22 loss: 0.4139 Lr: 0.00182 [2024-11-25 16:45:30,737 INFO misc.py line 119 2586773] Train: [1/50][327/376] Data 0.003 (0.003) Batch 0.516 (0.511) Remain 02:37:22 loss: 0.3711 Lr: 0.00182 [2024-11-25 16:45:31,269 INFO misc.py line 119 2586773] Train: [1/50][328/376] Data 0.003 (0.003) Batch 0.531 (0.511) Remain 02:37:23 loss: 0.4273 Lr: 0.00183 [2024-11-25 16:45:31,793 INFO misc.py line 119 2586773] Train: [1/50][329/376] Data 0.003 (0.003) Batch 0.524 (0.511) Remain 02:37:23 loss: 0.4090 Lr: 0.00184 [2024-11-25 16:45:32,311 INFO misc.py line 119 2586773] Train: [1/50][330/376] Data 0.003 (0.003) Batch 0.518 (0.511) Remain 02:37:23 loss: 0.4024 Lr: 0.00184 [2024-11-25 16:45:32,837 INFO misc.py line 119 2586773] Train: [1/50][331/376] Data 0.003 (0.003) Batch 0.525 (0.511) Remain 02:37:23 loss: 0.3939 Lr: 0.00185 [2024-11-25 16:45:33,332 INFO misc.py line 119 2586773] Train: [1/50][332/376] Data 0.002 (0.003) Batch 0.495 (0.511) Remain 02:37:22 loss: 0.3822 Lr: 0.00186 [2024-11-25 16:45:33,846 INFO misc.py line 119 2586773] Train: [1/50][333/376] Data 0.003 (0.003) Batch 0.514 (0.511) Remain 02:37:21 loss: 0.4089 Lr: 0.00187 [2024-11-25 16:45:34,321 INFO misc.py line 119 2586773] Train: [1/50][334/376] Data 0.002 (0.003) Batch 0.475 (0.511) Remain 02:37:19 loss: 0.3691 Lr: 0.00187 [2024-11-25 16:45:34,817 INFO misc.py line 119 2586773] Train: [1/50][335/376] Data 0.002 (0.003) Batch 0.496 (0.511) Remain 02:37:18 loss: 0.4158 Lr: 0.00188 [2024-11-25 16:45:35,339 INFO misc.py line 119 2586773] Train: [1/50][336/376] Data 0.003 (0.003) Batch 0.521 (0.511) Remain 02:37:18 loss: 0.4013 Lr: 0.00189 [2024-11-25 16:45:35,869 INFO misc.py line 119 2586773] Train: [1/50][337/376] Data 0.002 (0.003) Batch 0.531 (0.511) Remain 02:37:18 loss: 0.3493 Lr: 0.00190 [2024-11-25 16:45:36,386 INFO misc.py line 119 2586773] Train: [1/50][338/376] Data 0.003 (0.003) Batch 0.516 (0.511) Remain 02:37:18 loss: 0.4109 Lr: 0.00190 [2024-11-25 16:45:36,868 INFO misc.py line 119 2586773] Train: [1/50][339/376] Data 0.002 (0.003) Batch 0.482 (0.511) Remain 02:37:16 loss: 0.3923 Lr: 0.00191 [2024-11-25 16:45:37,415 INFO misc.py line 119 2586773] Train: [1/50][340/376] Data 0.002 (0.003) Batch 0.547 (0.511) Remain 02:37:17 loss: 0.3683 Lr: 0.00192 [2024-11-25 16:45:37,938 INFO misc.py line 119 2586773] Train: [1/50][341/376] Data 0.002 (0.003) Batch 0.523 (0.511) Remain 02:37:17 loss: 0.3630 Lr: 0.00193 [2024-11-25 16:45:38,435 INFO misc.py line 119 2586773] Train: [1/50][342/376] Data 0.002 (0.003) Batch 0.497 (0.511) Remain 02:37:16 loss: 0.3617 Lr: 0.00193 [2024-11-25 16:45:38,899 INFO misc.py line 119 2586773] Train: [1/50][343/376] Data 0.002 (0.003) Batch 0.464 (0.511) Remain 02:37:13 loss: 0.4209 Lr: 0.00194 [2024-11-25 16:45:39,575 INFO misc.py line 119 2586773] Train: [1/50][344/376] Data 0.002 (0.003) Batch 0.676 (0.512) Remain 02:37:21 loss: 0.3624 Lr: 0.00195 [2024-11-25 16:45:40,082 INFO misc.py line 119 2586773] Train: [1/50][345/376] Data 0.003 (0.003) Batch 0.507 (0.512) Remain 02:37:21 loss: 0.3824 Lr: 0.00196 [2024-11-25 16:45:40,598 INFO misc.py line 119 2586773] Train: [1/50][346/376] Data 0.003 (0.003) Batch 0.516 (0.512) Remain 02:37:20 loss: 0.3836 Lr: 0.00196 [2024-11-25 16:45:41,077 INFO misc.py line 119 2586773] Train: [1/50][347/376] Data 0.002 (0.003) Batch 0.479 (0.511) Remain 02:37:18 loss: 0.4054 Lr: 0.00197 [2024-11-25 16:45:41,585 INFO misc.py line 119 2586773] Train: [1/50][348/376] Data 0.003 (0.003) Batch 0.508 (0.511) Remain 02:37:17 loss: 0.3956 Lr: 0.00198 [2024-11-25 16:45:42,085 INFO misc.py line 119 2586773] Train: [1/50][349/376] Data 0.002 (0.003) Batch 0.499 (0.511) Remain 02:37:16 loss: 0.4001 Lr: 0.00199 [2024-11-25 16:45:42,614 INFO misc.py line 119 2586773] Train: [1/50][350/376] Data 0.002 (0.003) Batch 0.529 (0.512) Remain 02:37:17 loss: 0.3843 Lr: 0.00199 [2024-11-25 16:45:43,131 INFO misc.py line 119 2586773] Train: [1/50][351/376] Data 0.002 (0.003) Batch 0.517 (0.512) Remain 02:37:17 loss: 0.4017 Lr: 0.00200 [2024-11-25 16:45:43,625 INFO misc.py line 119 2586773] Train: [1/50][352/376] Data 0.003 (0.003) Batch 0.494 (0.511) Remain 02:37:15 loss: 0.3783 Lr: 0.00201 [2024-11-25 16:45:44,111 INFO misc.py line 119 2586773] Train: [1/50][353/376] Data 0.002 (0.003) Batch 0.486 (0.511) Remain 02:37:13 loss: 0.4264 Lr: 0.00202 [2024-11-25 16:45:44,598 INFO misc.py line 119 2586773] Train: [1/50][354/376] Data 0.002 (0.003) Batch 0.487 (0.511) Remain 02:37:11 loss: 0.3882 Lr: 0.00202 [2024-11-25 16:45:45,121 INFO misc.py line 119 2586773] Train: [1/50][355/376] Data 0.002 (0.003) Batch 0.523 (0.511) Remain 02:37:12 loss: 0.3637 Lr: 0.00203 [2024-11-25 16:45:45,619 INFO misc.py line 119 2586773] Train: [1/50][356/376] Data 0.002 (0.003) Batch 0.498 (0.511) Remain 02:37:10 loss: 0.4379 Lr: 0.00204 [2024-11-25 16:45:46,112 INFO misc.py line 119 2586773] Train: [1/50][357/376] Data 0.002 (0.003) Batch 0.494 (0.511) Remain 02:37:09 loss: 0.3847 Lr: 0.00205 [2024-11-25 16:45:46,631 INFO misc.py line 119 2586773] Train: [1/50][358/376] Data 0.002 (0.003) Batch 0.518 (0.511) Remain 02:37:09 loss: 0.4145 Lr: 0.00205 [2024-11-25 16:45:47,116 INFO misc.py line 119 2586773] Train: [1/50][359/376] Data 0.002 (0.003) Batch 0.486 (0.511) Remain 02:37:07 loss: 0.4072 Lr: 0.00206 [2024-11-25 16:45:47,682 INFO misc.py line 119 2586773] Train: [1/50][360/376] Data 0.002 (0.003) Batch 0.566 (0.511) Remain 02:37:09 loss: 0.4124 Lr: 0.00207 [2024-11-25 16:45:48,167 INFO misc.py line 119 2586773] Train: [1/50][361/376] Data 0.003 (0.003) Batch 0.485 (0.511) Remain 02:37:07 loss: 0.3927 Lr: 0.00208 [2024-11-25 16:45:48,652 INFO misc.py line 119 2586773] Train: [1/50][362/376] Data 0.002 (0.003) Batch 0.485 (0.511) Remain 02:37:06 loss: 0.3854 Lr: 0.00208 [2024-11-25 16:45:49,138 INFO misc.py line 119 2586773] Train: [1/50][363/376] Data 0.002 (0.003) Batch 0.485 (0.511) Remain 02:37:04 loss: 0.3644 Lr: 0.00209 [2024-11-25 16:45:49,660 INFO misc.py line 119 2586773] Train: [1/50][364/376] Data 0.002 (0.003) Batch 0.522 (0.511) Remain 02:37:04 loss: 0.3721 Lr: 0.00210 [2024-11-25 16:45:50,148 INFO misc.py line 119 2586773] Train: [1/50][365/376] Data 0.003 (0.003) Batch 0.488 (0.511) Remain 02:37:02 loss: 0.3939 Lr: 0.00211 [2024-11-25 16:45:50,626 INFO misc.py line 119 2586773] Train: [1/50][366/376] Data 0.002 (0.003) Batch 0.478 (0.511) Remain 02:37:00 loss: 0.3836 Lr: 0.00211 [2024-11-25 16:45:51,142 INFO misc.py line 119 2586773] Train: [1/50][367/376] Data 0.002 (0.003) Batch 0.516 (0.511) Remain 02:37:00 loss: 0.3552 Lr: 0.00212 [2024-11-25 16:45:51,615 INFO misc.py line 119 2586773] Train: [1/50][368/376] Data 0.002 (0.003) Batch 0.473 (0.511) Remain 02:36:57 loss: 0.3541 Lr: 0.00213 [2024-11-25 16:45:52,114 INFO misc.py line 119 2586773] Train: [1/50][369/376] Data 0.002 (0.003) Batch 0.499 (0.511) Remain 02:36:56 loss: 0.3584 Lr: 0.00214 [2024-11-25 16:45:52,608 INFO misc.py line 119 2586773] Train: [1/50][370/376] Data 0.002 (0.003) Batch 0.494 (0.511) Remain 02:36:55 loss: 0.3339 Lr: 0.00214 [2024-11-25 16:45:53,109 INFO misc.py line 119 2586773] Train: [1/50][371/376] Data 0.002 (0.003) Batch 0.502 (0.511) Remain 02:36:54 loss: 0.3580 Lr: 0.00215 [2024-11-25 16:45:53,634 INFO misc.py line 119 2586773] Train: [1/50][372/376] Data 0.002 (0.003) Batch 0.525 (0.511) Remain 02:36:54 loss: 0.3581 Lr: 0.00216 [2024-11-25 16:45:54,154 INFO misc.py line 119 2586773] Train: [1/50][373/376] Data 0.002 (0.003) Batch 0.520 (0.511) Remain 02:36:54 loss: 0.4216 Lr: 0.00217 [2024-11-25 16:45:54,633 INFO misc.py line 119 2586773] Train: [1/50][374/376] Data 0.002 (0.003) Batch 0.479 (0.511) Remain 02:36:52 loss: 0.3806 Lr: 0.00217 [2024-11-25 16:45:55,164 INFO misc.py line 119 2586773] Train: [1/50][375/376] Data 0.002 (0.003) Batch 0.531 (0.511) Remain 02:36:52 loss: 0.3648 Lr: 0.00218 [2024-11-25 16:45:55,618 INFO misc.py line 119 2586773] Train: [1/50][376/376] Data 0.002 (0.003) Batch 0.454 (0.511) Remain 02:36:49 loss: 0.4178 Lr: 0.00219 [2024-11-25 16:45:55,618 INFO misc.py line 136 2586773] Train result: loss: 0.4819 [2024-11-25 16:45:55,619 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 16:46:06,928 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.3728 [2024-11-25 16:46:07,183 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.3635 [2024-11-25 16:46:07,444 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3602 [2024-11-25 16:46:07,667 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.3638 [2024-11-25 16:46:07,926 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.4138 [2024-11-25 16:46:08,190 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.4040 [2024-11-25 16:46:08,411 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.3758 [2024-11-25 16:46:08,680 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.3896 [2024-11-25 16:46:08,903 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.3793 [2024-11-25 16:46:09,167 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.4300 [2024-11-25 16:46:09,397 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.4101 [2024-11-25 16:46:09,668 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.3707 [2024-11-25 16:46:09,933 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3730 [2024-11-25 16:46:10,194 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.4088 [2024-11-25 16:46:10,429 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.3596 [2024-11-25 16:46:10,667 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3920 [2024-11-25 16:46:10,935 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3965 [2024-11-25 16:46:11,180 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.3769 [2024-11-25 16:46:11,417 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.3725 [2024-11-25 16:46:11,677 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3832 [2024-11-25 16:46:11,910 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.3888 [2024-11-25 16:46:12,175 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.4062 [2024-11-25 16:46:12,410 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.3779 [2024-11-25 16:46:12,675 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.4136 [2024-11-25 16:46:12,935 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.4378 [2024-11-25 16:46:13,172 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3974 [2024-11-25 16:46:13,424 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.4372 [2024-11-25 16:46:13,669 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3921 [2024-11-25 16:46:13,933 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.4329 [2024-11-25 16:46:14,185 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.4179 [2024-11-25 16:46:14,418 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.4013 [2024-11-25 16:46:14,682 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.3994 [2024-11-25 16:46:14,900 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.3637 [2024-11-25 16:46:15,138 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.3621 [2024-11-25 16:46:15,399 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.3684 [2024-11-25 16:46:15,644 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.3799 [2024-11-25 16:46:15,872 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.3503 [2024-11-25 16:46:16,142 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.3841 [2024-11-25 16:46:16,376 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.4386 [2024-11-25 16:46:16,613 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3978 [2024-11-25 16:46:16,881 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3654 [2024-11-25 16:46:17,129 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.4246 [2024-11-25 16:46:17,365 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3848 [2024-11-25 16:46:17,596 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.3839 [2024-11-25 16:46:17,831 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.3776 [2024-11-25 16:46:18,079 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.4133 [2024-11-25 16:46:18,335 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3839 [2024-11-25 16:46:18,586 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3803 [2024-11-25 16:46:18,808 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.3785 [2024-11-25 16:46:19,042 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.4108 [2024-11-25 16:46:19,261 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.3786 [2024-11-25 16:46:19,514 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3875 [2024-11-25 16:46:19,780 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.3838 [2024-11-25 16:46:20,041 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3783 [2024-11-25 16:46:20,273 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.4200 [2024-11-25 16:46:20,512 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.3864 [2024-11-25 16:46:20,769 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.4497 [2024-11-25 16:46:21,035 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.3994 [2024-11-25 16:46:21,291 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3938 [2024-11-25 16:46:21,549 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3733 [2024-11-25 16:46:21,799 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.4081 [2024-11-25 16:46:22,066 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3953 [2024-11-25 16:46:22,298 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.3595 [2024-11-25 16:46:22,561 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.4153 [2024-11-25 16:46:22,827 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.4092 [2024-11-25 16:46:23,090 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.3878 [2024-11-25 16:46:23,333 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.3670 [2024-11-25 16:46:23,590 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3826 [2024-11-25 16:46:23,858 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.3828 [2024-11-25 16:46:24,120 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3981 [2024-11-25 16:46:24,364 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.3282 [2024-11-25 16:46:24,597 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.3814 [2024-11-25 16:46:24,852 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.4047 [2024-11-25 16:46:25,096 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.4048 [2024-11-25 16:46:25,313 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.3753 [2024-11-25 16:46:25,537 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.3746 [2024-11-25 16:46:25,806 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.4344 [2024-11-25 16:46:26,044 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.3527 [2024-11-25 16:46:26,304 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.3678 [2024-11-25 16:46:26,554 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.4119 [2024-11-25 16:46:26,797 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.3837 [2024-11-25 16:46:27,058 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3656 [2024-11-25 16:46:27,306 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.3705 [2024-11-25 16:46:27,556 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3945 [2024-11-25 16:46:27,825 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.3728 [2024-11-25 16:46:28,059 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.3908 [2024-11-25 16:46:28,320 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3878 [2024-11-25 16:46:28,579 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3933 [2024-11-25 16:46:28,827 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3919 [2024-11-25 16:46:29,073 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.3756 [2024-11-25 16:46:29,307 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.3634 [2024-11-25 16:46:29,560 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.4300 [2024-11-25 16:46:29,827 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.4164 [2024-11-25 16:46:30,091 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.3760 [2024-11-25 16:46:30,354 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.3513 [2024-11-25 16:46:30,601 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.3778 [2024-11-25 16:46:30,869 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3778 [2024-11-25 16:46:31,088 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3885 [2024-11-25 16:46:31,360 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.4233 [2024-11-25 16:46:31,598 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3960 [2024-11-25 16:46:31,867 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.4112 [2024-11-25 16:46:32,128 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.4237 [2024-11-25 16:46:32,386 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3920 [2024-11-25 16:46:32,638 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.4017 [2024-11-25 16:46:32,860 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.3733 [2024-11-25 16:46:33,094 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.3815 [2024-11-25 16:46:33,348 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.3807 [2024-11-25 16:46:33,617 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3748 [2024-11-25 16:46:33,850 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.4258 [2024-11-25 16:46:34,108 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.4134 [2024-11-25 16:46:34,369 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.3723 [2024-11-25 16:46:34,592 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.3891 [2024-11-25 16:46:34,828 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.3750 [2024-11-25 16:46:35,044 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.3975 [2024-11-25 16:46:35,268 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.3908 [2024-11-25 16:46:35,539 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3705 [2024-11-25 16:46:35,799 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3933 [2024-11-25 16:46:36,064 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3960 [2024-11-25 16:46:36,330 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.3888 [2024-11-25 16:46:36,592 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3831 [2024-11-25 16:46:36,851 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3871 [2024-11-25 16:46:37,115 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.3787 [2024-11-25 16:46:37,370 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.4169 [2024-11-25 16:46:37,636 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.4238 [2024-11-25 16:46:37,897 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.3543 [2024-11-25 16:46:38,147 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.4522 [2024-11-25 16:46:38,377 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.4037 [2024-11-25 16:46:38,636 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.4281 [2024-11-25 16:46:38,869 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.3926 [2024-11-25 16:46:39,095 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.3768 [2024-11-25 16:46:39,306 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.3850 [2024-11-25 16:46:39,525 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.3759 [2024-11-25 16:46:40,093 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.5495/0.5579/0.9935. [2024-11-25 16:46:40,093 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9935/0.9993 [2024-11-25 16:46:40,093 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.1054/0.1165 [2024-11-25 16:46:40,094 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 16:46:40,098 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.5495 [2024-11-25 16:46:40,098 INFO misc.py line 165 2586773] Currently Best mIoU: 0.5495 [2024-11-25 16:46:40,098 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 16:46:42,065 INFO misc.py line 119 2586773] Train: [2/50][1/376] Data 0.108 (0.108) Batch 0.609 (0.609) Remain 03:06:52 loss: 0.3372 Lr: 0.00220 [2024-11-25 16:46:42,607 INFO misc.py line 119 2586773] Train: [2/50][2/376] Data 0.002 (0.002) Batch 0.542 (0.542) Remain 02:46:16 loss: 0.3797 Lr: 0.00220 [2024-11-25 16:46:43,127 INFO misc.py line 119 2586773] Train: [2/50][3/376] Data 0.002 (0.002) Batch 0.520 (0.520) Remain 02:39:36 loss: 0.3832 Lr: 0.00221 [2024-11-25 16:46:43,641 INFO misc.py line 119 2586773] Train: [2/50][4/376] Data 0.002 (0.002) Batch 0.514 (0.514) Remain 02:37:53 loss: 0.4263 Lr: 0.00222 [2024-11-25 16:46:44,134 INFO misc.py line 119 2586773] Train: [2/50][5/376] Data 0.003 (0.003) Batch 0.494 (0.504) Remain 02:34:41 loss: 0.3823 Lr: 0.00223 [2024-11-25 16:46:44,636 INFO misc.py line 119 2586773] Train: [2/50][6/376] Data 0.002 (0.002) Batch 0.502 (0.503) Remain 02:34:27 loss: 0.3832 Lr: 0.00223 [2024-11-25 16:46:45,171 INFO misc.py line 119 2586773] Train: [2/50][7/376] Data 0.003 (0.003) Batch 0.535 (0.511) Remain 02:36:54 loss: 0.3873 Lr: 0.00224 [2024-11-25 16:46:45,682 INFO misc.py line 119 2586773] Train: [2/50][8/376] Data 0.002 (0.003) Batch 0.511 (0.511) Remain 02:36:52 loss: 0.3417 Lr: 0.00225 [2024-11-25 16:46:46,185 INFO misc.py line 119 2586773] Train: [2/50][9/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 02:36:24 loss: 0.3708 Lr: 0.00226 [2024-11-25 16:46:46,702 INFO misc.py line 119 2586773] Train: [2/50][10/376] Data 0.003 (0.003) Batch 0.518 (0.511) Remain 02:36:45 loss: 0.3780 Lr: 0.00226 [2024-11-25 16:46:47,217 INFO misc.py line 119 2586773] Train: [2/50][11/376] Data 0.003 (0.003) Batch 0.515 (0.511) Remain 02:36:54 loss: 0.3534 Lr: 0.00227 [2024-11-25 16:46:47,703 INFO misc.py line 119 2586773] Train: [2/50][12/376] Data 0.003 (0.003) Batch 0.486 (0.509) Remain 02:36:02 loss: 0.3655 Lr: 0.00228 [2024-11-25 16:46:48,333 INFO misc.py line 119 2586773] Train: [2/50][13/376] Data 0.003 (0.003) Batch 0.629 (0.521) Remain 02:39:44 loss: 0.3715 Lr: 0.00229 [2024-11-25 16:46:48,844 INFO misc.py line 119 2586773] Train: [2/50][14/376] Data 0.002 (0.003) Batch 0.511 (0.520) Remain 02:39:28 loss: 0.3346 Lr: 0.00229 [2024-11-25 16:46:49,324 INFO misc.py line 119 2586773] Train: [2/50][15/376] Data 0.002 (0.003) Batch 0.480 (0.516) Remain 02:38:27 loss: 0.3631 Lr: 0.00230 [2024-11-25 16:46:49,834 INFO misc.py line 119 2586773] Train: [2/50][16/376] Data 0.002 (0.003) Batch 0.510 (0.516) Remain 02:38:17 loss: 0.3988 Lr: 0.00231 [2024-11-25 16:46:50,375 INFO misc.py line 119 2586773] Train: [2/50][17/376] Data 0.002 (0.003) Batch 0.541 (0.518) Remain 02:38:49 loss: 0.3537 Lr: 0.00232 [2024-11-25 16:46:50,866 INFO misc.py line 119 2586773] Train: [2/50][18/376] Data 0.002 (0.003) Batch 0.492 (0.516) Remain 02:38:16 loss: 0.4416 Lr: 0.00232 [2024-11-25 16:46:51,354 INFO misc.py line 119 2586773] Train: [2/50][19/376] Data 0.003 (0.003) Batch 0.488 (0.514) Remain 02:37:43 loss: 0.3679 Lr: 0.00233 [2024-11-25 16:46:51,901 INFO misc.py line 119 2586773] Train: [2/50][20/376] Data 0.002 (0.003) Batch 0.547 (0.516) Remain 02:38:18 loss: 0.3731 Lr: 0.00234 [2024-11-25 16:46:52,399 INFO misc.py line 119 2586773] Train: [2/50][21/376] Data 0.003 (0.003) Batch 0.498 (0.515) Remain 02:38:00 loss: 0.3377 Lr: 0.00235 [2024-11-25 16:46:52,926 INFO misc.py line 119 2586773] Train: [2/50][22/376] Data 0.003 (0.003) Batch 0.526 (0.516) Remain 02:38:10 loss: 0.3550 Lr: 0.00235 [2024-11-25 16:46:53,417 INFO misc.py line 119 2586773] Train: [2/50][23/376] Data 0.002 (0.003) Batch 0.492 (0.515) Remain 02:37:47 loss: 0.3888 Lr: 0.00236 [2024-11-25 16:46:53,921 INFO misc.py line 119 2586773] Train: [2/50][24/376] Data 0.003 (0.003) Batch 0.504 (0.514) Remain 02:37:37 loss: 0.3992 Lr: 0.00237 [2024-11-25 16:46:54,390 INFO misc.py line 119 2586773] Train: [2/50][25/376] Data 0.003 (0.003) Batch 0.469 (0.512) Remain 02:36:59 loss: 0.3481 Lr: 0.00238 [2024-11-25 16:46:54,867 INFO misc.py line 119 2586773] Train: [2/50][26/376] Data 0.002 (0.003) Batch 0.478 (0.510) Remain 02:36:31 loss: 0.3922 Lr: 0.00238 [2024-11-25 16:46:55,351 INFO misc.py line 119 2586773] Train: [2/50][27/376] Data 0.003 (0.003) Batch 0.484 (0.509) Remain 02:36:10 loss: 0.4032 Lr: 0.00239 [2024-11-25 16:46:55,875 INFO misc.py line 119 2586773] Train: [2/50][28/376] Data 0.002 (0.003) Batch 0.524 (0.510) Remain 02:36:21 loss: 0.3630 Lr: 0.00240 [2024-11-25 16:46:56,407 INFO misc.py line 119 2586773] Train: [2/50][29/376] Data 0.003 (0.003) Batch 0.532 (0.511) Remain 02:36:35 loss: 0.3380 Lr: 0.00241 [2024-11-25 16:46:56,922 INFO misc.py line 119 2586773] Train: [2/50][30/376] Data 0.002 (0.003) Batch 0.515 (0.511) Remain 02:36:38 loss: 0.3872 Lr: 0.00241 [2024-11-25 16:46:57,457 INFO misc.py line 119 2586773] Train: [2/50][31/376] Data 0.003 (0.003) Batch 0.535 (0.512) Remain 02:36:53 loss: 0.3800 Lr: 0.00242 [2024-11-25 16:46:57,921 INFO misc.py line 119 2586773] Train: [2/50][32/376] Data 0.003 (0.003) Batch 0.464 (0.510) Remain 02:36:22 loss: 0.3695 Lr: 0.00243 [2024-11-25 16:46:58,427 INFO misc.py line 119 2586773] Train: [2/50][33/376] Data 0.002 (0.003) Batch 0.506 (0.510) Remain 02:36:19 loss: 0.3409 Lr: 0.00244 [2024-11-25 16:46:58,975 INFO misc.py line 119 2586773] Train: [2/50][34/376] Data 0.003 (0.003) Batch 0.548 (0.511) Remain 02:36:41 loss: 0.3742 Lr: 0.00244 [2024-11-25 16:46:59,449 INFO misc.py line 119 2586773] Train: [2/50][35/376] Data 0.003 (0.003) Batch 0.473 (0.510) Remain 02:36:19 loss: 0.3700 Lr: 0.00245 [2024-11-25 16:46:59,992 INFO misc.py line 119 2586773] Train: [2/50][36/376] Data 0.002 (0.003) Batch 0.543 (0.511) Remain 02:36:37 loss: 0.3821 Lr: 0.00246 [2024-11-25 16:47:00,559 INFO misc.py line 119 2586773] Train: [2/50][37/376] Data 0.003 (0.003) Batch 0.567 (0.513) Remain 02:37:07 loss: 0.3572 Lr: 0.00247 [2024-11-25 16:47:01,019 INFO misc.py line 119 2586773] Train: [2/50][38/376] Data 0.002 (0.003) Batch 0.460 (0.511) Remain 02:36:39 loss: 0.3577 Lr: 0.00247 [2024-11-25 16:47:01,566 INFO misc.py line 119 2586773] Train: [2/50][39/376] Data 0.002 (0.003) Batch 0.546 (0.512) Remain 02:36:56 loss: 0.3458 Lr: 0.00248 [2024-11-25 16:47:02,067 INFO misc.py line 119 2586773] Train: [2/50][40/376] Data 0.003 (0.003) Batch 0.501 (0.512) Remain 02:36:50 loss: 0.3877 Lr: 0.00249 [2024-11-25 16:47:02,564 INFO misc.py line 119 2586773] Train: [2/50][41/376] Data 0.002 (0.003) Batch 0.497 (0.511) Remain 02:36:42 loss: 0.4006 Lr: 0.00250 [2024-11-25 16:47:03,036 INFO misc.py line 119 2586773] Train: [2/50][42/376] Data 0.003 (0.003) Batch 0.472 (0.510) Remain 02:36:23 loss: 0.3967 Lr: 0.00250 [2024-11-25 16:47:03,507 INFO misc.py line 119 2586773] Train: [2/50][43/376] Data 0.002 (0.003) Batch 0.471 (0.510) Remain 02:36:05 loss: 0.4023 Lr: 0.00251 [2024-11-25 16:47:04,040 INFO misc.py line 119 2586773] Train: [2/50][44/376] Data 0.002 (0.003) Batch 0.533 (0.510) Remain 02:36:15 loss: 0.3776 Lr: 0.00252 [2024-11-25 16:47:04,535 INFO misc.py line 119 2586773] Train: [2/50][45/376] Data 0.003 (0.003) Batch 0.495 (0.510) Remain 02:36:08 loss: 0.3636 Lr: 0.00253 [2024-11-25 16:47:05,045 INFO misc.py line 119 2586773] Train: [2/50][46/376] Data 0.003 (0.003) Batch 0.510 (0.510) Remain 02:36:07 loss: 0.3700 Lr: 0.00253 [2024-11-25 16:47:05,514 INFO misc.py line 119 2586773] Train: [2/50][47/376] Data 0.002 (0.003) Batch 0.469 (0.509) Remain 02:35:50 loss: 0.3842 Lr: 0.00254 [2024-11-25 16:47:06,003 INFO misc.py line 119 2586773] Train: [2/50][48/376] Data 0.002 (0.003) Batch 0.489 (0.508) Remain 02:35:41 loss: 0.3710 Lr: 0.00255 [2024-11-25 16:47:06,490 INFO misc.py line 119 2586773] Train: [2/50][49/376] Data 0.003 (0.003) Batch 0.487 (0.508) Remain 02:35:32 loss: 0.3757 Lr: 0.00256 [2024-11-25 16:47:07,015 INFO misc.py line 119 2586773] Train: [2/50][50/376] Data 0.002 (0.003) Batch 0.524 (0.508) Remain 02:35:38 loss: 0.3389 Lr: 0.00256 [2024-11-25 16:47:07,516 INFO misc.py line 119 2586773] Train: [2/50][51/376] Data 0.002 (0.003) Batch 0.501 (0.508) Remain 02:35:35 loss: 0.3496 Lr: 0.00257 [2024-11-25 16:47:07,955 INFO misc.py line 119 2586773] Train: [2/50][52/376] Data 0.003 (0.003) Batch 0.439 (0.507) Remain 02:35:09 loss: 0.3376 Lr: 0.00258 [2024-11-25 16:47:08,490 INFO misc.py line 119 2586773] Train: [2/50][53/376] Data 0.003 (0.003) Batch 0.535 (0.507) Remain 02:35:18 loss: 0.3464 Lr: 0.00258 [2024-11-25 16:47:09,039 INFO misc.py line 119 2586773] Train: [2/50][54/376] Data 0.002 (0.003) Batch 0.549 (0.508) Remain 02:35:33 loss: 0.3413 Lr: 0.00259 [2024-11-25 16:47:09,522 INFO misc.py line 119 2586773] Train: [2/50][55/376] Data 0.003 (0.003) Batch 0.483 (0.508) Remain 02:35:24 loss: 0.4015 Lr: 0.00260 [2024-11-25 16:47:10,019 INFO misc.py line 119 2586773] Train: [2/50][56/376] Data 0.003 (0.003) Batch 0.497 (0.507) Remain 02:35:19 loss: 0.3383 Lr: 0.00261 [2024-11-25 16:47:10,540 INFO misc.py line 119 2586773] Train: [2/50][57/376] Data 0.003 (0.003) Batch 0.521 (0.508) Remain 02:35:24 loss: 0.3660 Lr: 0.00261 [2024-11-25 16:47:11,061 INFO misc.py line 119 2586773] Train: [2/50][58/376] Data 0.002 (0.003) Batch 0.521 (0.508) Remain 02:35:27 loss: 0.3844 Lr: 0.00262 [2024-11-25 16:47:11,577 INFO misc.py line 119 2586773] Train: [2/50][59/376] Data 0.003 (0.003) Batch 0.517 (0.508) Remain 02:35:30 loss: 0.3571 Lr: 0.00263 [2024-11-25 16:47:12,074 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Train: [2/50][336/376] Data 0.003 (0.003) Batch 0.481 (0.509) Remain 02:33:20 loss: 0.3408 Lr: 0.00397 [2024-11-25 16:49:33,008 INFO misc.py line 119 2586773] Train: [2/50][337/376] Data 0.003 (0.003) Batch 0.499 (0.509) Remain 02:33:19 loss: 0.3349 Lr: 0.00397 [2024-11-25 16:49:33,511 INFO misc.py line 119 2586773] Train: [2/50][338/376] Data 0.003 (0.003) Batch 0.503 (0.509) Remain 02:33:18 loss: 0.3283 Lr: 0.00397 [2024-11-25 16:49:34,012 INFO misc.py line 119 2586773] Train: [2/50][339/376] Data 0.003 (0.003) Batch 0.501 (0.509) Remain 02:33:17 loss: 0.3240 Lr: 0.00398 [2024-11-25 16:49:34,566 INFO misc.py line 119 2586773] Train: [2/50][340/376] Data 0.003 (0.003) Batch 0.554 (0.509) Remain 02:33:19 loss: 0.3271 Lr: 0.00398 [2024-11-25 16:49:35,036 INFO misc.py line 119 2586773] Train: [2/50][341/376] Data 0.003 (0.003) Batch 0.469 (0.509) Remain 02:33:17 loss: 0.3263 Lr: 0.00398 [2024-11-25 16:49:35,542 INFO misc.py line 119 2586773] Train: [2/50][342/376] Data 0.003 (0.003) Batch 0.506 (0.509) Remain 02:33:16 loss: 0.3432 Lr: 0.00398 [2024-11-25 16:49:36,036 INFO misc.py line 119 2586773] Train: [2/50][343/376] Data 0.003 (0.003) Batch 0.494 (0.509) Remain 02:33:15 loss: 0.3297 Lr: 0.00398 [2024-11-25 16:49:36,540 INFO misc.py line 119 2586773] Train: [2/50][344/376] Data 0.003 (0.003) Batch 0.504 (0.509) Remain 02:33:14 loss: 0.3700 Lr: 0.00398 [2024-11-25 16:49:37,052 INFO misc.py line 119 2586773] Train: [2/50][345/376] Data 0.003 (0.003) Batch 0.512 (0.509) Remain 02:33:14 loss: 0.3491 Lr: 0.00398 [2024-11-25 16:49:37,565 INFO misc.py line 119 2586773] Train: [2/50][346/376] Data 0.003 (0.003) Batch 0.513 (0.509) Remain 02:33:13 loss: 0.3057 Lr: 0.00398 [2024-11-25 16:49:38,086 INFO misc.py line 119 2586773] Train: [2/50][347/376] Data 0.003 (0.003) Batch 0.521 (0.509) Remain 02:33:13 loss: 0.3199 Lr: 0.00398 [2024-11-25 16:49:38,602 INFO misc.py line 119 2586773] Train: [2/50][348/376] Data 0.003 (0.003) Batch 0.516 (0.509) Remain 02:33:13 loss: 0.3263 Lr: 0.00399 [2024-11-25 16:49:39,126 INFO misc.py line 119 2586773] Train: [2/50][349/376] Data 0.003 (0.003) Batch 0.524 (0.509) Remain 02:33:14 loss: 0.3669 Lr: 0.00399 [2024-11-25 16:49:39,652 INFO misc.py line 119 2586773] Train: [2/50][350/376] Data 0.003 (0.003) Batch 0.526 (0.509) Remain 02:33:14 loss: 0.3436 Lr: 0.00399 [2024-11-25 16:49:40,122 INFO misc.py line 119 2586773] Train: [2/50][351/376] Data 0.003 (0.003) Batch 0.470 (0.509) Remain 02:33:12 loss: 0.3106 Lr: 0.00399 [2024-11-25 16:49:40,681 INFO misc.py line 119 2586773] Train: [2/50][352/376] Data 0.003 (0.003) Batch 0.559 (0.509) Remain 02:33:14 loss: 0.3121 Lr: 0.00399 [2024-11-25 16:49:41,198 INFO misc.py line 119 2586773] Train: [2/50][353/376] Data 0.003 (0.003) Batch 0.517 (0.509) Remain 02:33:14 loss: 0.2983 Lr: 0.00399 [2024-11-25 16:49:41,693 INFO misc.py line 119 2586773] Train: [2/50][354/376] Data 0.003 (0.003) Batch 0.496 (0.509) Remain 02:33:12 loss: 0.3051 Lr: 0.00399 [2024-11-25 16:49:42,216 INFO misc.py line 119 2586773] Train: [2/50][355/376] Data 0.003 (0.003) Batch 0.522 (0.509) Remain 02:33:13 loss: 0.3525 Lr: 0.00399 [2024-11-25 16:49:42,749 INFO misc.py line 119 2586773] Train: [2/50][356/376] Data 0.003 (0.003) Batch 0.533 (0.509) Remain 02:33:13 loss: 0.3098 Lr: 0.00399 [2024-11-25 16:49:43,249 INFO misc.py line 119 2586773] Train: [2/50][357/376] Data 0.003 (0.003) Batch 0.500 (0.509) Remain 02:33:12 loss: 0.3354 Lr: 0.00399 [2024-11-25 16:49:43,723 INFO misc.py line 119 2586773] Train: [2/50][358/376] Data 0.003 (0.003) Batch 0.475 (0.509) Remain 02:33:10 loss: 0.3276 Lr: 0.00399 [2024-11-25 16:49:44,213 INFO misc.py line 119 2586773] Train: [2/50][359/376] Data 0.003 (0.003) Batch 0.489 (0.509) Remain 02:33:09 loss: 0.3459 Lr: 0.00399 [2024-11-25 16:49:44,739 INFO misc.py line 119 2586773] Train: [2/50][360/376] Data 0.003 (0.003) Batch 0.527 (0.509) Remain 02:33:09 loss: 0.2971 Lr: 0.00399 [2024-11-25 16:49:45,212 INFO misc.py line 119 2586773] Train: [2/50][361/376] Data 0.002 (0.003) Batch 0.472 (0.509) Remain 02:33:07 loss: 0.3237 Lr: 0.00400 [2024-11-25 16:49:45,688 INFO misc.py line 119 2586773] Train: [2/50][362/376] Data 0.002 (0.003) Batch 0.476 (0.509) Remain 02:33:04 loss: 0.3126 Lr: 0.00400 [2024-11-25 16:49:46,161 INFO misc.py line 119 2586773] Train: [2/50][363/376] Data 0.002 (0.003) Batch 0.473 (0.508) Remain 02:33:02 loss: 0.2978 Lr: 0.00400 [2024-11-25 16:49:46,645 INFO misc.py line 119 2586773] Train: [2/50][364/376] Data 0.002 (0.003) Batch 0.484 (0.508) Remain 02:33:00 loss: 0.3582 Lr: 0.00400 [2024-11-25 16:49:47,132 INFO misc.py line 119 2586773] Train: [2/50][365/376] Data 0.002 (0.003) Batch 0.487 (0.508) Remain 02:32:59 loss: 0.3169 Lr: 0.00400 [2024-11-25 16:49:47,627 INFO misc.py line 119 2586773] Train: [2/50][366/376] Data 0.003 (0.003) Batch 0.495 (0.508) Remain 02:32:58 loss: 0.3002 Lr: 0.00400 [2024-11-25 16:49:48,114 INFO misc.py line 119 2586773] Train: [2/50][367/376] Data 0.003 (0.003) Batch 0.487 (0.508) Remain 02:32:56 loss: 0.3641 Lr: 0.00400 [2024-11-25 16:49:48,598 INFO misc.py line 119 2586773] Train: [2/50][368/376] Data 0.002 (0.003) Batch 0.483 (0.508) Remain 02:32:54 loss: 0.3351 Lr: 0.00400 [2024-11-25 16:49:49,115 INFO misc.py line 119 2586773] Train: [2/50][369/376] Data 0.003 (0.003) Batch 0.517 (0.508) Remain 02:32:54 loss: 0.3209 Lr: 0.00400 [2024-11-25 16:49:49,647 INFO misc.py line 119 2586773] Train: [2/50][370/376] Data 0.003 (0.003) Batch 0.532 (0.508) Remain 02:32:55 loss: 0.2893 Lr: 0.00400 [2024-11-25 16:49:50,149 INFO misc.py line 119 2586773] Train: [2/50][371/376] Data 0.003 (0.003) Batch 0.503 (0.508) Remain 02:32:54 loss: 0.3271 Lr: 0.00400 [2024-11-25 16:49:50,688 INFO misc.py line 119 2586773] Train: [2/50][372/376] Data 0.003 (0.003) Batch 0.538 (0.508) Remain 02:32:55 loss: 0.3616 Lr: 0.00400 [2024-11-25 16:49:51,198 INFO misc.py line 119 2586773] Train: [2/50][373/376] Data 0.003 (0.003) Batch 0.510 (0.508) Remain 02:32:55 loss: 0.3488 Lr: 0.00400 [2024-11-25 16:49:51,710 INFO misc.py line 119 2586773] Train: [2/50][374/376] Data 0.003 (0.003) Batch 0.512 (0.508) Remain 02:32:54 loss: 0.3328 Lr: 0.00400 [2024-11-25 16:49:52,216 INFO misc.py line 119 2586773] Train: [2/50][375/376] Data 0.003 (0.003) Batch 0.506 (0.508) Remain 02:32:54 loss: 0.3119 Lr: 0.00400 [2024-11-25 16:49:52,743 INFO misc.py line 119 2586773] Train: [2/50][376/376] Data 0.003 (0.003) Batch 0.527 (0.508) Remain 02:32:54 loss: 0.2798 Lr: 0.00400 [2024-11-25 16:49:52,744 INFO misc.py line 136 2586773] Train result: loss: 0.3481 [2024-11-25 16:49:52,744 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 16:50:03,638 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2973 [2024-11-25 16:50:03,897 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.3035 [2024-11-25 16:50:04,163 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3572 [2024-11-25 16:50:04,386 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.3304 [2024-11-25 16:50:04,649 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3425 [2024-11-25 16:50:04,917 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.3341 [2024-11-25 16:50:05,139 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.3090 [2024-11-25 16:50:05,410 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.3024 [2024-11-25 16:50:05,641 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.3061 [2024-11-25 16:50:05,903 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3661 [2024-11-25 16:50:06,135 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.3272 [2024-11-25 16:50:06,406 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.3084 [2024-11-25 16:50:06,673 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3223 [2024-11-25 16:50:06,937 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.3275 [2024-11-25 16:50:07,172 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.3386 [2024-11-25 16:50:07,414 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3316 [2024-11-25 16:50:07,679 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3754 [2024-11-25 16:50:07,925 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.3120 [2024-11-25 16:50:08,156 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.3082 [2024-11-25 16:50:08,425 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3317 [2024-11-25 16:50:08,659 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.3132 [2024-11-25 16:50:08,924 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3773 [2024-11-25 16:50:09,161 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.3304 [2024-11-25 16:50:09,427 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3464 [2024-11-25 16:50:09,688 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.3311 [2024-11-25 16:50:09,929 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3421 [2024-11-25 16:50:10,180 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3485 [2024-11-25 16:50:10,426 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3287 [2024-11-25 16:50:10,696 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3572 [2024-11-25 16:50:10,950 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3669 [2024-11-25 16:50:11,183 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3584 [2024-11-25 16:50:11,448 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.3234 [2024-11-25 16:50:11,670 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.3096 [2024-11-25 16:50:11,911 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.3066 [2024-11-25 16:50:12,172 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.3140 [2024-11-25 16:50:12,430 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.3071 [2024-11-25 16:50:12,658 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.3007 [2024-11-25 16:50:12,928 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.3449 [2024-11-25 16:50:13,160 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3333 [2024-11-25 16:50:13,393 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3504 [2024-11-25 16:50:13,663 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3072 [2024-11-25 16:50:13,912 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3643 [2024-11-25 16:50:14,149 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3636 [2024-11-25 16:50:14,379 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.3216 [2024-11-25 16:50:14,617 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.3355 [2024-11-25 16:50:14,867 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3379 [2024-11-25 16:50:15,131 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3415 [2024-11-25 16:50:15,381 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3630 [2024-11-25 16:50:15,611 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.3245 [2024-11-25 16:50:15,845 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.3234 [2024-11-25 16:50:16,065 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.3148 [2024-11-25 16:50:16,317 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3273 [2024-11-25 16:50:16,581 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.3200 [2024-11-25 16:50:16,844 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3311 [2024-11-25 16:50:17,076 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.3559 [2024-11-25 16:50:17,319 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.3252 [2024-11-25 16:50:17,575 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3607 [2024-11-25 16:50:17,842 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.3180 [2024-11-25 16:50:18,098 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3179 [2024-11-25 16:50:18,363 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3360 [2024-11-25 16:50:18,615 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.3242 [2024-11-25 16:50:18,882 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3439 [2024-11-25 16:50:19,112 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2970 [2024-11-25 16:50:19,369 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3909 [2024-11-25 16:50:19,637 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3278 [2024-11-25 16:50:19,907 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.3232 [2024-11-25 16:50:20,150 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.3113 [2024-11-25 16:50:20,404 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3256 [2024-11-25 16:50:20,671 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.3142 [2024-11-25 16:50:20,931 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3479 [2024-11-25 16:50:21,177 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2897 [2024-11-25 16:50:21,412 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.3307 [2024-11-25 16:50:21,668 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3464 [2024-11-25 16:50:21,916 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3458 [2024-11-25 16:50:22,132 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.3067 [2024-11-25 16:50:22,356 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.3106 [2024-11-25 16:50:22,625 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3488 [2024-11-25 16:50:22,863 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.3039 [2024-11-25 16:50:23,121 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.3035 [2024-11-25 16:50:23,371 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3822 [2024-11-25 16:50:23,614 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.3134 [2024-11-25 16:50:23,878 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3187 [2024-11-25 16:50:24,125 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.3002 [2024-11-25 16:50:24,376 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3192 [2024-11-25 16:50:24,647 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.3047 [2024-11-25 16:50:24,887 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.3240 [2024-11-25 16:50:25,148 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3819 [2024-11-25 16:50:25,407 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3392 [2024-11-25 16:50:25,653 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3351 [2024-11-25 16:50:25,910 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.3268 [2024-11-25 16:50:26,144 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.3021 [2024-11-25 16:50:26,395 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3366 [2024-11-25 16:50:26,661 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3560 [2024-11-25 16:50:26,927 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.3092 [2024-11-25 16:50:27,193 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.3092 [2024-11-25 16:50:27,451 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.3265 [2024-11-25 16:50:27,721 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3174 [2024-11-25 16:50:27,945 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3393 [2024-11-25 16:50:28,215 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.3350 [2024-11-25 16:50:28,453 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3277 [2024-11-25 16:50:28,726 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.3193 [2024-11-25 16:50:28,986 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3398 [2024-11-25 16:50:29,244 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3405 [2024-11-25 16:50:29,498 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3839 [2024-11-25 16:50:29,721 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.3175 [2024-11-25 16:50:29,967 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.3089 [2024-11-25 16:50:30,226 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.3349 [2024-11-25 16:50:30,497 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3378 [2024-11-25 16:50:30,731 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3442 [2024-11-25 16:50:30,997 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.3107 [2024-11-25 16:50:31,260 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.3208 [2024-11-25 16:50:31,483 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.3450 [2024-11-25 16:50:31,720 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.3231 [2024-11-25 16:50:31,938 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.3272 [2024-11-25 16:50:32,164 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.3159 [2024-11-25 16:50:32,434 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3867 [2024-11-25 16:50:32,694 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.4023 [2024-11-25 16:50:32,964 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3572 [2024-11-25 16:50:33,230 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.3409 [2024-11-25 16:50:33,490 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3782 [2024-11-25 16:50:33,750 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3229 [2024-11-25 16:50:34,014 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.3108 [2024-11-25 16:50:34,267 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3710 [2024-11-25 16:50:34,528 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3795 [2024-11-25 16:50:34,788 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.3073 [2024-11-25 16:50:35,038 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3616 [2024-11-25 16:50:35,271 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.3130 [2024-11-25 16:50:35,528 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3356 [2024-11-25 16:50:35,766 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.3245 [2024-11-25 16:50:35,992 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.3259 [2024-11-25 16:50:36,205 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.3236 [2024-11-25 16:50:36,422 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2978 [2024-11-25 16:50:37,104 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.6181/0.6317/0.9946. [2024-11-25 16:50:37,105 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9946/0.9994 [2024-11-25 16:50:37,105 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.2417/0.2640 [2024-11-25 16:50:37,105 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 16:50:37,106 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.6181 [2024-11-25 16:50:37,107 INFO misc.py line 165 2586773] Currently Best mIoU: 0.6181 [2024-11-25 16:50:37,107 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 16:50:41,501 INFO misc.py line 119 2586773] Train: [3/50][1/376] Data 0.093 (0.093) Batch 0.577 (0.577) Remain 02:53:30 loss: 0.3552 Lr: 0.00400 [2024-11-25 16:50:42,010 INFO misc.py line 119 2586773] Train: [3/50][2/376] Data 0.003 (0.003) Batch 0.509 (0.509) Remain 02:32:58 loss: 0.3284 Lr: 0.00400 [2024-11-25 16:50:42,487 INFO misc.py line 119 2586773] Train: [3/50][3/376] Data 0.003 (0.003) Batch 0.477 (0.477) Remain 02:23:22 loss: 0.2563 Lr: 0.00400 [2024-11-25 16:50:43,001 INFO misc.py line 119 2586773] Train: [3/50][4/376] Data 0.003 (0.003) Batch 0.514 (0.514) Remain 02:34:34 loss: 0.3317 Lr: 0.00400 [2024-11-25 16:50:43,542 INFO misc.py line 119 2586773] Train: [3/50][5/376] Data 0.004 (0.003) Batch 0.541 (0.527) Remain 02:38:33 loss: 0.3075 Lr: 0.00400 [2024-11-25 16:50:44,042 INFO misc.py line 119 2586773] Train: [3/50][6/376] Data 0.003 (0.003) Batch 0.501 (0.519) Remain 02:35:54 loss: 0.3281 Lr: 0.00400 [2024-11-25 16:50:44,581 INFO misc.py line 119 2586773] Train: [3/50][7/376] Data 0.003 (0.003) Batch 0.538 (0.523) Remain 02:37:23 loss: 0.3847 Lr: 0.00400 [2024-11-25 16:50:45,093 INFO misc.py line 119 2586773] Train: [3/50][8/376] Data 0.003 (0.003) Batch 0.513 (0.521) Remain 02:36:43 loss: 0.3697 Lr: 0.00400 [2024-11-25 16:50:45,613 INFO misc.py line 119 2586773] Train: [3/50][9/376] Data 0.003 (0.003) Batch 0.520 (0.521) Remain 02:36:38 loss: 0.3224 Lr: 0.00400 [2024-11-25 16:50:46,104 INFO misc.py line 119 2586773] Train: [3/50][10/376] Data 0.003 (0.003) Batch 0.492 (0.517) Remain 02:35:21 loss: 0.3390 Lr: 0.00400 [2024-11-25 16:50:46,585 INFO misc.py line 119 2586773] Train: [3/50][11/376] Data 0.003 (0.003) Batch 0.481 (0.512) Remain 02:33:59 loss: 0.4314 Lr: 0.00400 [2024-11-25 16:50:47,116 INFO misc.py line 119 2586773] Train: [3/50][12/376] Data 0.003 (0.003) Batch 0.531 (0.514) Remain 02:34:36 loss: 0.3476 Lr: 0.00400 [2024-11-25 16:50:47,638 INFO misc.py line 119 2586773] Train: [3/50][13/376] Data 0.003 (0.003) Batch 0.523 (0.515) Remain 02:34:50 loss: 0.3004 Lr: 0.00400 [2024-11-25 16:50:48,142 INFO misc.py line 119 2586773] Train: [3/50][14/376] Data 0.003 (0.003) Batch 0.504 (0.514) Remain 02:34:31 loss: 0.3096 Lr: 0.00400 [2024-11-25 16:50:48,614 INFO misc.py line 119 2586773] Train: [3/50][15/376] Data 0.003 (0.003) Batch 0.472 (0.511) Remain 02:33:28 loss: 0.3837 Lr: 0.00400 [2024-11-25 16:50:49,129 INFO misc.py line 119 2586773] Train: [3/50][16/376] Data 0.003 (0.003) Batch 0.514 (0.511) Remain 02:33:32 loss: 0.3098 Lr: 0.00400 [2024-11-25 16:50:49,612 INFO misc.py line 119 2586773] Train: [3/50][17/376] Data 0.003 (0.003) Batch 0.483 (0.509) Remain 02:32:56 loss: 0.3197 Lr: 0.00400 [2024-11-25 16:50:50,086 INFO misc.py line 119 2586773] Train: [3/50][18/376] Data 0.004 (0.003) Batch 0.474 (0.507) Remain 02:32:14 loss: 0.3198 Lr: 0.00400 [2024-11-25 16:50:50,589 INFO misc.py line 119 2586773] Train: [3/50][19/376] Data 0.003 (0.003) Batch 0.503 (0.506) Remain 02:32:09 loss: 0.3235 Lr: 0.00400 [2024-11-25 16:50:51,092 INFO misc.py line 119 2586773] Train: [3/50][20/376] Data 0.003 (0.003) Batch 0.503 (0.506) Remain 02:32:05 loss: 0.3204 Lr: 0.00400 [2024-11-25 16:50:51,588 INFO misc.py line 119 2586773] Train: [3/50][21/376] Data 0.003 (0.003) Batch 0.496 (0.506) Remain 02:31:54 loss: 0.3044 Lr: 0.00400 [2024-11-25 16:50:52,118 INFO misc.py line 119 2586773] Train: [3/50][22/376] Data 0.003 (0.003) Batch 0.529 (0.507) Remain 02:32:16 loss: 0.3516 Lr: 0.00400 [2024-11-25 16:50:52,651 INFO misc.py line 119 2586773] Train: [3/50][23/376] Data 0.004 (0.003) Batch 0.534 (0.508) Remain 02:32:40 loss: 0.3303 Lr: 0.00400 [2024-11-25 16:50:53,140 INFO misc.py line 119 2586773] Train: [3/50][24/376] Data 0.003 (0.003) Batch 0.489 (0.507) Remain 02:32:23 loss: 0.3115 Lr: 0.00400 [2024-11-25 16:50:53,700 INFO misc.py line 119 2586773] Train: [3/50][25/376] Data 0.003 (0.003) Batch 0.560 (0.510) Remain 02:33:06 loss: 0.3520 Lr: 0.00400 [2024-11-25 16:50:54,209 INFO misc.py line 119 2586773] Train: [3/50][26/376] Data 0.003 (0.003) Batch 0.509 (0.510) Remain 02:33:04 loss: 0.3120 Lr: 0.00400 [2024-11-25 16:50:54,728 INFO misc.py line 119 2586773] Train: [3/50][27/376] Data 0.003 (0.003) Batch 0.519 (0.510) Remain 02:33:11 loss: 0.3054 Lr: 0.00400 [2024-11-25 16:50:55,269 INFO misc.py line 119 2586773] Train: [3/50][28/376] Data 0.003 (0.003) Batch 0.542 (0.511) Remain 02:33:33 loss: 0.3059 Lr: 0.00400 [2024-11-25 16:50:55,780 INFO misc.py line 119 2586773] Train: [3/50][29/376] Data 0.003 (0.003) Batch 0.510 (0.511) Remain 02:33:32 loss: 0.3769 Lr: 0.00400 [2024-11-25 16:50:56,255 INFO misc.py line 119 2586773] Train: [3/50][30/376] Data 0.003 (0.003) Batch 0.475 (0.510) Remain 02:33:07 loss: 0.3345 Lr: 0.00400 [2024-11-25 16:50:56,741 INFO misc.py line 119 2586773] Train: [3/50][31/376] Data 0.003 (0.003) Batch 0.486 (0.509) Remain 02:32:51 loss: 0.3367 Lr: 0.00400 [2024-11-25 16:50:57,260 INFO misc.py line 119 2586773] Train: [3/50][32/376] Data 0.003 (0.003) Batch 0.517 (0.509) Remain 02:32:56 loss: 0.2764 Lr: 0.00400 [2024-11-25 16:50:57,739 INFO misc.py line 119 2586773] Train: [3/50][33/376] Data 0.004 (0.003) Batch 0.481 (0.508) Remain 02:32:39 loss: 0.3259 Lr: 0.00400 [2024-11-25 16:50:58,261 INFO misc.py line 119 2586773] Train: [3/50][34/376] Data 0.003 (0.003) Batch 0.522 (0.509) Remain 02:32:46 loss: 0.3219 Lr: 0.00400 [2024-11-25 16:50:58,758 INFO misc.py line 119 2586773] Train: [3/50][35/376] Data 0.003 (0.003) Batch 0.498 (0.508) Remain 02:32:39 loss: 0.3117 Lr: 0.00400 [2024-11-25 16:50:59,257 INFO misc.py line 119 2586773] Train: [3/50][36/376] Data 0.003 (0.003) Batch 0.498 (0.508) Remain 02:32:33 loss: 0.3387 Lr: 0.00400 [2024-11-25 16:50:59,776 INFO misc.py line 119 2586773] Train: [3/50][37/376] Data 0.003 (0.003) Batch 0.519 (0.508) Remain 02:32:38 loss: 0.3512 Lr: 0.00400 [2024-11-25 16:51:00,312 INFO misc.py line 119 2586773] Train: [3/50][38/376] Data 0.003 (0.003) Batch 0.537 (0.509) Remain 02:32:52 loss: 0.2989 Lr: 0.00400 [2024-11-25 16:51:00,802 INFO misc.py line 119 2586773] Train: [3/50][39/376] Data 0.003 (0.003) Batch 0.490 (0.509) Remain 02:32:42 loss: 0.2874 Lr: 0.00400 [2024-11-25 16:51:01,300 INFO misc.py line 119 2586773] Train: [3/50][40/376] Data 0.003 (0.003) Batch 0.497 (0.508) Remain 02:32:35 loss: 0.3080 Lr: 0.00400 [2024-11-25 16:51:01,787 INFO misc.py line 119 2586773] Train: [3/50][41/376] Data 0.004 (0.003) Batch 0.488 (0.508) Remain 02:32:25 loss: 0.3034 Lr: 0.00400 [2024-11-25 16:51:02,340 INFO misc.py line 119 2586773] Train: [3/50][42/376] Data 0.003 (0.003) Batch 0.554 (0.509) Remain 02:32:46 loss: 0.3251 Lr: 0.00400 [2024-11-25 16:51:02,834 INFO misc.py line 119 2586773] Train: [3/50][43/376] Data 0.003 (0.003) Batch 0.494 (0.509) Remain 02:32:38 loss: 0.3205 Lr: 0.00400 [2024-11-25 16:51:03,360 INFO misc.py line 119 2586773] Train: [3/50][44/376] Data 0.003 (0.003) Batch 0.525 (0.509) Remain 02:32:45 loss: 0.3500 Lr: 0.00400 [2024-11-25 16:51:03,845 INFO misc.py line 119 2586773] Train: [3/50][45/376] Data 0.003 (0.003) Batch 0.485 (0.509) Remain 02:32:34 loss: 0.3435 Lr: 0.00400 [2024-11-25 16:51:04,375 INFO misc.py line 119 2586773] Train: [3/50][46/376] Data 0.003 (0.003) Batch 0.531 (0.509) Remain 02:32:43 loss: 0.3969 Lr: 0.00400 [2024-11-25 16:51:04,868 INFO misc.py line 119 2586773] Train: [3/50][47/376] Data 0.003 (0.003) Batch 0.492 (0.509) Remain 02:32:36 loss: 0.3266 Lr: 0.00400 [2024-11-25 16:51:05,388 INFO misc.py line 119 2586773] Train: [3/50][48/376] Data 0.003 (0.003) Batch 0.520 (0.509) Remain 02:32:40 loss: 0.3002 Lr: 0.00400 [2024-11-25 16:51:05,903 INFO misc.py line 119 2586773] Train: [3/50][49/376] Data 0.003 (0.003) Batch 0.515 (0.509) Remain 02:32:42 loss: 0.3161 Lr: 0.00400 [2024-11-25 16:51:06,402 INFO misc.py line 119 2586773] Train: [3/50][50/376] Data 0.003 (0.003) Batch 0.499 (0.509) Remain 02:32:38 loss: 0.3253 Lr: 0.00400 [2024-11-25 16:51:06,931 INFO misc.py line 119 2586773] Train: [3/50][51/376] Data 0.003 (0.003) Batch 0.528 (0.509) Remain 02:32:44 loss: 0.3588 Lr: 0.00400 [2024-11-25 16:51:07,413 INFO misc.py line 119 2586773] Train: [3/50][52/376] Data 0.003 (0.003) Batch 0.483 (0.509) Remain 02:32:34 loss: 0.3226 Lr: 0.00400 [2024-11-25 16:51:07,930 INFO misc.py line 119 2586773] Train: [3/50][53/376] Data 0.003 (0.003) Batch 0.516 (0.509) Remain 02:32:36 loss: 0.3164 Lr: 0.00400 [2024-11-25 16:51:08,460 INFO misc.py line 119 2586773] Train: [3/50][54/376] Data 0.003 (0.003) Batch 0.530 (0.509) Remain 02:32:43 loss: 0.3203 Lr: 0.00400 [2024-11-25 16:51:08,910 INFO misc.py line 119 2586773] Train: [3/50][55/376] Data 0.003 (0.003) Batch 0.450 (0.508) Remain 02:32:22 loss: 0.3473 Lr: 0.00400 [2024-11-25 16:51:09,415 INFO misc.py line 119 2586773] Train: [3/50][56/376] Data 0.002 (0.003) Batch 0.506 (0.508) Remain 02:32:21 loss: 0.3062 Lr: 0.00400 [2024-11-25 16:51:09,946 INFO misc.py line 119 2586773] Train: [3/50][57/376] Data 0.003 (0.003) Batch 0.531 (0.509) Remain 02:32:28 loss: 0.3663 Lr: 0.00400 [2024-11-25 16:51:10,476 INFO misc.py line 119 2586773] Train: [3/50][58/376] Data 0.003 (0.003) Batch 0.530 (0.509) Remain 02:32:35 loss: 0.3027 Lr: 0.00400 [2024-11-25 16:51:11,007 INFO misc.py line 119 2586773] Train: [3/50][59/376] Data 0.003 (0.003) Batch 0.531 (0.509) Remain 02:32:41 loss: 0.3536 Lr: 0.00400 [2024-11-25 16:51:11,516 INFO misc.py line 119 2586773] Train: [3/50][60/376] Data 0.003 (0.003) Batch 0.508 (0.509) Remain 02:32:41 loss: 0.3265 Lr: 0.00400 [2024-11-25 16:51:12,047 INFO misc.py line 119 2586773] Train: [3/50][61/376] Data 0.003 (0.003) Batch 0.530 (0.510) Remain 02:32:47 loss: 0.2857 Lr: 0.00400 [2024-11-25 16:51:12,567 INFO misc.py line 119 2586773] Train: [3/50][62/376] Data 0.003 (0.003) Batch 0.520 (0.510) Remain 02:32:49 loss: 0.3089 Lr: 0.00400 [2024-11-25 16:51:13,046 INFO misc.py line 119 2586773] Train: [3/50][63/376] Data 0.003 (0.003) Batch 0.479 (0.509) Remain 02:32:39 loss: 0.3266 Lr: 0.00400 [2024-11-25 16:51:13,566 INFO misc.py line 119 2586773] Train: [3/50][64/376] Data 0.003 (0.003) Batch 0.520 (0.509) Remain 02:32:42 loss: 0.3491 Lr: 0.00400 [2024-11-25 16:51:14,068 INFO misc.py line 119 2586773] Train: [3/50][65/376] Data 0.003 (0.003) Batch 0.502 (0.509) Remain 02:32:40 loss: 0.2887 Lr: 0.00400 [2024-11-25 16:51:14,584 INFO misc.py line 119 2586773] Train: [3/50][66/376] Data 0.003 (0.003) Batch 0.515 (0.509) Remain 02:32:41 loss: 0.3317 Lr: 0.00400 [2024-11-25 16:51:15,123 INFO misc.py line 119 2586773] Train: [3/50][67/376] Data 0.003 (0.003) Batch 0.539 (0.510) Remain 02:32:49 loss: 0.3263 Lr: 0.00400 [2024-11-25 16:51:15,655 INFO misc.py line 119 2586773] Train: [3/50][68/376] Data 0.002 (0.003) Batch 0.532 (0.510) Remain 02:32:54 loss: 0.3234 Lr: 0.00400 [2024-11-25 16:51:16,159 INFO misc.py line 119 2586773] Train: [3/50][69/376] Data 0.003 (0.003) Batch 0.504 (0.510) Remain 02:32:52 loss: 0.3320 Lr: 0.00400 [2024-11-25 16:51:16,685 INFO misc.py line 119 2586773] Train: [3/50][70/376] Data 0.003 (0.003) Batch 0.526 (0.510) Remain 02:32:56 loss: 0.3570 Lr: 0.00400 [2024-11-25 16:51:17,217 INFO misc.py line 119 2586773] Train: [3/50][71/376] Data 0.003 (0.003) Batch 0.532 (0.511) Remain 02:33:01 loss: 0.3515 Lr: 0.00400 [2024-11-25 16:51:17,709 INFO misc.py line 119 2586773] Train: [3/50][72/376] Data 0.002 (0.003) Batch 0.492 (0.510) Remain 02:32:56 loss: 0.2965 Lr: 0.00400 [2024-11-25 16:51:18,233 INFO misc.py line 119 2586773] Train: [3/50][73/376] Data 0.003 (0.003) Batch 0.525 (0.511) Remain 02:32:59 loss: 0.3358 Lr: 0.00400 [2024-11-25 16:51:18,725 INFO misc.py line 119 2586773] Train: [3/50][74/376] Data 0.002 (0.003) Batch 0.492 (0.510) Remain 02:32:53 loss: 0.3452 Lr: 0.00400 [2024-11-25 16:51:19,229 INFO misc.py line 119 2586773] Train: [3/50][75/376] Data 0.003 (0.003) Batch 0.504 (0.510) Remain 02:32:51 loss: 0.3148 Lr: 0.00400 [2024-11-25 16:51:19,754 INFO misc.py line 119 2586773] Train: [3/50][76/376] Data 0.003 (0.003) Batch 0.524 (0.511) Remain 02:32:54 loss: 0.3752 Lr: 0.00400 [2024-11-25 16:51:20,220 INFO misc.py line 119 2586773] Train: [3/50][77/376] Data 0.002 (0.003) Batch 0.466 (0.510) Remain 02:32:43 loss: 0.3426 Lr: 0.00400 [2024-11-25 16:51:20,695 INFO misc.py line 119 2586773] Train: [3/50][78/376] Data 0.003 (0.003) Batch 0.475 (0.509) Remain 02:32:34 loss: 0.3101 Lr: 0.00400 [2024-11-25 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Train: [3/50][129/376] Data 0.003 (0.003) Batch 0.489 (0.510) Remain 02:32:13 loss: 0.3219 Lr: 0.00400 [2024-11-25 16:51:47,218 INFO misc.py line 119 2586773] Train: [3/50][130/376] Data 0.003 (0.003) Batch 0.506 (0.510) Remain 02:32:12 loss: 0.3470 Lr: 0.00400 [2024-11-25 16:51:47,734 INFO misc.py line 119 2586773] Train: [3/50][131/376] Data 0.003 (0.003) Batch 0.515 (0.510) Remain 02:32:13 loss: 0.3530 Lr: 0.00400 [2024-11-25 16:51:48,263 INFO misc.py line 119 2586773] Train: [3/50][132/376] Data 0.003 (0.003) Batch 0.529 (0.510) Remain 02:32:15 loss: 0.3031 Lr: 0.00400 [2024-11-25 16:51:48,767 INFO misc.py line 119 2586773] Train: [3/50][133/376] Data 0.003 (0.003) Batch 0.504 (0.510) Remain 02:32:13 loss: 0.4269 Lr: 0.00400 [2024-11-25 16:51:49,261 INFO misc.py line 119 2586773] Train: [3/50][134/376] Data 0.003 (0.003) Batch 0.494 (0.510) Remain 02:32:11 loss: 0.3273 Lr: 0.00400 [2024-11-25 16:51:49,749 INFO misc.py line 119 2586773] Train: [3/50][135/376] Data 0.003 (0.003) Batch 0.488 (0.510) Remain 02:32:07 loss: 0.2882 Lr: 0.00400 [2024-11-25 16:51:50,236 INFO misc.py line 119 2586773] Train: [3/50][136/376] Data 0.003 (0.003) Batch 0.487 (0.509) Remain 02:32:04 loss: 0.2904 Lr: 0.00400 [2024-11-25 16:51:50,761 INFO misc.py line 119 2586773] Train: [3/50][137/376] Data 0.003 (0.003) Batch 0.525 (0.510) Remain 02:32:05 loss: 0.3247 Lr: 0.00400 [2024-11-25 16:51:51,287 INFO misc.py line 119 2586773] Train: [3/50][138/376] Data 0.003 (0.003) Batch 0.526 (0.510) Remain 02:32:07 loss: 0.3715 Lr: 0.00400 [2024-11-25 16:51:51,804 INFO misc.py line 119 2586773] Train: [3/50][139/376] Data 0.003 (0.003) Batch 0.515 (0.510) Remain 02:32:07 loss: 0.2760 Lr: 0.00400 [2024-11-25 16:51:52,291 INFO misc.py line 119 2586773] Train: [3/50][140/376] Data 0.006 (0.003) Batch 0.489 (0.510) Remain 02:32:04 loss: 0.3141 Lr: 0.00400 [2024-11-25 16:51:52,817 INFO misc.py line 119 2586773] Train: [3/50][141/376] Data 0.003 (0.003) Batch 0.525 (0.510) Remain 02:32:06 loss: 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Train: [3/50][198/376] Data 0.003 (0.003) Batch 0.515 (0.508) Remain 02:31:13 loss: 0.3614 Lr: 0.00400 [2024-11-25 16:52:22,080 INFO misc.py line 119 2586773] Train: [3/50][199/376] Data 0.003 (0.003) Batch 0.473 (0.508) Remain 02:31:09 loss: 0.2602 Lr: 0.00400 [2024-11-25 16:52:22,579 INFO misc.py line 119 2586773] Train: [3/50][200/376] Data 0.003 (0.003) Batch 0.499 (0.508) Remain 02:31:08 loss: 0.2529 Lr: 0.00400 [2024-11-25 16:52:23,098 INFO misc.py line 119 2586773] Train: [3/50][201/376] Data 0.003 (0.003) Batch 0.519 (0.508) Remain 02:31:08 loss: 0.2762 Lr: 0.00400 [2024-11-25 16:52:23,606 INFO misc.py line 119 2586773] Train: [3/50][202/376] Data 0.003 (0.003) Batch 0.508 (0.508) Remain 02:31:08 loss: 0.3743 Lr: 0.00400 [2024-11-25 16:52:24,136 INFO misc.py line 119 2586773] Train: [3/50][203/376] Data 0.003 (0.003) Batch 0.529 (0.508) Remain 02:31:09 loss: 0.3918 Lr: 0.00400 [2024-11-25 16:52:24,638 INFO misc.py line 119 2586773] Train: [3/50][204/376] Data 0.003 (0.003) Batch 0.502 (0.508) Remain 02:31:08 loss: 0.3067 Lr: 0.00400 [2024-11-25 16:52:25,161 INFO misc.py line 119 2586773] Train: [3/50][205/376] Data 0.003 (0.003) Batch 0.523 (0.508) Remain 02:31:09 loss: 0.3596 Lr: 0.00400 [2024-11-25 16:52:25,707 INFO misc.py line 119 2586773] Train: [3/50][206/376] Data 0.004 (0.003) Batch 0.546 (0.508) Remain 02:31:12 loss: 0.3019 Lr: 0.00400 [2024-11-25 16:52:26,204 INFO misc.py line 119 2586773] Train: [3/50][207/376] Data 0.003 (0.003) Batch 0.497 (0.508) Remain 02:31:10 loss: 0.3247 Lr: 0.00400 [2024-11-25 16:52:26,704 INFO misc.py line 119 2586773] Train: [3/50][208/376] Data 0.003 (0.003) Batch 0.500 (0.508) Remain 02:31:09 loss: 0.3127 Lr: 0.00400 [2024-11-25 16:52:27,189 INFO misc.py line 119 2586773] Train: [3/50][209/376] Data 0.003 (0.003) Batch 0.485 (0.508) Remain 02:31:06 loss: 0.3196 Lr: 0.00400 [2024-11-25 16:52:27,695 INFO misc.py line 119 2586773] Train: [3/50][210/376] Data 0.003 (0.003) Batch 0.506 (0.508) Remain 02:31:06 loss: 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Train: [3/50][336/376] Data 0.003 (0.003) Batch 0.518 (0.509) Remain 02:30:16 loss: 0.2971 Lr: 0.00400 [2024-11-25 16:53:32,492 INFO misc.py line 119 2586773] Train: [3/50][337/376] Data 0.003 (0.003) Batch 0.478 (0.509) Remain 02:30:14 loss: 0.3433 Lr: 0.00400 [2024-11-25 16:53:32,982 INFO misc.py line 119 2586773] Train: [3/50][338/376] Data 0.003 (0.003) Batch 0.490 (0.509) Remain 02:30:13 loss: 0.3125 Lr: 0.00400 [2024-11-25 16:53:33,514 INFO misc.py line 119 2586773] Train: [3/50][339/376] Data 0.003 (0.003) Batch 0.532 (0.509) Remain 02:30:13 loss: 0.3041 Lr: 0.00400 [2024-11-25 16:53:34,026 INFO misc.py line 119 2586773] Train: [3/50][340/376] Data 0.003 (0.003) Batch 0.512 (0.509) Remain 02:30:13 loss: 0.2835 Lr: 0.00400 [2024-11-25 16:53:34,498 INFO misc.py line 119 2586773] Train: [3/50][341/376] Data 0.003 (0.003) Batch 0.473 (0.509) Remain 02:30:11 loss: 0.3558 Lr: 0.00400 [2024-11-25 16:53:34,969 INFO misc.py line 119 2586773] Train: [3/50][342/376] Data 0.003 (0.003) 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INFO misc.py line 119 2586773] Train: [3/50][355/376] Data 0.003 (0.003) Batch 0.507 (0.509) Remain 02:29:57 loss: 0.3460 Lr: 0.00400 [2024-11-25 16:53:42,046 INFO misc.py line 119 2586773] Train: [3/50][356/376] Data 0.003 (0.003) Batch 0.559 (0.509) Remain 02:29:59 loss: 0.3596 Lr: 0.00400 [2024-11-25 16:53:42,537 INFO misc.py line 119 2586773] Train: [3/50][357/376] Data 0.003 (0.003) Batch 0.491 (0.509) Remain 02:29:57 loss: 0.3355 Lr: 0.00400 [2024-11-25 16:53:43,061 INFO misc.py line 119 2586773] Train: [3/50][358/376] Data 0.004 (0.003) Batch 0.524 (0.509) Remain 02:29:58 loss: 0.3155 Lr: 0.00400 [2024-11-25 16:53:43,598 INFO misc.py line 119 2586773] Train: [3/50][359/376] Data 0.003 (0.003) Batch 0.536 (0.509) Remain 02:29:59 loss: 0.3306 Lr: 0.00400 [2024-11-25 16:53:44,125 INFO misc.py line 119 2586773] Train: [3/50][360/376] Data 0.003 (0.003) Batch 0.527 (0.509) Remain 02:29:59 loss: 0.3419 Lr: 0.00400 [2024-11-25 16:53:44,643 INFO misc.py line 119 2586773] Train: [3/50][361/376] Data 0.003 (0.003) Batch 0.518 (0.509) Remain 02:29:59 loss: 0.2976 Lr: 0.00400 [2024-11-25 16:53:45,126 INFO misc.py line 119 2586773] Train: [3/50][362/376] Data 0.003 (0.003) Batch 0.483 (0.509) Remain 02:29:57 loss: 0.2977 Lr: 0.00400 [2024-11-25 16:53:45,679 INFO misc.py line 119 2586773] Train: [3/50][363/376] Data 0.002 (0.003) Batch 0.554 (0.509) Remain 02:29:59 loss: 0.3102 Lr: 0.00400 [2024-11-25 16:53:46,139 INFO misc.py line 119 2586773] Train: [3/50][364/376] Data 0.003 (0.003) Batch 0.459 (0.509) Remain 02:29:56 loss: 0.3256 Lr: 0.00400 [2024-11-25 16:53:46,614 INFO misc.py line 119 2586773] Train: [3/50][365/376] Data 0.003 (0.003) Batch 0.475 (0.509) Remain 02:29:54 loss: 0.3701 Lr: 0.00400 [2024-11-25 16:53:47,089 INFO misc.py line 119 2586773] Train: [3/50][366/376] Data 0.002 (0.003) Batch 0.475 (0.509) Remain 02:29:52 loss: 0.2756 Lr: 0.00400 [2024-11-25 16:53:47,612 INFO misc.py line 119 2586773] Train: [3/50][367/376] Data 0.003 (0.003) Batch 0.523 (0.509) Remain 02:29:52 loss: 0.3226 Lr: 0.00400 [2024-11-25 16:53:48,108 INFO misc.py line 119 2586773] Train: [3/50][368/376] Data 0.002 (0.003) Batch 0.496 (0.509) Remain 02:29:51 loss: 0.2891 Lr: 0.00400 [2024-11-25 16:53:48,589 INFO misc.py line 119 2586773] Train: [3/50][369/376] Data 0.002 (0.003) Batch 0.481 (0.508) Remain 02:29:49 loss: 0.2522 Lr: 0.00400 [2024-11-25 16:53:49,052 INFO misc.py line 119 2586773] Train: [3/50][370/376] Data 0.003 (0.003) Batch 0.463 (0.508) Remain 02:29:46 loss: 0.2993 Lr: 0.00400 [2024-11-25 16:53:49,586 INFO misc.py line 119 2586773] Train: [3/50][371/376] Data 0.003 (0.003) Batch 0.534 (0.508) Remain 02:29:47 loss: 0.2852 Lr: 0.00400 [2024-11-25 16:53:50,060 INFO misc.py line 119 2586773] Train: [3/50][372/376] Data 0.003 (0.003) Batch 0.474 (0.508) Remain 02:29:45 loss: 0.2818 Lr: 0.00400 [2024-11-25 16:53:50,548 INFO misc.py line 119 2586773] Train: [3/50][373/376] Data 0.002 (0.003) Batch 0.488 (0.508) Remain 02:29:43 loss: 0.2848 Lr: 0.00400 [2024-11-25 16:53:51,055 INFO misc.py line 119 2586773] Train: [3/50][374/376] Data 0.002 (0.003) Batch 0.507 (0.508) Remain 02:29:43 loss: 0.3152 Lr: 0.00400 [2024-11-25 16:53:51,535 INFO misc.py line 119 2586773] Train: [3/50][375/376] Data 0.003 (0.003) Batch 0.480 (0.508) Remain 02:29:41 loss: 0.3282 Lr: 0.00400 [2024-11-25 16:53:52,022 INFO misc.py line 119 2586773] Train: [3/50][376/376] Data 0.003 (0.003) Batch 0.486 (0.508) Remain 02:29:39 loss: 0.2705 Lr: 0.00400 [2024-11-25 16:53:52,022 INFO misc.py line 136 2586773] Train result: loss: 0.3161 [2024-11-25 16:53:52,023 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 16:54:03,043 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2802 [2024-11-25 16:54:03,301 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2888 [2024-11-25 16:54:03,571 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3891 [2024-11-25 16:54:03,795 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.3275 [2024-11-25 16:54:04,056 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3716 [2024-11-25 16:54:04,326 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.3468 [2024-11-25 16:54:04,549 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2946 [2024-11-25 16:54:04,818 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.3025 [2024-11-25 16:54:05,042 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.3022 [2024-11-25 16:54:05,303 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3803 [2024-11-25 16:54:05,534 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.3092 [2024-11-25 16:54:05,805 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2988 [2024-11-25 16:54:06,070 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3198 [2024-11-25 16:54:06,334 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.3353 [2024-11-25 16:54:06,570 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.3167 [2024-11-25 16:54:06,808 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3194 [2024-11-25 16:54:07,075 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3994 [2024-11-25 16:54:07,321 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.3025 [2024-11-25 16:54:07,551 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.3145 [2024-11-25 16:54:07,812 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3194 [2024-11-25 16:54:08,046 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.3015 [2024-11-25 16:54:08,313 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3475 [2024-11-25 16:54:08,549 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2967 [2024-11-25 16:54:08,817 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3437 [2024-11-25 16:54:09,078 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.3122 [2024-11-25 16:54:09,313 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3269 [2024-11-25 16:54:09,566 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3630 [2024-11-25 16:54:09,815 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3345 [2024-11-25 16:54:10,081 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3439 [2024-11-25 16:54:10,335 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3705 [2024-11-25 16:54:10,571 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3312 [2024-11-25 16:54:10,834 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.3447 [2024-11-25 16:54:11,054 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.3078 [2024-11-25 16:54:11,293 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2877 [2024-11-25 16:54:11,553 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2925 [2024-11-25 16:54:11,799 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2956 [2024-11-25 16:54:12,027 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2829 [2024-11-25 16:54:12,298 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.3317 [2024-11-25 16:54:12,530 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3320 [2024-11-25 16:54:12,762 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3490 [2024-11-25 16:54:13,030 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2975 [2024-11-25 16:54:13,280 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3736 [2024-11-25 16:54:13,517 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3325 [2024-11-25 16:54:13,748 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.3096 [2024-11-25 16:54:13,983 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2960 [2024-11-25 16:54:14,233 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3230 [2024-11-25 16:54:14,493 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3412 [2024-11-25 16:54:14,744 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3536 [2024-11-25 16:54:14,967 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2951 [2024-11-25 16:54:15,201 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2990 [2024-11-25 16:54:15,421 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.3033 [2024-11-25 16:54:15,672 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3199 [2024-11-25 16:54:15,940 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.3174 [2024-11-25 16:54:16,201 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3356 [2024-11-25 16:54:16,433 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.3664 [2024-11-25 16:54:16,675 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.3209 [2024-11-25 16:54:16,932 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3564 [2024-11-25 16:54:17,198 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.3054 [2024-11-25 16:54:17,454 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3181 [2024-11-25 16:54:17,715 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3145 [2024-11-25 16:54:17,966 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.3447 [2024-11-25 16:54:18,235 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3329 [2024-11-25 16:54:18,464 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2841 [2024-11-25 16:54:18,722 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3875 [2024-11-25 16:54:18,991 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3517 [2024-11-25 16:54:19,261 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.3390 [2024-11-25 16:54:19,504 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2915 [2024-11-25 16:54:19,761 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3323 [2024-11-25 16:54:20,029 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.3061 [2024-11-25 16:54:20,292 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3406 [2024-11-25 16:54:20,537 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2840 [2024-11-25 16:54:20,771 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.3103 [2024-11-25 16:54:21,031 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3516 [2024-11-25 16:54:21,275 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3537 [2024-11-25 16:54:21,492 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.3021 [2024-11-25 16:54:21,713 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2845 [2024-11-25 16:54:21,984 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3316 [2024-11-25 16:54:22,220 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2869 [2024-11-25 16:54:22,480 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2979 [2024-11-25 16:54:22,732 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3598 [2024-11-25 16:54:22,972 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2953 [2024-11-25 16:54:23,232 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3194 [2024-11-25 16:54:23,483 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2800 [2024-11-25 16:54:23,733 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3349 [2024-11-25 16:54:24,005 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2967 [2024-11-25 16:54:24,248 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.3050 [2024-11-25 16:54:24,510 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3714 [2024-11-25 16:54:24,771 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3381 [2024-11-25 16:54:25,025 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3248 [2024-11-25 16:54:25,280 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.3156 [2024-11-25 16:54:25,520 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2923 [2024-11-25 16:54:25,772 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3267 [2024-11-25 16:54:26,038 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3445 [2024-11-25 16:54:26,304 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2866 [2024-11-25 16:54:26,570 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2797 [2024-11-25 16:54:26,820 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.3054 [2024-11-25 16:54:27,087 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3101 [2024-11-25 16:54:27,307 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3191 [2024-11-25 16:54:27,576 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.3279 [2024-11-25 16:54:27,813 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3268 [2024-11-25 16:54:28,085 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.3229 [2024-11-25 16:54:28,347 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3361 [2024-11-25 16:54:28,617 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3203 [2024-11-25 16:54:28,869 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3670 [2024-11-25 16:54:29,091 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.3116 [2024-11-25 16:54:29,326 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.3132 [2024-11-25 16:54:29,585 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.3005 [2024-11-25 16:54:29,855 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3588 [2024-11-25 16:54:30,089 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3574 [2024-11-25 16:54:30,349 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.3343 [2024-11-25 16:54:30,610 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.3023 [2024-11-25 16:54:30,832 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.3122 [2024-11-25 16:54:31,067 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.3171 [2024-11-25 16:54:31,285 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.3047 [2024-11-25 16:54:31,511 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2958 [2024-11-25 16:54:31,784 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3605 [2024-11-25 16:54:32,046 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3790 [2024-11-25 16:54:32,313 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3553 [2024-11-25 16:54:32,578 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.3355 [2024-11-25 16:54:32,838 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.4149 [2024-11-25 16:54:33,096 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3095 [2024-11-25 16:54:33,361 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2964 [2024-11-25 16:54:33,616 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3694 [2024-11-25 16:54:33,879 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3658 [2024-11-25 16:54:34,142 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2958 [2024-11-25 16:54:34,395 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3822 [2024-11-25 16:54:34,625 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.3354 [2024-11-25 16:54:34,885 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3800 [2024-11-25 16:54:35,119 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2971 [2024-11-25 16:54:35,344 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.3070 [2024-11-25 16:54:35,554 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.3123 [2024-11-25 16:54:35,773 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2845 [2024-11-25 16:54:36,501 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.6626/0.6929/0.9949. [2024-11-25 16:54:36,502 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9948/0.9989 [2024-11-25 16:54:36,502 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.3303/0.3870 [2024-11-25 16:54:36,502 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 16:54:36,504 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.6626 [2024-11-25 16:54:36,504 INFO misc.py line 165 2586773] Currently Best mIoU: 0.6626 [2024-11-25 16:54:36,505 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 16:54:40,809 INFO misc.py line 119 2586773] Train: [4/50][1/376] Data 0.085 (0.085) Batch 0.526 (0.526) Remain 02:34:46 loss: 0.3189 Lr: 0.00400 [2024-11-25 16:54:41,324 INFO misc.py line 119 2586773] Train: [4/50][2/376] Data 0.003 (0.003) Batch 0.515 (0.515) Remain 02:31:45 loss: 0.2897 Lr: 0.00400 [2024-11-25 16:54:41,811 INFO misc.py line 119 2586773] Train: [4/50][3/376] Data 0.003 (0.003) Batch 0.488 (0.488) Remain 02:23:37 loss: 0.3032 Lr: 0.00400 [2024-11-25 16:54:42,314 INFO misc.py line 119 2586773] Train: [4/50][4/376] Data 0.003 (0.003) Batch 0.502 (0.502) Remain 02:27:53 loss: 0.3113 Lr: 0.00400 [2024-11-25 16:54:42,834 INFO misc.py line 119 2586773] Train: [4/50][5/376] Data 0.003 (0.003) Batch 0.520 (0.511) Remain 02:30:26 loss: 0.3365 Lr: 0.00400 [2024-11-25 16:54:43,341 INFO misc.py line 119 2586773] Train: [4/50][6/376] Data 0.003 (0.003) Batch 0.508 (0.510) Remain 02:30:07 loss: 0.2627 Lr: 0.00400 [2024-11-25 16:54:43,842 INFO misc.py line 119 2586773] Train: [4/50][7/376] Data 0.003 (0.003) Batch 0.501 (0.508) Remain 02:29:26 loss: 0.3122 Lr: 0.00400 [2024-11-25 16:54:44,355 INFO misc.py line 119 2586773] Train: [4/50][8/376] Data 0.002 (0.003) Batch 0.513 (0.509) Remain 02:29:46 loss: 0.3242 Lr: 0.00400 [2024-11-25 16:54:44,872 INFO misc.py line 119 2586773] Train: [4/50][9/376] Data 0.003 (0.003) Batch 0.517 (0.510) Remain 02:30:08 loss: 0.3124 Lr: 0.00400 [2024-11-25 16:54:45,379 INFO misc.py line 119 2586773] Train: [4/50][10/376] Data 0.003 (0.003) Batch 0.507 (0.510) Remain 02:30:00 loss: 0.3263 Lr: 0.00400 [2024-11-25 16:54:45,867 INFO misc.py line 119 2586773] Train: [4/50][11/376] Data 0.003 (0.003) Batch 0.488 (0.507) Remain 02:29:12 loss: 0.3070 Lr: 0.00400 [2024-11-25 16:54:46,365 INFO misc.py line 119 2586773] Train: [4/50][12/376] Data 0.003 (0.003) Batch 0.499 (0.506) Remain 02:28:55 loss: 0.2628 Lr: 0.00400 [2024-11-25 16:54:46,925 INFO misc.py line 119 2586773] Train: [4/50][13/376] Data 0.003 (0.003) Batch 0.560 (0.511) Remain 02:30:30 loss: 0.3194 Lr: 0.00400 [2024-11-25 16:54:47,406 INFO misc.py line 119 2586773] Train: [4/50][14/376] Data 0.003 (0.003) Batch 0.481 (0.509) Remain 02:29:41 loss: 0.2712 Lr: 0.00400 [2024-11-25 16:54:47,908 INFO misc.py line 119 2586773] Train: [4/50][15/376] Data 0.003 (0.003) Batch 0.501 (0.508) Remain 02:29:29 loss: 0.2714 Lr: 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Train: [4/50][129/376] Data 0.003 (0.003) Batch 0.491 (0.505) Remain 02:27:35 loss: 0.2780 Lr: 0.00399 [2024-11-25 16:55:45,915 INFO misc.py line 119 2586773] Train: [4/50][130/376] Data 0.003 (0.003) Batch 0.503 (0.505) Remain 02:27:34 loss: 0.2780 Lr: 0.00399 [2024-11-25 16:55:46,424 INFO misc.py line 119 2586773] Train: [4/50][131/376] Data 0.002 (0.003) Batch 0.509 (0.505) Remain 02:27:34 loss: 0.3389 Lr: 0.00399 [2024-11-25 16:55:46,952 INFO misc.py line 119 2586773] Train: [4/50][132/376] Data 0.003 (0.003) Batch 0.528 (0.505) Remain 02:27:37 loss: 0.2789 Lr: 0.00399 [2024-11-25 16:55:47,481 INFO misc.py line 119 2586773] Train: [4/50][133/376] Data 0.003 (0.003) Batch 0.529 (0.505) Remain 02:27:39 loss: 0.2425 Lr: 0.00399 [2024-11-25 16:55:48,025 INFO misc.py line 119 2586773] Train: [4/50][134/376] Data 0.003 (0.003) Batch 0.545 (0.505) Remain 02:27:44 loss: 0.3096 Lr: 0.00399 [2024-11-25 16:55:48,529 INFO misc.py line 119 2586773] Train: [4/50][135/376] Data 0.003 (0.003) Batch 0.503 (0.505) Remain 02:27:43 loss: 0.3272 Lr: 0.00399 [2024-11-25 16:55:49,057 INFO misc.py line 119 2586773] Train: [4/50][136/376] Data 0.003 (0.003) Batch 0.529 (0.506) Remain 02:27:46 loss: 0.3066 Lr: 0.00399 [2024-11-25 16:55:49,557 INFO misc.py line 119 2586773] Train: [4/50][137/376] Data 0.003 (0.003) Batch 0.500 (0.506) Remain 02:27:44 loss: 0.2615 Lr: 0.00399 [2024-11-25 16:55:50,038 INFO misc.py line 119 2586773] Train: [4/50][138/376] Data 0.003 (0.003) Batch 0.482 (0.505) Remain 02:27:41 loss: 0.2692 Lr: 0.00399 [2024-11-25 16:55:50,530 INFO misc.py line 119 2586773] Train: [4/50][139/376] Data 0.003 (0.003) Batch 0.492 (0.505) Remain 02:27:39 loss: 0.2637 Lr: 0.00399 [2024-11-25 16:55:50,999 INFO misc.py line 119 2586773] Train: [4/50][140/376] Data 0.003 (0.003) Batch 0.469 (0.505) Remain 02:27:33 loss: 0.2857 Lr: 0.00399 [2024-11-25 16:55:51,480 INFO misc.py line 119 2586773] Train: [4/50][141/376] Data 0.003 (0.003) Batch 0.481 (0.505) Remain 02:27:30 loss: 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Train: [4/50][267/376] Data 0.003 (0.003) Batch 0.519 (0.506) Remain 02:26:41 loss: 0.2899 Lr: 0.00399 [2024-11-25 16:56:55,842 INFO misc.py line 119 2586773] Train: [4/50][268/376] Data 0.002 (0.003) Batch 0.534 (0.506) Remain 02:26:42 loss: 0.2400 Lr: 0.00399 [2024-11-25 16:56:56,316 INFO misc.py line 119 2586773] Train: [4/50][269/376] Data 0.003 (0.003) Batch 0.475 (0.506) Remain 02:26:39 loss: 0.2983 Lr: 0.00399 [2024-11-25 16:56:56,811 INFO misc.py line 119 2586773] Train: [4/50][270/376] Data 0.003 (0.003) Batch 0.495 (0.506) Remain 02:26:38 loss: 0.2833 Lr: 0.00399 [2024-11-25 16:56:57,360 INFO misc.py line 119 2586773] Train: [4/50][271/376] Data 0.003 (0.003) Batch 0.549 (0.506) Remain 02:26:41 loss: 0.2744 Lr: 0.00399 [2024-11-25 16:56:57,848 INFO misc.py line 119 2586773] Train: [4/50][272/376] Data 0.003 (0.003) Batch 0.488 (0.506) Remain 02:26:39 loss: 0.3493 Lr: 0.00399 [2024-11-25 16:56:58,345 INFO misc.py line 119 2586773] Train: [4/50][273/376] Data 0.003 (0.003) 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Train: [4/50][336/376] Data 0.003 (0.003) Batch 0.515 (0.506) Remain 02:26:17 loss: 0.3153 Lr: 0.00398 [2024-11-25 16:57:30,930 INFO misc.py line 119 2586773] Train: [4/50][337/376] Data 0.003 (0.003) Batch 0.515 (0.506) Remain 02:26:17 loss: 0.2821 Lr: 0.00398 [2024-11-25 16:57:31,412 INFO misc.py line 119 2586773] Train: [4/50][338/376] Data 0.003 (0.003) Batch 0.482 (0.506) Remain 02:26:15 loss: 0.2686 Lr: 0.00398 [2024-11-25 16:57:31,942 INFO misc.py line 119 2586773] Train: [4/50][339/376] Data 0.002 (0.003) Batch 0.530 (0.506) Remain 02:26:16 loss: 0.3193 Lr: 0.00398 [2024-11-25 16:57:32,475 INFO misc.py line 119 2586773] Train: [4/50][340/376] Data 0.003 (0.003) Batch 0.533 (0.506) Remain 02:26:17 loss: 0.3731 Lr: 0.00398 [2024-11-25 16:57:32,971 INFO misc.py line 119 2586773] Train: [4/50][341/376] Data 0.003 (0.003) Batch 0.495 (0.506) Remain 02:26:16 loss: 0.2985 Lr: 0.00398 [2024-11-25 16:57:33,491 INFO misc.py line 119 2586773] Train: [4/50][342/376] Data 0.002 (0.003) Batch 0.520 (0.506) Remain 02:26:16 loss: 0.2821 Lr: 0.00398 [2024-11-25 16:57:34,001 INFO misc.py line 119 2586773] Train: [4/50][343/376] Data 0.003 (0.003) Batch 0.510 (0.506) Remain 02:26:16 loss: 0.2472 Lr: 0.00398 [2024-11-25 16:57:34,533 INFO misc.py line 119 2586773] Train: [4/50][344/376] Data 0.003 (0.003) Batch 0.532 (0.507) Remain 02:26:16 loss: 0.3187 Lr: 0.00398 [2024-11-25 16:57:35,062 INFO misc.py line 119 2586773] Train: [4/50][345/376] Data 0.003 (0.003) Batch 0.530 (0.507) Remain 02:26:17 loss: 0.2779 Lr: 0.00398 [2024-11-25 16:57:35,544 INFO misc.py line 119 2586773] Train: [4/50][346/376] Data 0.003 (0.003) Batch 0.482 (0.507) Remain 02:26:15 loss: 0.3114 Lr: 0.00398 [2024-11-25 16:57:36,108 INFO misc.py line 119 2586773] Train: [4/50][347/376] Data 0.003 (0.003) Batch 0.563 (0.507) Remain 02:26:18 loss: 0.3230 Lr: 0.00398 [2024-11-25 16:57:36,642 INFO misc.py line 119 2586773] Train: [4/50][348/376] Data 0.003 (0.003) Batch 0.534 (0.507) Remain 02:26:18 loss: 0.3042 Lr: 0.00398 [2024-11-25 16:57:37,170 INFO misc.py line 119 2586773] Train: [4/50][349/376] Data 0.003 (0.003) Batch 0.528 (0.507) Remain 02:26:19 loss: 0.2586 Lr: 0.00398 [2024-11-25 16:57:37,649 INFO misc.py line 119 2586773] Train: [4/50][350/376] Data 0.003 (0.003) Batch 0.479 (0.507) Remain 02:26:17 loss: 0.2950 Lr: 0.00398 [2024-11-25 16:57:38,205 INFO misc.py line 119 2586773] Train: [4/50][351/376] Data 0.003 (0.003) Batch 0.555 (0.507) Remain 02:26:19 loss: 0.3020 Lr: 0.00398 [2024-11-25 16:57:38,728 INFO misc.py line 119 2586773] Train: [4/50][352/376] Data 0.003 (0.003) Batch 0.524 (0.507) Remain 02:26:19 loss: 0.2845 Lr: 0.00398 [2024-11-25 16:57:39,206 INFO misc.py line 119 2586773] Train: [4/50][353/376] Data 0.003 (0.003) Batch 0.478 (0.507) Remain 02:26:17 loss: 0.3345 Lr: 0.00398 [2024-11-25 16:57:39,681 INFO misc.py line 119 2586773] Train: [4/50][354/376] Data 0.003 (0.003) Batch 0.475 (0.507) Remain 02:26:15 loss: 0.2470 Lr: 0.00398 [2024-11-25 16:57:40,221 INFO misc.py line 119 2586773] Train: [4/50][355/376] Data 0.003 (0.003) Batch 0.540 (0.507) Remain 02:26:17 loss: 0.2943 Lr: 0.00398 [2024-11-25 16:57:40,712 INFO misc.py line 119 2586773] Train: [4/50][356/376] Data 0.003 (0.003) Batch 0.490 (0.507) Remain 02:26:15 loss: 0.2468 Lr: 0.00398 [2024-11-25 16:57:41,255 INFO misc.py line 119 2586773] Train: [4/50][357/376] Data 0.003 (0.003) Batch 0.543 (0.507) Remain 02:26:16 loss: 0.2830 Lr: 0.00398 [2024-11-25 16:57:41,759 INFO misc.py line 119 2586773] Train: [4/50][358/376] Data 0.003 (0.003) Batch 0.504 (0.507) Remain 02:26:16 loss: 0.2883 Lr: 0.00398 [2024-11-25 16:57:42,299 INFO misc.py line 119 2586773] Train: [4/50][359/376] Data 0.003 (0.003) Batch 0.540 (0.507) Remain 02:26:17 loss: 0.3064 Lr: 0.00398 [2024-11-25 16:57:42,772 INFO misc.py line 119 2586773] Train: [4/50][360/376] Data 0.003 (0.003) Batch 0.473 (0.507) Remain 02:26:15 loss: 0.2751 Lr: 0.00398 [2024-11-25 16:57:43,260 INFO misc.py line 119 2586773] Train: [4/50][361/376] Data 0.003 (0.003) Batch 0.488 (0.507) Remain 02:26:13 loss: 0.2508 Lr: 0.00398 [2024-11-25 16:57:43,703 INFO misc.py line 119 2586773] Train: [4/50][362/376] Data 0.003 (0.003) Batch 0.443 (0.507) Remain 02:26:10 loss: 0.2902 Lr: 0.00398 [2024-11-25 16:57:44,177 INFO misc.py line 119 2586773] Train: [4/50][363/376] Data 0.003 (0.003) Batch 0.473 (0.507) Remain 02:26:08 loss: 0.2861 Lr: 0.00398 [2024-11-25 16:57:44,715 INFO misc.py line 119 2586773] Train: [4/50][364/376] Data 0.003 (0.003) Batch 0.538 (0.507) Remain 02:26:09 loss: 0.3104 Lr: 0.00398 [2024-11-25 16:57:45,200 INFO misc.py line 119 2586773] Train: [4/50][365/376] Data 0.003 (0.003) Batch 0.485 (0.507) Remain 02:26:07 loss: 0.3023 Lr: 0.00398 [2024-11-25 16:57:45,738 INFO misc.py line 119 2586773] Train: [4/50][366/376] Data 0.003 (0.003) Batch 0.537 (0.507) Remain 02:26:08 loss: 0.2701 Lr: 0.00398 [2024-11-25 16:57:46,266 INFO misc.py line 119 2586773] Train: [4/50][367/376] Data 0.003 (0.003) Batch 0.528 (0.507) Remain 02:26:09 loss: 0.2857 Lr: 0.00398 [2024-11-25 16:57:46,732 INFO misc.py line 119 2586773] Train: [4/50][368/376] Data 0.003 (0.003) Batch 0.466 (0.507) Remain 02:26:06 loss: 0.3056 Lr: 0.00398 [2024-11-25 16:57:47,204 INFO misc.py line 119 2586773] Train: [4/50][369/376] Data 0.003 (0.003) Batch 0.473 (0.507) Remain 02:26:04 loss: 0.2716 Lr: 0.00398 [2024-11-25 16:57:47,717 INFO misc.py line 119 2586773] Train: [4/50][370/376] Data 0.003 (0.003) Batch 0.513 (0.507) Remain 02:26:04 loss: 0.2785 Lr: 0.00398 [2024-11-25 16:57:48,200 INFO misc.py line 119 2586773] Train: [4/50][371/376] Data 0.003 (0.003) Batch 0.483 (0.506) Remain 02:26:02 loss: 0.2569 Lr: 0.00398 [2024-11-25 16:57:48,703 INFO misc.py line 119 2586773] Train: [4/50][372/376] Data 0.003 (0.003) Batch 0.502 (0.506) Remain 02:26:02 loss: 0.1990 Lr: 0.00398 [2024-11-25 16:57:49,197 INFO misc.py line 119 2586773] Train: [4/50][373/376] Data 0.003 (0.003) Batch 0.494 (0.506) Remain 02:26:01 loss: 0.2803 Lr: 0.00398 [2024-11-25 16:57:49,688 INFO misc.py line 119 2586773] Train: [4/50][374/376] Data 0.003 (0.003) Batch 0.490 (0.506) Remain 02:25:59 loss: 0.2930 Lr: 0.00398 [2024-11-25 16:57:50,222 INFO misc.py line 119 2586773] Train: [4/50][375/376] Data 0.003 (0.003) Batch 0.534 (0.506) Remain 02:26:00 loss: 0.3286 Lr: 0.00398 [2024-11-25 16:57:50,714 INFO misc.py line 119 2586773] Train: [4/50][376/376] Data 0.003 (0.003) Batch 0.492 (0.506) Remain 02:25:59 loss: 0.2449 Lr: 0.00398 [2024-11-25 16:57:50,715 INFO misc.py line 136 2586773] Train result: loss: 0.2967 [2024-11-25 16:57:50,715 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 16:58:01,582 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2788 [2024-11-25 16:58:01,845 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2688 [2024-11-25 16:58:02,108 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3926 [2024-11-25 16:58:02,339 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2759 [2024-11-25 16:58:02,600 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3143 [2024-11-25 16:58:02,868 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.3021 [2024-11-25 16:58:03,101 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2911 [2024-11-25 16:58:03,370 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2873 [2024-11-25 16:58:03,596 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2892 [2024-11-25 16:58:03,860 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3420 [2024-11-25 16:58:04,098 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2899 [2024-11-25 16:58:04,370 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2834 [2024-11-25 16:58:04,634 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3143 [2024-11-25 16:58:04,894 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.3105 [2024-11-25 16:58:05,130 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.3102 [2024-11-25 16:58:05,367 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3176 [2024-11-25 16:58:05,632 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3819 [2024-11-25 16:58:05,884 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2842 [2024-11-25 16:58:06,117 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2978 [2024-11-25 16:58:06,380 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3356 [2024-11-25 16:58:06,613 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2897 [2024-11-25 16:58:06,880 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3378 [2024-11-25 16:58:07,123 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2946 [2024-11-25 16:58:07,390 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3199 [2024-11-25 16:58:07,661 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.3040 [2024-11-25 16:58:07,900 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3144 [2024-11-25 16:58:08,162 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3318 [2024-11-25 16:58:08,406 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3251 [2024-11-25 16:58:08,673 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3423 [2024-11-25 16:58:08,928 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3668 [2024-11-25 16:58:09,170 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3200 [2024-11-25 16:58:09,441 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.3168 [2024-11-25 16:58:09,660 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2947 [2024-11-25 16:58:09,903 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2856 [2024-11-25 16:58:10,164 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2805 [2024-11-25 16:58:10,408 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2892 [2024-11-25 16:58:10,637 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2711 [2024-11-25 16:58:10,907 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.3086 [2024-11-25 16:58:11,137 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3086 [2024-11-25 16:58:11,370 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3241 [2024-11-25 16:58:11,639 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2962 [2024-11-25 16:58:11,890 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3543 [2024-11-25 16:58:12,128 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3308 [2024-11-25 16:58:12,361 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.3015 [2024-11-25 16:58:12,597 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.3076 [2024-11-25 16:58:12,852 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3126 [2024-11-25 16:58:13,112 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3347 [2024-11-25 16:58:13,362 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3438 [2024-11-25 16:58:13,591 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2770 [2024-11-25 16:58:13,827 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2809 [2024-11-25 16:58:14,047 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2889 [2024-11-25 16:58:14,300 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3281 [2024-11-25 16:58:14,567 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2932 [2024-11-25 16:58:14,829 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3242 [2024-11-25 16:58:15,062 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.3177 [2024-11-25 16:58:15,306 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.3101 [2024-11-25 16:58:15,563 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3338 [2024-11-25 16:58:15,830 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2971 [2024-11-25 16:58:16,089 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3249 [2024-11-25 16:58:16,352 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3144 [2024-11-25 16:58:16,611 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.3054 [2024-11-25 16:58:16,891 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3106 [2024-11-25 16:58:17,120 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2764 [2024-11-25 16:58:17,389 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3656 [2024-11-25 16:58:17,653 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3264 [2024-11-25 16:58:17,933 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2953 [2024-11-25 16:58:18,186 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2750 [2024-11-25 16:58:18,443 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3344 [2024-11-25 16:58:18,712 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.3001 [2024-11-25 16:58:18,974 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3392 [2024-11-25 16:58:19,217 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2671 [2024-11-25 16:58:19,453 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.3058 [2024-11-25 16:58:19,710 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3104 [2024-11-25 16:58:19,953 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3582 [2024-11-25 16:58:20,179 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2867 [2024-11-25 16:58:20,407 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2664 [2024-11-25 16:58:20,675 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3082 [2024-11-25 16:58:20,919 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2854 [2024-11-25 16:58:21,189 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2713 [2024-11-25 16:58:21,439 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3686 [2024-11-25 16:58:21,692 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2945 [2024-11-25 16:58:21,951 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3084 [2024-11-25 16:58:22,203 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2739 [2024-11-25 16:58:22,449 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3075 [2024-11-25 16:58:22,728 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2893 [2024-11-25 16:58:22,966 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.3015 [2024-11-25 16:58:23,229 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3538 [2024-11-25 16:58:23,489 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3347 [2024-11-25 16:58:23,745 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3200 [2024-11-25 16:58:24,002 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2994 [2024-11-25 16:58:24,245 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2857 [2024-11-25 16:58:24,499 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3118 [2024-11-25 16:58:24,767 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3339 [2024-11-25 16:58:25,041 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2806 [2024-11-25 16:58:25,307 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2762 [2024-11-25 16:58:25,564 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2763 [2024-11-25 16:58:25,832 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3282 [2024-11-25 16:58:26,058 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3208 [2024-11-25 16:58:26,329 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2982 [2024-11-25 16:58:26,569 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3038 [2024-11-25 16:58:26,851 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2924 [2024-11-25 16:58:27,119 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3533 [2024-11-25 16:58:27,382 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3313 [2024-11-25 16:58:27,634 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3615 [2024-11-25 16:58:27,871 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2897 [2024-11-25 16:58:28,113 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2869 [2024-11-25 16:58:28,367 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.3013 [2024-11-25 16:58:28,642 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3553 [2024-11-25 16:58:28,878 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3317 [2024-11-25 16:58:29,139 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.3098 [2024-11-25 16:58:29,412 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2888 [2024-11-25 16:58:29,642 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2924 [2024-11-25 16:58:29,884 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2777 [2024-11-25 16:58:30,107 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2895 [2024-11-25 16:58:30,331 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2922 [2024-11-25 16:58:30,602 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3786 [2024-11-25 16:58:30,860 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3370 [2024-11-25 16:58:31,128 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3527 [2024-11-25 16:58:31,401 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2993 [2024-11-25 16:58:31,662 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3726 [2024-11-25 16:58:31,921 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3052 [2024-11-25 16:58:32,185 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2826 [2024-11-25 16:58:32,443 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3600 [2024-11-25 16:58:32,714 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3431 [2024-11-25 16:58:32,982 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2956 [2024-11-25 16:58:33,233 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3370 [2024-11-25 16:58:33,463 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.3096 [2024-11-25 16:58:33,733 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3315 [2024-11-25 16:58:33,968 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.3099 [2024-11-25 16:58:34,205 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2755 [2024-11-25 16:58:34,421 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2907 [2024-11-25 16:58:34,645 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2693 [2024-11-25 16:58:35,319 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.6856/0.7332/0.9949. [2024-11-25 16:58:35,319 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9949/0.9984 [2024-11-25 16:58:35,319 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.3762/0.4680 [2024-11-25 16:58:35,319 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 16:58:35,320 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.6856 [2024-11-25 16:58:35,320 INFO misc.py line 165 2586773] Currently Best mIoU: 0.6856 [2024-11-25 16:58:35,321 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 16:58:39,595 INFO misc.py line 119 2586773] Train: [5/50][1/376] Data 0.077 (0.077) Batch 0.616 (0.616) Remain 02:57:31 loss: 0.2841 Lr: 0.00398 [2024-11-25 16:58:40,113 INFO misc.py line 119 2586773] Train: [5/50][2/376] Data 0.003 (0.003) Batch 0.518 (0.518) Remain 02:29:12 loss: 0.2224 Lr: 0.00398 [2024-11-25 16:58:40,613 INFO misc.py line 119 2586773] Train: [5/50][3/376] Data 0.003 (0.003) Batch 0.500 (0.500) Remain 02:24:10 loss: 0.2690 Lr: 0.00398 [2024-11-25 16:58:41,156 INFO misc.py line 119 2586773] Train: [5/50][4/376] Data 0.003 (0.003) Batch 0.544 (0.544) Remain 02:36:42 loss: 0.2566 Lr: 0.00398 [2024-11-25 16:58:41,715 INFO misc.py line 119 2586773] Train: [5/50][5/376] Data 0.003 (0.003) Batch 0.559 (0.551) Remain 02:38:51 loss: 0.2903 Lr: 0.00398 [2024-11-25 16:58:42,220 INFO misc.py line 119 2586773] Train: [5/50][6/376] Data 0.003 (0.003) Batch 0.504 (0.536) Remain 02:34:21 loss: 0.3146 Lr: 0.00398 [2024-11-25 16:58:42,774 INFO misc.py line 119 2586773] Train: [5/50][7/376] Data 0.003 (0.003) Batch 0.555 (0.540) Remain 02:35:43 loss: 0.3011 Lr: 0.00398 [2024-11-25 16:58:43,299 INFO misc.py line 119 2586773] Train: [5/50][8/376] Data 0.003 (0.003) Batch 0.525 (0.537) Remain 02:34:48 loss: 0.3415 Lr: 0.00398 [2024-11-25 16:58:43,843 INFO misc.py line 119 2586773] Train: [5/50][9/376] Data 0.003 (0.003) Batch 0.544 (0.538) Remain 02:35:07 loss: 0.3105 Lr: 0.00398 [2024-11-25 16:58:44,383 INFO misc.py line 119 2586773] Train: [5/50][10/376] Data 0.003 (0.003) Batch 0.540 (0.539) Remain 02:35:09 loss: 0.2733 Lr: 0.00398 [2024-11-25 16:58:44,864 INFO misc.py line 119 2586773] Train: [5/50][11/376] Data 0.003 (0.003) Batch 0.481 (0.531) Remain 02:33:04 loss: 0.3025 Lr: 0.00398 [2024-11-25 16:58:45,349 INFO misc.py line 119 2586773] Train: [5/50][12/376] Data 0.003 (0.003) Batch 0.485 (0.526) Remain 02:31:36 loss: 0.3167 Lr: 0.00398 [2024-11-25 16:58:45,889 INFO misc.py line 119 2586773] Train: [5/50][13/376] Data 0.003 (0.003) Batch 0.540 (0.528) Remain 02:31:58 loss: 0.2553 Lr: 0.00398 [2024-11-25 16:58:46,458 INFO misc.py line 119 2586773] Train: [5/50][14/376] Data 0.003 (0.003) Batch 0.570 (0.531) Remain 02:33:04 loss: 0.2423 Lr: 0.00398 [2024-11-25 16:58:46,961 INFO misc.py line 119 2586773] Train: [5/50][15/376] Data 0.003 (0.003) Batch 0.503 (0.529) Remain 02:32:22 loss: 0.2797 Lr: 0.00398 [2024-11-25 16:58:47,474 INFO misc.py line 119 2586773] Train: [5/50][16/376] Data 0.003 (0.003) Batch 0.513 (0.528) Remain 02:32:00 loss: 0.3179 Lr: 0.00398 [2024-11-25 16:58:47,961 INFO misc.py line 119 2586773] Train: [5/50][17/376] Data 0.003 (0.003) Batch 0.487 (0.525) Remain 02:31:08 loss: 0.2701 Lr: 0.00398 [2024-11-25 16:58:48,485 INFO misc.py line 119 2586773] Train: [5/50][18/376] Data 0.003 (0.003) Batch 0.524 (0.525) Remain 02:31:07 loss: 0.2956 Lr: 0.00398 [2024-11-25 16:58:49,044 INFO misc.py line 119 2586773] Train: [5/50][19/376] Data 0.003 (0.003) Batch 0.559 (0.527) Remain 02:31:43 loss: 0.2919 Lr: 0.00398 [2024-11-25 16:58:49,539 INFO misc.py line 119 2586773] Train: [5/50][20/376] Data 0.003 (0.003) Batch 0.496 (0.525) Remain 02:31:11 loss: 0.3921 Lr: 0.00398 [2024-11-25 16:58:50,098 INFO misc.py line 119 2586773] Train: [5/50][21/376] Data 0.003 (0.003) Batch 0.558 (0.527) Remain 02:31:42 loss: 0.3013 Lr: 0.00398 [2024-11-25 16:58:50,622 INFO misc.py line 119 2586773] Train: [5/50][22/376] Data 0.003 (0.003) Batch 0.524 (0.527) Remain 02:31:39 loss: 0.2965 Lr: 0.00398 [2024-11-25 16:58:51,127 INFO misc.py line 119 2586773] Train: [5/50][23/376] Data 0.003 (0.003) Batch 0.505 (0.526) Remain 02:31:20 loss: 0.2988 Lr: 0.00398 [2024-11-25 16:58:51,632 INFO misc.py line 119 2586773] Train: [5/50][24/376] Data 0.003 (0.003) Batch 0.505 (0.525) Remain 02:31:03 loss: 0.2913 Lr: 0.00398 [2024-11-25 16:58:52,144 INFO misc.py line 119 2586773] Train: [5/50][25/376] Data 0.003 (0.003) Batch 0.512 (0.524) Remain 02:30:52 loss: 0.2999 Lr: 0.00398 [2024-11-25 16:58:52,683 INFO misc.py line 119 2586773] Train: [5/50][26/376] Data 0.003 (0.003) Batch 0.540 (0.525) Remain 02:31:03 loss: 0.2610 Lr: 0.00398 [2024-11-25 16:58:53,162 INFO misc.py line 119 2586773] Train: [5/50][27/376] Data 0.003 (0.003) Batch 0.478 (0.523) Remain 02:30:29 loss: 0.3125 Lr: 0.00398 [2024-11-25 16:58:53,689 INFO misc.py line 119 2586773] Train: [5/50][28/376] Data 0.003 (0.003) Batch 0.528 (0.523) Remain 02:30:32 loss: 0.2556 Lr: 0.00398 [2024-11-25 16:58:54,206 INFO misc.py line 119 2586773] Train: [5/50][29/376] Data 0.003 (0.003) Batch 0.517 (0.523) Remain 02:30:27 loss: 0.3279 Lr: 0.00398 [2024-11-25 16:58:54,715 INFO misc.py line 119 2586773] Train: [5/50][30/376] Data 0.003 (0.003) Batch 0.509 (0.522) Remain 02:30:18 loss: 0.2577 Lr: 0.00398 [2024-11-25 16:58:55,218 INFO misc.py line 119 2586773] Train: [5/50][31/376] Data 0.003 (0.003) Batch 0.502 (0.522) Remain 02:30:05 loss: 0.3043 Lr: 0.00398 [2024-11-25 16:58:55,715 INFO misc.py line 119 2586773] Train: [5/50][32/376] Data 0.002 (0.003) Batch 0.498 (0.521) Remain 02:29:50 loss: 0.2225 Lr: 0.00398 [2024-11-25 16:58:56,249 INFO misc.py line 119 2586773] Train: [5/50][33/376] Data 0.002 (0.003) Batch 0.534 (0.521) Remain 02:29:58 loss: 0.2536 Lr: 0.00398 [2024-11-25 16:58:56,761 INFO misc.py line 119 2586773] Train: [5/50][34/376] Data 0.002 (0.003) Batch 0.512 (0.521) Remain 02:29:52 loss: 0.3150 Lr: 0.00398 [2024-11-25 16:58:57,226 INFO misc.py line 119 2586773] Train: [5/50][35/376] Data 0.003 (0.003) Batch 0.466 (0.519) Remain 02:29:21 loss: 0.2566 Lr: 0.00398 [2024-11-25 16:58:57,767 INFO misc.py line 119 2586773] Train: [5/50][36/376] Data 0.003 (0.003) Batch 0.540 (0.520) Remain 02:29:32 loss: 0.3340 Lr: 0.00398 [2024-11-25 16:58:58,296 INFO misc.py line 119 2586773] Train: [5/50][37/376] Data 0.002 (0.003) Batch 0.529 (0.520) Remain 02:29:36 loss: 0.4610 Lr: 0.00398 [2024-11-25 16:58:58,813 INFO misc.py line 119 2586773] Train: [5/50][38/376] Data 0.002 (0.003) Batch 0.518 (0.520) Remain 02:29:34 loss: 0.2376 Lr: 0.00398 [2024-11-25 16:58:59,346 INFO misc.py line 119 2586773] Train: [5/50][39/376] Data 0.003 (0.003) Batch 0.533 (0.520) Remain 02:29:40 loss: 0.3162 Lr: 0.00398 [2024-11-25 16:58:59,851 INFO misc.py line 119 2586773] Train: [5/50][40/376] Data 0.003 (0.003) Batch 0.505 (0.520) Remain 02:29:32 loss: 0.3279 Lr: 0.00398 [2024-11-25 16:59:00,383 INFO 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02:24:24 loss: 0.3061 Lr: 0.00397 [2024-11-25 17:01:24,446 INFO misc.py line 119 2586773] Train: [5/50][324/376] Data 0.002 (0.002) Batch 0.480 (0.510) Remain 02:24:22 loss: 0.2721 Lr: 0.00397 [2024-11-25 17:01:24,938 INFO misc.py line 119 2586773] Train: [5/50][325/376] Data 0.002 (0.002) Batch 0.493 (0.510) Remain 02:24:20 loss: 0.3273 Lr: 0.00397 [2024-11-25 17:01:25,461 INFO misc.py line 119 2586773] Train: [5/50][326/376] Data 0.002 (0.002) Batch 0.523 (0.510) Remain 02:24:20 loss: 0.2762 Lr: 0.00397 [2024-11-25 17:01:25,959 INFO misc.py line 119 2586773] Train: [5/50][327/376] Data 0.002 (0.002) Batch 0.497 (0.510) Remain 02:24:19 loss: 0.2891 Lr: 0.00397 [2024-11-25 17:01:26,429 INFO misc.py line 119 2586773] Train: [5/50][328/376] Data 0.003 (0.002) Batch 0.470 (0.510) Remain 02:24:17 loss: 0.2878 Lr: 0.00396 [2024-11-25 17:01:26,924 INFO misc.py line 119 2586773] Train: [5/50][329/376] Data 0.002 (0.002) Batch 0.495 (0.510) Remain 02:24:15 loss: 0.2171 Lr: 0.00396 [2024-11-25 17:01:27,416 INFO misc.py line 119 2586773] Train: [5/50][330/376] Data 0.003 (0.002) Batch 0.492 (0.510) Remain 02:24:14 loss: 0.2905 Lr: 0.00396 [2024-11-25 17:01:27,909 INFO misc.py line 119 2586773] Train: [5/50][331/376] Data 0.003 (0.002) Batch 0.493 (0.510) Remain 02:24:12 loss: 0.3141 Lr: 0.00396 [2024-11-25 17:01:28,431 INFO misc.py line 119 2586773] Train: [5/50][332/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 02:24:13 loss: 0.2810 Lr: 0.00396 [2024-11-25 17:01:28,913 INFO misc.py line 119 2586773] Train: [5/50][333/376] Data 0.003 (0.002) Batch 0.482 (0.510) Remain 02:24:11 loss: 0.2959 Lr: 0.00396 [2024-11-25 17:01:29,427 INFO misc.py line 119 2586773] Train: [5/50][334/376] Data 0.003 (0.002) Batch 0.514 (0.510) Remain 02:24:10 loss: 0.3351 Lr: 0.00396 [2024-11-25 17:01:29,907 INFO misc.py line 119 2586773] Train: [5/50][335/376] Data 0.002 (0.002) Batch 0.480 (0.510) Remain 02:24:08 loss: 0.2603 Lr: 0.00396 [2024-11-25 17:01:30,456 INFO misc.py line 119 2586773] Train: [5/50][336/376] Data 0.002 (0.002) Batch 0.550 (0.510) Remain 02:24:10 loss: 0.3024 Lr: 0.00396 [2024-11-25 17:01:30,980 INFO misc.py line 119 2586773] Train: [5/50][337/376] Data 0.002 (0.002) Batch 0.523 (0.510) Remain 02:24:10 loss: 0.2764 Lr: 0.00396 [2024-11-25 17:01:31,486 INFO misc.py line 119 2586773] Train: [5/50][338/376] Data 0.003 (0.002) Batch 0.507 (0.510) Remain 02:24:09 loss: 0.2303 Lr: 0.00396 [2024-11-25 17:01:31,985 INFO misc.py line 119 2586773] Train: [5/50][339/376] Data 0.003 (0.002) Batch 0.499 (0.510) Remain 02:24:08 loss: 0.3679 Lr: 0.00396 [2024-11-25 17:01:32,504 INFO misc.py line 119 2586773] Train: [5/50][340/376] Data 0.002 (0.002) Batch 0.519 (0.510) Remain 02:24:08 loss: 0.2448 Lr: 0.00396 [2024-11-25 17:01:32,966 INFO misc.py line 119 2586773] Train: [5/50][341/376] Data 0.002 (0.002) Batch 0.462 (0.510) Remain 02:24:05 loss: 0.2877 Lr: 0.00396 [2024-11-25 17:01:33,453 INFO misc.py line 119 2586773] Train: [5/50][342/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 02:24:04 loss: 0.2867 Lr: 0.00396 [2024-11-25 17:01:33,909 INFO misc.py line 119 2586773] Train: [5/50][343/376] Data 0.002 (0.002) Batch 0.456 (0.510) Remain 02:24:00 loss: 0.3028 Lr: 0.00396 [2024-11-25 17:01:34,406 INFO misc.py line 119 2586773] Train: [5/50][344/376] Data 0.003 (0.002) Batch 0.496 (0.510) Remain 02:23:59 loss: 0.2625 Lr: 0.00396 [2024-11-25 17:01:34,932 INFO misc.py line 119 2586773] Train: [5/50][345/376] Data 0.002 (0.002) Batch 0.526 (0.510) Remain 02:23:59 loss: 0.2290 Lr: 0.00396 [2024-11-25 17:01:35,418 INFO misc.py line 119 2586773] Train: [5/50][346/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 02:23:58 loss: 0.2630 Lr: 0.00396 [2024-11-25 17:01:35,982 INFO misc.py line 119 2586773] Train: [5/50][347/376] Data 0.002 (0.002) Batch 0.563 (0.510) Remain 02:24:00 loss: 0.3064 Lr: 0.00396 [2024-11-25 17:01:36,486 INFO misc.py line 119 2586773] Train: [5/50][348/376] Data 0.002 (0.002) Batch 0.505 (0.510) Remain 02:23:59 loss: 0.2924 Lr: 0.00396 [2024-11-25 17:01:37,024 INFO misc.py line 119 2586773] Train: [5/50][349/376] Data 0.002 (0.002) Batch 0.538 (0.510) Remain 02:24:00 loss: 0.2793 Lr: 0.00396 [2024-11-25 17:01:37,536 INFO misc.py line 119 2586773] Train: [5/50][350/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 02:24:00 loss: 0.3042 Lr: 0.00396 [2024-11-25 17:01:38,054 INFO misc.py line 119 2586773] Train: [5/50][351/376] Data 0.003 (0.002) Batch 0.517 (0.510) Remain 02:24:00 loss: 0.2826 Lr: 0.00396 [2024-11-25 17:01:38,607 INFO misc.py line 119 2586773] Train: [5/50][352/376] Data 0.002 (0.002) Batch 0.553 (0.510) Remain 02:24:01 loss: 0.2638 Lr: 0.00396 [2024-11-25 17:01:39,107 INFO misc.py line 119 2586773] Train: [5/50][353/376] Data 0.003 (0.002) Batch 0.501 (0.510) Remain 02:24:00 loss: 0.2946 Lr: 0.00396 [2024-11-25 17:01:39,625 INFO misc.py line 119 2586773] Train: [5/50][354/376] Data 0.003 (0.002) Batch 0.518 (0.510) Remain 02:24:00 loss: 0.2621 Lr: 0.00396 [2024-11-25 17:01:40,145 INFO misc.py line 119 2586773] Train: [5/50][355/376] Data 0.002 (0.002) Batch 0.519 (0.510) Remain 02:24:00 loss: 0.2315 Lr: 0.00396 [2024-11-25 17:01:40,675 INFO misc.py line 119 2586773] Train: [5/50][356/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 02:24:00 loss: 0.2343 Lr: 0.00396 [2024-11-25 17:01:41,168 INFO misc.py line 119 2586773] Train: [5/50][357/376] Data 0.003 (0.002) Batch 0.493 (0.510) Remain 02:23:59 loss: 0.2962 Lr: 0.00396 [2024-11-25 17:01:41,689 INFO misc.py line 119 2586773] Train: [5/50][358/376] Data 0.003 (0.002) Batch 0.521 (0.510) Remain 02:23:59 loss: 0.2900 Lr: 0.00396 [2024-11-25 17:01:42,198 INFO misc.py line 119 2586773] Train: [5/50][359/376] Data 0.002 (0.002) Batch 0.509 (0.510) Remain 02:23:59 loss: 0.2652 Lr: 0.00396 [2024-11-25 17:01:42,674 INFO misc.py line 119 2586773] Train: [5/50][360/376] Data 0.002 (0.002) Batch 0.476 (0.510) Remain 02:23:56 loss: 0.2262 Lr: 0.00396 [2024-11-25 17:01:43,202 INFO misc.py line 119 2586773] Train: [5/50][361/376] Data 0.002 (0.002) Batch 0.528 (0.510) Remain 02:23:57 loss: 0.2870 Lr: 0.00396 [2024-11-25 17:01:43,712 INFO misc.py line 119 2586773] Train: [5/50][362/376] Data 0.002 (0.002) Batch 0.509 (0.510) Remain 02:23:56 loss: 0.3187 Lr: 0.00396 [2024-11-25 17:01:44,255 INFO misc.py line 119 2586773] Train: [5/50][363/376] Data 0.002 (0.002) Batch 0.544 (0.510) Remain 02:23:57 loss: 0.2650 Lr: 0.00396 [2024-11-25 17:01:44,772 INFO misc.py line 119 2586773] Train: [5/50][364/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 02:23:57 loss: 0.2365 Lr: 0.00396 [2024-11-25 17:01:45,299 INFO misc.py line 119 2586773] Train: [5/50][365/376] Data 0.002 (0.002) Batch 0.526 (0.510) Remain 02:23:57 loss: 0.2614 Lr: 0.00396 [2024-11-25 17:01:45,806 INFO misc.py line 119 2586773] Train: [5/50][366/376] Data 0.002 (0.002) Batch 0.507 (0.510) Remain 02:23:57 loss: 0.2569 Lr: 0.00396 [2024-11-25 17:01:46,319 INFO misc.py line 119 2586773] Train: [5/50][367/376] Data 0.002 (0.002) Batch 0.513 (0.510) Remain 02:23:56 loss: 0.2392 Lr: 0.00396 [2024-11-25 17:01:46,784 INFO misc.py line 119 2586773] Train: [5/50][368/376] Data 0.002 (0.002) Batch 0.465 (0.510) Remain 02:23:54 loss: 0.3069 Lr: 0.00396 [2024-11-25 17:01:47,295 INFO misc.py line 119 2586773] Train: [5/50][369/376] Data 0.002 (0.002) Batch 0.511 (0.510) Remain 02:23:53 loss: 0.2865 Lr: 0.00396 [2024-11-25 17:01:47,773 INFO misc.py line 119 2586773] Train: [5/50][370/376] Data 0.002 (0.002) Batch 0.477 (0.510) Remain 02:23:51 loss: 0.2820 Lr: 0.00396 [2024-11-25 17:01:48,246 INFO misc.py line 119 2586773] Train: [5/50][371/376] Data 0.002 (0.002) Batch 0.473 (0.510) Remain 02:23:49 loss: 0.2953 Lr: 0.00396 [2024-11-25 17:01:48,740 INFO misc.py line 119 2586773] Train: [5/50][372/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 02:23:48 loss: 0.2675 Lr: 0.00396 [2024-11-25 17:01:49,275 INFO misc.py line 119 2586773] Train: [5/50][373/376] Data 0.002 (0.002) Batch 0.536 (0.510) Remain 02:23:49 loss: 0.2949 Lr: 0.00396 [2024-11-25 17:01:49,752 INFO misc.py line 119 2586773] Train: [5/50][374/376] Data 0.002 (0.002) Batch 0.477 (0.510) Remain 02:23:46 loss: 0.2732 Lr: 0.00396 [2024-11-25 17:01:50,233 INFO misc.py line 119 2586773] Train: [5/50][375/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 02:23:45 loss: 0.3008 Lr: 0.00396 [2024-11-25 17:01:50,750 INFO misc.py line 119 2586773] Train: [5/50][376/376] Data 0.002 (0.002) Batch 0.516 (0.510) Remain 02:23:44 loss: 0.2441 Lr: 0.00396 [2024-11-25 17:01:50,751 INFO misc.py line 136 2586773] Train result: loss: 0.2857 [2024-11-25 17:01:50,751 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:02:01,679 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2516 [2024-11-25 17:02:01,941 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2526 [2024-11-25 17:02:02,202 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3748 [2024-11-25 17:02:02,431 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2679 [2024-11-25 17:02:02,692 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3509 [2024-11-25 17:02:02,961 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2758 [2024-11-25 17:02:03,185 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2641 [2024-11-25 17:02:03,458 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2737 [2024-11-25 17:02:03,682 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2906 [2024-11-25 17:02:03,943 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3498 [2024-11-25 17:02:04,174 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2776 [2024-11-25 17:02:04,445 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2671 [2024-11-25 17:02:04,712 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2880 [2024-11-25 17:02:04,976 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2995 [2024-11-25 17:02:05,208 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2920 [2024-11-25 17:02:05,448 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2911 [2024-11-25 17:02:05,714 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3583 [2024-11-25 17:02:05,960 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2623 [2024-11-25 17:02:06,194 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2731 [2024-11-25 17:02:06,455 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3322 [2024-11-25 17:02:06,688 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2703 [2024-11-25 17:02:06,956 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3571 [2024-11-25 17:02:07,195 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2823 [2024-11-25 17:02:07,463 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3398 [2024-11-25 17:02:07,726 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2880 [2024-11-25 17:02:07,970 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2893 [2024-11-25 17:02:08,223 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3215 [2024-11-25 17:02:08,470 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3227 [2024-11-25 17:02:08,737 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3589 [2024-11-25 17:02:08,990 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3869 [2024-11-25 17:02:09,225 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3113 [2024-11-25 17:02:09,490 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2834 [2024-11-25 17:02:09,708 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2750 [2024-11-25 17:02:09,953 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2702 [2024-11-25 17:02:10,215 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2721 [2024-11-25 17:02:10,461 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2933 [2024-11-25 17:02:10,687 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2581 [2024-11-25 17:02:10,956 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.3072 [2024-11-25 17:02:11,187 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3122 [2024-11-25 17:02:11,420 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3205 [2024-11-25 17:02:11,691 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2809 [2024-11-25 17:02:11,946 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3642 [2024-11-25 17:02:12,183 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3101 [2024-11-25 17:02:12,415 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2798 [2024-11-25 17:02:12,652 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2670 [2024-11-25 17:02:12,898 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3160 [2024-11-25 17:02:13,155 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3303 [2024-11-25 17:02:13,409 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3283 [2024-11-25 17:02:13,631 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2699 [2024-11-25 17:02:13,878 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2610 [2024-11-25 17:02:14,101 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2806 [2024-11-25 17:02:14,356 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3312 [2024-11-25 17:02:14,624 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.3018 [2024-11-25 17:02:14,885 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3112 [2024-11-25 17:02:15,117 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2993 [2024-11-25 17:02:15,355 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2928 [2024-11-25 17:02:15,611 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3414 [2024-11-25 17:02:15,881 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2766 [2024-11-25 17:02:16,137 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3114 [2024-11-25 17:02:16,396 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3223 [2024-11-25 17:02:16,649 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2892 [2024-11-25 17:02:16,918 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3192 [2024-11-25 17:02:17,148 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2702 [2024-11-25 17:02:17,409 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3392 [2024-11-25 17:02:17,673 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3255 [2024-11-25 17:02:17,939 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2937 [2024-11-25 17:02:18,183 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2641 [2024-11-25 17:02:18,438 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3565 [2024-11-25 17:02:18,707 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2689 [2024-11-25 17:02:18,969 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3284 [2024-11-25 17:02:19,213 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2502 [2024-11-25 17:02:19,446 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2962 [2024-11-25 17:02:19,702 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3336 [2024-11-25 17:02:19,947 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3281 [2024-11-25 17:02:20,163 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2705 [2024-11-25 17:02:20,386 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2444 [2024-11-25 17:02:20,657 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3164 [2024-11-25 17:02:20,893 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2708 [2024-11-25 17:02:21,152 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2567 [2024-11-25 17:02:21,402 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3757 [2024-11-25 17:02:21,642 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2714 [2024-11-25 17:02:21,905 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3045 [2024-11-25 17:02:22,153 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2508 [2024-11-25 17:02:22,404 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3303 [2024-11-25 17:02:22,675 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2832 [2024-11-25 17:02:22,911 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2739 [2024-11-25 17:02:23,177 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3217 [2024-11-25 17:02:23,438 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3480 [2024-11-25 17:02:23,685 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3000 [2024-11-25 17:02:23,931 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2878 [2024-11-25 17:02:24,164 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2569 [2024-11-25 17:02:24,419 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3078 [2024-11-25 17:02:24,687 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3198 [2024-11-25 17:02:24,955 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2661 [2024-11-25 17:02:25,218 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2599 [2024-11-25 17:02:25,469 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2752 [2024-11-25 17:02:25,738 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3112 [2024-11-25 17:02:25,958 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2947 [2024-11-25 17:02:26,230 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2950 [2024-11-25 17:02:26,469 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3047 [2024-11-25 17:02:26,740 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2797 [2024-11-25 17:02:27,004 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3553 [2024-11-25 17:02:27,264 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3382 [2024-11-25 17:02:27,519 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3457 [2024-11-25 17:02:27,743 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2711 [2024-11-25 17:02:27,979 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2638 [2024-11-25 17:02:28,234 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2832 [2024-11-25 17:02:28,499 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3134 [2024-11-25 17:02:28,735 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3439 [2024-11-25 17:02:28,993 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.3107 [2024-11-25 17:02:29,257 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2658 [2024-11-25 17:02:29,479 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.3054 [2024-11-25 17:02:29,715 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2768 [2024-11-25 17:02:29,935 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2913 [2024-11-25 17:02:30,160 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2809 [2024-11-25 17:02:30,434 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3707 [2024-11-25 17:02:30,695 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3546 [2024-11-25 17:02:30,964 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3263 [2024-11-25 17:02:31,228 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2737 [2024-11-25 17:02:31,489 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3758 [2024-11-25 17:02:31,747 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2975 [2024-11-25 17:02:32,015 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2753 [2024-11-25 17:02:32,270 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3307 [2024-11-25 17:02:32,533 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.4020 [2024-11-25 17:02:32,794 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2868 [2024-11-25 17:02:33,047 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3301 [2024-11-25 17:02:33,276 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.3036 [2024-11-25 17:02:33,534 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3285 [2024-11-25 17:02:33,770 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2937 [2024-11-25 17:02:33,995 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2799 [2024-11-25 17:02:34,206 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2825 [2024-11-25 17:02:34,423 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2705 [2024-11-25 17:02:35,136 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7016/0.7535/0.9952. [2024-11-25 17:02:35,136 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9951/0.9984 [2024-11-25 17:02:35,136 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4080/0.5086 [2024-11-25 17:02:35,137 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:02:35,138 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7016 [2024-11-25 17:02:35,138 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7016 [2024-11-25 17:02:35,138 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:02:39,522 INFO misc.py line 119 2586773] Train: [6/50][1/376] Data 0.087 (0.087) Batch 0.574 (0.574) Remain 02:41:48 loss: 0.3052 Lr: 0.00396 [2024-11-25 17:02:39,998 INFO misc.py line 119 2586773] Train: [6/50][2/376] Data 0.002 (0.002) Batch 0.475 (0.475) Remain 02:14:03 loss: 0.2753 Lr: 0.00396 [2024-11-25 17:02:40,503 INFO misc.py line 119 2586773] Train: [6/50][3/376] Data 0.002 (0.002) Batch 0.505 (0.505) Remain 02:22:23 loss: 0.2203 Lr: 0.00396 [2024-11-25 17:02:40,979 INFO misc.py line 119 2586773] Train: [6/50][4/376] Data 0.003 (0.003) Batch 0.476 (0.476) Remain 02:14:14 loss: 0.2638 Lr: 0.00396 [2024-11-25 17:02:41,468 INFO misc.py line 119 2586773] Train: [6/50][5/376] Data 0.003 (0.003) Batch 0.489 (0.483) Remain 02:16:02 loss: 0.2794 Lr: 0.00396 [2024-11-25 17:02:41,948 INFO misc.py line 119 2586773] Train: [6/50][6/376] Data 0.002 (0.003) Batch 0.480 (0.482) Remain 02:15:50 loss: 0.2434 Lr: 0.00396 [2024-11-25 17:02:42,450 INFO misc.py line 119 2586773] Train: [6/50][7/376] Data 0.002 (0.003) Batch 0.502 (0.487) Remain 02:17:13 loss: 0.2352 Lr: 0.00396 [2024-11-25 17:02:42,943 INFO misc.py line 119 2586773] Train: [6/50][8/376] Data 0.003 (0.003) Batch 0.493 (0.488) Remain 02:17:33 loss: 0.2770 Lr: 0.00396 [2024-11-25 17:02:43,503 INFO misc.py line 119 2586773] Train: [6/50][9/376] Data 0.002 (0.003) Batch 0.560 (0.500) Remain 02:20:55 loss: 0.2733 Lr: 0.00396 [2024-11-25 17:02:44,010 INFO misc.py line 119 2586773] Train: [6/50][10/376] Data 0.002 (0.003) Batch 0.507 (0.501) Remain 02:21:11 loss: 0.2701 Lr: 0.00396 [2024-11-25 17:02:44,505 INFO misc.py line 119 2586773] Train: [6/50][11/376] Data 0.002 (0.003) Batch 0.496 (0.500) Remain 02:20:59 loss: 0.2695 Lr: 0.00396 [2024-11-25 17:02:44,979 INFO misc.py line 119 2586773] Train: [6/50][12/376] Data 0.003 (0.003) Batch 0.474 (0.497) Remain 02:20:09 loss: 0.2984 Lr: 0.00396 [2024-11-25 17:02:45,481 INFO misc.py line 119 2586773] Train: [6/50][13/376] Data 0.003 (0.003) Batch 0.502 (0.498) Remain 02:20:17 loss: 0.2610 Lr: 0.00396 [2024-11-25 17:02:45,969 INFO misc.py line 119 2586773] Train: [6/50][14/376] Data 0.002 (0.003) Batch 0.487 (0.497) Remain 02:20:00 loss: 0.2808 Lr: 0.00396 [2024-11-25 17:02:46,453 INFO misc.py line 119 2586773] Train: [6/50][15/376] Data 0.002 (0.002) Batch 0.484 (0.496) Remain 02:19:42 loss: 0.3020 Lr: 0.00396 [2024-11-25 17:02:46,965 INFO misc.py line 119 2586773] Train: [6/50][16/376] Data 0.003 (0.002) Batch 0.512 (0.497) Remain 02:20:02 loss: 0.2986 Lr: 0.00396 [2024-11-25 17:02:47,499 INFO misc.py line 119 2586773] Train: [6/50][17/376] Data 0.003 (0.003) Batch 0.534 (0.500) Remain 02:20:46 loss: 0.2355 Lr: 0.00396 [2024-11-25 17:02:47,987 INFO misc.py line 119 2586773] Train: [6/50][18/376] Data 0.002 (0.002) Batch 0.489 (0.499) Remain 02:20:33 loss: 0.2977 Lr: 0.00396 [2024-11-25 17:02:48,502 INFO misc.py line 119 2586773] Train: [6/50][19/376] Data 0.002 (0.002) Batch 0.515 (0.500) Remain 02:20:49 loss: 0.3165 Lr: 0.00396 [2024-11-25 17:02:49,055 INFO misc.py line 119 2586773] Train: [6/50][20/376] Data 0.003 (0.002) Batch 0.553 (0.503) Remain 02:21:41 loss: 0.3021 Lr: 0.00396 [2024-11-25 17:02:49,541 INFO misc.py line 119 2586773] Train: [6/50][21/376] Data 0.002 (0.002) Batch 0.486 (0.502) Remain 02:21:25 loss: 0.2970 Lr: 0.00396 [2024-11-25 17:02:50,076 INFO misc.py line 119 2586773] Train: [6/50][22/376] Data 0.002 (0.002) Batch 0.535 (0.504) Remain 02:21:54 loss: 0.2601 Lr: 0.00396 [2024-11-25 17:02:50,580 INFO misc.py line 119 2586773] Train: [6/50][23/376] Data 0.003 (0.002) Batch 0.504 (0.504) Remain 02:21:53 loss: 0.2824 Lr: 0.00396 [2024-11-25 17:02:51,110 INFO misc.py line 119 2586773] Train: [6/50][24/376] Data 0.003 (0.002) Batch 0.530 (0.505) Remain 02:22:14 loss: 0.3168 Lr: 0.00396 [2024-11-25 17:02:51,660 INFO misc.py line 119 2586773] Train: [6/50][25/376] Data 0.003 (0.003) Batch 0.550 (0.507) Remain 02:22:47 loss: 0.3385 Lr: 0.00396 [2024-11-25 17:02:52,145 INFO misc.py line 119 2586773] Train: [6/50][26/376] Data 0.003 (0.003) Batch 0.485 (0.506) Remain 02:22:31 loss: 0.2749 Lr: 0.00396 [2024-11-25 17:02:52,677 INFO misc.py line 119 2586773] Train: [6/50][27/376] Data 0.003 (0.003) Batch 0.532 (0.507) Remain 02:22:48 loss: 0.2150 Lr: 0.00396 [2024-11-25 17:02:53,170 INFO misc.py line 119 2586773] Train: [6/50][28/376] Data 0.003 (0.003) Batch 0.493 (0.507) Remain 02:22:38 loss: 0.2402 Lr: 0.00396 [2024-11-25 17:02:53,700 INFO misc.py line 119 2586773] Train: [6/50][29/376] Data 0.003 (0.003) Batch 0.530 (0.508) Remain 02:22:53 loss: 0.3164 Lr: 0.00396 [2024-11-25 17:02:54,183 INFO misc.py line 119 2586773] Train: [6/50][30/376] Data 0.003 (0.003) Batch 0.483 (0.507) Remain 02:22:37 loss: 0.2897 Lr: 0.00396 [2024-11-25 17:02:54,679 INFO misc.py line 119 2586773] Train: [6/50][31/376] Data 0.002 (0.003) Batch 0.497 (0.506) Remain 02:22:30 loss: 0.2631 Lr: 0.00396 [2024-11-25 17:02:55,210 INFO misc.py line 119 2586773] Train: [6/50][32/376] Data 0.003 (0.003) Batch 0.531 (0.507) Remain 02:22:44 loss: 0.2104 Lr: 0.00396 [2024-11-25 17:02:55,677 INFO misc.py line 119 2586773] Train: [6/50][33/376] Data 0.002 (0.003) Batch 0.467 (0.506) Remain 02:22:21 loss: 0.2963 Lr: 0.00396 [2024-11-25 17:02:56,199 INFO misc.py line 119 2586773] Train: [6/50][34/376] Data 0.003 (0.003) Batch 0.522 (0.506) Remain 02:22:29 loss: 0.3157 Lr: 0.00396 [2024-11-25 17:02:56,724 INFO misc.py line 119 2586773] Train: [6/50][35/376] Data 0.002 (0.003) Batch 0.525 (0.507) Remain 02:22:39 loss: 0.2074 Lr: 0.00396 [2024-11-25 17:02:57,286 INFO misc.py line 119 2586773] Train: [6/50][36/376] Data 0.002 (0.003) Batch 0.562 (0.509) Remain 02:23:06 loss: 0.3271 Lr: 0.00396 [2024-11-25 17:02:57,756 INFO misc.py line 119 2586773] Train: [6/50][37/376] Data 0.003 (0.003) Batch 0.470 (0.507) Remain 02:22:47 loss: 0.2933 Lr: 0.00396 [2024-11-25 17:02:58,290 INFO misc.py line 119 2586773] Train: [6/50][38/376] Data 0.003 (0.003) Batch 0.534 (0.508) Remain 02:22:59 loss: 0.3037 Lr: 0.00396 [2024-11-25 17:02:58,769 INFO misc.py line 119 2586773] Train: [6/50][39/376] Data 0.002 (0.003) Batch 0.479 (0.507) Remain 02:22:45 loss: 0.3035 Lr: 0.00396 [2024-11-25 17:02:59,283 INFO misc.py line 119 2586773] Train: [6/50][40/376] Data 0.003 (0.003) Batch 0.514 (0.508) Remain 02:22:47 loss: 0.2698 Lr: 0.00396 [2024-11-25 17:02:59,746 INFO misc.py line 119 2586773] Train: [6/50][41/376] Data 0.003 (0.003) Batch 0.464 (0.506) Remain 02:22:27 loss: 0.3161 Lr: 0.00396 [2024-11-25 17:03:00,251 INFO misc.py line 119 2586773] Train: [6/50][42/376] Data 0.002 (0.003) Batch 0.504 (0.506) Remain 02:22:26 loss: 0.2647 Lr: 0.00396 [2024-11-25 17:03:00,764 INFO misc.py line 119 2586773] Train: [6/50][43/376] Data 0.002 (0.003) Batch 0.513 (0.507) Remain 02:22:28 loss: 0.3328 Lr: 0.00396 [2024-11-25 17:03:01,240 INFO misc.py line 119 2586773] Train: [6/50][44/376] Data 0.002 (0.003) Batch 0.477 (0.506) Remain 02:22:15 loss: 0.2890 Lr: 0.00396 [2024-11-25 17:03:01,760 INFO misc.py line 119 2586773] Train: [6/50][45/376] Data 0.002 (0.003) Batch 0.520 (0.506) Remain 02:22:20 loss: 0.2861 Lr: 0.00396 [2024-11-25 17:03:02,267 INFO misc.py line 119 2586773] Train: [6/50][46/376] Data 0.002 (0.003) Batch 0.506 (0.506) Remain 02:22:20 loss: 0.2683 Lr: 0.00396 [2024-11-25 17:03:02,815 INFO misc.py line 119 2586773] Train: [6/50][47/376] Data 0.002 (0.003) Batch 0.548 (0.507) Remain 02:22:36 loss: 0.3351 Lr: 0.00396 [2024-11-25 17:03:03,330 INFO misc.py line 119 2586773] Train: [6/50][48/376] Data 0.003 (0.003) Batch 0.516 (0.507) Remain 02:22:38 loss: 0.2604 Lr: 0.00396 [2024-11-25 17:03:03,854 INFO misc.py line 119 2586773] Train: [6/50][49/376] Data 0.002 (0.003) Batch 0.523 (0.508) Remain 02:22:44 loss: 0.2621 Lr: 0.00396 [2024-11-25 17:03:04,313 INFO misc.py line 119 2586773] Train: [6/50][50/376] Data 0.002 (0.003) Batch 0.459 (0.507) Remain 02:22:26 loss: 0.2718 Lr: 0.00396 [2024-11-25 17:03:04,807 INFO misc.py line 119 2586773] Train: [6/50][51/376] Data 0.002 (0.003) Batch 0.494 (0.506) Remain 02:22:21 loss: 0.3972 Lr: 0.00396 [2024-11-25 17:03:05,335 INFO misc.py line 119 2586773] Train: [6/50][52/376] Data 0.002 (0.003) Batch 0.528 (0.507) Remain 02:22:28 loss: 0.2847 Lr: 0.00396 [2024-11-25 17:03:05,876 INFO misc.py line 119 2586773] Train: [6/50][53/376] Data 0.002 (0.003) Batch 0.541 (0.507) Remain 02:22:39 loss: 0.3411 Lr: 0.00396 [2024-11-25 17:03:06,386 INFO misc.py line 119 2586773] Train: [6/50][54/376] Data 0.003 (0.003) Batch 0.509 (0.508) Remain 02:22:39 loss: 0.2436 Lr: 0.00396 [2024-11-25 17:03:06,917 INFO misc.py line 119 2586773] Train: [6/50][55/376] Data 0.003 (0.003) Batch 0.532 (0.508) Remain 02:22:46 loss: 0.2887 Lr: 0.00396 [2024-11-25 17:03:07,409 INFO misc.py line 119 2586773] Train: [6/50][56/376] Data 0.003 (0.003) Batch 0.492 (0.508) Remain 02:22:41 loss: 0.2844 Lr: 0.00396 [2024-11-25 17:03:07,898 INFO misc.py line 119 2586773] Train: [6/50][57/376] Data 0.002 (0.003) Batch 0.489 (0.507) Remain 02:22:35 loss: 0.2434 Lr: 0.00396 [2024-11-25 17:03:08,441 INFO misc.py line 119 2586773] Train: [6/50][58/376] Data 0.002 (0.003) Batch 0.542 (0.508) Remain 02:22:45 loss: 0.3128 Lr: 0.00396 [2024-11-25 17:03:08,991 INFO misc.py line 119 2586773] Train: [6/50][59/376] Data 0.002 (0.003) Batch 0.550 (0.509) Remain 02:22:57 loss: 0.2685 Lr: 0.00396 [2024-11-25 17:03:09,515 INFO misc.py line 119 2586773] Train: [6/50][60/376] Data 0.003 (0.003) Batch 0.524 (0.509) Remain 02:23:01 loss: 0.2815 Lr: 0.00396 [2024-11-25 17:03:10,029 INFO misc.py line 119 2586773] Train: [6/50][61/376] Data 0.002 (0.003) Batch 0.513 (0.509) Remain 02:23:02 loss: 0.3018 Lr: 0.00396 [2024-11-25 17:03:10,582 INFO misc.py line 119 2586773] Train: [6/50][62/376] Data 0.003 (0.003) Batch 0.553 (0.510) Remain 02:23:14 loss: 0.2401 Lr: 0.00396 [2024-11-25 17:03:11,068 INFO misc.py line 119 2586773] Train: [6/50][63/376] Data 0.002 (0.003) Batch 0.486 (0.509) Remain 02:23:07 loss: 0.2471 Lr: 0.00396 [2024-11-25 17:03:11,545 INFO misc.py line 119 2586773] Train: [6/50][64/376] Data 0.003 (0.003) Batch 0.477 (0.509) Remain 02:22:57 loss: 0.2753 Lr: 0.00396 [2024-11-25 17:03:12,045 INFO misc.py line 119 2586773] Train: [6/50][65/376] Data 0.002 (0.003) Batch 0.499 (0.509) Remain 02:22:54 loss: 0.2577 Lr: 0.00396 [2024-11-25 17:03:12,601 INFO misc.py line 119 2586773] Train: [6/50][66/376] 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Batch 0.509 (0.508) Remain 02:22:05 loss: 0.2874 Lr: 0.00395 [2024-11-25 17:03:48,071 INFO misc.py line 119 2586773] Train: [6/50][136/376] Data 0.003 (0.002) Batch 0.525 (0.508) Remain 02:22:06 loss: 0.3029 Lr: 0.00395 [2024-11-25 17:03:48,585 INFO misc.py line 119 2586773] Train: [6/50][137/376] Data 0.003 (0.003) Batch 0.514 (0.508) Remain 02:22:07 loss: 0.3002 Lr: 0.00395 [2024-11-25 17:03:49,103 INFO misc.py line 119 2586773] Train: [6/50][138/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 02:22:07 loss: 0.2421 Lr: 0.00395 [2024-11-25 17:03:49,626 INFO misc.py line 119 2586773] Train: [6/50][139/376] Data 0.003 (0.003) Batch 0.523 (0.508) Remain 02:22:09 loss: 0.2933 Lr: 0.00395 [2024-11-25 17:03:50,116 INFO misc.py line 119 2586773] Train: [6/50][140/376] Data 0.003 (0.003) Batch 0.490 (0.508) Remain 02:22:06 loss: 0.2995 Lr: 0.00395 [2024-11-25 17:03:50,674 INFO misc.py line 119 2586773] Train: [6/50][141/376] Data 0.003 (0.003) Batch 0.559 (0.508) Remain 02:22:11 loss: 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Train: [6/50][336/376] Data 0.002 (0.003) Batch 0.540 (0.509) Remain 02:20:33 loss: 0.2282 Lr: 0.00394 [2024-11-25 17:05:30,355 INFO misc.py line 119 2586773] Train: [6/50][337/376] Data 0.003 (0.003) Batch 0.510 (0.509) Remain 02:20:33 loss: 0.2663 Lr: 0.00394 [2024-11-25 17:05:30,865 INFO misc.py line 119 2586773] Train: [6/50][338/376] Data 0.002 (0.003) Batch 0.509 (0.509) Remain 02:20:32 loss: 0.2536 Lr: 0.00394 [2024-11-25 17:05:31,344 INFO misc.py line 119 2586773] Train: [6/50][339/376] Data 0.003 (0.003) Batch 0.480 (0.508) Remain 02:20:30 loss: 0.2982 Lr: 0.00394 [2024-11-25 17:05:31,864 INFO misc.py line 119 2586773] Train: [6/50][340/376] Data 0.002 (0.003) Batch 0.520 (0.508) Remain 02:20:30 loss: 0.2750 Lr: 0.00394 [2024-11-25 17:05:32,363 INFO misc.py line 119 2586773] Train: [6/50][341/376] Data 0.002 (0.003) Batch 0.499 (0.508) Remain 02:20:29 loss: 0.2683 Lr: 0.00394 [2024-11-25 17:05:32,874 INFO misc.py line 119 2586773] Train: [6/50][342/376] Data 0.002 (0.003) Batch 0.511 (0.508) Remain 02:20:29 loss: 0.2465 Lr: 0.00394 [2024-11-25 17:05:33,429 INFO misc.py line 119 2586773] Train: [6/50][343/376] Data 0.002 (0.003) Batch 0.555 (0.509) Remain 02:20:31 loss: 0.3022 Lr: 0.00394 [2024-11-25 17:05:33,960 INFO misc.py line 119 2586773] Train: [6/50][344/376] Data 0.003 (0.003) Batch 0.531 (0.509) Remain 02:20:31 loss: 0.2931 Lr: 0.00394 [2024-11-25 17:05:34,449 INFO misc.py line 119 2586773] Train: [6/50][345/376] Data 0.003 (0.003) Batch 0.488 (0.509) Remain 02:20:30 loss: 0.2808 Lr: 0.00393 [2024-11-25 17:05:34,993 INFO misc.py line 119 2586773] Train: [6/50][346/376] Data 0.002 (0.003) Batch 0.545 (0.509) Remain 02:20:31 loss: 0.3246 Lr: 0.00393 [2024-11-25 17:05:35,482 INFO misc.py line 119 2586773] Train: [6/50][347/376] Data 0.002 (0.003) Batch 0.488 (0.509) Remain 02:20:29 loss: 0.2169 Lr: 0.00393 [2024-11-25 17:05:35,963 INFO misc.py line 119 2586773] Train: [6/50][348/376] Data 0.003 (0.003) Batch 0.481 (0.509) Remain 02:20:28 loss: 0.3604 Lr: 0.00393 [2024-11-25 17:05:36,502 INFO misc.py line 119 2586773] Train: [6/50][349/376] Data 0.003 (0.003) Batch 0.539 (0.509) Remain 02:20:29 loss: 0.2563 Lr: 0.00393 [2024-11-25 17:05:36,982 INFO misc.py line 119 2586773] Train: [6/50][350/376] Data 0.002 (0.003) Batch 0.480 (0.509) Remain 02:20:27 loss: 0.2625 Lr: 0.00393 [2024-11-25 17:05:37,488 INFO misc.py line 119 2586773] Train: [6/50][351/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 02:20:26 loss: 0.3052 Lr: 0.00393 [2024-11-25 17:05:37,967 INFO misc.py line 119 2586773] Train: [6/50][352/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 02:20:24 loss: 0.2363 Lr: 0.00393 [2024-11-25 17:05:38,443 INFO misc.py line 119 2586773] Train: [6/50][353/376] Data 0.003 (0.003) Batch 0.476 (0.508) Remain 02:20:22 loss: 0.3057 Lr: 0.00393 [2024-11-25 17:05:38,897 INFO misc.py line 119 2586773] Train: [6/50][354/376] Data 0.002 (0.002) Batch 0.454 (0.508) Remain 02:20:19 loss: 0.2498 Lr: 0.00393 [2024-11-25 17:05:39,418 INFO misc.py line 119 2586773] Train: [6/50][355/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 02:20:19 loss: 0.2534 Lr: 0.00393 [2024-11-25 17:05:39,915 INFO misc.py line 119 2586773] Train: [6/50][356/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 02:20:18 loss: 0.2468 Lr: 0.00393 [2024-11-25 17:05:40,426 INFO misc.py line 119 2586773] Train: [6/50][357/376] Data 0.003 (0.003) Batch 0.512 (0.508) Remain 02:20:18 loss: 0.2219 Lr: 0.00393 [2024-11-25 17:05:40,931 INFO misc.py line 119 2586773] Train: [6/50][358/376] Data 0.003 (0.003) Batch 0.505 (0.508) Remain 02:20:17 loss: 0.3154 Lr: 0.00393 [2024-11-25 17:05:41,428 INFO misc.py line 119 2586773] Train: [6/50][359/376] Data 0.003 (0.003) Batch 0.496 (0.508) Remain 02:20:16 loss: 0.2197 Lr: 0.00393 [2024-11-25 17:05:41,965 INFO misc.py line 119 2586773] Train: [6/50][360/376] Data 0.003 (0.003) Batch 0.538 (0.508) Remain 02:20:17 loss: 0.2769 Lr: 0.00393 [2024-11-25 17:05:42,493 INFO misc.py line 119 2586773] Train: [6/50][361/376] Data 0.002 (0.003) Batch 0.528 (0.508) Remain 02:20:17 loss: 0.3138 Lr: 0.00393 [2024-11-25 17:05:42,985 INFO misc.py line 119 2586773] Train: [6/50][362/376] Data 0.002 (0.003) Batch 0.492 (0.508) Remain 02:20:16 loss: 0.2739 Lr: 0.00393 [2024-11-25 17:05:43,454 INFO misc.py line 119 2586773] Train: [6/50][363/376] Data 0.002 (0.003) Batch 0.470 (0.508) Remain 02:20:14 loss: 0.3145 Lr: 0.00393 [2024-11-25 17:05:44,007 INFO misc.py line 119 2586773] Train: [6/50][364/376] Data 0.002 (0.003) Batch 0.552 (0.508) Remain 02:20:15 loss: 0.3053 Lr: 0.00393 [2024-11-25 17:05:44,494 INFO misc.py line 119 2586773] Train: [6/50][365/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 02:20:14 loss: 0.2653 Lr: 0.00393 [2024-11-25 17:05:44,992 INFO misc.py line 119 2586773] Train: [6/50][366/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 02:20:13 loss: 0.2871 Lr: 0.00393 [2024-11-25 17:05:45,483 INFO misc.py line 119 2586773] Train: [6/50][367/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 02:20:12 loss: 0.2622 Lr: 0.00393 [2024-11-25 17:05:45,974 INFO misc.py line 119 2586773] Train: [6/50][368/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 02:20:10 loss: 0.2473 Lr: 0.00393 [2024-11-25 17:05:46,473 INFO misc.py line 119 2586773] Train: [6/50][369/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 02:20:09 loss: 0.2694 Lr: 0.00393 [2024-11-25 17:05:46,984 INFO misc.py line 119 2586773] Train: [6/50][370/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 02:20:09 loss: 0.2795 Lr: 0.00393 [2024-11-25 17:05:47,473 INFO misc.py line 119 2586773] Train: [6/50][371/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 02:20:08 loss: 0.2616 Lr: 0.00393 [2024-11-25 17:05:47,949 INFO misc.py line 119 2586773] Train: [6/50][372/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 02:20:06 loss: 0.3513 Lr: 0.00393 [2024-11-25 17:05:48,451 INFO misc.py line 119 2586773] Train: [6/50][373/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 02:20:05 loss: 0.2464 Lr: 0.00393 [2024-11-25 17:05:48,942 INFO misc.py line 119 2586773] Train: [6/50][374/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 02:20:04 loss: 0.2534 Lr: 0.00393 [2024-11-25 17:05:49,480 INFO misc.py line 119 2586773] Train: [6/50][375/376] Data 0.002 (0.002) Batch 0.538 (0.508) Remain 02:20:04 loss: 0.3067 Lr: 0.00393 [2024-11-25 17:05:49,977 INFO misc.py line 119 2586773] Train: [6/50][376/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 02:20:03 loss: 0.2906 Lr: 0.00393 [2024-11-25 17:05:49,977 INFO misc.py line 136 2586773] Train result: loss: 0.2769 [2024-11-25 17:05:49,978 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:06:01,031 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2329 [2024-11-25 17:06:01,285 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2447 [2024-11-25 17:06:01,546 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.4098 [2024-11-25 17:06:01,776 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2562 [2024-11-25 17:06:02,039 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3148 [2024-11-25 17:06:02,306 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2619 [2024-11-25 17:06:02,535 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2482 [2024-11-25 17:06:02,803 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2668 [2024-11-25 17:06:03,030 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2745 [2024-11-25 17:06:03,292 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3133 [2024-11-25 17:06:03,524 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2819 [2024-11-25 17:06:03,796 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2404 [2024-11-25 17:06:04,062 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3072 [2024-11-25 17:06:04,324 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2882 [2024-11-25 17:06:04,556 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2955 [2024-11-25 17:06:04,795 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3028 [2024-11-25 17:06:05,060 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3911 [2024-11-25 17:06:05,306 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2529 [2024-11-25 17:06:05,538 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2729 [2024-11-25 17:06:05,801 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3429 [2024-11-25 17:06:06,041 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2631 [2024-11-25 17:06:06,307 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3949 [2024-11-25 17:06:06,544 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2692 [2024-11-25 17:06:06,811 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3139 [2024-11-25 17:06:07,073 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2737 [2024-11-25 17:06:07,308 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3057 [2024-11-25 17:06:07,559 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3254 [2024-11-25 17:06:07,805 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3474 [2024-11-25 17:06:08,071 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3783 [2024-11-25 17:06:08,324 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3873 [2024-11-25 17:06:08,559 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2916 [2024-11-25 17:06:08,823 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2738 [2024-11-25 17:06:09,042 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2590 [2024-11-25 17:06:09,281 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2424 [2024-11-25 17:06:09,544 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2562 [2024-11-25 17:06:09,788 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2738 [2024-11-25 17:06:10,015 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2371 [2024-11-25 17:06:10,285 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2755 [2024-11-25 17:06:10,515 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2892 [2024-11-25 17:06:10,749 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2950 [2024-11-25 17:06:11,020 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2704 [2024-11-25 17:06:11,273 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3803 [2024-11-25 17:06:11,510 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2952 [2024-11-25 17:06:11,743 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2865 [2024-11-25 17:06:11,980 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2838 [2024-11-25 17:06:12,231 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2808 [2024-11-25 17:06:12,487 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3305 [2024-11-25 17:06:12,739 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3521 [2024-11-25 17:06:12,961 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2643 [2024-11-25 17:06:13,194 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2708 [2024-11-25 17:06:13,413 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2685 [2024-11-25 17:06:13,669 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3719 [2024-11-25 17:06:13,935 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2859 [2024-11-25 17:06:14,197 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3224 [2024-11-25 17:06:14,428 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2890 [2024-11-25 17:06:14,667 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2926 [2024-11-25 17:06:14,923 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3207 [2024-11-25 17:06:15,193 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2751 [2024-11-25 17:06:15,453 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3746 [2024-11-25 17:06:15,713 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3528 [2024-11-25 17:06:15,965 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2854 [2024-11-25 17:06:16,232 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2937 [2024-11-25 17:06:16,461 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2738 [2024-11-25 17:06:16,721 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3777 [2024-11-25 17:06:16,986 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3386 [2024-11-25 17:06:17,254 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2506 [2024-11-25 17:06:17,500 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2530 [2024-11-25 17:06:17,758 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3777 [2024-11-25 17:06:18,027 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2656 [2024-11-25 17:06:18,289 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3463 [2024-11-25 17:06:18,536 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2572 [2024-11-25 17:06:18,769 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2909 [2024-11-25 17:06:19,025 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3374 [2024-11-25 17:06:19,268 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3491 [2024-11-25 17:06:19,484 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2816 [2024-11-25 17:06:19,705 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2329 [2024-11-25 17:06:19,974 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3276 [2024-11-25 17:06:20,212 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2512 [2024-11-25 17:06:20,470 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2425 [2024-11-25 17:06:20,720 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3696 [2024-11-25 17:06:20,963 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2655 [2024-11-25 17:06:21,225 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2935 [2024-11-25 17:06:21,473 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2419 [2024-11-25 17:06:21,722 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3033 [2024-11-25 17:06:21,994 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2659 [2024-11-25 17:06:22,234 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.3046 [2024-11-25 17:06:22,498 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3304 [2024-11-25 17:06:22,759 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3477 [2024-11-25 17:06:23,008 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3051 [2024-11-25 17:06:23,257 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2981 [2024-11-25 17:06:23,490 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2571 [2024-11-25 17:06:23,742 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3104 [2024-11-25 17:06:24,012 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3343 [2024-11-25 17:06:24,279 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2452 [2024-11-25 17:06:24,545 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2514 [2024-11-25 17:06:24,793 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2623 [2024-11-25 17:06:25,064 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3477 [2024-11-25 17:06:25,286 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3211 [2024-11-25 17:06:25,557 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2952 [2024-11-25 17:06:25,796 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3010 [2024-11-25 17:06:26,065 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2632 [2024-11-25 17:06:26,324 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3396 [2024-11-25 17:06:26,584 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3461 [2024-11-25 17:06:26,835 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3792 [2024-11-25 17:06:27,057 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2613 [2024-11-25 17:06:27,295 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2698 [2024-11-25 17:06:27,553 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2884 [2024-11-25 17:06:27,823 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3068 [2024-11-25 17:06:28,056 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3186 [2024-11-25 17:06:28,323 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2844 [2024-11-25 17:06:28,587 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2590 [2024-11-25 17:06:28,809 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2757 [2024-11-25 17:06:29,047 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2679 [2024-11-25 17:06:29,265 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2787 [2024-11-25 17:06:29,493 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2754 [2024-11-25 17:06:29,764 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3976 [2024-11-25 17:06:30,023 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3731 [2024-11-25 17:06:30,291 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3145 [2024-11-25 17:06:30,556 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2854 [2024-11-25 17:06:30,817 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3943 [2024-11-25 17:06:31,079 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2939 [2024-11-25 17:06:31,345 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2516 [2024-11-25 17:06:31,599 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3436 [2024-11-25 17:06:31,865 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3530 [2024-11-25 17:06:32,131 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2681 [2024-11-25 17:06:32,381 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3254 [2024-11-25 17:06:32,611 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2867 [2024-11-25 17:06:32,871 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3331 [2024-11-25 17:06:33,107 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2746 [2024-11-25 17:06:33,332 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2464 [2024-11-25 17:06:33,543 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2859 [2024-11-25 17:06:33,759 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2442 [2024-11-25 17:06:34,404 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7067/0.7511/0.9954. [2024-11-25 17:06:34,404 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9954/0.9987 [2024-11-25 17:06:34,404 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4181/0.5036 [2024-11-25 17:06:34,405 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:06:34,405 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7067 [2024-11-25 17:06:34,405 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7067 [2024-11-25 17:06:34,406 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:06:38,715 INFO misc.py line 119 2586773] Train: [7/50][1/376] Data 0.089 (0.089) Batch 0.594 (0.594) Remain 02:43:50 loss: 0.2799 Lr: 0.00393 [2024-11-25 17:06:39,175 INFO misc.py line 119 2586773] Train: [7/50][2/376] Data 0.002 (0.002) Batch 0.460 (0.460) Remain 02:06:48 loss: 0.2789 Lr: 0.00393 [2024-11-25 17:06:39,702 INFO misc.py line 119 2586773] Train: [7/50][3/376] Data 0.002 (0.002) Batch 0.528 (0.528) Remain 02:25:27 loss: 0.2639 Lr: 0.00393 [2024-11-25 17:06:40,207 INFO misc.py line 119 2586773] Train: [7/50][4/376] Data 0.003 (0.003) Batch 0.504 (0.504) Remain 02:19:00 loss: 0.1998 Lr: 0.00393 [2024-11-25 17:06:40,764 INFO misc.py line 119 2586773] Train: [7/50][5/376] Data 0.002 (0.002) Batch 0.557 (0.531) Remain 02:26:19 loss: 0.2732 Lr: 0.00393 [2024-11-25 17:06:41,282 INFO misc.py line 119 2586773] Train: [7/50][6/376] Data 0.003 (0.003) Batch 0.518 (0.526) Remain 02:25:05 loss: 0.2693 Lr: 0.00393 [2024-11-25 17:06:41,771 INFO misc.py line 119 2586773] Train: [7/50][7/376] Data 0.002 (0.002) Batch 0.489 (0.517) Remain 02:22:31 loss: 0.2935 Lr: 0.00393 [2024-11-25 17:06:42,235 INFO misc.py line 119 2586773] Train: [7/50][8/376] Data 0.002 (0.002) Batch 0.464 (0.506) Remain 02:19:35 loss: 0.2557 Lr: 0.00393 [2024-11-25 17:06:42,720 INFO misc.py line 119 2586773] Train: [7/50][9/376] Data 0.002 (0.002) Batch 0.485 (0.503) Remain 02:18:36 loss: 0.3479 Lr: 0.00393 [2024-11-25 17:06:43,202 INFO misc.py line 119 2586773] Train: [7/50][10/376] Data 0.002 (0.002) Batch 0.482 (0.500) Remain 02:17:45 loss: 0.2581 Lr: 0.00393 [2024-11-25 17:06:43,676 INFO misc.py line 119 2586773] Train: [7/50][11/376] Data 0.002 (0.002) Batch 0.474 (0.497) Remain 02:16:52 loss: 0.2963 Lr: 0.00393 [2024-11-25 17:06:44,171 INFO misc.py line 119 2586773] Train: [7/50][12/376] Data 0.002 (0.002) Batch 0.495 (0.497) Remain 02:16:49 loss: 0.2185 Lr: 0.00393 [2024-11-25 17:06:44,736 INFO misc.py line 119 2586773] Train: [7/50][13/376] Data 0.002 (0.002) Batch 0.564 (0.503) Remain 02:18:40 loss: 0.2531 Lr: 0.00393 [2024-11-25 17:06:45,265 INFO misc.py line 119 2586773] Train: [7/50][14/376] Data 0.003 (0.002) Batch 0.529 (0.506) Remain 02:19:18 loss: 0.2830 Lr: 0.00393 [2024-11-25 17:06:45,787 INFO misc.py line 119 2586773] Train: [7/50][15/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 02:19:41 loss: 0.2532 Lr: 0.00393 [2024-11-25 17:06:46,301 INFO misc.py line 119 2586773] Train: [7/50][16/376] Data 0.003 (0.002) Batch 0.514 (0.508) Remain 02:19:49 loss: 0.2915 Lr: 0.00393 [2024-11-25 17:06:46,806 INFO misc.py line 119 2586773] Train: [7/50][17/376] Data 0.003 (0.002) Batch 0.505 (0.507) Remain 02:19:45 loss: 0.2534 Lr: 0.00393 [2024-11-25 17:06:47,368 INFO misc.py line 119 2586773] Train: [7/50][18/376] Data 0.003 (0.002) Batch 0.562 (0.511) Remain 02:20:45 loss: 0.2970 Lr: 0.00393 [2024-11-25 17:06:47,852 INFO misc.py line 119 2586773] Train: [7/50][19/376] Data 0.003 (0.002) Batch 0.484 (0.509) Remain 02:20:17 loss: 0.2590 Lr: 0.00393 [2024-11-25 17:06:48,347 INFO misc.py line 119 2586773] Train: [7/50][20/376] Data 0.003 (0.002) Batch 0.494 (0.508) Remain 02:20:02 loss: 0.2897 Lr: 0.00393 [2024-11-25 17:06:48,847 INFO misc.py line 119 2586773] Train: [7/50][21/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 02:19:54 loss: 0.2872 Lr: 0.00393 [2024-11-25 17:06:49,384 INFO misc.py line 119 2586773] Train: [7/50][22/376] Data 0.002 (0.002) Batch 0.537 (0.510) Remain 02:20:18 loss: 0.2265 Lr: 0.00393 [2024-11-25 17:06:49,856 INFO misc.py line 119 2586773] Train: [7/50][23/376] Data 0.002 (0.002) Batch 0.472 (0.508) Remain 02:19:47 loss: 0.4147 Lr: 0.00393 [2024-11-25 17:06:50,371 INFO misc.py line 119 2586773] Train: [7/50][24/376] Data 0.002 (0.002) Batch 0.515 (0.508) Remain 02:19:52 loss: 0.2691 Lr: 0.00393 [2024-11-25 17:06:50,850 INFO misc.py line 119 2586773] Train: [7/50][25/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 02:19:30 loss: 0.2258 Lr: 0.00393 [2024-11-25 17:06:51,378 INFO misc.py line 119 2586773] Train: [7/50][26/376] Data 0.003 (0.002) Batch 0.528 (0.508) Remain 02:19:44 loss: 0.2431 Lr: 0.00393 [2024-11-25 17:06:51,921 INFO misc.py line 119 2586773] Train: [7/50][27/376] Data 0.003 (0.002) Batch 0.543 (0.509) Remain 02:20:08 loss: 0.2320 Lr: 0.00393 [2024-11-25 17:06:52,473 INFO misc.py line 119 2586773] Train: [7/50][28/376] Data 0.003 (0.002) Batch 0.553 (0.511) Remain 02:20:37 loss: 0.2924 Lr: 0.00393 [2024-11-25 17:06:53,001 INFO misc.py line 119 2586773] Train: [7/50][29/376] Data 0.002 (0.002) Batch 0.528 (0.511) Remain 02:20:47 loss: 0.3058 Lr: 0.00393 [2024-11-25 17:06:53,484 INFO misc.py line 119 2586773] Train: [7/50][30/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 02:20:29 loss: 0.2284 Lr: 0.00393 [2024-11-25 17:06:53,987 INFO misc.py line 119 2586773] Train: [7/50][31/376] Data 0.002 (0.002) Batch 0.503 (0.510) Remain 02:20:24 loss: 0.2406 Lr: 0.00393 [2024-11-25 17:06:54,511 INFO misc.py line 119 2586773] Train: [7/50][32/376] Data 0.003 (0.002) Batch 0.523 (0.511) Remain 02:20:31 loss: 0.2942 Lr: 0.00393 [2024-11-25 17:06:55,033 INFO misc.py line 119 2586773] Train: [7/50][33/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 02:20:37 loss: 0.3100 Lr: 0.00393 [2024-11-25 17:06:55,586 INFO misc.py line 119 2586773] Train: [7/50][34/376] Data 0.002 (0.002) Batch 0.553 (0.512) Remain 02:20:59 loss: 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misc.py line 119 2586773] Train: [7/50][41/376] Data 0.002 (0.002) Batch 0.489 (0.509) Remain 02:19:58 loss: 0.2794 Lr: 0.00393 [2024-11-25 17:06:59,527 INFO misc.py line 119 2586773] Train: [7/50][42/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 02:19:48 loss: 0.2799 Lr: 0.00393 [2024-11-25 17:07:00,012 INFO misc.py line 119 2586773] Train: [7/50][43/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 02:19:38 loss: 0.2584 Lr: 0.00393 [2024-11-25 17:07:00,515 INFO misc.py line 119 2586773] Train: [7/50][44/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 02:19:35 loss: 0.2581 Lr: 0.00393 [2024-11-25 17:07:01,029 INFO misc.py line 119 2586773] Train: [7/50][45/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 02:19:37 loss: 0.2960 Lr: 0.00393 [2024-11-25 17:07:01,558 INFO misc.py line 119 2586773] Train: [7/50][46/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 02:19:45 loss: 0.2149 Lr: 0.00393 [2024-11-25 17:07:02,057 INFO misc.py line 119 2586773] Train: [7/50][47/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 02:19:41 loss: 0.3418 Lr: 0.00393 [2024-11-25 17:07:02,590 INFO misc.py line 119 2586773] Train: [7/50][48/376] Data 0.002 (0.002) Batch 0.533 (0.509) Remain 02:19:49 loss: 0.2310 Lr: 0.00393 [2024-11-25 17:07:03,064 INFO misc.py line 119 2586773] Train: [7/50][49/376] Data 0.003 (0.002) Batch 0.474 (0.508) Remain 02:19:37 loss: 0.3323 Lr: 0.00393 [2024-11-25 17:07:03,575 INFO misc.py line 119 2586773] Train: [7/50][50/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 02:19:37 loss: 0.2885 Lr: 0.00393 [2024-11-25 17:07:04,098 INFO misc.py line 119 2586773] Train: [7/50][51/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 02:19:42 loss: 0.2729 Lr: 0.00393 [2024-11-25 17:07:04,588 INFO misc.py line 119 2586773] Train: [7/50][52/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 02:19:35 loss: 0.2153 Lr: 0.00393 [2024-11-25 17:07:05,110 INFO misc.py line 119 2586773] Train: [7/50][53/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 02:19:39 loss: 0.2485 Lr: 0.00393 [2024-11-25 17:07:05,608 INFO misc.py line 119 2586773] Train: [7/50][54/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 02:19:36 loss: 0.2315 Lr: 0.00393 [2024-11-25 17:07:06,089 INFO misc.py line 119 2586773] Train: [7/50][55/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 02:19:27 loss: 0.4159 Lr: 0.00393 [2024-11-25 17:07:06,565 INFO misc.py line 119 2586773] Train: [7/50][56/376] Data 0.002 (0.002) Batch 0.476 (0.507) Remain 02:19:16 loss: 0.2827 Lr: 0.00393 [2024-11-25 17:07:07,072 INFO misc.py line 119 2586773] Train: [7/50][57/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 02:19:16 loss: 0.2683 Lr: 0.00393 [2024-11-25 17:07:07,578 INFO misc.py line 119 2586773] Train: [7/50][58/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 02:19:15 loss: 0.2808 Lr: 0.00393 [2024-11-25 17:07:08,108 INFO misc.py line 119 2586773] Train: [7/50][59/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 02:19:21 loss: 0.2416 Lr: 0.00393 [2024-11-25 17:07:08,579 INFO misc.py line 119 2586773] Train: [7/50][60/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 02:19:10 loss: 0.2895 Lr: 0.00393 [2024-11-25 17:07:09,081 INFO misc.py line 119 2586773] Train: [7/50][61/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 02:19:09 loss: 0.2709 Lr: 0.00393 [2024-11-25 17:07:09,598 INFO misc.py line 119 2586773] Train: [7/50][62/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 02:19:11 loss: 0.2441 Lr: 0.00393 [2024-11-25 17:07:10,075 INFO misc.py line 119 2586773] Train: [7/50][63/376] Data 0.002 (0.002) Batch 0.478 (0.506) Remain 02:19:02 loss: 0.2803 Lr: 0.00393 [2024-11-25 17:07:10,619 INFO misc.py line 119 2586773] Train: [7/50][64/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 02:19:12 loss: 0.2401 Lr: 0.00393 [2024-11-25 17:07:11,130 INFO misc.py line 119 2586773] Train: [7/50][65/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 02:19:12 loss: 0.2875 Lr: 0.00393 [2024-11-25 17:07:11,661 INFO misc.py line 119 2586773] Train: [7/50][66/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 02:19:18 loss: 0.2636 Lr: 0.00393 [2024-11-25 17:07:12,131 INFO misc.py line 119 2586773] Train: [7/50][67/376] Data 0.002 (0.002) Batch 0.470 (0.507) Remain 02:19:08 loss: 0.3024 Lr: 0.00393 [2024-11-25 17:07:12,664 INFO misc.py line 119 2586773] Train: [7/50][68/376] Data 0.002 (0.002) Batch 0.534 (0.507) Remain 02:19:15 loss: 0.2837 Lr: 0.00393 [2024-11-25 17:07:13,195 INFO misc.py line 119 2586773] Train: [7/50][69/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 02:19:20 loss: 0.2834 Lr: 0.00393 [2024-11-25 17:07:13,749 INFO misc.py line 119 2586773] Train: [7/50][70/376] Data 0.002 (0.002) Batch 0.554 (0.508) Remain 02:19:31 loss: 0.2718 Lr: 0.00393 [2024-11-25 17:07:14,276 INFO misc.py line 119 2586773] Train: [7/50][71/376] Data 0.002 (0.002) Batch 0.527 (0.508) Remain 02:19:35 loss: 0.2634 Lr: 0.00393 [2024-11-25 17:07:14,800 INFO misc.py line 119 2586773] Train: [7/50][72/376] Data 0.002 (0.002) Batch 0.524 (0.509) Remain 02:19:38 loss: 0.2761 Lr: 0.00393 [2024-11-25 17:07:15,330 INFO misc.py line 119 2586773] Train: [7/50][73/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 02:19:43 loss: 0.2927 Lr: 0.00393 [2024-11-25 17:07:15,859 INFO misc.py line 119 2586773] Train: [7/50][74/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 02:19:47 loss: 0.2744 Lr: 0.00393 [2024-11-25 17:07:16,436 INFO misc.py line 119 2586773] Train: [7/50][75/376] Data 0.002 (0.002) Batch 0.577 (0.510) Remain 02:20:02 loss: 0.2737 Lr: 0.00393 [2024-11-25 17:07:16,940 INFO misc.py line 119 2586773] Train: [7/50][76/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 02:20:00 loss: 0.2687 Lr: 0.00393 [2024-11-25 17:07:17,482 INFO misc.py line 119 2586773] Train: [7/50][77/376] Data 0.003 (0.002) Batch 0.542 (0.511) Remain 02:20:06 loss: 0.2620 Lr: 0.00393 [2024-11-25 17:07:18,017 INFO misc.py line 119 2586773] Train: [7/50][78/376] Data 0.003 (0.002) Batch 0.536 (0.511) Remain 02:20:11 loss: 0.2512 Lr: 0.00393 [2024-11-25 17:07:18,507 INFO misc.py line 119 2586773] Train: [7/50][79/376] Data 0.003 (0.002) Batch 0.490 (0.511) Remain 02:20:06 loss: 0.1994 Lr: 0.00392 [2024-11-25 17:07:18,975 INFO misc.py line 119 2586773] Train: [7/50][80/376] Data 0.003 (0.002) Batch 0.467 (0.510) Remain 02:19:57 loss: 0.2587 Lr: 0.00392 [2024-11-25 17:07:19,502 INFO misc.py line 119 2586773] Train: [7/50][81/376] Data 0.002 (0.002) Batch 0.527 (0.510) Remain 02:20:00 loss: 0.3086 Lr: 0.00392 [2024-11-25 17:07:19,986 INFO misc.py line 119 2586773] Train: [7/50][82/376] Data 0.003 (0.002) Batch 0.484 (0.510) Remain 02:19:54 loss: 0.2682 Lr: 0.00392 [2024-11-25 17:07:20,499 INFO misc.py line 119 2586773] Train: [7/50][83/376] Data 0.003 (0.002) Batch 0.512 (0.510) Remain 02:19:54 loss: 0.2477 Lr: 0.00392 [2024-11-25 17:07:21,005 INFO misc.py line 119 2586773] Train: [7/50][84/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 02:19:53 loss: 0.3186 Lr: 0.00392 [2024-11-25 17:07:21,513 INFO misc.py line 119 2586773] Train: 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Batch 0.550 (0.508) Remain 02:18:54 loss: 0.2658 Lr: 0.00392 [2024-11-25 17:07:47,254 INFO misc.py line 119 2586773] Train: [7/50][136/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 02:18:53 loss: 0.2966 Lr: 0.00392 [2024-11-25 17:07:47,758 INFO misc.py line 119 2586773] Train: [7/50][137/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 02:18:52 loss: 0.3033 Lr: 0.00392 [2024-11-25 17:07:48,309 INFO misc.py line 119 2586773] Train: [7/50][138/376] Data 0.003 (0.002) Batch 0.551 (0.508) Remain 02:18:57 loss: 0.2700 Lr: 0.00392 [2024-11-25 17:07:48,832 INFO misc.py line 119 2586773] Train: [7/50][139/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 02:18:58 loss: 0.2723 Lr: 0.00392 [2024-11-25 17:07:49,319 INFO misc.py line 119 2586773] Train: [7/50][140/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 02:18:55 loss: 0.2663 Lr: 0.00392 [2024-11-25 17:07:49,812 INFO misc.py line 119 2586773] Train: [7/50][141/376] Data 0.003 (0.002) Batch 0.492 (0.508) Remain 02:18:53 loss: 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Batch 0.480 (0.508) Remain 02:17:41 loss: 0.2693 Lr: 0.00391 [2024-11-25 17:08:57,295 INFO misc.py line 119 2586773] Train: [7/50][274/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 02:17:40 loss: 0.2870 Lr: 0.00391 [2024-11-25 17:08:57,799 INFO misc.py line 119 2586773] Train: [7/50][275/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 02:17:39 loss: 0.2892 Lr: 0.00391 [2024-11-25 17:08:58,271 INFO misc.py line 119 2586773] Train: [7/50][276/376] Data 0.003 (0.002) Batch 0.471 (0.508) Remain 02:17:37 loss: 0.2979 Lr: 0.00391 [2024-11-25 17:08:58,767 INFO misc.py line 119 2586773] Train: [7/50][277/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 02:17:36 loss: 0.2069 Lr: 0.00391 [2024-11-25 17:08:59,261 INFO misc.py line 119 2586773] Train: [7/50][278/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 02:17:34 loss: 0.2210 Lr: 0.00390 [2024-11-25 17:08:59,724 INFO misc.py line 119 2586773] Train: [7/50][279/376] Data 0.002 (0.002) Batch 0.463 (0.507) Remain 02:17:31 loss: 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02:17:36 loss: 0.2224 Lr: 0.00390 [2024-11-25 17:09:23,097 INFO misc.py line 119 2586773] Train: [7/50][324/376] Data 0.002 (0.002) Batch 0.518 (0.509) Remain 02:17:36 loss: 0.2837 Lr: 0.00390 [2024-11-25 17:09:23,656 INFO misc.py line 119 2586773] Train: [7/50][325/376] Data 0.002 (0.002) Batch 0.560 (0.509) Remain 02:17:38 loss: 0.2893 Lr: 0.00390 [2024-11-25 17:09:24,181 INFO misc.py line 119 2586773] Train: [7/50][326/376] Data 0.002 (0.002) Batch 0.525 (0.509) Remain 02:17:38 loss: 0.2840 Lr: 0.00390 [2024-11-25 17:09:24,695 INFO misc.py line 119 2586773] Train: [7/50][327/376] Data 0.002 (0.002) Batch 0.514 (0.509) Remain 02:17:38 loss: 0.2866 Lr: 0.00390 [2024-11-25 17:09:25,183 INFO misc.py line 119 2586773] Train: [7/50][328/376] Data 0.003 (0.002) Batch 0.488 (0.509) Remain 02:17:36 loss: 0.2575 Lr: 0.00390 [2024-11-25 17:09:25,710 INFO misc.py line 119 2586773] Train: [7/50][329/376] Data 0.002 (0.002) Batch 0.527 (0.509) Remain 02:17:37 loss: 0.2285 Lr: 0.00390 [2024-11-25 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Train: [7/50][336/376] Data 0.002 (0.002) Batch 0.465 (0.509) Remain 02:17:29 loss: 0.2691 Lr: 0.00390 [2024-11-25 17:09:29,727 INFO misc.py line 119 2586773] Train: [7/50][337/376] Data 0.002 (0.002) Batch 0.541 (0.509) Remain 02:17:30 loss: 0.2240 Lr: 0.00390 [2024-11-25 17:09:30,198 INFO misc.py line 119 2586773] Train: [7/50][338/376] Data 0.002 (0.002) Batch 0.471 (0.509) Remain 02:17:27 loss: 0.2904 Lr: 0.00390 [2024-11-25 17:09:30,726 INFO misc.py line 119 2586773] Train: [7/50][339/376] Data 0.002 (0.002) Batch 0.528 (0.509) Remain 02:17:28 loss: 0.2798 Lr: 0.00390 [2024-11-25 17:09:31,234 INFO misc.py line 119 2586773] Train: [7/50][340/376] Data 0.002 (0.002) Batch 0.508 (0.509) Remain 02:17:27 loss: 0.3543 Lr: 0.00390 [2024-11-25 17:09:31,702 INFO misc.py line 119 2586773] Train: [7/50][341/376] Data 0.002 (0.002) Batch 0.468 (0.509) Remain 02:17:25 loss: 0.3061 Lr: 0.00390 [2024-11-25 17:09:32,198 INFO misc.py line 119 2586773] Train: [7/50][342/376] Data 0.003 (0.002) Batch 0.496 (0.509) Remain 02:17:24 loss: 0.3091 Lr: 0.00390 [2024-11-25 17:09:32,716 INFO misc.py line 119 2586773] Train: [7/50][343/376] Data 0.002 (0.002) Batch 0.518 (0.509) Remain 02:17:24 loss: 0.2538 Lr: 0.00390 [2024-11-25 17:09:33,196 INFO misc.py line 119 2586773] Train: [7/50][344/376] Data 0.003 (0.002) Batch 0.480 (0.509) Remain 02:17:22 loss: 0.2516 Lr: 0.00390 [2024-11-25 17:09:33,721 INFO misc.py line 119 2586773] Train: [7/50][345/376] Data 0.002 (0.002) Batch 0.525 (0.509) Remain 02:17:22 loss: 0.2104 Lr: 0.00390 [2024-11-25 17:09:34,215 INFO misc.py line 119 2586773] Train: [7/50][346/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 02:17:21 loss: 0.2028 Lr: 0.00390 [2024-11-25 17:09:34,716 INFO misc.py line 119 2586773] Train: [7/50][347/376] Data 0.002 (0.002) Batch 0.501 (0.509) Remain 02:17:20 loss: 0.2525 Lr: 0.00390 [2024-11-25 17:09:35,197 INFO misc.py line 119 2586773] Train: [7/50][348/376] Data 0.003 (0.002) Batch 0.480 (0.509) Remain 02:17:18 loss: 0.2750 Lr: 0.00390 [2024-11-25 17:09:35,683 INFO misc.py line 119 2586773] Train: [7/50][349/376] Data 0.002 (0.002) Batch 0.486 (0.509) Remain 02:17:16 loss: 0.2697 Lr: 0.00390 [2024-11-25 17:09:36,197 INFO misc.py line 119 2586773] Train: [7/50][350/376] Data 0.002 (0.002) Batch 0.514 (0.509) Remain 02:17:16 loss: 0.2957 Lr: 0.00390 [2024-11-25 17:09:36,693 INFO misc.py line 119 2586773] Train: [7/50][351/376] Data 0.002 (0.002) Batch 0.497 (0.509) Remain 02:17:15 loss: 0.2511 Lr: 0.00390 [2024-11-25 17:09:37,198 INFO misc.py line 119 2586773] Train: [7/50][352/376] Data 0.002 (0.002) Batch 0.504 (0.509) Remain 02:17:14 loss: 0.2592 Lr: 0.00390 [2024-11-25 17:09:37,720 INFO misc.py line 119 2586773] Train: [7/50][353/376] Data 0.002 (0.002) Batch 0.522 (0.509) Remain 02:17:15 loss: 0.2868 Lr: 0.00390 [2024-11-25 17:09:38,244 INFO misc.py line 119 2586773] Train: [7/50][354/376] Data 0.002 (0.002) Batch 0.524 (0.509) Remain 02:17:15 loss: 0.2819 Lr: 0.00390 [2024-11-25 17:09:38,732 INFO misc.py line 119 2586773] Train: [7/50][355/376] Data 0.002 (0.002) Batch 0.488 (0.509) Remain 02:17:13 loss: 0.2166 Lr: 0.00390 [2024-11-25 17:09:39,251 INFO misc.py line 119 2586773] Train: [7/50][356/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 02:17:13 loss: 0.2944 Lr: 0.00390 [2024-11-25 17:09:39,777 INFO misc.py line 119 2586773] Train: [7/50][357/376] Data 0.002 (0.002) Batch 0.525 (0.509) Remain 02:17:14 loss: 0.2145 Lr: 0.00390 [2024-11-25 17:09:40,269 INFO misc.py line 119 2586773] Train: [7/50][358/376] Data 0.002 (0.002) Batch 0.493 (0.509) Remain 02:17:12 loss: 0.2276 Lr: 0.00390 [2024-11-25 17:09:40,805 INFO misc.py line 119 2586773] Train: [7/50][359/376] Data 0.002 (0.002) Batch 0.536 (0.509) Remain 02:17:13 loss: 0.2204 Lr: 0.00390 [2024-11-25 17:09:41,315 INFO misc.py line 119 2586773] Train: [7/50][360/376] Data 0.003 (0.002) Batch 0.510 (0.509) Remain 02:17:13 loss: 0.3009 Lr: 0.00390 [2024-11-25 17:09:41,849 INFO misc.py line 119 2586773] Train: [7/50][361/376] Data 0.002 (0.002) Batch 0.533 (0.509) Remain 02:17:13 loss: 0.2881 Lr: 0.00390 [2024-11-25 17:09:42,344 INFO misc.py line 119 2586773] Train: [7/50][362/376] Data 0.002 (0.002) Batch 0.496 (0.509) Remain 02:17:12 loss: 0.2652 Lr: 0.00390 [2024-11-25 17:09:42,834 INFO misc.py line 119 2586773] Train: [7/50][363/376] Data 0.002 (0.002) Batch 0.489 (0.509) Remain 02:17:11 loss: 0.2898 Lr: 0.00390 [2024-11-25 17:09:43,345 INFO misc.py line 119 2586773] Train: [7/50][364/376] Data 0.002 (0.002) Batch 0.511 (0.509) Remain 02:17:10 loss: 0.2571 Lr: 0.00390 [2024-11-25 17:09:43,835 INFO misc.py line 119 2586773] Train: [7/50][365/376] Data 0.002 (0.002) Batch 0.490 (0.509) Remain 02:17:09 loss: 0.2723 Lr: 0.00390 [2024-11-25 17:09:44,340 INFO misc.py line 119 2586773] Train: [7/50][366/376] Data 0.002 (0.002) Batch 0.505 (0.509) Remain 02:17:08 loss: 0.2587 Lr: 0.00390 [2024-11-25 17:09:44,852 INFO misc.py line 119 2586773] Train: [7/50][367/376] Data 0.003 (0.002) Batch 0.512 (0.509) Remain 02:17:08 loss: 0.2794 Lr: 0.00390 [2024-11-25 17:09:45,372 INFO misc.py line 119 2586773] Train: [7/50][368/376] Data 0.002 (0.002) Batch 0.521 (0.509) Remain 02:17:08 loss: 0.2476 Lr: 0.00390 [2024-11-25 17:09:45,859 INFO misc.py line 119 2586773] Train: [7/50][369/376] Data 0.002 (0.002) Batch 0.486 (0.509) Remain 02:17:06 loss: 0.2689 Lr: 0.00390 [2024-11-25 17:09:46,337 INFO misc.py line 119 2586773] Train: [7/50][370/376] Data 0.003 (0.002) Batch 0.479 (0.509) Remain 02:17:05 loss: 0.2557 Lr: 0.00389 [2024-11-25 17:09:46,892 INFO misc.py line 119 2586773] Train: [7/50][371/376] Data 0.002 (0.002) Batch 0.554 (0.509) Remain 02:17:06 loss: 0.2880 Lr: 0.00389 [2024-11-25 17:09:47,386 INFO misc.py line 119 2586773] Train: [7/50][372/376] Data 0.003 (0.002) Batch 0.495 (0.509) Remain 02:17:05 loss: 0.2708 Lr: 0.00389 [2024-11-25 17:09:47,935 INFO misc.py line 119 2586773] Train: [7/50][373/376] Data 0.002 (0.002) Batch 0.549 (0.509) Remain 02:17:06 loss: 0.2709 Lr: 0.00389 [2024-11-25 17:09:48,396 INFO misc.py line 119 2586773] Train: [7/50][374/376] Data 0.002 (0.002) Batch 0.461 (0.509) Remain 02:17:04 loss: 0.2409 Lr: 0.00389 [2024-11-25 17:09:48,872 INFO misc.py line 119 2586773] Train: [7/50][375/376] Data 0.002 (0.002) Batch 0.475 (0.509) Remain 02:17:02 loss: 0.2288 Lr: 0.00389 [2024-11-25 17:09:49,350 INFO misc.py line 119 2586773] Train: [7/50][376/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 02:17:00 loss: 0.2656 Lr: 0.00389 [2024-11-25 17:09:49,350 INFO misc.py line 136 2586773] Train result: loss: 0.2698 [2024-11-25 17:09:49,350 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:10:00,424 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2401 [2024-11-25 17:10:00,680 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2508 [2024-11-25 17:10:00,947 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3524 [2024-11-25 17:10:01,169 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2511 [2024-11-25 17:10:01,434 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3530 [2024-11-25 17:10:01,701 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2643 [2024-11-25 17:10:01,927 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2577 [2024-11-25 17:10:02,197 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2445 [2024-11-25 17:10:02,420 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2876 [2024-11-25 17:10:02,682 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3310 [2024-11-25 17:10:02,913 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2857 [2024-11-25 17:10:03,184 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2599 [2024-11-25 17:10:03,451 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.3026 [2024-11-25 17:10:03,713 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2917 [2024-11-25 17:10:03,948 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2917 [2024-11-25 17:10:04,188 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3107 [2024-11-25 17:10:04,454 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3687 [2024-11-25 17:10:04,705 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2552 [2024-11-25 17:10:04,935 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2671 [2024-11-25 17:10:05,196 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3452 [2024-11-25 17:10:05,434 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2814 [2024-11-25 17:10:05,699 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3497 [2024-11-25 17:10:05,935 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2586 [2024-11-25 17:10:06,201 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3305 [2024-11-25 17:10:06,464 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.3154 [2024-11-25 17:10:06,699 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2904 [2024-11-25 17:10:06,955 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3426 [2024-11-25 17:10:07,203 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.3015 [2024-11-25 17:10:07,470 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3962 [2024-11-25 17:10:07,723 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3865 [2024-11-25 17:10:07,960 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3126 [2024-11-25 17:10:08,226 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2978 [2024-11-25 17:10:08,444 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2854 [2024-11-25 17:10:08,685 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2607 [2024-11-25 17:10:08,949 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2607 [2024-11-25 17:10:09,197 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2835 [2024-11-25 17:10:09,425 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2415 [2024-11-25 17:10:09,697 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2996 [2024-11-25 17:10:09,929 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3263 [2024-11-25 17:10:10,162 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.3209 [2024-11-25 17:10:10,434 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2767 [2024-11-25 17:10:10,682 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3567 [2024-11-25 17:10:10,931 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3079 [2024-11-25 17:10:11,162 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2909 [2024-11-25 17:10:11,397 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2816 [2024-11-25 17:10:11,643 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3163 [2024-11-25 17:10:11,902 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3369 [2024-11-25 17:10:12,161 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3522 [2024-11-25 17:10:12,386 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2732 [2024-11-25 17:10:12,620 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2795 [2024-11-25 17:10:12,840 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2853 [2024-11-25 17:10:13,093 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3085 [2024-11-25 17:10:13,361 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2775 [2024-11-25 17:10:13,622 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3193 [2024-11-25 17:10:13,854 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2960 [2024-11-25 17:10:14,093 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.3004 [2024-11-25 17:10:14,350 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3593 [2024-11-25 17:10:14,617 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2868 [2024-11-25 17:10:14,873 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3181 [2024-11-25 17:10:15,136 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3495 [2024-11-25 17:10:15,388 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2878 [2024-11-25 17:10:15,666 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.3147 [2024-11-25 17:10:15,896 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2728 [2024-11-25 17:10:16,155 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3427 [2024-11-25 17:10:16,423 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3216 [2024-11-25 17:10:16,687 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2946 [2024-11-25 17:10:16,931 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2510 [2024-11-25 17:10:17,188 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3559 [2024-11-25 17:10:17,455 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2720 [2024-11-25 17:10:17,714 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3451 [2024-11-25 17:10:17,958 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2378 [2024-11-25 17:10:18,192 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2934 [2024-11-25 17:10:18,448 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3358 [2024-11-25 17:10:18,697 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3340 [2024-11-25 17:10:18,913 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2771 [2024-11-25 17:10:19,136 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2387 [2024-11-25 17:10:19,405 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.3161 [2024-11-25 17:10:19,646 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2574 [2024-11-25 17:10:19,904 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2499 [2024-11-25 17:10:20,154 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3902 [2024-11-25 17:10:20,397 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2705 [2024-11-25 17:10:20,659 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2998 [2024-11-25 17:10:20,908 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2533 [2024-11-25 17:10:21,156 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3189 [2024-11-25 17:10:21,428 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2796 [2024-11-25 17:10:21,664 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2889 [2024-11-25 17:10:21,940 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3628 [2024-11-25 17:10:22,199 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3625 [2024-11-25 17:10:22,447 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3151 [2024-11-25 17:10:22,695 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2872 [2024-11-25 17:10:22,928 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2671 [2024-11-25 17:10:23,182 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.3282 [2024-11-25 17:10:23,449 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3258 [2024-11-25 17:10:23,725 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2524 [2024-11-25 17:10:23,991 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2463 [2024-11-25 17:10:24,239 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2577 [2024-11-25 17:10:24,505 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.3332 [2024-11-25 17:10:24,725 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3121 [2024-11-25 17:10:24,994 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.3126 [2024-11-25 17:10:25,232 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.3232 [2024-11-25 17:10:25,505 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2717 [2024-11-25 17:10:25,774 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3642 [2024-11-25 17:10:26,034 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3424 [2024-11-25 17:10:26,286 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3449 [2024-11-25 17:10:26,512 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2698 [2024-11-25 17:10:26,747 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2663 [2024-11-25 17:10:27,004 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2958 [2024-11-25 17:10:27,275 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3494 [2024-11-25 17:10:27,508 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3393 [2024-11-25 17:10:27,766 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2867 [2024-11-25 17:10:28,032 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2670 [2024-11-25 17:10:28,254 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2756 [2024-11-25 17:10:28,489 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2573 [2024-11-25 17:10:28,709 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.3094 [2024-11-25 17:10:28,934 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2885 [2024-11-25 17:10:29,208 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3880 [2024-11-25 17:10:29,468 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3428 [2024-11-25 17:10:29,735 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3453 [2024-11-25 17:10:30,000 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2869 [2024-11-25 17:10:30,262 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3994 [2024-11-25 17:10:30,523 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2983 [2024-11-25 17:10:30,786 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2543 [2024-11-25 17:10:31,041 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3319 [2024-11-25 17:10:31,305 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3464 [2024-11-25 17:10:31,569 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2787 [2024-11-25 17:10:31,820 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3397 [2024-11-25 17:10:32,050 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2714 [2024-11-25 17:10:32,310 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3389 [2024-11-25 17:10:32,544 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2837 [2024-11-25 17:10:32,771 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2443 [2024-11-25 17:10:32,985 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2864 [2024-11-25 17:10:33,200 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2533 [2024-11-25 17:10:33,933 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7078/0.7475/0.9955. [2024-11-25 17:10:33,934 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9955/0.9988 [2024-11-25 17:10:33,934 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4201/0.4961 [2024-11-25 17:10:33,934 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:10:33,935 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7078 [2024-11-25 17:10:33,935 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7078 [2024-11-25 17:10:33,935 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:10:38,227 INFO misc.py line 119 2586773] Train: [8/50][1/376] Data 0.107 (0.107) Batch 0.584 (0.584) Remain 02:37:15 loss: 0.2595 Lr: 0.00389 [2024-11-25 17:10:38,692 INFO misc.py line 119 2586773] Train: [8/50][2/376] Data 0.002 (0.002) Batch 0.466 (0.466) Remain 02:05:26 loss: 0.2240 Lr: 0.00389 [2024-11-25 17:10:39,169 INFO misc.py line 119 2586773] Train: [8/50][3/376] Data 0.002 (0.002) Batch 0.477 (0.477) Remain 02:08:29 loss: 0.3237 Lr: 0.00389 [2024-11-25 17:10:39,665 INFO misc.py line 119 2586773] Train: [8/50][4/376] Data 0.002 (0.002) Batch 0.496 (0.496) Remain 02:13:42 loss: 0.4172 Lr: 0.00389 [2024-11-25 17:10:40,187 INFO misc.py line 119 2586773] Train: [8/50][5/376] Data 0.002 (0.002) Batch 0.522 (0.509) Remain 02:17:06 loss: 0.2996 Lr: 0.00389 [2024-11-25 17:10:40,721 INFO misc.py line 119 2586773] Train: [8/50][6/376] Data 0.003 (0.002) Batch 0.534 (0.517) Remain 02:19:22 loss: 0.2714 Lr: 0.00389 [2024-11-25 17:10:41,179 INFO misc.py line 119 2586773] Train: [8/50][7/376] Data 0.002 (0.002) Batch 0.458 (0.503) Remain 02:15:21 loss: 0.2292 Lr: 0.00389 [2024-11-25 17:10:41,656 INFO misc.py line 119 2586773] Train: [8/50][8/376] Data 0.002 (0.002) Batch 0.477 (0.497) Remain 02:13:58 loss: 0.2158 Lr: 0.00389 [2024-11-25 17:10:42,190 INFO misc.py line 119 2586773] Train: [8/50][9/376] Data 0.002 (0.002) Batch 0.534 (0.504) Remain 02:15:36 loss: 0.2956 Lr: 0.00389 [2024-11-25 17:10:42,704 INFO misc.py line 119 2586773] Train: [8/50][10/376] Data 0.002 (0.002) Batch 0.514 (0.505) Remain 02:15:59 loss: 0.2669 Lr: 0.00389 [2024-11-25 17:10:43,222 INFO misc.py line 119 2586773] Train: [8/50][11/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 02:16:25 loss: 0.3317 Lr: 0.00389 [2024-11-25 17:10:43,717 INFO misc.py line 119 2586773] Train: [8/50][12/376] Data 0.003 (0.002) Batch 0.496 (0.505) Remain 02:16:04 loss: 0.2719 Lr: 0.00389 [2024-11-25 17:10:44,227 INFO misc.py line 119 2586773] Train: [8/50][13/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 02:16:10 loss: 0.2686 Lr: 0.00389 [2024-11-25 17:10:44,746 INFO misc.py line 119 2586773] Train: [8/50][14/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 02:16:29 loss: 0.2930 Lr: 0.00389 [2024-11-25 17:10:45,294 INFO misc.py line 119 2586773] Train: [8/50][15/376] Data 0.002 (0.002) Batch 0.548 (0.510) Remain 02:17:24 loss: 0.2853 Lr: 0.00389 [2024-11-25 17:10:45,808 INFO misc.py line 119 2586773] Train: [8/50][16/376] Data 0.002 (0.002) Batch 0.514 (0.511) Remain 02:17:28 loss: 0.2510 Lr: 0.00389 [2024-11-25 17:10:46,359 INFO misc.py line 119 2586773] Train: [8/50][17/376] Data 0.002 (0.002) Batch 0.550 (0.514) Remain 02:18:13 loss: 0.2366 Lr: 0.00389 [2024-11-25 17:10:46,862 INFO misc.py line 119 2586773] Train: [8/50][18/376] Data 0.002 (0.002) Batch 0.503 (0.513) Remain 02:18:02 loss: 0.3360 Lr: 0.00389 [2024-11-25 17:10:47,383 INFO misc.py line 119 2586773] Train: [8/50][19/376] Data 0.003 (0.002) Batch 0.522 (0.513) Remain 02:18:10 loss: 0.2561 Lr: 0.00389 [2024-11-25 17:10:47,898 INFO misc.py line 119 2586773] Train: [8/50][20/376] Data 0.002 (0.002) Batch 0.515 (0.513) Remain 02:18:11 loss: 0.2878 Lr: 0.00389 [2024-11-25 17:10:48,374 INFO misc.py line 119 2586773] Train: [8/50][21/376] Data 0.002 (0.002) Batch 0.475 (0.511) Remain 02:17:36 loss: 0.2318 Lr: 0.00389 [2024-11-25 17:10:48,860 INFO misc.py line 119 2586773] Train: [8/50][22/376] Data 0.002 (0.002) Batch 0.486 (0.510) Remain 02:17:15 loss: 0.3252 Lr: 0.00389 [2024-11-25 17:10:49,351 INFO misc.py line 119 2586773] Train: [8/50][23/376] Data 0.002 (0.002) Batch 0.490 (0.509) Remain 02:16:58 loss: 0.2937 Lr: 0.00389 [2024-11-25 17:10:49,875 INFO misc.py line 119 2586773] Train: [8/50][24/376] Data 0.003 (0.002) Batch 0.524 (0.510) Remain 02:17:09 loss: 0.2296 Lr: 0.00389 [2024-11-25 17:10:50,364 INFO misc.py line 119 2586773] Train: [8/50][25/376] Data 0.003 (0.002) Batch 0.490 (0.509) Remain 02:16:54 loss: 0.3334 Lr: 0.00389 [2024-11-25 17:10:50,876 INFO misc.py line 119 2586773] Train: [8/50][26/376] Data 0.002 (0.002) Batch 0.511 (0.509) Remain 02:16:55 loss: 0.2237 Lr: 0.00389 [2024-11-25 17:10:51,365 INFO misc.py line 119 2586773] Train: [8/50][27/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 02:16:42 loss: 0.2954 Lr: 0.00389 [2024-11-25 17:10:51,900 INFO misc.py line 119 2586773] Train: [8/50][28/376] Data 0.002 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(0.508) Remain 02:14:14 loss: 0.3163 Lr: 0.00386 [2024-11-25 17:13:09,406 INFO misc.py line 119 2586773] Train: [8/50][299/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 02:14:14 loss: 0.2341 Lr: 0.00386 [2024-11-25 17:13:09,935 INFO misc.py line 119 2586773] Train: [8/50][300/376] Data 0.002 (0.002) Batch 0.530 (0.508) Remain 02:14:15 loss: 0.2107 Lr: 0.00386 [2024-11-25 17:13:10,448 INFO misc.py line 119 2586773] Train: [8/50][301/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 02:14:14 loss: 0.2887 Lr: 0.00386 [2024-11-25 17:13:10,944 INFO misc.py line 119 2586773] Train: [8/50][302/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 02:14:13 loss: 0.2739 Lr: 0.00386 [2024-11-25 17:13:11,461 INFO misc.py line 119 2586773] Train: [8/50][303/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 02:14:13 loss: 0.1983 Lr: 0.00386 [2024-11-25 17:13:11,932 INFO misc.py line 119 2586773] Train: [8/50][304/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 02:14:11 loss: 0.3040 Lr: 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line 119 2586773] Train: [8/50][311/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 02:14:08 loss: 0.2619 Lr: 0.00386 [2024-11-25 17:13:15,984 INFO misc.py line 119 2586773] Train: [8/50][312/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 02:14:06 loss: 0.2460 Lr: 0.00386 [2024-11-25 17:13:16,458 INFO misc.py line 119 2586773] Train: [8/50][313/376] Data 0.002 (0.002) Batch 0.474 (0.507) Remain 02:14:04 loss: 0.2525 Lr: 0.00386 [2024-11-25 17:13:16,962 INFO misc.py line 119 2586773] Train: [8/50][314/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:14:03 loss: 0.4369 Lr: 0.00386 [2024-11-25 17:13:17,442 INFO misc.py line 119 2586773] Train: [8/50][315/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 02:14:01 loss: 0.2806 Lr: 0.00386 [2024-11-25 17:13:17,902 INFO misc.py line 119 2586773] Train: [8/50][316/376] Data 0.002 (0.002) Batch 0.460 (0.507) Remain 02:13:59 loss: 0.2493 Lr: 0.00386 [2024-11-25 17:13:18,442 INFO misc.py line 119 2586773] Train: [8/50][317/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 02:14:00 loss: 0.2326 Lr: 0.00386 [2024-11-25 17:13:18,959 INFO misc.py line 119 2586773] Train: [8/50][318/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 02:14:00 loss: 0.2480 Lr: 0.00386 [2024-11-25 17:13:19,463 INFO misc.py line 119 2586773] Train: [8/50][319/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:13:59 loss: 0.2733 Lr: 0.00386 [2024-11-25 17:13:19,942 INFO misc.py line 119 2586773] Train: [8/50][320/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 02:13:57 loss: 0.2536 Lr: 0.00386 [2024-11-25 17:13:20,474 INFO misc.py line 119 2586773] Train: [8/50][321/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 02:13:58 loss: 0.2287 Lr: 0.00386 [2024-11-25 17:13:20,950 INFO misc.py line 119 2586773] Train: [8/50][322/376] Data 0.002 (0.002) Batch 0.477 (0.507) Remain 02:13:56 loss: 0.3153 Lr: 0.00386 [2024-11-25 17:13:21,458 INFO misc.py line 119 2586773] Train: [8/50][323/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 02:13:55 loss: 0.3103 Lr: 0.00386 [2024-11-25 17:13:21,954 INFO misc.py line 119 2586773] Train: [8/50][324/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 02:13:54 loss: 0.2619 Lr: 0.00386 [2024-11-25 17:13:22,474 INFO misc.py line 119 2586773] Train: [8/50][325/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 02:13:54 loss: 0.2548 Lr: 0.00386 [2024-11-25 17:13:22,981 INFO misc.py line 119 2586773] Train: [8/50][326/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 02:13:54 loss: 0.2698 Lr: 0.00385 [2024-11-25 17:13:23,468 INFO misc.py line 119 2586773] Train: [8/50][327/376] Data 0.003 (0.002) Batch 0.488 (0.507) Remain 02:13:52 loss: 0.2683 Lr: 0.00385 [2024-11-25 17:13:23,921 INFO misc.py line 119 2586773] Train: [8/50][328/376] Data 0.002 (0.002) Batch 0.452 (0.507) Remain 02:13:49 loss: 0.2293 Lr: 0.00385 [2024-11-25 17:13:24,405 INFO misc.py line 119 2586773] Train: [8/50][329/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 02:13:48 loss: 0.2193 Lr: 0.00385 [2024-11-25 17:13:24,917 INFO misc.py line 119 2586773] Train: [8/50][330/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 02:13:47 loss: 0.2689 Lr: 0.00385 [2024-11-25 17:13:25,430 INFO misc.py line 119 2586773] Train: [8/50][331/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 02:13:47 loss: 0.2780 Lr: 0.00385 [2024-11-25 17:13:25,930 INFO misc.py line 119 2586773] Train: [8/50][332/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 02:13:46 loss: 0.2665 Lr: 0.00385 [2024-11-25 17:13:26,453 INFO misc.py line 119 2586773] Train: [8/50][333/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 02:13:47 loss: 0.2725 Lr: 0.00385 [2024-11-25 17:13:26,977 INFO misc.py line 119 2586773] Train: [8/50][334/376] Data 0.003 (0.002) Batch 0.524 (0.507) Remain 02:13:47 loss: 0.2787 Lr: 0.00385 [2024-11-25 17:13:27,468 INFO misc.py line 119 2586773] Train: [8/50][335/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 02:13:46 loss: 0.2497 Lr: 0.00385 [2024-11-25 17:13:27,998 INFO misc.py line 119 2586773] Train: [8/50][336/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 02:13:46 loss: 0.2567 Lr: 0.00385 [2024-11-25 17:13:28,498 INFO misc.py line 119 2586773] Train: [8/50][337/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 02:13:45 loss: 0.2274 Lr: 0.00385 [2024-11-25 17:13:28,995 INFO misc.py line 119 2586773] Train: [8/50][338/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 02:13:44 loss: 0.2707 Lr: 0.00385 [2024-11-25 17:13:29,477 INFO misc.py line 119 2586773] Train: [8/50][339/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 02:13:43 loss: 0.2812 Lr: 0.00385 [2024-11-25 17:13:29,960 INFO misc.py line 119 2586773] Train: [8/50][340/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 02:13:41 loss: 0.2585 Lr: 0.00385 [2024-11-25 17:13:30,429 INFO misc.py line 119 2586773] Train: [8/50][341/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 02:13:39 loss: 0.2650 Lr: 0.00385 [2024-11-25 17:13:30,930 INFO misc.py line 119 2586773] Train: [8/50][342/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 02:13:38 loss: 0.2099 Lr: 0.00385 [2024-11-25 17:13:31,414 INFO misc.py line 119 2586773] Train: [8/50][343/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 02:13:36 loss: 0.2322 Lr: 0.00385 [2024-11-25 17:13:31,945 INFO misc.py line 119 2586773] Train: [8/50][344/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 02:13:37 loss: 0.3021 Lr: 0.00385 [2024-11-25 17:13:32,429 INFO misc.py line 119 2586773] Train: [8/50][345/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 02:13:36 loss: 0.2135 Lr: 0.00385 [2024-11-25 17:13:32,949 INFO misc.py line 119 2586773] Train: [8/50][346/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 02:13:36 loss: 0.2328 Lr: 0.00385 [2024-11-25 17:13:33,467 INFO misc.py line 119 2586773] Train: [8/50][347/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 02:13:36 loss: 0.2911 Lr: 0.00385 [2024-11-25 17:13:33,983 INFO misc.py line 119 2586773] Train: [8/50][348/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 02:13:36 loss: 0.2669 Lr: 0.00385 [2024-11-25 17:13:34,487 INFO misc.py line 119 2586773] Train: [8/50][349/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:13:35 loss: 0.2594 Lr: 0.00385 [2024-11-25 17:13:34,997 INFO misc.py line 119 2586773] Train: [8/50][350/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 02:13:35 loss: 0.2271 Lr: 0.00385 [2024-11-25 17:13:35,545 INFO misc.py line 119 2586773] Train: [8/50][351/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 02:13:36 loss: 0.3090 Lr: 0.00385 [2024-11-25 17:13:36,016 INFO misc.py line 119 2586773] Train: [8/50][352/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 02:13:34 loss: 0.2495 Lr: 0.00385 [2024-11-25 17:13:36,516 INFO misc.py line 119 2586773] Train: [8/50][353/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 02:13:33 loss: 0.2364 Lr: 0.00385 [2024-11-25 17:13:37,041 INFO misc.py line 119 2586773] Train: [8/50][354/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 02:13:33 loss: 0.2009 Lr: 0.00385 [2024-11-25 17:13:37,563 INFO misc.py line 119 2586773] Train: [8/50][355/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 02:13:34 loss: 0.2456 Lr: 0.00385 [2024-11-25 17:13:38,089 INFO misc.py line 119 2586773] Train: [8/50][356/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 02:13:34 loss: 0.2285 Lr: 0.00385 [2024-11-25 17:13:38,598 INFO misc.py line 119 2586773] Train: [8/50][357/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 02:13:33 loss: 0.2977 Lr: 0.00385 [2024-11-25 17:13:39,109 INFO misc.py line 119 2586773] Train: [8/50][358/376] Data 0.002 (0.002) Batch 0.511 (0.507) Remain 02:13:33 loss: 0.2857 Lr: 0.00385 [2024-11-25 17:13:39,631 INFO misc.py line 119 2586773] Train: [8/50][359/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 02:13:33 loss: 0.2614 Lr: 0.00385 [2024-11-25 17:13:40,152 INFO misc.py line 119 2586773] Train: [8/50][360/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 02:13:33 loss: 0.2820 Lr: 0.00385 [2024-11-25 17:13:40,637 INFO misc.py line 119 2586773] Train: [8/50][361/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 02:13:32 loss: 0.2716 Lr: 0.00385 [2024-11-25 17:13:41,121 INFO misc.py line 119 2586773] Train: [8/50][362/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 02:13:30 loss: 0.2430 Lr: 0.00385 [2024-11-25 17:13:41,653 INFO misc.py line 119 2586773] Train: [8/50][363/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 02:13:31 loss: 0.2995 Lr: 0.00385 [2024-11-25 17:13:42,165 INFO misc.py line 119 2586773] Train: [8/50][364/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 02:13:31 loss: 0.2755 Lr: 0.00385 [2024-11-25 17:13:42,643 INFO misc.py line 119 2586773] Train: [8/50][365/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 02:13:29 loss: 0.2350 Lr: 0.00385 [2024-11-25 17:13:43,144 INFO misc.py line 119 2586773] Train: [8/50][366/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:13:28 loss: 0.3019 Lr: 0.00385 [2024-11-25 17:13:43,660 INFO misc.py line 119 2586773] Train: [8/50][367/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 02:13:28 loss: 0.2438 Lr: 0.00385 [2024-11-25 17:13:44,189 INFO misc.py line 119 2586773] Train: [8/50][368/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 02:13:29 loss: 0.2412 Lr: 0.00385 [2024-11-25 17:13:44,695 INFO misc.py line 119 2586773] Train: [8/50][369/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:13:28 loss: 0.1943 Lr: 0.00385 [2024-11-25 17:13:45,224 INFO misc.py line 119 2586773] Train: [8/50][370/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 02:13:28 loss: 0.3047 Lr: 0.00385 [2024-11-25 17:13:45,706 INFO misc.py line 119 2586773] Train: [8/50][371/376] Data 0.003 (0.002) Batch 0.483 (0.507) Remain 02:13:27 loss: 0.2758 Lr: 0.00385 [2024-11-25 17:13:46,241 INFO misc.py line 119 2586773] Train: [8/50][372/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 02:13:28 loss: 0.2997 Lr: 0.00385 [2024-11-25 17:13:46,803 INFO misc.py line 119 2586773] Train: [8/50][373/376] Data 0.002 (0.002) Batch 0.562 (0.507) Remain 02:13:29 loss: 0.2990 Lr: 0.00385 [2024-11-25 17:13:47,271 INFO misc.py line 119 2586773] Train: [8/50][374/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 02:13:27 loss: 0.2607 Lr: 0.00385 [2024-11-25 17:13:47,797 INFO misc.py line 119 2586773] Train: [8/50][375/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 02:13:28 loss: 0.2776 Lr: 0.00385 [2024-11-25 17:13:48,299 INFO misc.py line 119 2586773] Train: [8/50][376/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 02:13:27 loss: 0.2047 Lr: 0.00385 [2024-11-25 17:13:48,299 INFO misc.py line 136 2586773] Train result: loss: 0.2706 [2024-11-25 17:13:48,300 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:13:59,265 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2336 [2024-11-25 17:13:59,543 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2455 [2024-11-25 17:13:59,805 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2971 [2024-11-25 17:14:00,031 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2382 [2024-11-25 17:14:00,295 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3352 [2024-11-25 17:14:00,564 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2536 [2024-11-25 17:14:00,788 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2473 [2024-11-25 17:14:01,058 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2459 [2024-11-25 17:14:01,294 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2942 [2024-11-25 17:14:01,556 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2935 [2024-11-25 17:14:01,786 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2466 [2024-11-25 17:14:02,059 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2521 [2024-11-25 17:14:02,326 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2832 [2024-11-25 17:14:02,589 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2879 [2024-11-25 17:14:02,824 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2885 [2024-11-25 17:14:03,064 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3192 [2024-11-25 17:14:03,331 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3369 [2024-11-25 17:14:03,578 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2364 [2024-11-25 17:14:03,810 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2501 [2024-11-25 17:14:04,070 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3010 [2024-11-25 17:14:04,304 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2639 [2024-11-25 17:14:04,571 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3077 [2024-11-25 17:14:04,809 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2663 [2024-11-25 17:14:05,076 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2980 [2024-11-25 17:14:05,341 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2599 [2024-11-25 17:14:05,579 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2818 [2024-11-25 17:14:05,830 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3132 [2024-11-25 17:14:06,081 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2769 [2024-11-25 17:14:06,348 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3419 [2024-11-25 17:14:06,603 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3496 [2024-11-25 17:14:06,838 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3096 [2024-11-25 17:14:07,101 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2575 [2024-11-25 17:14:07,320 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2795 [2024-11-25 17:14:07,560 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2368 [2024-11-25 17:14:07,819 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2586 [2024-11-25 17:14:08,063 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2800 [2024-11-25 17:14:08,289 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2388 [2024-11-25 17:14:08,560 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2578 [2024-11-25 17:14:08,790 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2854 [2024-11-25 17:14:09,023 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2842 [2024-11-25 17:14:09,293 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2749 [2024-11-25 17:14:09,549 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3146 [2024-11-25 17:14:09,786 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3052 [2024-11-25 17:14:10,017 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2671 [2024-11-25 17:14:10,254 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2627 [2024-11-25 17:14:10,503 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.3074 [2024-11-25 17:14:10,761 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2876 [2024-11-25 17:14:11,012 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3140 [2024-11-25 17:14:11,236 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2483 [2024-11-25 17:14:11,472 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2397 [2024-11-25 17:14:11,698 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2620 [2024-11-25 17:14:11,953 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2885 [2024-11-25 17:14:12,220 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2695 [2024-11-25 17:14:12,481 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3212 [2024-11-25 17:14:12,712 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2591 [2024-11-25 17:14:12,952 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2668 [2024-11-25 17:14:13,209 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3033 [2024-11-25 17:14:13,482 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2681 [2024-11-25 17:14:13,748 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2875 [2024-11-25 17:14:14,009 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2912 [2024-11-25 17:14:14,265 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2680 [2024-11-25 17:14:14,535 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2917 [2024-11-25 17:14:14,764 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2589 [2024-11-25 17:14:15,021 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2936 [2024-11-25 17:14:15,290 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3172 [2024-11-25 17:14:15,560 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2962 [2024-11-25 17:14:15,803 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2460 [2024-11-25 17:14:16,060 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3164 [2024-11-25 17:14:16,329 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2575 [2024-11-25 17:14:16,590 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2991 [2024-11-25 17:14:16,839 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2343 [2024-11-25 17:14:17,073 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2927 [2024-11-25 17:14:17,330 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3113 [2024-11-25 17:14:17,573 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3000 [2024-11-25 17:14:17,795 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2883 [2024-11-25 17:14:18,015 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2394 [2024-11-25 17:14:18,286 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2752 [2024-11-25 17:14:18,524 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2554 [2024-11-25 17:14:18,780 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2376 [2024-11-25 17:14:19,051 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3446 [2024-11-25 17:14:19,294 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2441 [2024-11-25 17:14:19,553 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2872 [2024-11-25 17:14:19,807 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2459 [2024-11-25 17:14:20,056 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3025 [2024-11-25 17:14:20,327 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2678 [2024-11-25 17:14:20,567 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2836 [2024-11-25 17:14:20,829 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3210 [2024-11-25 17:14:21,092 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2929 [2024-11-25 17:14:21,340 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2975 [2024-11-25 17:14:21,588 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2899 [2024-11-25 17:14:21,820 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2576 [2024-11-25 17:14:22,073 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2956 [2024-11-25 17:14:22,340 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2808 [2024-11-25 17:14:22,604 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2259 [2024-11-25 17:14:22,871 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2515 [2024-11-25 17:14:23,119 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2429 [2024-11-25 17:14:23,386 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2913 [2024-11-25 17:14:23,605 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3141 [2024-11-25 17:14:23,875 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2673 [2024-11-25 17:14:24,115 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2956 [2024-11-25 17:14:24,385 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2620 [2024-11-25 17:14:24,646 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3240 [2024-11-25 17:14:24,906 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3109 [2024-11-25 17:14:25,160 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3109 [2024-11-25 17:14:25,382 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2463 [2024-11-25 17:14:25,616 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2612 [2024-11-25 17:14:25,873 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2547 [2024-11-25 17:14:26,141 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3051 [2024-11-25 17:14:26,375 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3085 [2024-11-25 17:14:26,635 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2689 [2024-11-25 17:14:26,899 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2489 [2024-11-25 17:14:27,121 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2954 [2024-11-25 17:14:27,357 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2545 [2024-11-25 17:14:27,578 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2696 [2024-11-25 17:14:27,804 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2734 [2024-11-25 17:14:28,076 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3414 [2024-11-25 17:14:28,333 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3050 [2024-11-25 17:14:28,604 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3052 [2024-11-25 17:14:28,869 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2566 [2024-11-25 17:14:29,131 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3433 [2024-11-25 17:14:29,392 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2986 [2024-11-25 17:14:29,657 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2344 [2024-11-25 17:14:29,913 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2881 [2024-11-25 17:14:30,177 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3073 [2024-11-25 17:14:30,439 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2721 [2024-11-25 17:14:30,689 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3121 [2024-11-25 17:14:30,919 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2596 [2024-11-25 17:14:31,179 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3182 [2024-11-25 17:14:31,416 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2796 [2024-11-25 17:14:31,642 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2404 [2024-11-25 17:14:31,854 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2622 [2024-11-25 17:14:32,072 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2503 [2024-11-25 17:14:32,762 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7323/0.7975/0.9956. [2024-11-25 17:14:32,762 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9956/0.9982 [2024-11-25 17:14:32,762 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4690/0.5967 [2024-11-25 17:14:32,763 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:14:32,763 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7323 [2024-11-25 17:14:32,764 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7323 [2024-11-25 17:14:32,764 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:14:37,242 INFO misc.py line 119 2586773] Train: [9/50][1/376] Data 0.093 (0.093) Batch 0.595 (0.595) Remain 02:36:31 loss: 0.2645 Lr: 0.00385 [2024-11-25 17:14:37,792 INFO misc.py line 119 2586773] Train: [9/50][2/376] Data 0.002 (0.002) Batch 0.550 (0.550) Remain 02:24:47 loss: 0.2644 Lr: 0.00385 [2024-11-25 17:14:38,309 INFO misc.py line 119 2586773] Train: [9/50][3/376] Data 0.002 (0.002) Batch 0.517 (0.517) Remain 02:16:03 loss: 0.2135 Lr: 0.00385 [2024-11-25 17:14:38,859 INFO misc.py line 119 2586773] Train: [9/50][4/376] Data 0.002 (0.002) Batch 0.550 (0.550) Remain 02:24:49 loss: 0.2196 Lr: 0.00385 [2024-11-25 17:14:39,392 INFO misc.py line 119 2586773] Train: [9/50][5/376] Data 0.002 (0.002) Batch 0.533 (0.542) Remain 02:22:28 loss: 0.3641 Lr: 0.00385 [2024-11-25 17:14:39,860 INFO misc.py line 119 2586773] Train: [9/50][6/376] Data 0.003 (0.002) Batch 0.468 (0.517) Remain 02:15:59 loss: 0.2402 Lr: 0.00385 [2024-11-25 17:14:40,344 INFO misc.py line 119 2586773] Train: [9/50][7/376] Data 0.002 (0.002) Batch 0.484 (0.509) Remain 02:13:50 loss: 0.2514 Lr: 0.00385 [2024-11-25 17:14:40,828 INFO misc.py line 119 2586773] Train: [9/50][8/376] Data 0.002 (0.002) Batch 0.484 (0.504) Remain 02:12:30 loss: 0.2342 Lr: 0.00385 [2024-11-25 17:14:41,344 INFO misc.py line 119 2586773] Train: [9/50][9/376] Data 0.003 (0.002) Batch 0.516 (0.506) Remain 02:13:03 loss: 0.2924 Lr: 0.00385 [2024-11-25 17:14:41,823 INFO misc.py line 119 2586773] Train: [9/50][10/376] Data 0.002 (0.002) Batch 0.480 (0.502) Remain 02:12:03 loss: 0.2644 Lr: 0.00385 [2024-11-25 17:14:42,340 INFO misc.py line 119 2586773] Train: [9/50][11/376] Data 0.002 (0.002) Batch 0.517 (0.504) Remain 02:12:32 loss: 0.1961 Lr: 0.00385 [2024-11-25 17:14:42,834 INFO misc.py line 119 2586773] Train: [9/50][12/376] Data 0.002 (0.002) Batch 0.494 (0.503) Remain 02:12:14 loss: 0.2394 Lr: 0.00385 [2024-11-25 17:14:43,326 INFO misc.py line 119 2586773] Train: [9/50][13/376] Data 0.002 (0.002) Batch 0.492 (0.502) Remain 02:11:56 loss: 0.2692 Lr: 0.00385 [2024-11-25 17:14:43,831 INFO misc.py line 119 2586773] Train: [9/50][14/376] Data 0.002 (0.002) Batch 0.505 (0.502) Remain 02:12:00 loss: 0.2068 Lr: 0.00385 [2024-11-25 17:14:44,325 INFO misc.py line 119 2586773] Train: [9/50][15/376] Data 0.002 (0.002) Batch 0.494 (0.501) Remain 02:11:49 loss: 0.2589 Lr: 0.00385 [2024-11-25 17:14:44,809 INFO misc.py line 119 2586773] Train: [9/50][16/376] Data 0.002 (0.002) Batch 0.484 (0.500) Remain 02:11:28 loss: 0.2099 Lr: 0.00385 [2024-11-25 17:14:45,334 INFO misc.py line 119 2586773] Train: [9/50][17/376] Data 0.002 (0.002) Batch 0.525 (0.502) Remain 02:11:55 loss: 0.2762 Lr: 0.00385 [2024-11-25 17:14:45,816 INFO misc.py line 119 2586773] Train: [9/50][18/376] Data 0.002 (0.002) Batch 0.482 (0.500) Remain 02:11:34 loss: 0.2630 Lr: 0.00385 [2024-11-25 17:14:46,302 INFO misc.py line 119 2586773] Train: [9/50][19/376] Data 0.002 (0.002) Batch 0.486 (0.500) Remain 02:11:19 loss: 0.2167 Lr: 0.00385 [2024-11-25 17:14:46,818 INFO misc.py line 119 2586773] Train: [9/50][20/376] Data 0.002 (0.002) Batch 0.516 (0.501) Remain 02:11:34 loss: 0.2263 Lr: 0.00385 [2024-11-25 17:14:47,302 INFO misc.py line 119 2586773] Train: [9/50][21/376] Data 0.003 (0.002) Batch 0.484 (0.500) Remain 02:11:19 loss: 0.2791 Lr: 0.00385 [2024-11-25 17:14:47,827 INFO misc.py line 119 2586773] Train: [9/50][22/376] Data 0.002 (0.002) Batch 0.525 (0.501) Remain 02:11:39 loss: 0.2042 Lr: 0.00385 [2024-11-25 17:14:48,318 INFO misc.py line 119 2586773] Train: [9/50][23/376] Data 0.003 (0.002) Batch 0.492 (0.500) Remain 02:11:31 loss: 0.2328 Lr: 0.00385 [2024-11-25 17:14:48,804 INFO misc.py line 119 2586773] Train: [9/50][24/376] Data 0.003 (0.002) Batch 0.486 (0.500) Remain 02:11:20 loss: 0.3290 Lr: 0.00385 [2024-11-25 17:14:49,307 INFO misc.py line 119 2586773] Train: [9/50][25/376] Data 0.003 (0.002) Batch 0.503 (0.500) Remain 02:11:22 loss: 0.2398 Lr: 0.00384 [2024-11-25 17:14:49,777 INFO misc.py line 119 2586773] Train: [9/50][26/376] Data 0.002 (0.002) Batch 0.469 (0.499) Remain 02:11:00 loss: 0.2383 Lr: 0.00384 [2024-11-25 17:14:50,281 INFO misc.py line 119 2586773] Train: [9/50][27/376] Data 0.003 (0.002) Batch 0.504 (0.499) Remain 02:11:03 loss: 0.2141 Lr: 0.00384 [2024-11-25 17:14:50,771 INFO misc.py line 119 2586773] Train: [9/50][28/376] Data 0.002 (0.002) Batch 0.490 (0.498) Remain 02:10:57 loss: 0.2617 Lr: 0.00384 [2024-11-25 17:14:51,316 INFO misc.py line 119 2586773] Train: [9/50][29/376] Data 0.002 (0.002) Batch 0.546 (0.500) Remain 02:11:26 loss: 0.2423 Lr: 0.00384 [2024-11-25 17:14:51,816 INFO misc.py line 119 2586773] Train: [9/50][30/376] Data 0.002 (0.002) Batch 0.500 (0.500) Remain 02:11:25 loss: 0.2550 Lr: 0.00384 [2024-11-25 17:14:52,346 INFO misc.py line 119 2586773] Train: [9/50][31/376] Data 0.002 (0.002) Batch 0.530 (0.501) Remain 02:11:41 loss: 0.2085 Lr: 0.00384 [2024-11-25 17:14:52,855 INFO misc.py line 119 2586773] Train: [9/50][32/376] Data 0.002 (0.002) Batch 0.508 (0.502) Remain 02:11:44 loss: 0.2532 Lr: 0.00384 [2024-11-25 17:14:53,365 INFO misc.py line 119 2586773] Train: [9/50][33/376] Data 0.002 (0.002) Batch 0.510 (0.502) Remain 02:11:48 loss: 0.2459 Lr: 0.00384 [2024-11-25 17:14:53,845 INFO misc.py line 119 2586773] Train: [9/50][34/376] Data 0.002 (0.002) Batch 0.481 (0.501) Remain 02:11:37 loss: 0.2976 Lr: 0.00384 [2024-11-25 17:14:54,336 INFO misc.py line 119 2586773] Train: [9/50][35/376] Data 0.002 (0.002) Batch 0.490 (0.501) Remain 02:11:31 loss: 0.2076 Lr: 0.00384 [2024-11-25 17:14:54,858 INFO misc.py line 119 2586773] Train: [9/50][36/376] Data 0.002 (0.002) Batch 0.522 (0.501) Remain 02:11:41 loss: 0.2465 Lr: 0.00384 [2024-11-25 17:14:55,345 INFO misc.py line 119 2586773] Train: [9/50][37/376] Data 0.002 (0.002) Batch 0.487 (0.501) Remain 02:11:34 loss: 0.2715 Lr: 0.00384 [2024-11-25 17:14:55,842 INFO misc.py line 119 2586773] Train: [9/50][38/376] Data 0.002 (0.002) Batch 0.497 (0.501) Remain 02:11:31 loss: 0.2929 Lr: 0.00384 [2024-11-25 17:14:56,390 INFO misc.py line 119 2586773] Train: [9/50][39/376] Data 0.002 (0.002) Batch 0.548 (0.502) Remain 02:11:51 loss: 0.2319 Lr: 0.00384 [2024-11-25 17:14:56,930 INFO misc.py line 119 2586773] Train: [9/50][40/376] Data 0.002 (0.002) Batch 0.540 (0.503) Remain 02:12:07 loss: 0.2096 Lr: 0.00384 [2024-11-25 17:14:57,491 INFO 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0.002 (0.002) Batch 0.491 (0.505) Remain 02:12:27 loss: 0.2255 Lr: 0.00384 [2024-11-25 17:15:01,027 INFO misc.py line 119 2586773] Train: [9/50][48/376] Data 0.002 (0.002) Batch 0.509 (0.505) Remain 02:12:28 loss: 0.3218 Lr: 0.00384 [2024-11-25 17:15:01,555 INFO misc.py line 119 2586773] Train: [9/50][49/376] Data 0.002 (0.002) Batch 0.528 (0.505) Remain 02:12:35 loss: 0.2878 Lr: 0.00384 [2024-11-25 17:15:02,040 INFO misc.py line 119 2586773] Train: [9/50][50/376] Data 0.002 (0.002) Batch 0.485 (0.505) Remain 02:12:28 loss: 0.2260 Lr: 0.00384 [2024-11-25 17:15:02,568 INFO misc.py line 119 2586773] Train: [9/50][51/376] Data 0.002 (0.002) Batch 0.527 (0.505) Remain 02:12:35 loss: 0.3060 Lr: 0.00384 [2024-11-25 17:15:03,089 INFO misc.py line 119 2586773] Train: [9/50][52/376] Data 0.002 (0.002) Batch 0.521 (0.506) Remain 02:12:39 loss: 0.2649 Lr: 0.00384 [2024-11-25 17:15:03,596 INFO misc.py line 119 2586773] Train: [9/50][53/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 02:12:39 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Train: [9/50][336/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 02:10:48 loss: 0.2457 Lr: 0.00380 [2024-11-25 17:17:27,898 INFO misc.py line 119 2586773] Train: [9/50][337/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 02:10:47 loss: 0.2028 Lr: 0.00380 [2024-11-25 17:17:28,442 INFO misc.py line 119 2586773] Train: [9/50][338/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 02:10:48 loss: 0.3102 Lr: 0.00380 [2024-11-25 17:17:28,919 INFO misc.py line 119 2586773] Train: [9/50][339/376] Data 0.002 (0.002) Batch 0.477 (0.508) Remain 02:10:46 loss: 0.2432 Lr: 0.00380 [2024-11-25 17:17:29,455 INFO misc.py line 119 2586773] Train: [9/50][340/376] Data 0.002 (0.002) Batch 0.536 (0.508) Remain 02:10:47 loss: 0.2387 Lr: 0.00380 [2024-11-25 17:17:29,951 INFO misc.py line 119 2586773] Train: [9/50][341/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 02:10:46 loss: 0.2529 Lr: 0.00380 [2024-11-25 17:17:30,440 INFO misc.py line 119 2586773] Train: [9/50][342/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 02:10:44 loss: 0.2575 Lr: 0.00380 [2024-11-25 17:17:30,920 INFO misc.py line 119 2586773] Train: [9/50][343/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 02:10:43 loss: 0.2285 Lr: 0.00380 [2024-11-25 17:17:31,376 INFO misc.py line 119 2586773] Train: [9/50][344/376] Data 0.003 (0.002) Batch 0.456 (0.508) Remain 02:10:40 loss: 0.2688 Lr: 0.00380 [2024-11-25 17:17:31,854 INFO misc.py line 119 2586773] Train: [9/50][345/376] Data 0.003 (0.002) Batch 0.478 (0.507) Remain 02:10:38 loss: 0.2640 Lr: 0.00380 [2024-11-25 17:17:32,355 INFO misc.py line 119 2586773] Train: [9/50][346/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:10:37 loss: 0.2678 Lr: 0.00380 [2024-11-25 17:17:32,840 INFO misc.py line 119 2586773] Train: [9/50][347/376] Data 0.003 (0.002) Batch 0.485 (0.507) Remain 02:10:36 loss: 0.2317 Lr: 0.00380 [2024-11-25 17:17:33,339 INFO misc.py line 119 2586773] Train: [9/50][348/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 02:10:35 loss: 0.2614 Lr: 0.00380 [2024-11-25 17:17:33,846 INFO misc.py line 119 2586773] Train: [9/50][349/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 02:10:34 loss: 0.2138 Lr: 0.00380 [2024-11-25 17:17:34,336 INFO misc.py line 119 2586773] Train: [9/50][350/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 02:10:33 loss: 0.2612 Lr: 0.00380 [2024-11-25 17:17:34,862 INFO misc.py line 119 2586773] Train: [9/50][351/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 02:10:33 loss: 0.2521 Lr: 0.00380 [2024-11-25 17:17:35,365 INFO misc.py line 119 2586773] Train: [9/50][352/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 02:10:33 loss: 0.2517 Lr: 0.00380 [2024-11-25 17:17:35,878 INFO misc.py line 119 2586773] Train: [9/50][353/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 02:10:32 loss: 0.2219 Lr: 0.00380 [2024-11-25 17:17:36,402 INFO misc.py line 119 2586773] Train: [9/50][354/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 02:10:33 loss: 0.2539 Lr: 0.00380 [2024-11-25 17:17:36,906 INFO misc.py line 119 2586773] Train: [9/50][355/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 02:10:32 loss: 0.2366 Lr: 0.00380 [2024-11-25 17:17:37,429 INFO misc.py line 119 2586773] Train: [9/50][356/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 02:10:32 loss: 0.2757 Lr: 0.00380 [2024-11-25 17:17:37,921 INFO misc.py line 119 2586773] Train: [9/50][357/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 02:10:31 loss: 0.2607 Lr: 0.00380 [2024-11-25 17:17:38,474 INFO misc.py line 119 2586773] Train: [9/50][358/376] Data 0.002 (0.002) Batch 0.554 (0.508) Remain 02:10:32 loss: 0.3533 Lr: 0.00380 [2024-11-25 17:17:38,993 INFO misc.py line 119 2586773] Train: [9/50][359/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 02:10:32 loss: 0.2101 Lr: 0.00380 [2024-11-25 17:17:39,520 INFO misc.py line 119 2586773] Train: [9/50][360/376] Data 0.002 (0.002) Batch 0.528 (0.508) Remain 02:10:33 loss: 0.2504 Lr: 0.00380 [2024-11-25 17:17:40,001 INFO misc.py line 119 2586773] Train: [9/50][361/376] Data 0.002 (0.002) Batch 0.481 (0.508) Remain 02:10:31 loss: 0.2164 Lr: 0.00380 [2024-11-25 17:17:40,513 INFO misc.py line 119 2586773] Train: [9/50][362/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 02:10:31 loss: 0.2348 Lr: 0.00380 [2024-11-25 17:17:41,000 INFO misc.py line 119 2586773] Train: [9/50][363/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 02:10:29 loss: 0.2634 Lr: 0.00380 [2024-11-25 17:17:41,572 INFO misc.py line 119 2586773] Train: [9/50][364/376] Data 0.002 (0.002) Batch 0.572 (0.508) Remain 02:10:32 loss: 0.2910 Lr: 0.00380 [2024-11-25 17:17:41,997 INFO misc.py line 119 2586773] Train: [9/50][365/376] Data 0.002 (0.002) Batch 0.425 (0.507) Remain 02:10:28 loss: 0.2390 Lr: 0.00380 [2024-11-25 17:17:42,514 INFO misc.py line 119 2586773] Train: [9/50][366/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 02:10:27 loss: 0.2036 Lr: 0.00380 [2024-11-25 17:17:43,012 INFO misc.py line 119 2586773] Train: [9/50][367/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 02:10:27 loss: 0.2217 Lr: 0.00380 [2024-11-25 17:17:43,502 INFO misc.py line 119 2586773] Train: [9/50][368/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 02:10:25 loss: 0.2460 Lr: 0.00380 [2024-11-25 17:17:44,017 INFO misc.py line 119 2586773] Train: [9/50][369/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 02:10:25 loss: 0.2248 Lr: 0.00380 [2024-11-25 17:17:44,527 INFO misc.py line 119 2586773] Train: [9/50][370/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 02:10:25 loss: 0.2309 Lr: 0.00380 [2024-11-25 17:17:45,003 INFO misc.py line 119 2586773] Train: [9/50][371/376] Data 0.002 (0.002) Batch 0.476 (0.507) Remain 02:10:23 loss: 0.2839 Lr: 0.00380 [2024-11-25 17:17:45,486 INFO misc.py line 119 2586773] Train: [9/50][372/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 02:10:21 loss: 0.2404 Lr: 0.00380 [2024-11-25 17:17:45,989 INFO misc.py line 119 2586773] Train: [9/50][373/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 02:10:21 loss: 0.2949 Lr: 0.00380 [2024-11-25 17:17:46,515 INFO misc.py line 119 2586773] Train: [9/50][374/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 02:10:21 loss: 0.2046 Lr: 0.00379 [2024-11-25 17:17:47,036 INFO misc.py line 119 2586773] Train: [9/50][375/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 02:10:21 loss: 0.1899 Lr: 0.00379 [2024-11-25 17:17:47,543 INFO misc.py line 119 2586773] Train: [9/50][376/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:10:20 loss: 0.2485 Lr: 0.00379 [2024-11-25 17:17:47,543 INFO misc.py line 136 2586773] Train result: loss: 0.2576 [2024-11-25 17:17:47,544 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:17:58,484 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2246 [2024-11-25 17:17:58,746 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2337 [2024-11-25 17:17:59,007 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3167 [2024-11-25 17:17:59,230 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2379 [2024-11-25 17:17:59,491 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3094 [2024-11-25 17:17:59,757 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2411 [2024-11-25 17:17:59,979 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2318 [2024-11-25 17:18:00,251 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2532 [2024-11-25 17:18:00,475 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2802 [2024-11-25 17:18:00,738 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2928 [2024-11-25 17:18:00,970 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2428 [2024-11-25 17:18:01,244 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2249 [2024-11-25 17:18:01,510 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2884 [2024-11-25 17:18:01,773 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2880 [2024-11-25 17:18:02,017 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2712 [2024-11-25 17:18:02,254 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3144 [2024-11-25 17:18:02,528 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3651 [2024-11-25 17:18:02,774 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2297 [2024-11-25 17:18:03,012 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2410 [2024-11-25 17:18:03,271 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3205 [2024-11-25 17:18:03,503 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2651 [2024-11-25 17:18:03,769 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3038 [2024-11-25 17:18:04,005 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2426 [2024-11-25 17:18:04,272 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2712 [2024-11-25 17:18:04,538 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2549 [2024-11-25 17:18:04,775 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2954 [2024-11-25 17:18:05,028 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3091 [2024-11-25 17:18:05,273 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2843 [2024-11-25 17:18:05,539 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3047 [2024-11-25 17:18:05,794 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3280 [2024-11-25 17:18:06,028 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2638 [2024-11-25 17:18:06,291 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2570 [2024-11-25 17:18:06,517 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2599 [2024-11-25 17:18:06,758 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2317 [2024-11-25 17:18:07,027 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2447 [2024-11-25 17:18:07,272 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2892 [2024-11-25 17:18:07,497 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2255 [2024-11-25 17:18:07,774 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2721 [2024-11-25 17:18:08,004 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2839 [2024-11-25 17:18:08,238 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2675 [2024-11-25 17:18:08,507 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2697 [2024-11-25 17:18:08,756 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2998 [2024-11-25 17:18:08,994 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2946 [2024-11-25 17:18:09,225 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2661 [2024-11-25 17:18:09,463 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2520 [2024-11-25 17:18:09,712 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2712 [2024-11-25 17:18:09,973 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2796 [2024-11-25 17:18:10,222 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3218 [2024-11-25 17:18:10,446 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2447 [2024-11-25 17:18:10,680 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2446 [2024-11-25 17:18:10,902 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2576 [2024-11-25 17:18:11,153 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3018 [2024-11-25 17:18:11,420 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2691 [2024-11-25 17:18:11,682 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3285 [2024-11-25 17:18:11,915 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2673 [2024-11-25 17:18:12,154 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2684 [2024-11-25 17:18:12,415 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3011 [2024-11-25 17:18:12,681 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2882 [2024-11-25 17:18:12,939 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2907 [2024-11-25 17:18:13,199 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2747 [2024-11-25 17:18:13,450 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2475 [2024-11-25 17:18:13,718 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2833 [2024-11-25 17:18:13,950 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2558 [2024-11-25 17:18:14,208 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3020 [2024-11-25 17:18:14,479 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2812 [2024-11-25 17:18:14,745 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2431 [2024-11-25 17:18:14,993 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2437 [2024-11-25 17:18:15,248 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3105 [2024-11-25 17:18:15,516 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2461 [2024-11-25 17:18:15,779 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3114 [2024-11-25 17:18:16,025 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2319 [2024-11-25 17:18:16,260 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2982 [2024-11-25 17:18:16,518 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2933 [2024-11-25 17:18:16,762 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3078 [2024-11-25 17:18:16,980 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2851 [2024-11-25 17:18:17,203 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2199 [2024-11-25 17:18:17,475 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2807 [2024-11-25 17:18:17,712 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2392 [2024-11-25 17:18:17,969 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2349 [2024-11-25 17:18:18,219 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3282 [2024-11-25 17:18:18,463 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2565 [2024-11-25 17:18:18,722 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2831 [2024-11-25 17:18:18,972 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2180 [2024-11-25 17:18:19,225 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2867 [2024-11-25 17:18:19,499 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2611 [2024-11-25 17:18:19,735 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2923 [2024-11-25 17:18:19,999 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3292 [2024-11-25 17:18:20,258 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2861 [2024-11-25 17:18:20,508 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2906 [2024-11-25 17:18:20,756 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2722 [2024-11-25 17:18:20,990 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2571 [2024-11-25 17:18:21,243 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2876 [2024-11-25 17:18:21,510 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2871 [2024-11-25 17:18:21,774 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2246 [2024-11-25 17:18:22,041 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2474 [2024-11-25 17:18:22,288 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2388 [2024-11-25 17:18:22,556 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2864 [2024-11-25 17:18:22,775 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3199 [2024-11-25 17:18:23,045 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2760 [2024-11-25 17:18:23,284 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2873 [2024-11-25 17:18:23,556 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2403 [2024-11-25 17:18:23,823 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3086 [2024-11-25 17:18:24,086 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3230 [2024-11-25 17:18:24,338 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3125 [2024-11-25 17:18:24,567 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2576 [2024-11-25 17:18:24,813 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2477 [2024-11-25 17:18:25,068 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2523 [2024-11-25 17:18:25,345 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3148 [2024-11-25 17:18:25,580 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2880 [2024-11-25 17:18:25,839 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2585 [2024-11-25 17:18:26,112 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2456 [2024-11-25 17:18:26,342 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2598 [2024-11-25 17:18:26,588 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2464 [2024-11-25 17:18:26,812 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2559 [2024-11-25 17:18:27,039 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2526 [2024-11-25 17:18:27,313 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3458 [2024-11-25 17:18:27,577 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3011 [2024-11-25 17:18:27,848 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2805 [2024-11-25 17:18:28,121 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2608 [2024-11-25 17:18:28,385 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3672 [2024-11-25 17:18:28,644 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2877 [2024-11-25 17:18:28,909 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2355 [2024-11-25 17:18:29,166 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2946 [2024-11-25 17:18:29,440 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2976 [2024-11-25 17:18:29,700 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2539 [2024-11-25 17:18:29,950 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2928 [2024-11-25 17:18:30,181 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2578 [2024-11-25 17:18:30,445 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3100 [2024-11-25 17:18:30,680 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2668 [2024-11-25 17:18:30,913 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2289 [2024-11-25 17:18:31,130 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2673 [2024-11-25 17:18:31,356 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2322 [2024-11-25 17:18:31,916 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7379/0.8012/0.9957. [2024-11-25 17:18:31,917 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9983 [2024-11-25 17:18:31,917 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4801/0.6041 [2024-11-25 17:18:31,917 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:18:31,918 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7379 [2024-11-25 17:18:31,918 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7379 [2024-11-25 17:18:31,918 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:18:36,268 INFO misc.py line 119 2586773] Train: [10/50][1/376] Data 0.075 (0.075) Batch 0.574 (0.574) Remain 02:27:34 loss: 0.3063 Lr: 0.00379 [2024-11-25 17:18:36,756 INFO misc.py line 119 2586773] Train: [10/50][2/376] Data 0.002 (0.002) Batch 0.488 (0.488) Remain 02:05:17 loss: 0.2712 Lr: 0.00379 [2024-11-25 17:18:37,251 INFO misc.py line 119 2586773] Train: [10/50][3/376] Data 0.002 (0.002) Batch 0.495 (0.495) Remain 02:07:16 loss: 0.2854 Lr: 0.00379 [2024-11-25 17:18:37,811 INFO misc.py line 119 2586773] Train: [10/50][4/376] Data 0.002 (0.002) Batch 0.560 (0.560) Remain 02:23:47 loss: 0.2334 Lr: 0.00379 [2024-11-25 17:18:38,290 INFO misc.py line 119 2586773] Train: [10/50][5/376] Data 0.002 (0.002) Batch 0.479 (0.519) Remain 02:13:23 loss: 0.2796 Lr: 0.00379 [2024-11-25 17:18:38,843 INFO misc.py line 119 2586773] Train: [10/50][6/376] Data 0.003 (0.002) Batch 0.553 (0.531) Remain 02:16:16 loss: 0.3454 Lr: 0.00379 [2024-11-25 17:18:39,338 INFO misc.py line 119 2586773] Train: [10/50][7/376] Data 0.002 (0.002) Batch 0.495 (0.522) Remain 02:13:59 loss: 0.2752 Lr: 0.00379 [2024-11-25 17:18:39,878 INFO misc.py line 119 2586773] Train: [10/50][8/376] Data 0.003 (0.002) Batch 0.540 (0.525) Remain 02:14:54 loss: 0.2626 Lr: 0.00379 [2024-11-25 17:18:40,368 INFO misc.py line 119 2586773] Train: [10/50][9/376] Data 0.002 (0.002) Batch 0.490 (0.519) Remain 02:13:23 loss: 0.2439 Lr: 0.00379 [2024-11-25 17:18:40,876 INFO misc.py line 119 2586773] Train: [10/50][10/376] Data 0.003 (0.002) Batch 0.508 (0.518) Remain 02:12:57 loss: 0.2198 Lr: 0.00379 [2024-11-25 17:18:41,366 INFO misc.py line 119 2586773] Train: [10/50][11/376] Data 0.002 (0.002) Batch 0.490 (0.514) Remain 02:12:04 loss: 0.2862 Lr: 0.00379 [2024-11-25 17:18:41,877 INFO misc.py line 119 2586773] Train: [10/50][12/376] Data 0.002 (0.002) Batch 0.510 (0.514) Remain 02:11:56 loss: 0.2853 Lr: 0.00379 [2024-11-25 17:18:42,359 INFO misc.py line 119 2586773] Train: [10/50][13/376] Data 0.002 (0.002) Batch 0.483 (0.511) Remain 02:11:08 loss: 0.2392 Lr: 0.00379 [2024-11-25 17:18:42,892 INFO misc.py line 119 2586773] Train: [10/50][14/376] Data 0.002 (0.002) Batch 0.533 (0.513) Remain 02:11:38 loss: 0.2715 Lr: 0.00379 [2024-11-25 17:18:43,406 INFO misc.py line 119 2586773] Train: [10/50][15/376] Data 0.002 (0.002) Batch 0.513 (0.513) Remain 02:11:38 loss: 0.3724 Lr: 0.00379 [2024-11-25 17:18:43,880 INFO misc.py line 119 2586773] Train: [10/50][16/376] Data 0.003 (0.002) Batch 0.475 (0.510) Remain 02:10:53 loss: 0.2492 Lr: 0.00379 [2024-11-25 17:18:44,413 INFO misc.py line 119 2586773] Train: [10/50][17/376] Data 0.002 (0.002) Batch 0.532 (0.512) Remain 02:11:17 loss: 0.1895 Lr: 0.00379 [2024-11-25 17:18:44,932 INFO misc.py line 119 2586773] Train: [10/50][18/376] Data 0.002 (0.002) Batch 0.520 (0.512) Remain 02:11:25 loss: 0.2257 Lr: 0.00379 [2024-11-25 17:18:45,459 INFO misc.py line 119 2586773] Train: [10/50][19/376] Data 0.002 (0.002) Batch 0.526 (0.513) Remain 02:11:38 loss: 0.3531 Lr: 0.00379 [2024-11-25 17:18:45,993 INFO misc.py line 119 2586773] Train: [10/50][20/376] Data 0.002 (0.002) Batch 0.534 (0.514) Remain 02:11:57 loss: 0.1848 Lr: 0.00379 [2024-11-25 17:18:46,486 INFO misc.py line 119 2586773] Train: [10/50][21/376] Data 0.002 (0.002) Batch 0.493 (0.513) Remain 02:11:38 loss: 0.1944 Lr: 0.00379 [2024-11-25 17:18:47,007 INFO misc.py line 119 2586773] Train: [10/50][22/376] Data 0.002 (0.002) Batch 0.521 (0.513) Remain 02:11:44 loss: 0.2727 Lr: 0.00379 [2024-11-25 17:18:47,495 INFO misc.py line 119 2586773] Train: [10/50][23/376] Data 0.003 (0.002) Batch 0.488 (0.512) Remain 02:11:23 loss: 0.2471 Lr: 0.00379 [2024-11-25 17:18:47,979 INFO misc.py line 119 2586773] Train: [10/50][24/376] Data 0.003 (0.002) Batch 0.484 (0.511) Remain 02:11:02 loss: 0.2843 Lr: 0.00379 [2024-11-25 17:18:48,451 INFO misc.py line 119 2586773] Train: [10/50][25/376] Data 0.003 (0.002) Batch 0.472 (0.509) Remain 02:10:35 loss: 0.2969 Lr: 0.00379 [2024-11-25 17:18:48,991 INFO misc.py line 119 2586773] Train: [10/50][26/376] Data 0.002 (0.002) Batch 0.540 (0.510) Remain 02:10:55 loss: 0.2656 Lr: 0.00379 [2024-11-25 17:18:49,481 INFO misc.py line 119 2586773] Train: [10/50][27/376] Data 0.003 (0.002) Batch 0.490 (0.510) Remain 02:10:41 loss: 0.2734 Lr: 0.00379 [2024-11-25 17:18:50,012 INFO misc.py line 119 2586773] Train: [10/50][28/376] Data 0.002 (0.002) Batch 0.531 (0.510) Remain 02:10:54 loss: 0.2755 Lr: 0.00379 [2024-11-25 17:18:50,501 INFO misc.py line 119 2586773] Train: [10/50][29/376] Data 0.002 (0.002) Batch 0.489 (0.510) Remain 02:10:41 loss: 0.2478 Lr: 0.00379 [2024-11-25 17:18:50,998 INFO misc.py line 119 2586773] Train: [10/50][30/376] Data 0.002 (0.002) Batch 0.497 (0.509) Remain 02:10:33 loss: 0.2945 Lr: 0.00379 [2024-11-25 17:18:51,491 INFO misc.py line 119 2586773] Train: [10/50][31/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 02:10:24 loss: 0.2017 Lr: 0.00379 [2024-11-25 17:18:52,007 INFO misc.py line 119 2586773] Train: [10/50][32/376] Data 0.002 (0.002) Batch 0.516 (0.509) Remain 02:10:27 loss: 0.2390 Lr: 0.00379 [2024-11-25 17:18:52,491 INFO misc.py line 119 2586773] Train: [10/50][33/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 02:10:14 loss: 0.2355 Lr: 0.00379 [2024-11-25 17:18:52,964 INFO misc.py line 119 2586773] Train: [10/50][34/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 02:09:56 loss: 0.2501 Lr: 0.00379 [2024-11-25 17:18:53,470 INFO misc.py line 119 2586773] Train: [10/50][35/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:09:55 loss: 0.2163 Lr: 0.00379 [2024-11-25 17:18:54,005 INFO misc.py line 119 2586773] Train: [10/50][36/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 02:10:08 loss: 0.2275 Lr: 0.00379 [2024-11-25 17:18:54,515 INFO misc.py line 119 2586773] Train: [10/50][37/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 02:10:08 loss: 0.2319 Lr: 0.00379 [2024-11-25 17:18:54,999 INFO misc.py line 119 2586773] Train: [10/50][38/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 02:09:58 loss: 0.2244 Lr: 0.00379 [2024-11-25 17:18:55,449 INFO misc.py line 119 2586773] Train: [10/50][39/376] Data 0.002 (0.002) Batch 0.450 (0.506) Remain 02:09:33 loss: 0.2707 Lr: 0.00379 [2024-11-25 17:18:55,971 INFO misc.py line 119 2586773] Train: [10/50][40/376] Data 0.002 (0.002) Batch 0.522 (0.506) Remain 02:09:39 loss: 0.2161 Lr: 0.00379 [2024-11-25 17:18:56,510 INFO misc.py line 119 2586773] Train: [10/50][41/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 02:09:52 loss: 0.2071 Lr: 0.00379 [2024-11-25 17:18:56,984 INFO misc.py line 119 2586773] Train: [10/50][42/376] Data 0.002 (0.002) Batch 0.474 (0.506) Remain 02:09:38 loss: 0.2398 Lr: 0.00379 [2024-11-25 17:18:57,483 INFO misc.py line 119 2586773] Train: [10/50][43/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 02:09:35 loss: 0.2711 Lr: 0.00379 [2024-11-25 17:18:57,999 INFO misc.py line 119 2586773] Train: [10/50][44/376] Data 0.002 (0.002) Batch 0.517 (0.506) Remain 02:09:39 loss: 0.2926 Lr: 0.00379 [2024-11-25 17:18:58,476 INFO misc.py line 119 2586773] Train: [10/50][45/376] Data 0.003 (0.002) Batch 0.477 (0.505) Remain 02:09:27 loss: 0.2383 Lr: 0.00379 [2024-11-25 17:18:58,983 INFO misc.py line 119 2586773] Train: [10/50][46/376] Data 0.002 (0.002) Batch 0.507 (0.505) Remain 02:09:28 loss: 0.2686 Lr: 0.00379 [2024-11-25 17:18:59,507 INFO misc.py line 119 2586773] Train: [10/50][47/376] Data 0.002 (0.002) Batch 0.523 (0.506) Remain 02:09:33 loss: 0.3002 Lr: 0.00379 [2024-11-25 17:19:00,041 INFO misc.py line 119 2586773] Train: [10/50][48/376] Data 0.002 (0.002) Batch 0.535 (0.506) Remain 02:09:43 loss: 0.2685 Lr: 0.00379 [2024-11-25 17:19:00,546 INFO misc.py line 119 2586773] Train: [10/50][49/376] Data 0.002 (0.002) Batch 0.505 (0.506) Remain 02:09:42 loss: 0.2417 Lr: 0.00379 [2024-11-25 17:19:01,035 INFO misc.py line 119 2586773] Train: [10/50][50/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 02:09:35 loss: 0.2653 Lr: 0.00379 [2024-11-25 17:19:01,517 INFO misc.py line 119 2586773] Train: [10/50][51/376] Data 0.002 (0.002) Batch 0.482 (0.506) Remain 02:09:27 loss: 0.3395 Lr: 0.00379 [2024-11-25 17:19:02,007 INFO misc.py line 119 2586773] Train: [10/50][52/376] Data 0.002 (0.002) Batch 0.490 (0.505) Remain 02:09:22 loss: 0.2177 Lr: 0.00379 [2024-11-25 17:19:02,531 INFO misc.py line 119 2586773] Train: [10/50][53/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 02:09:27 loss: 0.2607 Lr: 0.00379 [2024-11-25 17:19:03,039 INFO misc.py line 119 2586773] Train: [10/50][54/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 02:09:27 loss: 0.2498 Lr: 0.00379 [2024-11-25 17:19:03,574 INFO misc.py line 119 2586773] Train: [10/50][55/376] Data 0.002 (0.002) Batch 0.535 (0.506) Remain 02:09:35 loss: 0.2946 Lr: 0.00379 [2024-11-25 17:19:04,073 INFO misc.py line 119 2586773] Train: [10/50][56/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 02:09:33 loss: 0.2328 Lr: 0.00379 [2024-11-25 17:19:04,617 INFO misc.py line 119 2586773] Train: [10/50][57/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 02:09:43 loss: 0.2675 Lr: 0.00379 [2024-11-25 17:19:05,150 INFO misc.py line 119 2586773] Train: [10/50][58/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 02:09:50 loss: 0.2837 Lr: 0.00379 [2024-11-25 17:19:05,670 INFO misc.py line 119 2586773] Train: [10/50][59/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 02:09:53 loss: 0.2548 Lr: 0.00379 [2024-11-25 17:19:06,175 INFO misc.py line 119 2586773] Train: [10/50][60/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 02:09:52 loss: 0.2531 Lr: 0.00379 [2024-11-25 17:19:06,703 INFO misc.py line 119 2586773] Train: [10/50][61/376] Data 0.002 (0.002) Batch 0.528 (0.508) Remain 02:09:57 loss: 0.2416 Lr: 0.00379 [2024-11-25 17:19:07,177 INFO misc.py line 119 2586773] Train: [10/50][62/376] Data 0.002 (0.002) Batch 0.474 (0.507) Remain 02:09:47 loss: 0.2484 Lr: 0.00378 [2024-11-25 17:19:07,669 INFO misc.py line 119 2586773] Train: [10/50][63/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 02:09:43 loss: 0.2299 Lr: 0.00378 [2024-11-25 17:19:08,168 INFO misc.py line 119 2586773] Train: [10/50][64/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 02:09:40 loss: 0.2644 Lr: 0.00378 [2024-11-25 17:19:08,705 INFO misc.py line 119 2586773] Train: [10/50][65/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 02:09:47 loss: 0.3033 Lr: 0.00378 [2024-11-25 17:19:09,224 INFO misc.py line 119 2586773] Train: [10/50][66/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 02:09:50 loss: 0.3005 Lr: 0.00378 [2024-11-25 17:19:09,743 INFO misc.py line 119 2586773] Train: [10/50][67/376] Data 0.002 (0.002) Batch 0.519 (0.508) Remain 02:09:52 loss: 0.2083 Lr: 0.00378 [2024-11-25 17:19:10,224 INFO misc.py line 119 2586773] Train: [10/50][68/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 02:09:45 loss: 0.2136 Lr: 0.00378 [2024-11-25 17:19:10,731 INFO misc.py line 119 2586773] Train: [10/50][69/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 02:09:45 loss: 0.2662 Lr: 0.00378 [2024-11-25 17:19:11,232 INFO misc.py line 119 2586773] Train: [10/50][70/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:09:43 loss: 0.2304 Lr: 0.00378 [2024-11-25 17:19:11,733 INFO misc.py line 119 2586773] Train: [10/50][71/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:09:41 loss: 0.2672 Lr: 0.00378 [2024-11-25 17:19:12,223 INFO misc.py line 119 2586773] Train: [10/50][72/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 02:09:36 loss: 0.2213 Lr: 0.00378 [2024-11-25 17:19:12,695 INFO misc.py line 119 2586773] Train: [10/50][73/376] Data 0.002 (0.002) Batch 0.472 (0.506) Remain 02:09:28 loss: 0.2346 Lr: 0.00378 [2024-11-25 17:19:13,184 INFO misc.py line 119 2586773] Train: [10/50][74/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 02:09:24 loss: 0.2470 Lr: 0.00378 [2024-11-25 17:19:13,693 INFO misc.py line 119 2586773] Train: [10/50][75/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 02:09:24 loss: 0.2130 Lr: 0.00378 [2024-11-25 17:19:14,222 INFO misc.py line 119 2586773] Train: [10/50][76/376] Data 0.002 (0.002) Batch 0.529 (0.506) Remain 02:09:28 loss: 0.2627 Lr: 0.00378 [2024-11-25 17:19:14,741 INFO misc.py line 119 2586773] Train: [10/50][77/376] Data 0.003 (0.002) Batch 0.519 (0.507) Remain 02:09:31 loss: 0.2436 Lr: 0.00378 [2024-11-25 17:19:15,249 INFO misc.py line 119 2586773] Train: [10/50][78/376] Data 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Batch 0.495 (0.508) Remain 02:08:13 loss: 0.2942 Lr: 0.00375 [2024-11-25 17:20:53,940 INFO misc.py line 119 2586773] Train: [10/50][272/376] Data 0.002 (0.002) Batch 0.545 (0.508) Remain 02:08:15 loss: 0.3031 Lr: 0.00375 [2024-11-25 17:20:54,450 INFO misc.py line 119 2586773] Train: [10/50][273/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 02:08:14 loss: 0.2231 Lr: 0.00375 [2024-11-25 17:20:54,993 INFO misc.py line 119 2586773] Train: [10/50][274/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 02:08:16 loss: 0.2188 Lr: 0.00375 [2024-11-25 17:20:55,537 INFO misc.py line 119 2586773] Train: [10/50][275/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 02:08:17 loss: 0.2967 Lr: 0.00375 [2024-11-25 17:20:56,029 INFO misc.py line 119 2586773] Train: [10/50][276/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 02:08:16 loss: 0.3533 Lr: 0.00375 [2024-11-25 17:20:56,528 INFO misc.py line 119 2586773] Train: [10/50][277/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 02:08:15 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2586773] Train: [10/50][346/376] Data 0.002 (0.002) Batch 0.524 (0.509) Remain 02:07:44 loss: 0.2494 Lr: 0.00374 [2024-11-25 17:21:32,199 INFO misc.py line 119 2586773] Train: [10/50][347/376] Data 0.002 (0.002) Batch 0.503 (0.509) Remain 02:07:43 loss: 0.2138 Lr: 0.00374 [2024-11-25 17:21:32,669 INFO misc.py line 119 2586773] Train: [10/50][348/376] Data 0.002 (0.002) Batch 0.470 (0.508) Remain 02:07:41 loss: 0.2247 Lr: 0.00374 [2024-11-25 17:21:33,229 INFO misc.py line 119 2586773] Train: [10/50][349/376] Data 0.002 (0.002) Batch 0.560 (0.509) Remain 02:07:43 loss: 0.3156 Lr: 0.00374 [2024-11-25 17:21:33,750 INFO misc.py line 119 2586773] Train: [10/50][350/376] Data 0.002 (0.002) Batch 0.520 (0.509) Remain 02:07:43 loss: 0.1831 Lr: 0.00374 [2024-11-25 17:21:34,255 INFO misc.py line 119 2586773] Train: [10/50][351/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 02:07:42 loss: 0.2661 Lr: 0.00374 [2024-11-25 17:21:34,783 INFO misc.py line 119 2586773] Train: [10/50][352/376] Data 0.002 (0.002) Batch 0.528 (0.509) Remain 02:07:42 loss: 0.2483 Lr: 0.00374 [2024-11-25 17:21:35,244 INFO misc.py line 119 2586773] Train: [10/50][353/376] Data 0.002 (0.002) Batch 0.461 (0.509) Remain 02:07:40 loss: 0.2642 Lr: 0.00374 [2024-11-25 17:21:35,715 INFO misc.py line 119 2586773] Train: [10/50][354/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 02:07:38 loss: 0.3107 Lr: 0.00374 [2024-11-25 17:21:36,233 INFO misc.py line 119 2586773] Train: [10/50][355/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 02:07:38 loss: 0.2439 Lr: 0.00374 [2024-11-25 17:21:36,702 INFO misc.py line 119 2586773] Train: [10/50][356/376] Data 0.002 (0.002) Batch 0.469 (0.508) Remain 02:07:35 loss: 0.2255 Lr: 0.00374 [2024-11-25 17:21:37,178 INFO misc.py line 119 2586773] Train: [10/50][357/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 02:07:33 loss: 0.2758 Lr: 0.00374 [2024-11-25 17:21:37,679 INFO misc.py line 119 2586773] Train: [10/50][358/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 02:07:33 loss: 0.2667 Lr: 0.00374 [2024-11-25 17:21:38,229 INFO misc.py line 119 2586773] Train: [10/50][359/376] Data 0.002 (0.002) Batch 0.550 (0.508) Remain 02:07:34 loss: 0.2467 Lr: 0.00374 [2024-11-25 17:21:38,738 INFO misc.py line 119 2586773] Train: [10/50][360/376] Data 0.003 (0.002) Batch 0.509 (0.508) Remain 02:07:33 loss: 0.2629 Lr: 0.00374 [2024-11-25 17:21:39,261 INFO misc.py line 119 2586773] Train: [10/50][361/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 02:07:34 loss: 0.2004 Lr: 0.00374 [2024-11-25 17:21:39,738 INFO misc.py line 119 2586773] Train: [10/50][362/376] Data 0.002 (0.002) Batch 0.477 (0.508) Remain 02:07:32 loss: 0.2443 Lr: 0.00374 [2024-11-25 17:21:40,233 INFO misc.py line 119 2586773] Train: [10/50][363/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 02:07:31 loss: 0.2314 Lr: 0.00374 [2024-11-25 17:21:40,733 INFO misc.py line 119 2586773] Train: [10/50][364/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 02:07:30 loss: 0.2405 Lr: 0.00374 [2024-11-25 17:21:41,207 INFO misc.py line 119 2586773] Train: [10/50][365/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 02:07:28 loss: 0.2427 Lr: 0.00373 [2024-11-25 17:21:41,695 INFO misc.py line 119 2586773] Train: [10/50][366/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 02:07:27 loss: 0.2401 Lr: 0.00373 [2024-11-25 17:21:42,173 INFO misc.py line 119 2586773] Train: [10/50][367/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 02:07:25 loss: 0.3024 Lr: 0.00373 [2024-11-25 17:21:42,647 INFO misc.py line 119 2586773] Train: [10/50][368/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 02:07:23 loss: 0.2353 Lr: 0.00373 [2024-11-25 17:21:43,187 INFO misc.py line 119 2586773] Train: [10/50][369/376] Data 0.002 (0.002) Batch 0.539 (0.508) Remain 02:07:24 loss: 0.1698 Lr: 0.00373 [2024-11-25 17:21:43,657 INFO misc.py line 119 2586773] Train: [10/50][370/376] Data 0.002 (0.002) Batch 0.470 (0.508) Remain 02:07:22 loss: 0.2681 Lr: 0.00373 [2024-11-25 17:21:44,182 INFO misc.py line 119 2586773] Train: [10/50][371/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 02:07:22 loss: 0.2424 Lr: 0.00373 [2024-11-25 17:21:44,677 INFO misc.py line 119 2586773] Train: [10/50][372/376] Data 0.003 (0.002) Batch 0.494 (0.508) Remain 02:07:21 loss: 0.2822 Lr: 0.00373 [2024-11-25 17:21:45,196 INFO misc.py line 119 2586773] Train: [10/50][373/376] Data 0.002 (0.002) Batch 0.519 (0.508) Remain 02:07:21 loss: 0.3978 Lr: 0.00373 [2024-11-25 17:21:45,733 INFO misc.py line 119 2586773] Train: [10/50][374/376] Data 0.002 (0.002) Batch 0.538 (0.508) Remain 02:07:21 loss: 0.2456 Lr: 0.00373 [2024-11-25 17:21:46,266 INFO misc.py line 119 2586773] Train: [10/50][375/376] Data 0.002 (0.002) Batch 0.532 (0.508) Remain 02:07:22 loss: 0.2383 Lr: 0.00373 [2024-11-25 17:21:46,742 INFO misc.py line 119 2586773] Train: [10/50][376/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 02:07:20 loss: 0.2566 Lr: 0.00373 [2024-11-25 17:21:46,743 INFO misc.py line 136 2586773] Train result: loss: 0.2590 [2024-11-25 17:21:46,743 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:21:57,460 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2331 [2024-11-25 17:21:57,777 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2394 [2024-11-25 17:21:58,040 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3577 [2024-11-25 17:21:58,271 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2428 [2024-11-25 17:21:58,534 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3093 [2024-11-25 17:21:58,800 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2378 [2024-11-25 17:21:59,031 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2565 [2024-11-25 17:21:59,299 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2240 [2024-11-25 17:21:59,526 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2948 [2024-11-25 17:21:59,788 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2972 [2024-11-25 17:22:00,027 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2571 [2024-11-25 17:22:00,299 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2761 [2024-11-25 17:22:00,564 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2915 [2024-11-25 17:22:00,826 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2804 [2024-11-25 17:22:01,058 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2846 [2024-11-25 17:22:01,300 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3273 [2024-11-25 17:22:01,566 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3573 [2024-11-25 17:22:01,819 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2297 [2024-11-25 17:22:02,047 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2526 [2024-11-25 17:22:02,307 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3014 [2024-11-25 17:22:02,548 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2757 [2024-11-25 17:22:02,814 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3282 [2024-11-25 17:22:03,066 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2462 [2024-11-25 17:22:03,334 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2884 [2024-11-25 17:22:03,605 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2563 [2024-11-25 17:22:03,842 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2765 [2024-11-25 17:22:04,102 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3068 [2024-11-25 17:22:04,348 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2783 [2024-11-25 17:22:04,613 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3160 [2024-11-25 17:22:04,867 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3441 [2024-11-25 17:22:05,112 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2887 [2024-11-25 17:22:05,383 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2504 [2024-11-25 17:22:05,606 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.3014 [2024-11-25 17:22:05,849 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2342 [2024-11-25 17:22:06,110 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2394 [2024-11-25 17:22:06,353 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2728 [2024-11-25 17:22:06,581 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2311 [2024-11-25 17:22:06,854 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2663 [2024-11-25 17:22:07,083 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2915 [2024-11-25 17:22:07,324 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2959 [2024-11-25 17:22:07,594 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2852 [2024-11-25 17:22:07,851 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3189 [2024-11-25 17:22:08,087 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.3049 [2024-11-25 17:22:08,318 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2770 [2024-11-25 17:22:08,557 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2737 [2024-11-25 17:22:08,806 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2773 [2024-11-25 17:22:09,065 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3003 [2024-11-25 17:22:09,316 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3412 [2024-11-25 17:22:09,539 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2432 [2024-11-25 17:22:09,775 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2390 [2024-11-25 17:22:09,997 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2623 [2024-11-25 17:22:10,270 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2929 [2024-11-25 17:22:10,535 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2622 [2024-11-25 17:22:10,796 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3153 [2024-11-25 17:22:11,026 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2649 [2024-11-25 17:22:11,266 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2761 [2024-11-25 17:22:11,523 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3144 [2024-11-25 17:22:11,792 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2881 [2024-11-25 17:22:12,046 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2949 [2024-11-25 17:22:12,308 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2935 [2024-11-25 17:22:12,559 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2545 [2024-11-25 17:22:12,827 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2819 [2024-11-25 17:22:13,062 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2638 [2024-11-25 17:22:13,320 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3416 [2024-11-25 17:22:13,589 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2961 [2024-11-25 17:22:13,858 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2469 [2024-11-25 17:22:14,101 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2418 [2024-11-25 17:22:14,359 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3131 [2024-11-25 17:22:14,625 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2652 [2024-11-25 17:22:14,887 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3106 [2024-11-25 17:22:15,139 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2425 [2024-11-25 17:22:15,373 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2926 [2024-11-25 17:22:15,628 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2801 [2024-11-25 17:22:15,876 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3338 [2024-11-25 17:22:16,092 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2809 [2024-11-25 17:22:16,315 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2332 [2024-11-25 17:22:16,585 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2886 [2024-11-25 17:22:16,821 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2416 [2024-11-25 17:22:17,079 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2440 [2024-11-25 17:22:17,333 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3388 [2024-11-25 17:22:17,576 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2629 [2024-11-25 17:22:17,836 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2877 [2024-11-25 17:22:18,089 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2394 [2024-11-25 17:22:18,339 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2821 [2024-11-25 17:22:18,608 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2816 [2024-11-25 17:22:18,842 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2845 [2024-11-25 17:22:19,107 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3118 [2024-11-25 17:22:19,368 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2892 [2024-11-25 17:22:19,615 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3119 [2024-11-25 17:22:19,862 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2823 [2024-11-25 17:22:20,093 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2681 [2024-11-25 17:22:20,348 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2857 [2024-11-25 17:22:20,616 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3138 [2024-11-25 17:22:20,882 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2392 [2024-11-25 17:22:21,149 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2680 [2024-11-25 17:22:21,397 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2372 [2024-11-25 17:22:21,665 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2820 [2024-11-25 17:22:21,886 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3057 [2024-11-25 17:22:22,158 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2730 [2024-11-25 17:22:22,395 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2897 [2024-11-25 17:22:22,669 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2435 [2024-11-25 17:22:22,929 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3048 [2024-11-25 17:22:23,189 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2947 [2024-11-25 17:22:23,441 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3355 [2024-11-25 17:22:23,667 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2551 [2024-11-25 17:22:23,903 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2581 [2024-11-25 17:22:24,158 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2819 [2024-11-25 17:22:24,425 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3006 [2024-11-25 17:22:24,659 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3023 [2024-11-25 17:22:24,922 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2633 [2024-11-25 17:22:25,187 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2619 [2024-11-25 17:22:25,409 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2661 [2024-11-25 17:22:25,646 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2464 [2024-11-25 17:22:25,865 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2622 [2024-11-25 17:22:26,092 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2631 [2024-11-25 17:22:26,364 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3462 [2024-11-25 17:22:26,623 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3243 [2024-11-25 17:22:26,890 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3091 [2024-11-25 17:22:27,155 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2650 [2024-11-25 17:22:27,416 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3774 [2024-11-25 17:22:27,677 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2884 [2024-11-25 17:22:27,941 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2335 [2024-11-25 17:22:28,195 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3348 [2024-11-25 17:22:28,458 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2762 [2024-11-25 17:22:28,727 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2679 [2024-11-25 17:22:28,979 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3195 [2024-11-25 17:22:29,211 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2356 [2024-11-25 17:22:29,468 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3151 [2024-11-25 17:22:29,702 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2770 [2024-11-25 17:22:29,929 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2172 [2024-11-25 17:22:30,141 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2703 [2024-11-25 17:22:30,360 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2459 [2024-11-25 17:22:31,087 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7305/0.7967/0.9955. [2024-11-25 17:22:31,087 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9955/0.9982 [2024-11-25 17:22:31,087 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4655/0.5952 [2024-11-25 17:22:31,087 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:22:31,088 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7379 [2024-11-25 17:22:31,088 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:22:33,812 INFO misc.py line 119 2586773] Train: [11/50][1/376] Data 0.125 (0.125) Batch 0.565 (0.565) Remain 02:21:44 loss: 0.2861 Lr: 0.00373 [2024-11-25 17:22:34,319 INFO misc.py line 119 2586773] Train: [11/50][2/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 02:07:03 loss: 0.2836 Lr: 0.00373 [2024-11-25 17:22:34,861 INFO misc.py line 119 2586773] Train: [11/50][3/376] Data 0.002 (0.002) Batch 0.542 (0.542) Remain 02:15:49 loss: 0.3115 Lr: 0.00373 [2024-11-25 17:22:35,362 INFO misc.py line 119 2586773] Train: [11/50][4/376] Data 0.002 (0.002) Batch 0.501 (0.501) Remain 02:05:33 loss: 0.2813 Lr: 0.00373 [2024-11-25 17:22:35,839 INFO misc.py line 119 2586773] Train: [11/50][5/376] Data 0.003 (0.002) Batch 0.477 (0.489) Remain 02:02:29 loss: 0.2672 Lr: 0.00373 [2024-11-25 17:22:36,312 INFO misc.py line 119 2586773] Train: [11/50][6/376] Data 0.002 (0.002) Batch 0.474 (0.484) Remain 02:01:14 loss: 0.2720 Lr: 0.00373 [2024-11-25 17:22:36,798 INFO misc.py line 119 2586773] Train: [11/50][7/376] Data 0.003 (0.002) Batch 0.486 (0.484) Remain 02:01:22 loss: 0.2272 Lr: 0.00373 [2024-11-25 17:22:37,284 INFO misc.py line 119 2586773] Train: [11/50][8/376] Data 0.003 (0.002) Batch 0.485 (0.485) Remain 02:01:24 loss: 0.2857 Lr: 0.00373 [2024-11-25 17:22:37,822 INFO misc.py line 119 2586773] Train: [11/50][9/376] Data 0.003 (0.002) Batch 0.538 (0.493) Remain 02:03:37 loss: 0.2309 Lr: 0.00373 [2024-11-25 17:22:38,334 INFO misc.py line 119 2586773] Train: [11/50][10/376] Data 0.002 (0.002) Batch 0.513 (0.496) Remain 02:04:17 loss: 0.2590 Lr: 0.00373 [2024-11-25 17:22:38,877 INFO misc.py line 119 2586773] Train: [11/50][11/376] Data 0.002 (0.002) Batch 0.542 (0.502) Remain 02:05:44 loss: 0.2568 Lr: 0.00373 [2024-11-25 17:22:39,343 INFO misc.py line 119 2586773] Train: [11/50][12/376] Data 0.002 (0.002) Batch 0.466 (0.498) Remain 02:04:44 loss: 0.2796 Lr: 0.00373 [2024-11-25 17:22:39,835 INFO misc.py line 119 2586773] Train: [11/50][13/376] Data 0.003 (0.002) Batch 0.492 (0.497) Remain 02:04:34 loss: 0.2633 Lr: 0.00373 [2024-11-25 17:22:40,331 INFO misc.py line 119 2586773] Train: [11/50][14/376] Data 0.002 (0.002) Batch 0.496 (0.497) Remain 02:04:31 loss: 0.2526 Lr: 0.00373 [2024-11-25 17:22:40,835 INFO misc.py line 119 2586773] Train: [11/50][15/376] Data 0.003 (0.002) Batch 0.504 (0.498) Remain 02:04:40 loss: 0.2832 Lr: 0.00373 [2024-11-25 17:22:41,335 INFO misc.py line 119 2586773] Train: [11/50][16/376] Data 0.003 (0.002) Batch 0.500 (0.498) Remain 02:04:41 loss: 0.2952 Lr: 0.00373 [2024-11-25 17:22:41,829 INFO misc.py line 119 2586773] Train: [11/50][17/376] Data 0.002 (0.002) Batch 0.494 (0.498) Remain 02:04:37 loss: 0.2355 Lr: 0.00373 [2024-11-25 17:22:42,312 INFO misc.py line 119 2586773] Train: [11/50][18/376] Data 0.003 (0.002) Batch 0.483 (0.497) Remain 02:04:22 loss: 0.2398 Lr: 0.00373 [2024-11-25 17:22:42,811 INFO misc.py line 119 2586773] Train: [11/50][19/376] Data 0.002 (0.002) Batch 0.498 (0.497) Remain 02:04:23 loss: 0.2803 Lr: 0.00373 [2024-11-25 17:22:43,307 INFO misc.py line 119 2586773] Train: [11/50][20/376] Data 0.003 (0.002) Batch 0.497 (0.497) Remain 02:04:22 loss: 0.2462 Lr: 0.00373 [2024-11-25 17:22:43,828 INFO misc.py line 119 2586773] Train: [11/50][21/376] Data 0.002 (0.002) Batch 0.520 (0.498) Remain 02:04:41 loss: 0.2386 Lr: 0.00373 [2024-11-25 17:22:44,390 INFO misc.py line 119 2586773] Train: [11/50][22/376] Data 0.002 (0.002) Batch 0.563 (0.502) Remain 02:05:32 loss: 0.2902 Lr: 0.00373 [2024-11-25 17:22:44,873 INFO misc.py line 119 2586773] Train: [11/50][23/376] Data 0.002 (0.002) Batch 0.483 (0.501) Remain 02:05:17 loss: 0.2653 Lr: 0.00373 [2024-11-25 17:22:45,405 INFO misc.py line 119 2586773] Train: [11/50][24/376] Data 0.003 (0.002) Batch 0.532 (0.502) Remain 02:05:39 loss: 0.2458 Lr: 0.00373 [2024-11-25 17:22:45,962 INFO misc.py line 119 2586773] Train: [11/50][25/376] Data 0.002 (0.002) Batch 0.557 (0.505) Remain 02:06:16 loss: 0.2490 Lr: 0.00373 [2024-11-25 17:22:46,462 INFO misc.py line 119 2586773] Train: [11/50][26/376] Data 0.002 (0.002) Batch 0.500 (0.504) Remain 02:06:12 loss: 0.2029 Lr: 0.00373 [2024-11-25 17:22:46,951 INFO misc.py line 119 2586773] Train: [11/50][27/376] Data 0.002 (0.002) Batch 0.489 (0.504) Remain 02:06:03 loss: 0.2192 Lr: 0.00373 [2024-11-25 17:22:47,442 INFO misc.py line 119 2586773] Train: [11/50][28/376] Data 0.003 (0.002) Batch 0.490 (0.503) Remain 02:05:54 loss: 0.2609 Lr: 0.00373 [2024-11-25 17:22:47,939 INFO misc.py line 119 2586773] Train: [11/50][29/376] Data 0.003 (0.002) Batch 0.498 (0.503) Remain 02:05:50 loss: 0.2577 Lr: 0.00373 [2024-11-25 17:22:48,478 INFO misc.py line 119 2586773] Train: [11/50][30/376] Data 0.003 (0.002) Batch 0.539 (0.504) Remain 02:06:10 loss: 0.2969 Lr: 0.00373 [2024-11-25 17:22:48,989 INFO misc.py line 119 2586773] Train: [11/50][31/376] Data 0.003 (0.002) Batch 0.511 (0.505) Remain 02:06:13 loss: 0.2590 Lr: 0.00373 [2024-11-25 17:22:49,517 INFO misc.py line 119 2586773] Train: [11/50][32/376] Data 0.003 (0.002) Batch 0.528 (0.505) Remain 02:06:24 loss: 0.2414 Lr: 0.00373 [2024-11-25 17:22:50,015 INFO misc.py line 119 2586773] Train: [11/50][33/376] Data 0.002 (0.002) Batch 0.497 (0.505) Remain 02:06:20 loss: 0.2736 Lr: 0.00373 [2024-11-25 17:22:50,527 INFO misc.py line 119 2586773] Train: [11/50][34/376] Data 0.002 (0.002) Batch 0.513 (0.505) Remain 02:06:23 loss: 0.2761 Lr: 0.00373 [2024-11-25 17:22:51,044 INFO misc.py line 119 2586773] Train: [11/50][35/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 02:06:28 loss: 0.2176 Lr: 0.00373 [2024-11-25 17:22:51,575 INFO misc.py line 119 2586773] Train: [11/50][36/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 02:06:39 loss: 0.2535 Lr: 0.00373 [2024-11-25 17:22:52,097 INFO misc.py line 119 2586773] Train: [11/50][37/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 02:06:45 loss: 0.2281 Lr: 0.00373 [2024-11-25 17:22:52,632 INFO misc.py line 119 2586773] Train: [11/50][38/376] Data 0.002 (0.002) Batch 0.536 (0.508) Remain 02:06:57 loss: 0.2380 Lr: 0.00373 [2024-11-25 17:22:53,153 INFO misc.py line 119 2586773] Train: [11/50][39/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 02:07:02 loss: 0.2164 Lr: 0.00373 [2024-11-25 17:22:53,679 INFO misc.py line 119 2586773] Train: [11/50][40/376] Data 0.003 (0.002) Batch 0.526 (0.509) Remain 02:07:09 loss: 0.2247 Lr: 0.00373 [2024-11-25 17:22:54,216 INFO misc.py line 119 2586773] Train: [11/50][41/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 02:07:19 loss: 0.3292 Lr: 0.00373 [2024-11-25 17:22:54,715 INFO misc.py line 119 2586773] Train: [11/50][42/376] Data 0.002 (0.002) Batch 0.499 (0.509) Remain 02:07:15 loss: 0.2862 Lr: 0.00373 [2024-11-25 17:22:55,270 INFO misc.py line 119 2586773] Train: [11/50][43/376] Data 0.003 (0.002) Batch 0.555 (0.510) Remain 02:07:31 loss: 0.2683 Lr: 0.00373 [2024-11-25 17:22:55,760 INFO misc.py line 119 2586773] Train: [11/50][44/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 02:07:23 loss: 0.2829 Lr: 0.00373 [2024-11-25 17:22:56,267 INFO misc.py line 119 2586773] Train: [11/50][45/376] Data 0.002 (0.002) Batch 0.508 (0.510) Remain 02:07:22 loss: 0.2384 Lr: 0.00373 [2024-11-25 17:22:56,796 INFO misc.py line 119 2586773] Train: [11/50][46/376] Data 0.002 (0.002) Batch 0.528 (0.510) Remain 02:07:28 loss: 0.2487 Lr: 0.00372 [2024-11-25 17:22:57,316 INFO misc.py line 119 2586773] Train: [11/50][47/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 02:07:31 loss: 0.1804 Lr: 0.00372 [2024-11-25 17:22:57,815 INFO misc.py line 119 2586773] Train: [11/50][48/376] Data 0.002 (0.002) Batch 0.499 (0.510) Remain 02:07:27 loss: 0.2634 Lr: 0.00372 [2024-11-25 17:22:58,316 INFO misc.py line 119 2586773] Train: [11/50][49/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 02:07:23 loss: 0.2549 Lr: 0.00372 [2024-11-25 17:22:58,840 INFO misc.py line 119 2586773] Train: [11/50][50/376] Data 0.002 (0.002) Batch 0.524 (0.510) Remain 02:07:27 loss: 0.3002 Lr: 0.00372 [2024-11-25 17:22:59,397 INFO misc.py line 119 2586773] Train: [11/50][51/376] Data 0.002 (0.002) Batch 0.558 (0.511) Remain 02:07:42 loss: 0.2702 Lr: 0.00372 [2024-11-25 17:22:59,921 INFO misc.py line 119 2586773] Train: [11/50][52/376] Data 0.002 (0.002) Batch 0.523 (0.511) Remain 02:07:45 loss: 0.2178 Lr: 0.00372 [2024-11-25 17:23:00,391 INFO misc.py line 119 2586773] Train: [11/50][53/376] Data 0.002 (0.002) Batch 0.470 (0.511) Remain 02:07:32 loss: 0.2351 Lr: 0.00372 [2024-11-25 17:23:00,884 INFO misc.py line 119 2586773] Train: [11/50][54/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 02:07:26 loss: 0.3160 Lr: 0.00372 [2024-11-25 17:23:01,427 INFO misc.py line 119 2586773] Train: [11/50][55/376] Data 0.002 (0.002) Batch 0.542 (0.511) Remain 02:07:35 loss: 0.2854 Lr: 0.00372 [2024-11-25 17:23:01,935 INFO misc.py line 119 2586773] Train: [11/50][56/376] Data 0.002 (0.002) Batch 0.508 (0.511) Remain 02:07:34 loss: 0.2728 Lr: 0.00372 [2024-11-25 17:23:02,423 INFO misc.py line 119 2586773] Train: [11/50][57/376] Data 0.002 (0.002) Batch 0.488 (0.510) Remain 02:07:27 loss: 0.2961 Lr: 0.00372 [2024-11-25 17:23:02,974 INFO misc.py line 119 2586773] Train: [11/50][58/376] Data 0.003 (0.002) Batch 0.551 (0.511) Remain 02:07:37 loss: 0.2077 Lr: 0.00372 [2024-11-25 17:23:03,479 INFO misc.py line 119 2586773] Train: [11/50][59/376] Data 0.002 (0.002) Batch 0.506 (0.511) Remain 02:07:35 loss: 0.2352 Lr: 0.00372 [2024-11-25 17:23:03,967 INFO misc.py line 119 2586773] Train: [11/50][60/376] Data 0.003 (0.002) Batch 0.488 (0.511) Remain 02:07:29 loss: 0.2835 Lr: 0.00372 [2024-11-25 17:23:04,452 INFO misc.py line 119 2586773] Train: [11/50][61/376] Data 0.002 (0.002) Batch 0.485 (0.510) Remain 02:07:22 loss: 0.2273 Lr: 0.00372 [2024-11-25 17:23:04,973 INFO misc.py line 119 2586773] Train: [11/50][62/376] Data 0.002 (0.002) Batch 0.521 (0.510) Remain 02:07:24 loss: 0.2218 Lr: 0.00372 [2024-11-25 17:23:05,495 INFO misc.py line 119 2586773] Train: [11/50][63/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 02:07:26 loss: 0.2725 Lr: 0.00372 [2024-11-25 17:23:05,995 INFO misc.py line 119 2586773] Train: [11/50][64/376] Data 0.003 (0.002) Batch 0.500 (0.510) Remain 02:07:23 loss: 0.2730 Lr: 0.00372 [2024-11-25 17:23:06,492 INFO misc.py line 119 2586773] Train: [11/50][65/376] Data 0.002 (0.002) Batch 0.497 (0.510) Remain 02:07:19 loss: 0.2297 Lr: 0.00372 [2024-11-25 17:23:07,009 INFO misc.py line 119 2586773] Train: [11/50][66/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 02:07:21 loss: 0.2920 Lr: 0.00372 [2024-11-25 17:23:07,527 INFO misc.py line 119 2586773] Train: [11/50][67/376] Data 0.002 (0.002) Batch 0.518 (0.510) Remain 02:07:22 loss: 0.2755 Lr: 0.00372 [2024-11-25 17:23:08,022 INFO misc.py line 119 2586773] Train: [11/50][68/376] Data 0.003 (0.002) Batch 0.495 (0.510) Remain 02:07:18 loss: 0.2417 Lr: 0.00372 [2024-11-25 17:23:08,513 INFO misc.py line 119 2586773] Train: [11/50][69/376] Data 0.003 (0.002) Batch 0.491 (0.510) Remain 02:07:13 loss: 0.2771 Lr: 0.00372 [2024-11-25 17:23:09,055 INFO misc.py line 119 2586773] Train: [11/50][70/376] Data 0.003 (0.002) Batch 0.542 (0.510) Remain 02:07:20 loss: 0.2294 Lr: 0.00372 [2024-11-25 17:23:09,560 INFO misc.py line 119 2586773] Train: [11/50][71/376] Data 0.003 (0.002) Batch 0.505 (0.510) Remain 02:07:18 loss: 0.2752 Lr: 0.00372 [2024-11-25 17:23:10,078 INFO misc.py line 119 2586773] Train: [11/50][72/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 02:07:19 loss: 0.2721 Lr: 0.00372 [2024-11-25 17:23:10,581 INFO misc.py line 119 2586773] Train: [11/50][73/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 02:07:17 loss: 0.2461 Lr: 0.00372 [2024-11-25 17:23:11,087 INFO misc.py line 119 2586773] Train: [11/50][74/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 02:07:16 loss: 0.2471 Lr: 0.00372 [2024-11-25 17:23:11,613 INFO misc.py line 119 2586773] Train: [11/50][75/376] Data 0.002 (0.002) Batch 0.526 (0.510) Remain 02:07:18 loss: 0.2393 Lr: 0.00372 [2024-11-25 17:23:12,135 INFO misc.py line 119 2586773] Train: [11/50][76/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 02:07:20 loss: 0.2726 Lr: 0.00372 [2024-11-25 17:23:12,632 INFO misc.py line 119 2586773] Train: [11/50][77/376] Data 0.002 (0.002) Batch 0.497 (0.510) Remain 02:07:17 loss: 0.2336 Lr: 0.00372 [2024-11-25 17:23:13,145 INFO misc.py line 119 2586773] Train: [11/50][78/376] Data 0.002 (0.002) Batch 0.513 (0.510) Remain 02:07:17 loss: 0.2685 Lr: 0.00372 [2024-11-25 17:23:13,619 INFO misc.py line 119 2586773] Train: [11/50][79/376] Data 0.002 (0.002) Batch 0.474 (0.510) Remain 02:07:09 loss: 0.2576 Lr: 0.00372 [2024-11-25 17:23:14,149 INFO misc.py line 119 2586773] Train: [11/50][80/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 02:07:13 loss: 0.2114 Lr: 0.00372 [2024-11-25 17:23:14,617 INFO misc.py line 119 2586773] Train: [11/50][81/376] Data 0.002 (0.002) Batch 0.468 (0.510) Remain 02:07:04 loss: 0.1883 Lr: 0.00372 [2024-11-25 17:23:15,109 INFO misc.py line 119 2586773] Train: [11/50][82/376] Data 0.002 (0.002) Batch 0.492 (0.509) Remain 02:07:00 loss: 0.2502 Lr: 0.00372 [2024-11-25 17:23:15,591 INFO misc.py line 119 2586773] Train: [11/50][83/376] Data 0.002 (0.002) Batch 0.482 (0.509) Remain 02:06:55 loss: 0.2100 Lr: 0.00372 [2024-11-25 17:23:16,071 INFO misc.py line 119 2586773] Train: [11/50][84/376] Data 0.002 (0.002) Batch 0.480 (0.509) Remain 02:06:49 loss: 0.2803 Lr: 0.00372 [2024-11-25 17:23:16,563 INFO misc.py line 119 2586773] Train: 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(0.508) Remain 02:06:33 loss: 0.2924 Lr: 0.00372 [2024-11-25 17:23:20,063 INFO misc.py line 119 2586773] Train: [11/50][92/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 02:06:32 loss: 0.2268 Lr: 0.00372 [2024-11-25 17:23:20,573 INFO misc.py line 119 2586773] Train: [11/50][93/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 02:06:31 loss: 0.2148 Lr: 0.00372 [2024-11-25 17:23:21,115 INFO misc.py line 119 2586773] Train: [11/50][94/376] Data 0.002 (0.002) Batch 0.542 (0.508) Remain 02:06:36 loss: 0.2389 Lr: 0.00372 [2024-11-25 17:23:21,624 INFO misc.py line 119 2586773] Train: [11/50][95/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 02:06:36 loss: 0.1916 Lr: 0.00372 [2024-11-25 17:23:22,170 INFO misc.py line 119 2586773] Train: [11/50][96/376] Data 0.002 (0.002) Batch 0.546 (0.509) Remain 02:06:41 loss: 0.2374 Lr: 0.00372 [2024-11-25 17:23:22,732 INFO misc.py line 119 2586773] Train: [11/50][97/376] Data 0.003 (0.002) Batch 0.562 (0.509) Remain 02:06:49 loss: 0.2617 Lr: 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Batch 0.538 (0.510) Remain 02:06:51 loss: 0.2029 Lr: 0.00371 [2024-11-25 17:23:33,023 INFO misc.py line 119 2586773] Train: [11/50][117/376] Data 0.002 (0.002) Batch 0.532 (0.510) Remain 02:06:53 loss: 0.2598 Lr: 0.00371 [2024-11-25 17:23:33,562 INFO misc.py line 119 2586773] Train: [11/50][118/376] Data 0.002 (0.002) Batch 0.539 (0.510) Remain 02:06:56 loss: 0.2053 Lr: 0.00371 [2024-11-25 17:23:34,084 INFO misc.py line 119 2586773] Train: [11/50][119/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 02:06:57 loss: 0.2448 Lr: 0.00371 [2024-11-25 17:23:34,597 INFO misc.py line 119 2586773] Train: [11/50][120/376] Data 0.002 (0.002) Batch 0.513 (0.511) Remain 02:06:57 loss: 0.2011 Lr: 0.00371 [2024-11-25 17:23:35,087 INFO misc.py line 119 2586773] Train: [11/50][121/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 02:06:54 loss: 0.2535 Lr: 0.00371 [2024-11-25 17:23:35,602 INFO misc.py line 119 2586773] Train: [11/50][122/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 02:06:54 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Batch 0.532 (0.508) Remain 02:05:56 loss: 0.2292 Lr: 0.00370 [2024-11-25 17:24:01,276 INFO misc.py line 119 2586773] Train: [11/50][173/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 02:05:57 loss: 0.3233 Lr: 0.00370 [2024-11-25 17:24:01,777 INFO misc.py line 119 2586773] Train: [11/50][174/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 02:05:56 loss: 0.2327 Lr: 0.00370 [2024-11-25 17:24:02,270 INFO misc.py line 119 2586773] Train: [11/50][175/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 02:05:54 loss: 0.2684 Lr: 0.00370 [2024-11-25 17:24:02,773 INFO misc.py line 119 2586773] Train: [11/50][176/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 02:05:53 loss: 0.2398 Lr: 0.00370 [2024-11-25 17:24:03,255 INFO misc.py line 119 2586773] Train: [11/50][177/376] Data 0.002 (0.002) Batch 0.482 (0.508) Remain 02:05:50 loss: 0.2001 Lr: 0.00370 [2024-11-25 17:24:03,740 INFO misc.py line 119 2586773] Train: [11/50][178/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 02:05:48 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line 119 2586773] Train: [11/50][272/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 02:04:40 loss: 0.2887 Lr: 0.00368 [2024-11-25 17:24:51,623 INFO misc.py line 119 2586773] Train: [11/50][273/376] Data 0.002 (0.002) Batch 0.511 (0.507) Remain 02:04:39 loss: 0.2306 Lr: 0.00368 [2024-11-25 17:24:52,150 INFO misc.py line 119 2586773] Train: [11/50][274/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 02:04:40 loss: 0.1898 Lr: 0.00368 [2024-11-25 17:24:52,652 INFO misc.py line 119 2586773] Train: [11/50][275/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 02:04:39 loss: 0.2014 Lr: 0.00368 [2024-11-25 17:24:53,161 INFO misc.py line 119 2586773] Train: [11/50][276/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 02:04:39 loss: 0.2251 Lr: 0.00368 [2024-11-25 17:24:53,665 INFO misc.py line 119 2586773] Train: [11/50][277/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:04:38 loss: 0.2753 Lr: 0.00368 [2024-11-25 17:24:54,156 INFO misc.py line 119 2586773] Train: 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Batch 0.495 (0.507) Remain 02:04:36 loss: 0.2032 Lr: 0.00368 [2024-11-25 17:24:57,717 INFO misc.py line 119 2586773] Train: [11/50][285/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 02:04:34 loss: 0.2304 Lr: 0.00368 [2024-11-25 17:24:58,221 INFO misc.py line 119 2586773] Train: [11/50][286/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 02:04:33 loss: 0.2503 Lr: 0.00368 [2024-11-25 17:24:58,712 INFO misc.py line 119 2586773] Train: [11/50][287/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 02:04:32 loss: 0.2709 Lr: 0.00368 [2024-11-25 17:24:59,208 INFO misc.py line 119 2586773] Train: [11/50][288/376] Data 0.002 (0.002) Batch 0.497 (0.506) Remain 02:04:31 loss: 0.2597 Lr: 0.00368 [2024-11-25 17:24:59,763 INFO misc.py line 119 2586773] Train: [11/50][289/376] Data 0.002 (0.002) Batch 0.555 (0.507) Remain 02:04:33 loss: 0.2963 Lr: 0.00368 [2024-11-25 17:25:00,327 INFO misc.py line 119 2586773] Train: [11/50][290/376] Data 0.002 (0.002) Batch 0.563 (0.507) Remain 02:04:36 loss: 0.3007 Lr: 0.00368 [2024-11-25 17:25:00,805 INFO misc.py line 119 2586773] Train: [11/50][291/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 02:04:34 loss: 0.2086 Lr: 0.00368 [2024-11-25 17:25:01,286 INFO misc.py line 119 2586773] Train: [11/50][292/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 02:04:32 loss: 0.2345 Lr: 0.00368 [2024-11-25 17:25:01,784 INFO misc.py line 119 2586773] Train: [11/50][293/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 02:04:31 loss: 0.2902 Lr: 0.00368 [2024-11-25 17:25:02,300 INFO misc.py line 119 2586773] Train: [11/50][294/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 02:04:31 loss: 0.2503 Lr: 0.00368 [2024-11-25 17:25:02,843 INFO misc.py line 119 2586773] Train: [11/50][295/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 02:04:32 loss: 0.2383 Lr: 0.00368 [2024-11-25 17:25:03,331 INFO misc.py line 119 2586773] Train: [11/50][296/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 02:04:31 loss: 0.2743 Lr: 0.00368 [2024-11-25 17:25:03,832 INFO misc.py line 119 2586773] Train: [11/50][297/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 02:04:30 loss: 0.2918 Lr: 0.00368 [2024-11-25 17:25:04,326 INFO misc.py line 119 2586773] Train: [11/50][298/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 02:04:29 loss: 0.2404 Lr: 0.00368 [2024-11-25 17:25:04,840 INFO misc.py line 119 2586773] Train: [11/50][299/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 02:04:29 loss: 0.2060 Lr: 0.00368 [2024-11-25 17:25:05,341 INFO misc.py line 119 2586773] Train: [11/50][300/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:04:28 loss: 0.3155 Lr: 0.00368 [2024-11-25 17:25:05,820 INFO misc.py line 119 2586773] Train: [11/50][301/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 02:04:26 loss: 0.2968 Lr: 0.00368 [2024-11-25 17:25:06,324 INFO misc.py line 119 2586773] Train: [11/50][302/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:04:25 loss: 0.2240 Lr: 0.00368 [2024-11-25 17:25:06,821 INFO misc.py line 119 2586773] Train: [11/50][303/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 02:04:24 loss: 0.2402 Lr: 0.00368 [2024-11-25 17:25:07,296 INFO misc.py line 119 2586773] Train: [11/50][304/376] Data 0.002 (0.002) Batch 0.474 (0.506) Remain 02:04:22 loss: 0.3436 Lr: 0.00368 [2024-11-25 17:25:07,821 INFO misc.py line 119 2586773] Train: [11/50][305/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 02:04:23 loss: 0.2391 Lr: 0.00368 [2024-11-25 17:25:08,353 INFO misc.py line 119 2586773] Train: [11/50][306/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 02:04:23 loss: 0.2884 Lr: 0.00368 [2024-11-25 17:25:08,847 INFO misc.py line 119 2586773] Train: [11/50][307/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 02:04:22 loss: 0.2786 Lr: 0.00368 [2024-11-25 17:25:09,393 INFO misc.py line 119 2586773] Train: [11/50][308/376] Data 0.002 (0.002) Batch 0.546 (0.507) Remain 02:04:24 loss: 0.2169 Lr: 0.00368 [2024-11-25 17:25:09,863 INFO misc.py line 119 2586773] Train: [11/50][309/376] Data 0.002 (0.002) Batch 0.470 (0.507) Remain 02:04:21 loss: 0.2862 Lr: 0.00368 [2024-11-25 17:25:10,388 INFO misc.py line 119 2586773] Train: [11/50][310/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 02:04:22 loss: 0.2025 Lr: 0.00368 [2024-11-25 17:25:10,905 INFO misc.py line 119 2586773] Train: [11/50][311/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 02:04:22 loss: 0.2144 Lr: 0.00368 [2024-11-25 17:25:11,418 INFO misc.py line 119 2586773] Train: [11/50][312/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 02:04:22 loss: 0.2477 Lr: 0.00368 [2024-11-25 17:25:11,912 INFO misc.py line 119 2586773] Train: [11/50][313/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 02:04:20 loss: 0.2238 Lr: 0.00368 [2024-11-25 17:25:12,423 INFO misc.py line 119 2586773] Train: [11/50][314/376] Data 0.002 (0.002) Batch 0.511 (0.507) Remain 02:04:20 loss: 0.2557 Lr: 0.00368 [2024-11-25 17:25:12,956 INFO misc.py line 119 2586773] Train: [11/50][315/376] Data 0.003 (0.002) Batch 0.534 (0.507) Remain 02:04:21 loss: 0.3064 Lr: 0.00368 [2024-11-25 17:25:13,475 INFO misc.py line 119 2586773] Train: [11/50][316/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 02:04:21 loss: 0.2646 Lr: 0.00368 [2024-11-25 17:25:13,981 INFO misc.py line 119 2586773] Train: [11/50][317/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:04:20 loss: 0.2605 Lr: 0.00368 [2024-11-25 17:25:14,486 INFO misc.py line 119 2586773] Train: [11/50][318/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 02:04:20 loss: 0.2321 Lr: 0.00368 [2024-11-25 17:25:15,039 INFO misc.py line 119 2586773] Train: [11/50][319/376] Data 0.002 (0.002) Batch 0.553 (0.507) Remain 02:04:21 loss: 0.2217 Lr: 0.00367 [2024-11-25 17:25:15,558 INFO misc.py line 119 2586773] Train: [11/50][320/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 02:04:22 loss: 0.2872 Lr: 0.00367 [2024-11-25 17:25:16,084 INFO misc.py line 119 2586773] Train: [11/50][321/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 02:04:22 loss: 0.2640 Lr: 0.00367 [2024-11-25 17:25:16,574 INFO misc.py line 119 2586773] Train: [11/50][322/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 02:04:21 loss: 0.2725 Lr: 0.00367 [2024-11-25 17:25:17,088 INFO misc.py line 119 2586773] Train: [11/50][323/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 02:04:20 loss: 0.2050 Lr: 0.00367 [2024-11-25 17:25:17,578 INFO misc.py line 119 2586773] Train: [11/50][324/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 02:04:19 loss: 0.2282 Lr: 0.00367 [2024-11-25 17:25:18,049 INFO misc.py line 119 2586773] Train: [11/50][325/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 02:04:17 loss: 0.2972 Lr: 0.00367 [2024-11-25 17:25:18,558 INFO misc.py line 119 2586773] Train: [11/50][326/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 02:04:17 loss: 0.2110 Lr: 0.00367 [2024-11-25 17:25:19,108 INFO misc.py line 119 2586773] Train: [11/50][327/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 02:04:18 loss: 0.2509 Lr: 0.00367 [2024-11-25 17:25:19,645 INFO misc.py line 119 2586773] Train: [11/50][328/376] Data 0.002 (0.002) Batch 0.536 (0.507) Remain 02:04:19 loss: 0.2677 Lr: 0.00367 [2024-11-25 17:25:20,147 INFO misc.py line 119 2586773] Train: [11/50][329/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 02:04:18 loss: 0.2561 Lr: 0.00367 [2024-11-25 17:25:20,640 INFO misc.py line 119 2586773] Train: [11/50][330/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 02:04:17 loss: 0.2650 Lr: 0.00367 [2024-11-25 17:25:21,172 INFO misc.py line 119 2586773] Train: [11/50][331/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 02:04:18 loss: 0.2311 Lr: 0.00367 [2024-11-25 17:25:21,674 INFO misc.py line 119 2586773] Train: [11/50][332/376] Data 0.003 (0.002) Batch 0.502 (0.507) Remain 02:04:17 loss: 0.2644 Lr: 0.00367 [2024-11-25 17:25:22,169 INFO misc.py line 119 2586773] Train: [11/50][333/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 02:04:16 loss: 0.2228 Lr: 0.00367 [2024-11-25 17:25:22,693 INFO misc.py line 119 2586773] Train: 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Batch 0.507 (0.507) Remain 02:04:14 loss: 0.3726 Lr: 0.00367 [2024-11-25 17:25:26,296 INFO misc.py line 119 2586773] Train: [11/50][341/376] Data 0.003 (0.002) Batch 0.543 (0.507) Remain 02:04:15 loss: 0.2151 Lr: 0.00367 [2024-11-25 17:25:26,811 INFO misc.py line 119 2586773] Train: [11/50][342/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 02:04:15 loss: 0.2566 Lr: 0.00367 [2024-11-25 17:25:27,346 INFO misc.py line 119 2586773] Train: [11/50][343/376] Data 0.002 (0.002) Batch 0.534 (0.507) Remain 02:04:15 loss: 0.2737 Lr: 0.00367 [2024-11-25 17:25:27,811 INFO misc.py line 119 2586773] Train: [11/50][344/376] Data 0.003 (0.002) Batch 0.466 (0.507) Remain 02:04:13 loss: 0.2667 Lr: 0.00367 [2024-11-25 17:25:28,352 INFO misc.py line 119 2586773] Train: [11/50][345/376] Data 0.002 (0.002) Batch 0.541 (0.507) Remain 02:04:14 loss: 0.2197 Lr: 0.00367 [2024-11-25 17:25:28,837 INFO misc.py line 119 2586773] Train: [11/50][346/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 02:04:13 loss: 0.2399 Lr: 0.00367 [2024-11-25 17:25:29,325 INFO misc.py line 119 2586773] Train: [11/50][347/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 02:04:11 loss: 0.2375 Lr: 0.00367 [2024-11-25 17:25:29,861 INFO misc.py line 119 2586773] Train: [11/50][348/376] Data 0.002 (0.002) Batch 0.536 (0.507) Remain 02:04:12 loss: 0.2096 Lr: 0.00367 [2024-11-25 17:25:30,373 INFO misc.py line 119 2586773] Train: [11/50][349/376] Data 0.003 (0.002) Batch 0.513 (0.507) Remain 02:04:12 loss: 0.2499 Lr: 0.00367 [2024-11-25 17:25:30,864 INFO misc.py line 119 2586773] Train: [11/50][350/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 02:04:10 loss: 0.2200 Lr: 0.00367 [2024-11-25 17:25:31,369 INFO misc.py line 119 2586773] Train: [11/50][351/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 02:04:10 loss: 0.2702 Lr: 0.00367 [2024-11-25 17:25:31,873 INFO misc.py line 119 2586773] Train: [11/50][352/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 02:04:09 loss: 0.2812 Lr: 0.00367 [2024-11-25 17:25:32,370 INFO misc.py line 119 2586773] Train: [11/50][353/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 02:04:08 loss: 0.2436 Lr: 0.00367 [2024-11-25 17:25:32,908 INFO misc.py line 119 2586773] Train: [11/50][354/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 02:04:09 loss: 0.3076 Lr: 0.00367 [2024-11-25 17:25:33,409 INFO misc.py line 119 2586773] Train: [11/50][355/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:04:08 loss: 0.1866 Lr: 0.00367 [2024-11-25 17:25:33,922 INFO misc.py line 119 2586773] Train: [11/50][356/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 02:04:08 loss: 0.2197 Lr: 0.00367 [2024-11-25 17:25:34,420 INFO misc.py line 119 2586773] Train: [11/50][357/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 02:04:07 loss: 0.2708 Lr: 0.00367 [2024-11-25 17:25:34,913 INFO misc.py line 119 2586773] Train: [11/50][358/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 02:04:06 loss: 0.2985 Lr: 0.00367 [2024-11-25 17:25:35,360 INFO misc.py line 119 2586773] Train: [11/50][359/376] Data 0.002 (0.002) Batch 0.446 (0.507) Remain 02:04:03 loss: 0.2761 Lr: 0.00367 [2024-11-25 17:25:35,855 INFO misc.py line 119 2586773] Train: [11/50][360/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 02:04:02 loss: 0.2180 Lr: 0.00367 [2024-11-25 17:25:36,364 INFO misc.py line 119 2586773] Train: [11/50][361/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 02:04:02 loss: 0.2362 Lr: 0.00367 [2024-11-25 17:25:36,894 INFO misc.py line 119 2586773] Train: [11/50][362/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 02:04:02 loss: 0.1961 Lr: 0.00367 [2024-11-25 17:25:37,378 INFO misc.py line 119 2586773] Train: [11/50][363/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 02:04:01 loss: 0.2918 Lr: 0.00367 [2024-11-25 17:25:37,929 INFO misc.py line 119 2586773] Train: [11/50][364/376] Data 0.002 (0.002) Batch 0.551 (0.507) Remain 02:04:02 loss: 0.2729 Lr: 0.00367 [2024-11-25 17:25:38,395 INFO misc.py line 119 2586773] Train: [11/50][365/376] Data 0.002 (0.002) Batch 0.466 (0.507) Remain 02:04:00 loss: 0.2452 Lr: 0.00367 [2024-11-25 17:25:38,901 INFO misc.py line 119 2586773] Train: [11/50][366/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:03:59 loss: 0.2224 Lr: 0.00367 [2024-11-25 17:25:39,387 INFO misc.py line 119 2586773] Train: [11/50][367/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 02:03:58 loss: 0.2254 Lr: 0.00367 [2024-11-25 17:25:39,916 INFO misc.py line 119 2586773] Train: [11/50][368/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 02:03:58 loss: 0.2321 Lr: 0.00367 [2024-11-25 17:25:40,448 INFO misc.py line 119 2586773] Train: [11/50][369/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 02:03:59 loss: 0.2515 Lr: 0.00367 [2024-11-25 17:25:40,998 INFO misc.py line 119 2586773] Train: [11/50][370/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 02:04:00 loss: 0.2141 Lr: 0.00367 [2024-11-25 17:25:41,540 INFO misc.py line 119 2586773] Train: [11/50][371/376] Data 0.002 (0.002) Batch 0.542 (0.507) Remain 02:04:01 loss: 0.2111 Lr: 0.00367 [2024-11-25 17:25:42,041 INFO misc.py line 119 2586773] Train: [11/50][372/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 02:04:00 loss: 0.2580 Lr: 0.00366 [2024-11-25 17:25:42,548 INFO misc.py line 119 2586773] Train: [11/50][373/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 02:03:59 loss: 0.2798 Lr: 0.00366 [2024-11-25 17:25:43,068 INFO misc.py line 119 2586773] Train: [11/50][374/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 02:04:00 loss: 0.2419 Lr: 0.00366 [2024-11-25 17:25:43,581 INFO misc.py line 119 2586773] Train: [11/50][375/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 02:03:59 loss: 0.2399 Lr: 0.00366 [2024-11-25 17:25:44,087 INFO misc.py line 119 2586773] Train: [11/50][376/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 02:03:59 loss: 0.2843 Lr: 0.00366 [2024-11-25 17:25:44,087 INFO misc.py line 136 2586773] Train result: loss: 0.2530 [2024-11-25 17:25:44,088 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:25:54,948 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2123 [2024-11-25 17:25:55,213 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2339 [2024-11-25 17:25:55,473 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3150 [2024-11-25 17:25:55,697 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2281 [2024-11-25 17:25:55,964 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3162 [2024-11-25 17:25:56,234 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2448 [2024-11-25 17:25:56,459 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2397 [2024-11-25 17:25:56,729 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2365 [2024-11-25 17:25:56,954 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2774 [2024-11-25 17:25:57,215 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3268 [2024-11-25 17:25:57,444 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2474 [2024-11-25 17:25:57,718 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2634 [2024-11-25 17:25:57,987 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2635 [2024-11-25 17:25:58,249 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2925 [2024-11-25 17:25:58,482 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2814 [2024-11-25 17:25:58,720 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3037 [2024-11-25 17:25:58,995 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3400 [2024-11-25 17:25:59,241 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2195 [2024-11-25 17:25:59,471 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2372 [2024-11-25 17:25:59,733 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2984 [2024-11-25 17:25:59,966 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2705 [2024-11-25 17:26:00,233 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3117 [2024-11-25 17:26:00,470 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2626 [2024-11-25 17:26:00,738 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2979 [2024-11-25 17:26:01,001 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2448 [2024-11-25 17:26:01,237 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2764 [2024-11-25 17:26:01,490 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3081 [2024-11-25 17:26:01,737 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2550 [2024-11-25 17:26:02,005 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3224 [2024-11-25 17:26:02,261 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3458 [2024-11-25 17:26:02,496 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3196 [2024-11-25 17:26:02,760 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2434 [2024-11-25 17:26:02,982 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.3023 [2024-11-25 17:26:03,222 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2524 [2024-11-25 17:26:03,483 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2433 [2024-11-25 17:26:03,734 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2640 [2024-11-25 17:26:03,961 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2397 [2024-11-25 17:26:04,232 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2758 [2024-11-25 17:26:04,463 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2939 [2024-11-25 17:26:04,713 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2738 [2024-11-25 17:26:04,983 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2853 [2024-11-25 17:26:05,235 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3301 [2024-11-25 17:26:05,472 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2904 [2024-11-25 17:26:05,704 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2497 [2024-11-25 17:26:05,942 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2567 [2024-11-25 17:26:06,188 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2592 [2024-11-25 17:26:06,445 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2935 [2024-11-25 17:26:06,696 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3161 [2024-11-25 17:26:06,920 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2388 [2024-11-25 17:26:07,156 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2504 [2024-11-25 17:26:07,376 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2442 [2024-11-25 17:26:07,629 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2849 [2024-11-25 17:26:07,896 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2694 [2024-11-25 17:26:08,156 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3200 [2024-11-25 17:26:08,388 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2695 [2024-11-25 17:26:08,630 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2614 [2024-11-25 17:26:08,886 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3166 [2024-11-25 17:26:09,155 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2688 [2024-11-25 17:26:09,410 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2724 [2024-11-25 17:26:09,670 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3110 [2024-11-25 17:26:09,922 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2733 [2024-11-25 17:26:10,191 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2859 [2024-11-25 17:26:10,420 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2628 [2024-11-25 17:26:10,682 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3091 [2024-11-25 17:26:10,952 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3149 [2024-11-25 17:26:11,218 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2636 [2024-11-25 17:26:11,466 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2428 [2024-11-25 17:26:11,721 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3144 [2024-11-25 17:26:11,988 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2601 [2024-11-25 17:26:12,249 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3088 [2024-11-25 17:26:12,493 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2441 [2024-11-25 17:26:12,727 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2917 [2024-11-25 17:26:12,984 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.3007 [2024-11-25 17:26:13,228 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2939 [2024-11-25 17:26:13,444 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2752 [2024-11-25 17:26:13,666 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2248 [2024-11-25 17:26:13,932 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2838 [2024-11-25 17:26:14,170 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2515 [2024-11-25 17:26:14,429 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2354 [2024-11-25 17:26:14,680 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3148 [2024-11-25 17:26:14,922 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2389 [2024-11-25 17:26:15,182 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2843 [2024-11-25 17:26:15,433 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2330 [2024-11-25 17:26:15,681 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2963 [2024-11-25 17:26:15,956 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2648 [2024-11-25 17:26:16,193 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2646 [2024-11-25 17:26:16,455 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3042 [2024-11-25 17:26:16,717 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2675 [2024-11-25 17:26:16,965 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2849 [2024-11-25 17:26:17,213 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2921 [2024-11-25 17:26:17,448 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2635 [2024-11-25 17:26:17,702 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2925 [2024-11-25 17:26:17,971 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2829 [2024-11-25 17:26:18,238 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2484 [2024-11-25 17:26:18,509 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2549 [2024-11-25 17:26:18,761 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2439 [2024-11-25 17:26:19,030 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2856 [2024-11-25 17:26:19,250 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3094 [2024-11-25 17:26:19,522 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2732 [2024-11-25 17:26:19,759 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2929 [2024-11-25 17:26:20,028 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2649 [2024-11-25 17:26:20,290 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3238 [2024-11-25 17:26:20,551 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3116 [2024-11-25 17:26:20,803 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3178 [2024-11-25 17:26:21,024 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2594 [2024-11-25 17:26:21,260 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2476 [2024-11-25 17:26:21,515 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2519 [2024-11-25 17:26:21,785 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3173 [2024-11-25 17:26:22,018 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3214 [2024-11-25 17:26:22,278 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2710 [2024-11-25 17:26:22,542 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2599 [2024-11-25 17:26:22,763 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2649 [2024-11-25 17:26:23,001 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2366 [2024-11-25 17:26:23,219 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2582 [2024-11-25 17:26:23,445 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2504 [2024-11-25 17:26:23,717 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3280 [2024-11-25 17:26:23,978 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3114 [2024-11-25 17:26:24,246 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2958 [2024-11-25 17:26:24,512 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2633 [2024-11-25 17:26:24,774 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3466 [2024-11-25 17:26:25,033 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2814 [2024-11-25 17:26:25,296 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2157 [2024-11-25 17:26:25,552 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2798 [2024-11-25 17:26:25,815 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3257 [2024-11-25 17:26:26,078 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2714 [2024-11-25 17:26:26,331 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3185 [2024-11-25 17:26:26,562 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2536 [2024-11-25 17:26:26,820 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3027 [2024-11-25 17:26:27,055 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2662 [2024-11-25 17:26:27,282 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2177 [2024-11-25 17:26:27,494 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2509 [2024-11-25 17:26:27,709 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2428 [2024-11-25 17:26:28,420 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7350/0.7969/0.9957. [2024-11-25 17:26:28,420 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9983 [2024-11-25 17:26:28,420 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4743/0.5956 [2024-11-25 17:26:28,421 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:26:28,421 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7379 [2024-11-25 17:26:28,422 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:26:31,004 INFO misc.py line 119 2586773] Train: [12/50][1/376] Data 0.084 (0.084) Batch 0.519 (0.519) Remain 02:06:50 loss: 0.3050 Lr: 0.00366 [2024-11-25 17:26:31,545 INFO misc.py line 119 2586773] Train: [12/50][2/376] Data 0.002 (0.002) Batch 0.541 (0.541) Remain 02:12:11 loss: 0.2870 Lr: 0.00366 [2024-11-25 17:26:32,059 INFO misc.py line 119 2586773] Train: [12/50][3/376] Data 0.003 (0.003) Batch 0.514 (0.514) Remain 02:05:36 loss: 0.2534 Lr: 0.00366 [2024-11-25 17:26:32,560 INFO misc.py line 119 2586773] Train: [12/50][4/376] Data 0.002 (0.002) Batch 0.502 (0.502) Remain 02:02:35 loss: 0.2479 Lr: 0.00366 [2024-11-25 17:26:33,072 INFO misc.py line 119 2586773] Train: [12/50][5/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 02:03:47 loss: 0.2494 Lr: 0.00366 [2024-11-25 17:26:33,579 INFO misc.py line 119 2586773] Train: [12/50][6/376] Data 0.003 (0.002) Batch 0.507 (0.507) Remain 02:03:50 loss: 0.2482 Lr: 0.00366 [2024-11-25 17:26:34,074 INFO misc.py line 119 2586773] Train: [12/50][7/376] Data 0.002 (0.002) Batch 0.495 (0.504) Remain 02:03:04 loss: 0.2393 Lr: 0.00366 [2024-11-25 17:26:34,598 INFO misc.py line 119 2586773] Train: [12/50][8/376] Data 0.003 (0.002) Batch 0.524 (0.508) Remain 02:04:02 loss: 0.2522 Lr: 0.00366 [2024-11-25 17:26:35,089 INFO misc.py line 119 2586773] Train: [12/50][9/376] Data 0.003 (0.003) Batch 0.491 (0.505) Remain 02:03:21 loss: 0.2832 Lr: 0.00366 [2024-11-25 17:26:35,616 INFO misc.py line 119 2586773] Train: [12/50][10/376] Data 0.002 (0.003) Batch 0.527 (0.508) Remain 02:04:07 loss: 0.3830 Lr: 0.00366 [2024-11-25 17:26:36,132 INFO misc.py line 119 2586773] Train: [12/50][11/376] Data 0.002 (0.002) Batch 0.516 (0.509) Remain 02:04:21 loss: 0.2403 Lr: 0.00366 [2024-11-25 17:26:36,625 INFO misc.py line 119 2586773] Train: [12/50][12/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 02:03:53 loss: 0.2665 Lr: 0.00366 [2024-11-25 17:26:37,117 INFO misc.py line 119 2586773] Train: [12/50][13/376] Data 0.002 (0.002) Batch 0.492 (0.506) Remain 02:03:30 loss: 0.2769 Lr: 0.00366 [2024-11-25 17:26:37,653 INFO misc.py line 119 2586773] Train: [12/50][14/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 02:04:11 loss: 0.1962 Lr: 0.00366 [2024-11-25 17:26:38,188 INFO misc.py line 119 2586773] Train: [12/50][15/376] Data 0.002 (0.002) Batch 0.535 (0.511) Remain 02:04:42 loss: 0.2692 Lr: 0.00366 [2024-11-25 17:26:38,736 INFO misc.py line 119 2586773] Train: [12/50][16/376] Data 0.003 (0.002) Batch 0.548 (0.514) Remain 02:05:23 loss: 0.2483 Lr: 0.00366 [2024-11-25 17:26:39,216 INFO misc.py line 119 2586773] Train: [12/50][17/376] Data 0.002 (0.002) Batch 0.481 (0.511) Remain 02:04:48 loss: 0.2126 Lr: 0.00366 [2024-11-25 17:26:39,775 INFO misc.py line 119 2586773] Train: [12/50][18/376] Data 0.002 (0.002) Batch 0.559 (0.514) Remain 02:05:34 loss: 0.2273 Lr: 0.00366 [2024-11-25 17:26:40,281 INFO misc.py line 119 2586773] Train: [12/50][19/376] Data 0.002 (0.002) Batch 0.505 (0.514) Remain 02:05:25 loss: 0.2116 Lr: 0.00366 [2024-11-25 17:26:40,777 INFO misc.py line 119 2586773] Train: [12/50][20/376] Data 0.003 (0.002) Batch 0.497 (0.513) Remain 02:05:10 loss: 0.2715 Lr: 0.00366 [2024-11-25 17:26:41,268 INFO misc.py line 119 2586773] Train: [12/50][21/376] Data 0.002 (0.002) Batch 0.490 (0.512) Remain 02:04:51 loss: 0.2029 Lr: 0.00366 [2024-11-25 17:26:41,773 INFO misc.py line 119 2586773] Train: [12/50][22/376] Data 0.002 (0.002) Batch 0.505 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loss: 0.2695 Lr: 0.00361 [2024-11-25 17:28:58,505 INFO misc.py line 119 2586773] Train: [12/50][291/376] Data 0.002 (0.002) Batch 0.547 (0.508) Remain 02:01:48 loss: 0.2900 Lr: 0.00361 [2024-11-25 17:28:59,004 INFO misc.py line 119 2586773] Train: [12/50][292/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 02:01:47 loss: 0.2262 Lr: 0.00361 [2024-11-25 17:28:59,475 INFO misc.py line 119 2586773] Train: [12/50][293/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 02:01:45 loss: 0.2599 Lr: 0.00361 [2024-11-25 17:29:00,025 INFO misc.py line 119 2586773] Train: [12/50][294/376] Data 0.002 (0.002) Batch 0.551 (0.508) Remain 02:01:46 loss: 0.2460 Lr: 0.00361 [2024-11-25 17:29:00,551 INFO misc.py line 119 2586773] Train: [12/50][295/376] Data 0.002 (0.002) Batch 0.526 (0.509) Remain 02:01:47 loss: 0.2448 Lr: 0.00360 [2024-11-25 17:29:01,043 INFO misc.py line 119 2586773] Train: [12/50][296/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 02:01:45 loss: 0.3002 Lr: 0.00360 [2024-11-25 17:29:01,535 INFO misc.py line 119 2586773] Train: [12/50][297/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 02:01:44 loss: 0.2343 Lr: 0.00360 [2024-11-25 17:29:02,047 INFO misc.py line 119 2586773] Train: [12/50][298/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 02:01:44 loss: 0.2510 Lr: 0.00360 [2024-11-25 17:29:02,575 INFO misc.py line 119 2586773] Train: [12/50][299/376] Data 0.002 (0.002) Batch 0.527 (0.508) Remain 02:01:44 loss: 0.2432 Lr: 0.00360 [2024-11-25 17:29:03,089 INFO misc.py line 119 2586773] Train: [12/50][300/376] Data 0.002 (0.002) Batch 0.515 (0.509) Remain 02:01:44 loss: 0.2576 Lr: 0.00360 [2024-11-25 17:29:03,566 INFO misc.py line 119 2586773] Train: [12/50][301/376] Data 0.002 (0.002) Batch 0.477 (0.508) Remain 02:01:42 loss: 0.2368 Lr: 0.00360 [2024-11-25 17:29:04,105 INFO misc.py line 119 2586773] Train: [12/50][302/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 02:01:43 loss: 0.2494 Lr: 0.00360 [2024-11-25 17:29:04,650 INFO misc.py line 119 2586773] Train: [12/50][303/376] Data 0.002 (0.002) Batch 0.545 (0.509) Remain 02:01:44 loss: 0.2609 Lr: 0.00360 [2024-11-25 17:29:05,182 INFO misc.py line 119 2586773] Train: [12/50][304/376] Data 0.003 (0.002) Batch 0.532 (0.509) Remain 02:01:45 loss: 0.2323 Lr: 0.00360 [2024-11-25 17:29:05,695 INFO misc.py line 119 2586773] Train: [12/50][305/376] Data 0.002 (0.002) Batch 0.512 (0.509) Remain 02:01:44 loss: 0.2248 Lr: 0.00360 [2024-11-25 17:29:06,214 INFO misc.py line 119 2586773] Train: [12/50][306/376] Data 0.003 (0.002) Batch 0.520 (0.509) Remain 02:01:44 loss: 0.2390 Lr: 0.00360 [2024-11-25 17:29:06,741 INFO misc.py line 119 2586773] Train: [12/50][307/376] Data 0.002 (0.002) Batch 0.527 (0.509) Remain 02:01:45 loss: 0.2523 Lr: 0.00360 [2024-11-25 17:29:07,245 INFO misc.py line 119 2586773] Train: [12/50][308/376] Data 0.003 (0.002) Batch 0.503 (0.509) Remain 02:01:44 loss: 0.2261 Lr: 0.00360 [2024-11-25 17:29:07,774 INFO misc.py line 119 2586773] Train: [12/50][309/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 02:01:44 loss: 0.2299 Lr: 0.00360 [2024-11-25 17:29:08,280 INFO misc.py line 119 2586773] Train: [12/50][310/376] Data 0.002 (0.002) Batch 0.507 (0.509) Remain 02:01:44 loss: 0.2648 Lr: 0.00360 [2024-11-25 17:29:08,736 INFO misc.py line 119 2586773] Train: [12/50][311/376] Data 0.002 (0.002) Batch 0.456 (0.509) Remain 02:01:41 loss: 0.2168 Lr: 0.00360 [2024-11-25 17:29:09,277 INFO misc.py line 119 2586773] Train: [12/50][312/376] Data 0.002 (0.002) Batch 0.540 (0.509) Remain 02:01:42 loss: 0.2543 Lr: 0.00360 [2024-11-25 17:29:09,766 INFO misc.py line 119 2586773] Train: [12/50][313/376] Data 0.002 (0.002) Batch 0.489 (0.509) Remain 02:01:40 loss: 0.2109 Lr: 0.00360 [2024-11-25 17:29:10,264 INFO misc.py line 119 2586773] Train: [12/50][314/376] Data 0.002 (0.002) Batch 0.498 (0.509) Remain 02:01:39 loss: 0.2245 Lr: 0.00360 [2024-11-25 17:29:10,766 INFO misc.py line 119 2586773] Train: [12/50][315/376] Data 0.002 (0.002) Batch 0.502 (0.509) Remain 02:01:38 loss: 0.3186 Lr: 0.00360 [2024-11-25 17:29:11,288 INFO misc.py line 119 2586773] Train: [12/50][316/376] Data 0.002 (0.002) Batch 0.523 (0.509) Remain 02:01:39 loss: 0.2752 Lr: 0.00360 [2024-11-25 17:29:11,786 INFO misc.py line 119 2586773] Train: [12/50][317/376] Data 0.002 (0.002) Batch 0.498 (0.509) Remain 02:01:38 loss: 0.2232 Lr: 0.00360 [2024-11-25 17:29:12,300 INFO misc.py line 119 2586773] Train: [12/50][318/376] Data 0.002 (0.002) Batch 0.514 (0.509) Remain 02:01:37 loss: 0.2200 Lr: 0.00360 [2024-11-25 17:29:12,835 INFO misc.py line 119 2586773] Train: [12/50][319/376] Data 0.003 (0.002) Batch 0.534 (0.509) Remain 02:01:38 loss: 0.2962 Lr: 0.00360 [2024-11-25 17:29:13,380 INFO misc.py line 119 2586773] Train: [12/50][320/376] Data 0.002 (0.002) Batch 0.546 (0.509) Remain 02:01:39 loss: 0.2010 Lr: 0.00360 [2024-11-25 17:29:13,872 INFO misc.py line 119 2586773] Train: [12/50][321/376] Data 0.002 (0.002) Batch 0.492 (0.509) Remain 02:01:38 loss: 0.2336 Lr: 0.00360 [2024-11-25 17:29:14,384 INFO misc.py line 119 2586773] Train: [12/50][322/376] Data 0.002 (0.002) Batch 0.512 (0.509) Remain 02:01:38 loss: 0.2995 Lr: 0.00360 [2024-11-25 17:29:14,921 INFO misc.py line 119 2586773] Train: [12/50][323/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 02:01:38 loss: 0.2521 Lr: 0.00360 [2024-11-25 17:29:15,434 INFO misc.py line 119 2586773] Train: [12/50][324/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 02:01:38 loss: 0.2174 Lr: 0.00360 [2024-11-25 17:29:15,990 INFO misc.py line 119 2586773] Train: [12/50][325/376] Data 0.002 (0.002) Batch 0.556 (0.509) Remain 02:01:40 loss: 0.2000 Lr: 0.00360 [2024-11-25 17:29:16,507 INFO misc.py line 119 2586773] Train: [12/50][326/376] Data 0.002 (0.002) Batch 0.517 (0.509) Remain 02:01:39 loss: 0.2016 Lr: 0.00360 [2024-11-25 17:29:17,041 INFO misc.py line 119 2586773] Train: [12/50][327/376] Data 0.002 (0.002) Batch 0.534 (0.509) Remain 02:01:40 loss: 0.2916 Lr: 0.00360 [2024-11-25 17:29:17,514 INFO misc.py line 119 2586773] Train: [12/50][328/376] Data 0.003 (0.002) Batch 0.473 (0.509) Remain 02:01:38 loss: 0.2599 Lr: 0.00360 [2024-11-25 17:29:18,043 INFO misc.py line 119 2586773] Train: [12/50][329/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 02:01:38 loss: 0.2364 Lr: 0.00360 [2024-11-25 17:29:18,554 INFO misc.py line 119 2586773] Train: [12/50][330/376] Data 0.002 (0.002) Batch 0.511 (0.509) Remain 02:01:38 loss: 0.2825 Lr: 0.00360 [2024-11-25 17:29:19,076 INFO misc.py line 119 2586773] Train: [12/50][331/376] Data 0.003 (0.002) Batch 0.522 (0.509) Remain 02:01:38 loss: 0.2005 Lr: 0.00360 [2024-11-25 17:29:19,574 INFO misc.py line 119 2586773] Train: [12/50][332/376] Data 0.002 (0.002) Batch 0.498 (0.509) Remain 02:01:37 loss: 0.2291 Lr: 0.00360 [2024-11-25 17:29:20,091 INFO misc.py line 119 2586773] Train: [12/50][333/376] Data 0.002 (0.002) Batch 0.517 (0.509) Remain 02:01:37 loss: 0.2856 Lr: 0.00360 [2024-11-25 17:29:20,585 INFO misc.py line 119 2586773] Train: [12/50][334/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 02:01:35 loss: 0.2782 Lr: 0.00360 [2024-11-25 17:29:21,046 INFO misc.py line 119 2586773] Train: [12/50][335/376] Data 0.002 (0.002) Batch 0.461 (0.509) Remain 02:01:33 loss: 0.2160 Lr: 0.00360 [2024-11-25 17:29:21,571 INFO misc.py line 119 2586773] Train: [12/50][336/376] Data 0.002 (0.002) Batch 0.526 (0.509) Remain 02:01:33 loss: 0.2251 Lr: 0.00360 [2024-11-25 17:29:22,066 INFO misc.py line 119 2586773] Train: [12/50][337/376] Data 0.002 (0.002) Batch 0.495 (0.509) Remain 02:01:32 loss: 0.2239 Lr: 0.00360 [2024-11-25 17:29:22,557 INFO misc.py line 119 2586773] Train: [12/50][338/376] Data 0.002 (0.002) Batch 0.491 (0.509) Remain 02:01:31 loss: 0.2458 Lr: 0.00360 [2024-11-25 17:29:23,106 INFO misc.py line 119 2586773] Train: [12/50][339/376] Data 0.002 (0.002) Batch 0.549 (0.509) Remain 02:01:32 loss: 0.1814 Lr: 0.00360 [2024-11-25 17:29:23,582 INFO misc.py line 119 2586773] Train: [12/50][340/376] Data 0.002 (0.002) Batch 0.476 (0.509) Remain 02:01:30 loss: 0.2745 Lr: 0.00360 [2024-11-25 17:29:24,058 INFO misc.py line 119 2586773] Train: [12/50][341/376] Data 0.002 (0.002) Batch 0.476 (0.509) Remain 02:01:28 loss: 0.3009 Lr: 0.00360 [2024-11-25 17:29:24,565 INFO misc.py line 119 2586773] Train: [12/50][342/376] Data 0.003 (0.002) Batch 0.507 (0.509) Remain 02:01:27 loss: 0.2590 Lr: 0.00360 [2024-11-25 17:29:25,064 INFO misc.py line 119 2586773] Train: [12/50][343/376] Data 0.003 (0.002) Batch 0.499 (0.509) Remain 02:01:27 loss: 0.2859 Lr: 0.00359 [2024-11-25 17:29:25,603 INFO misc.py line 119 2586773] Train: [12/50][344/376] Data 0.003 (0.002) Batch 0.539 (0.509) Remain 02:01:27 loss: 0.2053 Lr: 0.00359 [2024-11-25 17:29:26,075 INFO misc.py line 119 2586773] Train: [12/50][345/376] Data 0.006 (0.002) Batch 0.472 (0.509) Remain 02:01:25 loss: 0.2179 Lr: 0.00359 [2024-11-25 17:29:26,593 INFO misc.py line 119 2586773] Train: [12/50][346/376] Data 0.002 (0.002) Batch 0.518 (0.509) Remain 02:01:25 loss: 0.2115 Lr: 0.00359 [2024-11-25 17:29:27,041 INFO misc.py line 119 2586773] Train: [12/50][347/376] Data 0.002 (0.002) Batch 0.448 (0.509) Remain 02:01:22 loss: 0.2601 Lr: 0.00359 [2024-11-25 17:29:27,571 INFO misc.py line 119 2586773] Train: [12/50][348/376] Data 0.002 (0.002) Batch 0.530 (0.509) Remain 02:01:22 loss: 0.2718 Lr: 0.00359 [2024-11-25 17:29:28,014 INFO misc.py line 119 2586773] Train: [12/50][349/376] Data 0.002 (0.002) Batch 0.443 (0.509) Remain 02:01:19 loss: 0.2333 Lr: 0.00359 [2024-11-25 17:29:28,514 INFO misc.py line 119 2586773] Train: [12/50][350/376] Data 0.002 (0.002) Batch 0.499 (0.509) Remain 02:01:18 loss: 0.2412 Lr: 0.00359 [2024-11-25 17:29:29,033 INFO misc.py line 119 2586773] Train: [12/50][351/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 02:01:18 loss: 0.2789 Lr: 0.00359 [2024-11-25 17:29:29,571 INFO misc.py line 119 2586773] Train: [12/50][352/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 02:01:19 loss: 0.2567 Lr: 0.00359 [2024-11-25 17:29:30,071 INFO misc.py line 119 2586773] Train: [12/50][353/376] Data 0.002 (0.002) Batch 0.500 (0.509) Remain 02:01:18 loss: 0.2437 Lr: 0.00359 [2024-11-25 17:29:30,570 INFO misc.py line 119 2586773] Train: [12/50][354/376] Data 0.002 (0.002) Batch 0.500 (0.509) Remain 02:01:17 loss: 0.2700 Lr: 0.00359 [2024-11-25 17:29:31,102 INFO misc.py line 119 2586773] Train: [12/50][355/376] Data 0.002 (0.002) Batch 0.531 (0.509) Remain 02:01:18 loss: 0.2448 Lr: 0.00359 [2024-11-25 17:29:31,555 INFO misc.py line 119 2586773] Train: [12/50][356/376] Data 0.002 (0.002) Batch 0.453 (0.508) Remain 02:01:15 loss: 0.2421 Lr: 0.00359 [2024-11-25 17:29:32,091 INFO misc.py line 119 2586773] Train: [12/50][357/376] Data 0.002 (0.002) Batch 0.536 (0.509) Remain 02:01:16 loss: 0.2262 Lr: 0.00359 [2024-11-25 17:29:32,578 INFO misc.py line 119 2586773] Train: [12/50][358/376] Data 0.002 (0.002) Batch 0.487 (0.509) Remain 02:01:14 loss: 0.2254 Lr: 0.00359 [2024-11-25 17:29:33,120 INFO misc.py line 119 2586773] Train: [12/50][359/376] Data 0.002 (0.002) Batch 0.541 (0.509) Remain 02:01:15 loss: 0.2195 Lr: 0.00359 [2024-11-25 17:29:33,595 INFO misc.py line 119 2586773] Train: [12/50][360/376] Data 0.002 (0.002) Batch 0.476 (0.509) Remain 02:01:13 loss: 0.2346 Lr: 0.00359 [2024-11-25 17:29:34,118 INFO misc.py line 119 2586773] Train: [12/50][361/376] Data 0.002 (0.002) Batch 0.523 (0.509) Remain 02:01:13 loss: 0.3087 Lr: 0.00359 [2024-11-25 17:29:34,620 INFO misc.py line 119 2586773] Train: [12/50][362/376] Data 0.002 (0.002) Batch 0.501 (0.509) Remain 02:01:12 loss: 0.2674 Lr: 0.00359 [2024-11-25 17:29:35,128 INFO misc.py line 119 2586773] Train: [12/50][363/376] Data 0.003 (0.002) Batch 0.508 (0.509) Remain 02:01:12 loss: 0.3120 Lr: 0.00359 [2024-11-25 17:29:35,621 INFO misc.py line 119 2586773] Train: [12/50][364/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 02:01:11 loss: 0.2562 Lr: 0.00359 [2024-11-25 17:29:36,130 INFO misc.py line 119 2586773] Train: [12/50][365/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 02:01:10 loss: 0.2689 Lr: 0.00359 [2024-11-25 17:29:36,669 INFO misc.py line 119 2586773] Train: [12/50][366/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 02:01:11 loss: 0.2416 Lr: 0.00359 [2024-11-25 17:29:37,185 INFO misc.py line 119 2586773] Train: [12/50][367/376] Data 0.002 (0.002) Batch 0.516 (0.509) Remain 02:01:11 loss: 0.1961 Lr: 0.00359 [2024-11-25 17:29:37,724 INFO misc.py line 119 2586773] Train: [12/50][368/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 02:01:11 loss: 0.2822 Lr: 0.00359 [2024-11-25 17:29:38,246 INFO misc.py line 119 2586773] Train: [12/50][369/376] Data 0.002 (0.002) Batch 0.522 (0.509) Remain 02:01:11 loss: 0.2459 Lr: 0.00359 [2024-11-25 17:29:38,751 INFO misc.py line 119 2586773] Train: [12/50][370/376] Data 0.002 (0.002) Batch 0.504 (0.509) Remain 02:01:11 loss: 0.2244 Lr: 0.00359 [2024-11-25 17:29:39,236 INFO misc.py line 119 2586773] Train: [12/50][371/376] Data 0.002 (0.002) Batch 0.485 (0.509) Remain 02:01:09 loss: 0.2321 Lr: 0.00359 [2024-11-25 17:29:39,734 INFO misc.py line 119 2586773] Train: [12/50][372/376] Data 0.002 (0.002) Batch 0.498 (0.509) Remain 02:01:08 loss: 0.2619 Lr: 0.00359 [2024-11-25 17:29:40,244 INFO misc.py line 119 2586773] Train: [12/50][373/376] Data 0.002 (0.002) Batch 0.510 (0.509) Remain 02:01:08 loss: 0.2548 Lr: 0.00359 [2024-11-25 17:29:40,750 INFO misc.py line 119 2586773] Train: [12/50][374/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 02:01:07 loss: 0.2571 Lr: 0.00359 [2024-11-25 17:29:41,262 INFO misc.py line 119 2586773] Train: [12/50][375/376] Data 0.002 (0.002) Batch 0.512 (0.509) Remain 02:01:07 loss: 0.3222 Lr: 0.00359 [2024-11-25 17:29:41,775 INFO misc.py line 119 2586773] Train: [12/50][376/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 02:01:07 loss: 0.2701 Lr: 0.00359 [2024-11-25 17:29:41,776 INFO misc.py line 136 2586773] Train result: loss: 0.2507 [2024-11-25 17:29:41,776 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:29:52,780 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2164 [2024-11-25 17:29:53,034 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2290 [2024-11-25 17:29:53,296 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2903 [2024-11-25 17:29:53,527 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2216 [2024-11-25 17:29:53,790 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2855 [2024-11-25 17:29:54,059 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2211 [2024-11-25 17:29:54,289 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2197 [2024-11-25 17:29:54,560 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2223 [2024-11-25 17:29:54,789 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2598 [2024-11-25 17:29:55,048 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2960 [2024-11-25 17:29:55,290 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2461 [2024-11-25 17:29:55,562 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2102 [2024-11-25 17:29:55,829 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2579 [2024-11-25 17:29:56,090 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2808 [2024-11-25 17:29:56,324 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2517 [2024-11-25 17:29:56,562 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3066 [2024-11-25 17:29:56,831 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3040 [2024-11-25 17:29:57,080 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2342 [2024-11-25 17:29:57,311 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2285 [2024-11-25 17:29:57,573 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2648 [2024-11-25 17:29:57,809 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2229 [2024-11-25 17:29:58,076 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3133 [2024-11-25 17:29:58,311 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2253 [2024-11-25 17:29:58,578 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2573 [2024-11-25 17:29:58,839 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2719 [2024-11-25 17:29:59,077 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2725 [2024-11-25 17:29:59,330 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3020 [2024-11-25 17:29:59,576 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2870 [2024-11-25 17:29:59,843 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2928 [2024-11-25 17:30:00,097 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3263 [2024-11-25 17:30:00,330 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2846 [2024-11-25 17:30:00,593 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2486 [2024-11-25 17:30:00,811 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2427 [2024-11-25 17:30:01,051 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2177 [2024-11-25 17:30:01,316 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2314 [2024-11-25 17:30:01,560 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2638 [2024-11-25 17:30:01,786 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2072 [2024-11-25 17:30:02,056 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2462 [2024-11-25 17:30:02,299 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.3024 [2024-11-25 17:30:02,535 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2510 [2024-11-25 17:30:02,806 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2617 [2024-11-25 17:30:03,056 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3195 [2024-11-25 17:30:03,293 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2612 [2024-11-25 17:30:03,526 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2647 [2024-11-25 17:30:03,762 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2451 [2024-11-25 17:30:04,011 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2617 [2024-11-25 17:30:04,280 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2829 [2024-11-25 17:30:04,530 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3111 [2024-11-25 17:30:04,753 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2425 [2024-11-25 17:30:04,986 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2637 [2024-11-25 17:30:05,206 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2450 [2024-11-25 17:30:05,462 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2908 [2024-11-25 17:30:05,729 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2579 [2024-11-25 17:30:05,991 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3286 [2024-11-25 17:30:06,222 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2526 [2024-11-25 17:30:06,467 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2551 [2024-11-25 17:30:06,729 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2822 [2024-11-25 17:30:06,997 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2571 [2024-11-25 17:30:07,254 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2833 [2024-11-25 17:30:07,516 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2902 [2024-11-25 17:30:07,767 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2606 [2024-11-25 17:30:08,035 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2599 [2024-11-25 17:30:08,265 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2483 [2024-11-25 17:30:08,523 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3102 [2024-11-25 17:30:08,790 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2801 [2024-11-25 17:30:09,061 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2225 [2024-11-25 17:30:09,304 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2259 [2024-11-25 17:30:09,565 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3048 [2024-11-25 17:30:09,833 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2447 [2024-11-25 17:30:10,095 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3224 [2024-11-25 17:30:10,342 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2158 [2024-11-25 17:30:10,584 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2814 [2024-11-25 17:30:10,839 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2621 [2024-11-25 17:30:11,081 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3126 [2024-11-25 17:30:11,298 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2583 [2024-11-25 17:30:11,520 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2211 [2024-11-25 17:30:11,787 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2722 [2024-11-25 17:30:12,023 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2237 [2024-11-25 17:30:12,282 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2271 [2024-11-25 17:30:12,534 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3046 [2024-11-25 17:30:12,781 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2359 [2024-11-25 17:30:13,042 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2801 [2024-11-25 17:30:13,291 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2183 [2024-11-25 17:30:13,539 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2670 [2024-11-25 17:30:13,808 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2442 [2024-11-25 17:30:14,045 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2787 [2024-11-25 17:30:14,308 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2934 [2024-11-25 17:30:14,570 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2557 [2024-11-25 17:30:14,824 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2786 [2024-11-25 17:30:15,078 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2659 [2024-11-25 17:30:15,315 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2156 [2024-11-25 17:30:15,567 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2977 [2024-11-25 17:30:15,833 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2844 [2024-11-25 17:30:16,099 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2194 [2024-11-25 17:30:16,364 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2380 [2024-11-25 17:30:16,612 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2378 [2024-11-25 17:30:16,880 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2554 [2024-11-25 17:30:17,102 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3260 [2024-11-25 17:30:17,374 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2809 [2024-11-25 17:30:17,610 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2854 [2024-11-25 17:30:17,880 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2330 [2024-11-25 17:30:18,140 INFO evaluator.py line 159 2586773] Test: 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[2024-11-25 17:30:21,087 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2501 [2024-11-25 17:30:21,312 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2536 [2024-11-25 17:30:21,584 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3291 [2024-11-25 17:30:21,846 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2816 [2024-11-25 17:30:22,113 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.3034 [2024-11-25 17:30:22,378 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2417 [2024-11-25 17:30:22,639 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3104 [2024-11-25 17:30:22,898 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2757 [2024-11-25 17:30:23,163 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2136 [2024-11-25 17:30:23,420 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3136 [2024-11-25 17:30:23,684 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2672 [2024-11-25 17:30:23,947 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2586 [2024-11-25 17:30:24,199 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2966 [2024-11-25 17:30:24,428 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2214 [2024-11-25 17:30:24,689 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2897 [2024-11-25 17:30:24,924 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2785 [2024-11-25 17:30:25,153 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2094 [2024-11-25 17:30:25,365 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2577 [2024-11-25 17:30:25,581 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2203 [2024-11-25 17:30:26,224 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7469/0.8196/0.9958. [2024-11-25 17:30:26,225 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9981 [2024-11-25 17:30:26,225 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4981/0.6410 [2024-11-25 17:30:26,225 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:30:26,226 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7469 [2024-11-25 17:30:26,226 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7469 [2024-11-25 17:30:26,226 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:30:30,773 INFO misc.py line 119 2586773] Train: [13/50][1/376] Data 0.110 (0.110) Batch 0.577 (0.577) Remain 02:17:25 loss: 0.2938 Lr: 0.00359 [2024-11-25 17:30:31,313 INFO misc.py line 119 2586773] Train: [13/50][2/376] Data 0.002 (0.002) Batch 0.540 (0.540) Remain 02:08:35 loss: 0.2949 Lr: 0.00359 [2024-11-25 17:30:31,824 INFO misc.py line 119 2586773] Train: [13/50][3/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 02:01:33 loss: 0.2589 Lr: 0.00359 [2024-11-25 17:30:32,332 INFO misc.py line 119 2586773] Train: [13/50][4/376] Data 0.002 (0.002) Batch 0.509 (0.509) Remain 02:01:05 loss: 0.2632 Lr: 0.00359 [2024-11-25 17:30:32,827 INFO misc.py line 119 2586773] Train: [13/50][5/376] Data 0.002 (0.002) Batch 0.494 (0.501) Remain 01:59:21 loss: 0.3015 Lr: 0.00359 [2024-11-25 17:30:33,369 INFO misc.py line 119 2586773] Train: [13/50][6/376] Data 0.002 (0.002) Batch 0.543 (0.515) Remain 02:02:38 loss: 0.2421 Lr: 0.00359 [2024-11-25 17:30:33,903 INFO misc.py line 119 2586773] Train: [13/50][7/376] Data 0.002 (0.002) Batch 0.533 (0.520) Remain 02:03:41 loss: 0.3197 Lr: 0.00359 [2024-11-25 17:30:34,423 INFO misc.py line 119 2586773] Train: [13/50][8/376] Data 0.002 (0.002) Batch 0.520 (0.520) Remain 02:03:43 loss: 0.3014 Lr: 0.00359 [2024-11-25 17:30:34,943 INFO misc.py line 119 2586773] Train: [13/50][9/376] Data 0.003 (0.002) Batch 0.520 (0.520) Remain 02:03:42 loss: 0.2568 Lr: 0.00359 [2024-11-25 17:30:35,447 INFO misc.py line 119 2586773] Train: [13/50][10/376] Data 0.003 (0.002) Batch 0.504 (0.518) Remain 02:03:09 loss: 0.2767 Lr: 0.00359 [2024-11-25 17:30:35,935 INFO misc.py line 119 2586773] Train: [13/50][11/376] Data 0.002 (0.002) Batch 0.488 (0.514) Remain 02:02:16 loss: 0.3082 Lr: 0.00359 [2024-11-25 17:30:36,492 INFO misc.py line 119 2586773] Train: [13/50][12/376] Data 0.003 (0.002) Batch 0.557 (0.519) Remain 02:03:24 loss: 0.2101 Lr: 0.00359 [2024-11-25 17:30:36,978 INFO misc.py line 119 2586773] Train: [13/50][13/376] Data 0.002 (0.002) Batch 0.486 (0.515) Remain 02:02:37 loss: 0.2313 Lr: 0.00359 [2024-11-25 17:30:37,502 INFO misc.py line 119 2586773] Train: [13/50][14/376] Data 0.002 (0.002) Batch 0.524 (0.516) Remain 02:02:48 loss: 0.2431 Lr: 0.00359 [2024-11-25 17:30:37,996 INFO misc.py line 119 2586773] Train: [13/50][15/376] Data 0.002 (0.002) Batch 0.494 (0.514) Remain 02:02:21 loss: 0.2497 Lr: 0.00358 [2024-11-25 17:30:38,530 INFO misc.py line 119 2586773] Train: [13/50][16/376] Data 0.002 (0.002) Batch 0.534 (0.516) Remain 02:02:42 loss: 0.2608 Lr: 0.00358 [2024-11-25 17:30:39,047 INFO misc.py line 119 2586773] Train: [13/50][17/376] Data 0.003 (0.002) Batch 0.516 (0.516) Remain 02:02:42 loss: 0.2931 Lr: 0.00358 [2024-11-25 17:30:39,534 INFO misc.py line 119 2586773] Train: [13/50][18/376] Data 0.003 (0.002) Batch 0.487 (0.514) Remain 02:02:15 loss: 0.2408 Lr: 0.00358 [2024-11-25 17:30:40,039 INFO misc.py line 119 2586773] Train: [13/50][19/376] Data 0.003 (0.002) Batch 0.505 (0.513) Remain 02:02:06 loss: 0.2581 Lr: 0.00358 [2024-11-25 17:30:40,502 INFO misc.py line 119 2586773] Train: [13/50][20/376] Data 0.003 (0.002) Batch 0.463 (0.510) Remain 02:01:23 loss: 0.3087 Lr: 0.00358 [2024-11-25 17:30:41,036 INFO misc.py line 119 2586773] Train: [13/50][21/376] Data 0.002 (0.002) Batch 0.535 (0.512) Remain 02:01:41 loss: 0.2822 Lr: 0.00358 [2024-11-25 17:30:41,587 INFO misc.py line 119 2586773] Train: [13/50][22/376] Data 0.003 (0.002) Batch 0.550 (0.514) Remain 02:02:10 loss: 0.2784 Lr: 0.00358 [2024-11-25 17:30:42,089 INFO misc.py line 119 2586773] Train: [13/50][23/376] Data 0.003 (0.002) Batch 0.503 (0.513) Remain 02:02:01 loss: 0.2359 Lr: 0.00358 [2024-11-25 17:30:42,574 INFO misc.py line 119 2586773] Train: [13/50][24/376] Data 0.002 (0.002) Batch 0.485 (0.512) Remain 02:01:42 loss: 0.3144 Lr: 0.00358 [2024-11-25 17:30:43,099 INFO misc.py line 119 2586773] Train: [13/50][25/376] Data 0.003 (0.002) Batch 0.525 (0.513) Remain 02:01:50 loss: 0.2086 Lr: 0.00358 [2024-11-25 17:30:43,665 INFO misc.py line 119 2586773] Train: [13/50][26/376] Data 0.003 (0.002) Batch 0.565 (0.515) Remain 02:02:22 loss: 0.2905 Lr: 0.00358 [2024-11-25 17:30:44,204 INFO misc.py line 119 2586773] Train: [13/50][27/376] Data 0.003 (0.003) Batch 0.540 (0.516) Remain 02:02:36 loss: 0.2892 Lr: 0.00358 [2024-11-25 17:30:44,714 INFO misc.py line 119 2586773] Train: [13/50][28/376] Data 0.003 (0.003) Batch 0.510 (0.516) Remain 02:02:32 loss: 0.2324 Lr: 0.00358 [2024-11-25 17:30:45,231 INFO misc.py line 119 2586773] Train: [13/50][29/376] Data 0.002 (0.003) Batch 0.517 (0.516) Remain 02:02:33 loss: 0.2550 Lr: 0.00358 [2024-11-25 17:30:45,736 INFO misc.py line 119 2586773] Train: [13/50][30/376] Data 0.002 (0.002) Batch 0.505 (0.515) Remain 02:02:26 loss: 0.2205 Lr: 0.00358 [2024-11-25 17:30:46,215 INFO misc.py line 119 2586773] Train: [13/50][31/376] Data 0.002 (0.002) Batch 0.478 (0.514) Remain 02:02:07 loss: 0.2395 Lr: 0.00358 [2024-11-25 17:30:46,696 INFO misc.py line 119 2586773] Train: [13/50][32/376] Data 0.002 (0.002) Batch 0.481 (0.513) Remain 02:01:50 loss: 0.2228 Lr: 0.00358 [2024-11-25 17:30:47,260 INFO misc.py line 119 2586773] Train: [13/50][33/376] Data 0.002 (0.002) Batch 0.565 (0.515) Remain 02:02:14 loss: 0.3197 Lr: 0.00358 [2024-11-25 17:30:47,797 INFO misc.py line 119 2586773] Train: [13/50][34/376] Data 0.003 (0.002) Batch 0.536 (0.515) Remain 02:02:24 loss: 0.2241 Lr: 0.00358 [2024-11-25 17:30:48,322 INFO misc.py line 119 2586773] Train: [13/50][35/376] Data 0.002 (0.002) Batch 0.525 (0.516) Remain 02:02:28 loss: 0.2848 Lr: 0.00358 [2024-11-25 17:30:48,831 INFO misc.py line 119 2586773] Train: [13/50][36/376] Data 0.002 (0.002) Batch 0.509 (0.515) Remain 02:02:24 loss: 0.2382 Lr: 0.00358 [2024-11-25 17:30:49,337 INFO misc.py line 119 2586773] Train: [13/50][37/376] Data 0.002 (0.002) Batch 0.506 (0.515) Remain 02:02:20 loss: 0.2635 Lr: 0.00358 [2024-11-25 17:30:49,864 INFO misc.py line 119 2586773] Train: [13/50][38/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 02:02:24 loss: 0.2834 Lr: 0.00358 [2024-11-25 17:30:50,411 INFO misc.py line 119 2586773] Train: [13/50][39/376] Data 0.002 (0.002) Batch 0.547 (0.516) Remain 02:02:36 loss: 0.2924 Lr: 0.00358 [2024-11-25 17:30:50,913 INFO misc.py line 119 2586773] Train: [13/50][40/376] Data 0.002 (0.002) Batch 0.502 (0.516) Remain 02:02:30 loss: 0.2416 Lr: 0.00358 [2024-11-25 17:30:51,403 INFO misc.py line 119 2586773] Train: [13/50][41/376] Data 0.002 (0.002) Batch 0.491 (0.515) Remain 02:02:20 loss: 0.2878 Lr: 0.00358 [2024-11-25 17:30:51,959 INFO misc.py line 119 2586773] Train: [13/50][42/376] Data 0.002 (0.002) Batch 0.556 (0.516) Remain 02:02:35 loss: 0.2900 Lr: 0.00358 [2024-11-25 17:30:52,426 INFO misc.py line 119 2586773] Train: [13/50][43/376] Data 0.003 (0.002) Batch 0.467 (0.515) Remain 02:02:17 loss: 0.2015 Lr: 0.00358 [2024-11-25 17:30:52,919 INFO misc.py line 119 2586773] Train: [13/50][44/376] Data 0.002 (0.002) Batch 0.493 (0.515) Remain 02:02:08 loss: 0.2616 Lr: 0.00358 [2024-11-25 17:30:53,406 INFO misc.py line 119 2586773] Train: [13/50][45/376] Data 0.002 (0.002) Batch 0.488 (0.514) Remain 02:01:59 loss: 0.2299 Lr: 0.00358 [2024-11-25 17:30:53,872 INFO misc.py line 119 2586773] Train: [13/50][46/376] Data 0.002 (0.002) Batch 0.466 (0.513) Remain 02:01:42 loss: 0.1989 Lr: 0.00358 [2024-11-25 17:30:54,381 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line 119 2586773] Train: [13/50][315/376] Data 0.002 (0.002) Batch 0.509 (0.515) Remain 01:59:59 loss: 0.2735 Lr: 0.00352 [2024-11-25 17:33:13,062 INFO misc.py line 119 2586773] Train: [13/50][316/376] Data 0.002 (0.002) Batch 0.483 (0.515) Remain 01:59:57 loss: 0.2148 Lr: 0.00352 [2024-11-25 17:33:13,551 INFO misc.py line 119 2586773] Train: [13/50][317/376] Data 0.002 (0.002) Batch 0.490 (0.515) Remain 01:59:55 loss: 0.2474 Lr: 0.00352 [2024-11-25 17:33:14,046 INFO misc.py line 119 2586773] Train: [13/50][318/376] Data 0.002 (0.002) Batch 0.495 (0.515) Remain 01:59:54 loss: 0.2488 Lr: 0.00352 [2024-11-25 17:33:14,538 INFO misc.py line 119 2586773] Train: [13/50][319/376] Data 0.003 (0.002) Batch 0.492 (0.515) Remain 01:59:52 loss: 0.2734 Lr: 0.00352 [2024-11-25 17:33:15,050 INFO misc.py line 119 2586773] Train: [13/50][320/376] Data 0.002 (0.002) Batch 0.512 (0.515) Remain 01:59:52 loss: 0.2230 Lr: 0.00352 [2024-11-25 17:33:15,549 INFO misc.py line 119 2586773] Train: 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Batch 0.485 (0.515) Remain 01:59:46 loss: 0.1741 Lr: 0.00352 [2024-11-25 17:33:19,116 INFO misc.py line 119 2586773] Train: [13/50][328/376] Data 0.002 (0.002) Batch 0.507 (0.515) Remain 01:59:45 loss: 0.2305 Lr: 0.00352 [2024-11-25 17:33:19,612 INFO misc.py line 119 2586773] Train: [13/50][329/376] Data 0.002 (0.002) Batch 0.496 (0.515) Remain 01:59:44 loss: 0.2179 Lr: 0.00352 [2024-11-25 17:33:20,166 INFO misc.py line 119 2586773] Train: [13/50][330/376] Data 0.002 (0.002) Batch 0.554 (0.515) Remain 01:59:45 loss: 0.2128 Lr: 0.00352 [2024-11-25 17:33:20,653 INFO misc.py line 119 2586773] Train: [13/50][331/376] Data 0.002 (0.002) Batch 0.487 (0.515) Remain 01:59:43 loss: 0.2234 Lr: 0.00352 [2024-11-25 17:33:21,212 INFO misc.py line 119 2586773] Train: [13/50][332/376] Data 0.003 (0.002) Batch 0.559 (0.515) Remain 01:59:45 loss: 0.2608 Lr: 0.00352 [2024-11-25 17:33:21,712 INFO misc.py line 119 2586773] Train: [13/50][333/376] Data 0.002 (0.002) Batch 0.500 (0.515) Remain 01:59:44 loss: 0.2599 Lr: 0.00351 [2024-11-25 17:33:22,164 INFO misc.py line 119 2586773] Train: [13/50][334/376] Data 0.002 (0.002) Batch 0.452 (0.515) Remain 01:59:41 loss: 0.2661 Lr: 0.00351 [2024-11-25 17:33:22,639 INFO misc.py line 119 2586773] Train: [13/50][335/376] Data 0.002 (0.002) Batch 0.475 (0.515) Remain 01:59:38 loss: 0.2142 Lr: 0.00351 [2024-11-25 17:33:23,160 INFO misc.py line 119 2586773] Train: [13/50][336/376] Data 0.002 (0.002) Batch 0.521 (0.515) Remain 01:59:38 loss: 0.2423 Lr: 0.00351 [2024-11-25 17:33:23,661 INFO misc.py line 119 2586773] Train: [13/50][337/376] Data 0.002 (0.002) Batch 0.501 (0.514) Remain 01:59:37 loss: 0.3601 Lr: 0.00351 [2024-11-25 17:33:24,172 INFO misc.py line 119 2586773] Train: [13/50][338/376] Data 0.002 (0.002) Batch 0.511 (0.514) Remain 01:59:36 loss: 0.3062 Lr: 0.00351 [2024-11-25 17:33:24,731 INFO misc.py line 119 2586773] Train: [13/50][339/376] Data 0.002 (0.002) Batch 0.559 (0.515) Remain 01:59:38 loss: 0.2585 Lr: 0.00351 [2024-11-25 17:33:25,235 INFO misc.py line 119 2586773] Train: [13/50][340/376] Data 0.002 (0.002) Batch 0.504 (0.515) Remain 01:59:37 loss: 0.2755 Lr: 0.00351 [2024-11-25 17:33:25,733 INFO misc.py line 119 2586773] Train: [13/50][341/376] Data 0.002 (0.002) Batch 0.497 (0.515) Remain 01:59:36 loss: 0.2713 Lr: 0.00351 [2024-11-25 17:33:26,233 INFO misc.py line 119 2586773] Train: [13/50][342/376] Data 0.002 (0.002) Batch 0.500 (0.514) Remain 01:59:34 loss: 0.2556 Lr: 0.00351 [2024-11-25 17:33:26,744 INFO misc.py line 119 2586773] Train: [13/50][343/376] Data 0.002 (0.002) Batch 0.511 (0.514) Remain 01:59:34 loss: 0.2247 Lr: 0.00351 [2024-11-25 17:33:27,271 INFO misc.py line 119 2586773] Train: [13/50][344/376] Data 0.002 (0.002) Batch 0.528 (0.515) Remain 01:59:34 loss: 0.2782 Lr: 0.00351 [2024-11-25 17:33:27,756 INFO misc.py line 119 2586773] Train: [13/50][345/376] Data 0.002 (0.002) Batch 0.484 (0.514) Remain 01:59:32 loss: 0.2877 Lr: 0.00351 [2024-11-25 17:33:28,264 INFO misc.py line 119 2586773] Train: [13/50][346/376] Data 0.002 (0.002) Batch 0.508 (0.514) Remain 01:59:31 loss: 0.1988 Lr: 0.00351 [2024-11-25 17:33:28,778 INFO misc.py line 119 2586773] Train: [13/50][347/376] Data 0.002 (0.002) Batch 0.514 (0.514) Remain 01:59:31 loss: 0.2463 Lr: 0.00351 [2024-11-25 17:33:29,321 INFO misc.py line 119 2586773] Train: [13/50][348/376] Data 0.002 (0.002) Batch 0.543 (0.514) Remain 01:59:31 loss: 0.3102 Lr: 0.00351 [2024-11-25 17:33:29,821 INFO misc.py line 119 2586773] Train: [13/50][349/376] Data 0.002 (0.002) Batch 0.500 (0.514) Remain 01:59:30 loss: 0.2924 Lr: 0.00351 [2024-11-25 17:33:30,332 INFO misc.py line 119 2586773] Train: [13/50][350/376] Data 0.002 (0.002) Batch 0.510 (0.514) Remain 01:59:30 loss: 0.2140 Lr: 0.00351 [2024-11-25 17:33:30,833 INFO misc.py line 119 2586773] Train: [13/50][351/376] Data 0.002 (0.002) Batch 0.501 (0.514) Remain 01:59:29 loss: 0.2586 Lr: 0.00351 [2024-11-25 17:33:31,307 INFO misc.py line 119 2586773] Train: [13/50][352/376] Data 0.002 (0.002) Batch 0.474 (0.514) Remain 01:59:26 loss: 0.2300 Lr: 0.00351 [2024-11-25 17:33:31,810 INFO misc.py line 119 2586773] Train: [13/50][353/376] Data 0.002 (0.002) Batch 0.503 (0.514) Remain 01:59:25 loss: 0.2689 Lr: 0.00351 [2024-11-25 17:33:32,296 INFO misc.py line 119 2586773] Train: [13/50][354/376] Data 0.002 (0.002) Batch 0.485 (0.514) Remain 01:59:24 loss: 0.2499 Lr: 0.00351 [2024-11-25 17:33:32,829 INFO misc.py line 119 2586773] Train: [13/50][355/376] Data 0.002 (0.002) Batch 0.534 (0.514) Remain 01:59:24 loss: 0.2491 Lr: 0.00351 [2024-11-25 17:33:33,343 INFO misc.py line 119 2586773] Train: [13/50][356/376] Data 0.002 (0.002) Batch 0.514 (0.514) Remain 01:59:24 loss: 0.2186 Lr: 0.00351 [2024-11-25 17:33:33,858 INFO misc.py line 119 2586773] Train: [13/50][357/376] Data 0.002 (0.002) Batch 0.515 (0.514) Remain 01:59:23 loss: 0.2267 Lr: 0.00351 [2024-11-25 17:33:34,333 INFO misc.py line 119 2586773] Train: [13/50][358/376] Data 0.002 (0.002) Batch 0.475 (0.514) Remain 01:59:21 loss: 0.2792 Lr: 0.00351 [2024-11-25 17:33:34,822 INFO misc.py line 119 2586773] Train: [13/50][359/376] Data 0.002 (0.002) Batch 0.488 (0.514) Remain 01:59:20 loss: 0.2523 Lr: 0.00351 [2024-11-25 17:33:35,308 INFO misc.py line 119 2586773] Train: [13/50][360/376] Data 0.003 (0.002) Batch 0.486 (0.514) Remain 01:59:18 loss: 0.2776 Lr: 0.00351 [2024-11-25 17:33:35,778 INFO misc.py line 119 2586773] Train: [13/50][361/376] Data 0.002 (0.002) Batch 0.471 (0.514) Remain 01:59:16 loss: 0.2439 Lr: 0.00351 [2024-11-25 17:33:36,257 INFO misc.py line 119 2586773] Train: [13/50][362/376] Data 0.002 (0.002) Batch 0.479 (0.514) Remain 01:59:14 loss: 0.2593 Lr: 0.00351 [2024-11-25 17:33:36,710 INFO misc.py line 119 2586773] Train: [13/50][363/376] Data 0.002 (0.002) Batch 0.453 (0.514) Remain 01:59:11 loss: 0.2112 Lr: 0.00351 [2024-11-25 17:33:37,238 INFO misc.py line 119 2586773] Train: [13/50][364/376] Data 0.002 (0.002) Batch 0.528 (0.514) Remain 01:59:11 loss: 0.2915 Lr: 0.00351 [2024-11-25 17:33:37,754 INFO misc.py line 119 2586773] Train: [13/50][365/376] Data 0.002 (0.002) Batch 0.516 (0.514) Remain 01:59:11 loss: 0.2232 Lr: 0.00351 [2024-11-25 17:33:38,229 INFO misc.py line 119 2586773] Train: [13/50][366/376] Data 0.002 (0.002) Batch 0.475 (0.514) Remain 01:59:09 loss: 0.2492 Lr: 0.00351 [2024-11-25 17:33:38,723 INFO misc.py line 119 2586773] Train: [13/50][367/376] Data 0.002 (0.002) Batch 0.494 (0.513) Remain 01:59:07 loss: 0.2476 Lr: 0.00351 [2024-11-25 17:33:39,228 INFO misc.py line 119 2586773] Train: [13/50][368/376] Data 0.003 (0.002) Batch 0.506 (0.513) Remain 01:59:07 loss: 0.2470 Lr: 0.00351 [2024-11-25 17:33:39,753 INFO misc.py line 119 2586773] Train: [13/50][369/376] Data 0.003 (0.002) Batch 0.525 (0.513) Remain 01:59:06 loss: 0.2317 Lr: 0.00351 [2024-11-25 17:33:40,251 INFO misc.py line 119 2586773] Train: [13/50][370/376] Data 0.002 (0.002) Batch 0.498 (0.513) Remain 01:59:05 loss: 0.2273 Lr: 0.00351 [2024-11-25 17:33:40,778 INFO misc.py line 119 2586773] Train: [13/50][371/376] Data 0.002 (0.002) Batch 0.527 (0.513) Remain 01:59:05 loss: 0.2242 Lr: 0.00351 [2024-11-25 17:33:41,246 INFO misc.py line 119 2586773] Train: [13/50][372/376] Data 0.002 (0.002) Batch 0.468 (0.513) Remain 01:59:03 loss: 0.2291 Lr: 0.00351 [2024-11-25 17:33:41,760 INFO misc.py line 119 2586773] Train: [13/50][373/376] Data 0.002 (0.002) Batch 0.514 (0.513) Remain 01:59:03 loss: 0.2139 Lr: 0.00351 [2024-11-25 17:33:42,259 INFO misc.py line 119 2586773] Train: [13/50][374/376] Data 0.002 (0.002) Batch 0.498 (0.513) Remain 01:59:02 loss: 0.1942 Lr: 0.00351 [2024-11-25 17:33:42,770 INFO misc.py line 119 2586773] Train: [13/50][375/376] Data 0.002 (0.002) Batch 0.511 (0.513) Remain 01:59:01 loss: 0.2822 Lr: 0.00351 [2024-11-25 17:33:43,242 INFO misc.py line 119 2586773] Train: [13/50][376/376] Data 0.002 (0.002) Batch 0.472 (0.513) Remain 01:58:59 loss: 0.2279 Lr: 0.00351 [2024-11-25 17:33:43,242 INFO misc.py line 136 2586773] Train result: loss: 0.2496 [2024-11-25 17:33:43,243 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:33:54,130 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2045 [2024-11-25 17:33:54,386 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2461 [2024-11-25 17:33:54,654 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3008 [2024-11-25 17:33:54,886 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2135 [2024-11-25 17:33:55,151 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3149 [2024-11-25 17:33:55,418 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2407 [2024-11-25 17:33:55,650 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2599 [2024-11-25 17:33:55,920 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2491 [2024-11-25 17:33:56,144 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2754 [2024-11-25 17:33:56,404 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3248 [2024-11-25 17:33:56,635 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2468 [2024-11-25 17:33:56,908 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2234 [2024-11-25 17:33:57,172 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2617 [2024-11-25 17:33:57,437 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2699 [2024-11-25 17:33:57,670 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2558 [2024-11-25 17:33:57,909 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2878 [2024-11-25 17:33:58,174 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3162 [2024-11-25 17:33:58,421 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2411 [2024-11-25 17:33:58,653 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2369 [2024-11-25 17:33:58,913 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3071 [2024-11-25 17:33:59,150 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2278 [2024-11-25 17:33:59,416 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3216 [2024-11-25 17:33:59,652 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2379 [2024-11-25 17:33:59,924 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.3167 [2024-11-25 17:34:00,186 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2539 [2024-11-25 17:34:00,422 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2692 [2024-11-25 17:34:00,673 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3051 [2024-11-25 17:34:00,919 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2659 [2024-11-25 17:34:01,185 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3457 [2024-11-25 17:34:01,441 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3495 [2024-11-25 17:34:01,673 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2803 [2024-11-25 17:34:01,937 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2547 [2024-11-25 17:34:02,158 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2498 [2024-11-25 17:34:02,396 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2530 [2024-11-25 17:34:02,656 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2496 [2024-11-25 17:34:02,901 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2435 [2024-11-25 17:34:03,138 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2225 [2024-11-25 17:34:03,410 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2533 [2024-11-25 17:34:03,640 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2832 [2024-11-25 17:34:03,882 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2671 [2024-11-25 17:34:04,157 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3040 [2024-11-25 17:34:04,408 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3456 [2024-11-25 17:34:04,644 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2935 [2024-11-25 17:34:04,877 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2758 [2024-11-25 17:34:05,113 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2538 [2024-11-25 17:34:05,364 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2824 [2024-11-25 17:34:05,623 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2690 [2024-11-25 17:34:05,872 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3300 [2024-11-25 17:34:06,096 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2399 [2024-11-25 17:34:06,330 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2438 [2024-11-25 17:34:06,553 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2773 [2024-11-25 17:34:06,808 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.3136 [2024-11-25 17:34:07,075 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2763 [2024-11-25 17:34:07,337 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2995 [2024-11-25 17:34:07,571 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2634 [2024-11-25 17:34:07,810 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2542 [2024-11-25 17:34:08,067 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3129 [2024-11-25 17:34:08,333 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2470 [2024-11-25 17:34:08,588 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.3003 [2024-11-25 17:34:08,848 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2655 [2024-11-25 17:34:09,101 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2451 [2024-11-25 17:34:09,372 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2638 [2024-11-25 17:34:09,602 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2305 [2024-11-25 17:34:09,859 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3192 [2024-11-25 17:34:10,125 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3134 [2024-11-25 17:34:10,394 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2405 [2024-11-25 17:34:10,637 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2408 [2024-11-25 17:34:10,893 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3172 [2024-11-25 17:34:11,161 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2549 [2024-11-25 17:34:11,424 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3154 [2024-11-25 17:34:11,666 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2197 [2024-11-25 17:34:11,899 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2742 [2024-11-25 17:34:12,155 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2863 [2024-11-25 17:34:12,399 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3075 [2024-11-25 17:34:12,620 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2800 [2024-11-25 17:34:12,844 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2267 [2024-11-25 17:34:13,113 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2903 [2024-11-25 17:34:13,350 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2646 [2024-11-25 17:34:13,610 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2381 [2024-11-25 17:34:13,860 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3254 [2024-11-25 17:34:14,104 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2685 [2024-11-25 17:34:14,369 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2646 [2024-11-25 17:34:14,618 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2069 [2024-11-25 17:34:14,864 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2955 [2024-11-25 17:34:15,133 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2464 [2024-11-25 17:34:15,372 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2839 [2024-11-25 17:34:15,632 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3212 [2024-11-25 17:34:15,891 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.3330 [2024-11-25 17:34:16,138 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2958 [2024-11-25 17:34:16,386 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2609 [2024-11-25 17:34:16,621 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2701 [2024-11-25 17:34:16,874 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2914 [2024-11-25 17:34:17,141 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.3191 [2024-11-25 17:34:17,408 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2630 [2024-11-25 17:34:17,677 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2464 [2024-11-25 17:34:17,925 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2356 [2024-11-25 17:34:18,192 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2977 [2024-11-25 17:34:18,410 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3330 [2024-11-25 17:34:18,684 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2770 [2024-11-25 17:34:18,920 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2797 [2024-11-25 17:34:19,189 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2387 [2024-11-25 17:34:19,449 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3501 [2024-11-25 17:34:19,710 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3239 [2024-11-25 17:34:19,965 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3271 [2024-11-25 17:34:20,188 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2767 [2024-11-25 17:34:20,424 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2603 [2024-11-25 17:34:20,678 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2400 [2024-11-25 17:34:20,947 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2963 [2024-11-25 17:34:21,181 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3283 [2024-11-25 17:34:21,441 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2856 [2024-11-25 17:34:21,703 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2455 [2024-11-25 17:34:21,927 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2505 [2024-11-25 17:34:22,162 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2368 [2024-11-25 17:34:22,380 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2564 [2024-11-25 17:34:22,606 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2363 [2024-11-25 17:34:22,875 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3668 [2024-11-25 17:34:23,134 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3030 [2024-11-25 17:34:23,406 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2833 [2024-11-25 17:34:23,671 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2520 [2024-11-25 17:34:23,932 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.4103 [2024-11-25 17:34:24,193 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3041 [2024-11-25 17:34:24,459 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2265 [2024-11-25 17:34:24,713 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.3184 [2024-11-25 17:34:24,976 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3196 [2024-11-25 17:34:25,241 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2876 [2024-11-25 17:34:25,491 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3469 [2024-11-25 17:34:25,721 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2364 [2024-11-25 17:34:25,980 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3018 [2024-11-25 17:34:26,215 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2750 [2024-11-25 17:34:26,442 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2047 [2024-11-25 17:34:26,653 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2795 [2024-11-25 17:34:26,870 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2094 [2024-11-25 17:34:27,551 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7387/0.8040/0.9957. [2024-11-25 17:34:27,552 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9982 [2024-11-25 17:34:27,552 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4818/0.6097 [2024-11-25 17:34:27,552 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:34:27,553 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7469 [2024-11-25 17:34:27,553 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:34:30,263 INFO misc.py line 119 2586773] Train: [14/50][1/376] Data 0.082 (0.082) Batch 0.569 (0.569) Remain 02:11:55 loss: 0.2117 Lr: 0.00350 [2024-11-25 17:34:30,798 INFO misc.py line 119 2586773] Train: [14/50][2/376] Data 0.003 (0.003) Batch 0.535 (0.535) Remain 02:04:06 loss: 0.2503 Lr: 0.00350 [2024-11-25 17:34:31,307 INFO misc.py line 119 2586773] Train: [14/50][3/376] Data 0.002 (0.002) Batch 0.509 (0.509) Remain 01:57:57 loss: 0.2277 Lr: 0.00350 [2024-11-25 17:34:31,863 INFO misc.py line 119 2586773] Train: [14/50][4/376] Data 0.002 (0.002) Batch 0.556 (0.556) Remain 02:08:52 loss: 0.2821 Lr: 0.00350 [2024-11-25 17:34:32,371 INFO misc.py line 119 2586773] Train: [14/50][5/376] Data 0.003 (0.002) Batch 0.508 (0.532) Remain 02:03:20 loss: 0.2203 Lr: 0.00350 [2024-11-25 17:34:32,842 INFO misc.py line 119 2586773] Train: [14/50][6/376] Data 0.002 (0.002) Batch 0.470 (0.512) Remain 01:58:33 loss: 0.2226 Lr: 0.00350 [2024-11-25 17:34:33,352 INFO misc.py line 119 2586773] Train: [14/50][7/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 01:58:27 loss: 0.2247 Lr: 0.00350 [2024-11-25 17:34:33,829 INFO misc.py line 119 2586773] Train: [14/50][8/376] Data 0.002 (0.002) Batch 0.478 (0.504) Remain 01:56:54 loss: 0.3071 Lr: 0.00350 [2024-11-25 17:34:34,347 INFO misc.py line 119 2586773] Train: [14/50][9/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:57:23 loss: 0.2655 Lr: 0.00350 [2024-11-25 17:34:34,860 INFO misc.py line 119 2586773] Train: [14/50][10/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 01:57:36 loss: 0.2499 Lr: 0.00350 [2024-11-25 17:34:35,339 INFO misc.py line 119 2586773] Train: [14/50][11/376] Data 0.002 (0.002) Batch 0.478 (0.504) Remain 01:56:45 loss: 0.2243 Lr: 0.00350 [2024-11-25 17:34:35,815 INFO misc.py line 119 2586773] Train: [14/50][12/376] Data 0.003 (0.002) Batch 0.476 (0.501) Remain 01:56:02 loss: 0.2343 Lr: 0.00350 [2024-11-25 17:34:36,279 INFO misc.py line 119 2586773] Train: [14/50][13/376] Data 0.002 (0.002) Batch 0.464 (0.497) Remain 01:55:10 loss: 0.2130 Lr: 0.00350 [2024-11-25 17:34:36,773 INFO misc.py line 119 2586773] Train: [14/50][14/376] Data 0.002 (0.002) Batch 0.494 (0.497) Remain 01:55:06 loss: 0.2330 Lr: 0.00350 [2024-11-25 17:34:37,291 INFO misc.py line 119 2586773] Train: [14/50][15/376] Data 0.002 (0.002) Batch 0.518 (0.499) Remain 01:55:29 loss: 0.2423 Lr: 0.00350 [2024-11-25 17:34:37,818 INFO misc.py line 119 2586773] Train: [14/50][16/376] Data 0.002 (0.002) Batch 0.528 (0.501) Remain 01:56:00 loss: 0.2323 Lr: 0.00350 [2024-11-25 17:34:38,372 INFO misc.py line 119 2586773] Train: [14/50][17/376] Data 0.002 (0.002) Batch 0.554 (0.505) Remain 01:56:52 loss: 0.2291 Lr: 0.00350 [2024-11-25 17:34:38,901 INFO misc.py line 119 2586773] Train: [14/50][18/376] Data 0.002 (0.002) Batch 0.529 (0.506) Remain 01:57:14 loss: 0.2957 Lr: 0.00350 [2024-11-25 17:34:39,384 INFO misc.py line 119 2586773] Train: [14/50][19/376] Data 0.003 (0.002) Batch 0.483 (0.505) Remain 01:56:53 loss: 0.2779 Lr: 0.00350 [2024-11-25 17:34:39,884 INFO misc.py line 119 2586773] Train: [14/50][20/376] Data 0.002 (0.002) Batch 0.500 (0.505) Remain 01:56:48 loss: 0.2129 Lr: 0.00350 [2024-11-25 17:34:40,429 INFO misc.py line 119 2586773] Train: [14/50][21/376] Data 0.002 (0.002) Batch 0.545 (0.507) Remain 01:57:19 loss: 0.2863 Lr: 0.00350 [2024-11-25 17:34:40,917 INFO misc.py line 119 2586773] Train: [14/50][22/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 01:57:05 loss: 0.2179 Lr: 0.00350 [2024-11-25 17:34:41,412 INFO misc.py line 119 2586773] Train: [14/50][23/376] Data 0.003 (0.002) Batch 0.495 (0.505) Remain 01:56:57 loss: 0.2553 Lr: 0.00350 [2024-11-25 17:34:41,891 INFO misc.py line 119 2586773] Train: [14/50][24/376] Data 0.002 (0.002) Batch 0.479 (0.504) Remain 01:56:39 loss: 0.2386 Lr: 0.00350 [2024-11-25 17:34:42,373 INFO misc.py line 119 2586773] Train: [14/50][25/376] Data 0.002 (0.002) Batch 0.482 (0.503) Remain 01:56:25 loss: 0.2625 Lr: 0.00350 [2024-11-25 17:34:42,902 INFO misc.py line 119 2586773] Train: [14/50][26/376] Data 0.003 (0.002) Batch 0.529 (0.504) Remain 01:56:40 loss: 0.2028 Lr: 0.00350 [2024-11-25 17:34:43,398 INFO misc.py line 119 2586773] Train: [14/50][27/376] Data 0.003 (0.002) Batch 0.496 (0.504) Remain 01:56:35 loss: 0.2526 Lr: 0.00350 [2024-11-25 17:34:43,915 INFO misc.py line 119 2586773] Train: [14/50][28/376] Data 0.003 (0.002) Batch 0.516 (0.504) Remain 01:56:41 loss: 0.2665 Lr: 0.00350 [2024-11-25 17:34:44,466 INFO misc.py line 119 2586773] Train: [14/50][29/376] Data 0.003 (0.002) Batch 0.551 (0.506) Remain 01:57:06 loss: 0.2270 Lr: 0.00350 [2024-11-25 17:34:45,001 INFO misc.py line 119 2586773] Train: [14/50][30/376] Data 0.003 (0.002) Batch 0.535 (0.507) Remain 01:57:20 loss: 0.2670 Lr: 0.00350 [2024-11-25 17:34:45,521 INFO misc.py line 119 2586773] Train: [14/50][31/376] Data 0.003 (0.002) Batch 0.520 (0.508) Remain 01:57:26 loss: 0.2641 Lr: 0.00350 [2024-11-25 17:34:46,025 INFO misc.py line 119 2586773] Train: [14/50][32/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 01:57:24 loss: 0.2191 Lr: 0.00350 [2024-11-25 17:34:46,527 INFO misc.py line 119 2586773] Train: [14/50][33/376] Data 0.003 (0.002) Batch 0.502 (0.507) Remain 01:57:21 loss: 0.2895 Lr: 0.00350 [2024-11-25 17:34:47,000 INFO misc.py line 119 2586773] Train: [14/50][34/376] Data 0.003 (0.002) Batch 0.472 (0.506) Remain 01:57:05 loss: 0.2651 Lr: 0.00350 [2024-11-25 17:34:47,533 INFO misc.py line 119 2586773] Train: [14/50][35/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 01:57:16 loss: 0.2222 Lr: 0.00350 [2024-11-25 17:34:48,046 INFO misc.py line 119 2586773] Train: [14/50][36/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 01:57:18 loss: 0.2316 Lr: 0.00350 [2024-11-25 17:34:48,584 INFO misc.py line 119 2586773] Train: [14/50][37/376] Data 0.003 (0.002) Batch 0.538 (0.508) Remain 01:57:30 loss: 0.2127 Lr: 0.00350 [2024-11-25 17:34:49,098 INFO misc.py line 119 2586773] Train: [14/50][38/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 01:57:32 loss: 0.2209 Lr: 0.00350 [2024-11-25 17:34:49,590 INFO misc.py line 119 2586773] Train: [14/50][39/376] Data 0.003 (0.002) Batch 0.493 (0.508) Remain 01:57:25 loss: 0.2078 Lr: 0.00350 [2024-11-25 17:34:50,078 INFO misc.py line 119 2586773] Train: [14/50][40/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:57:17 loss: 0.2192 Lr: 0.00350 [2024-11-25 17:34:50,538 INFO misc.py line 119 2586773] Train: [14/50][41/376] Data 0.003 (0.002) Batch 0.460 (0.506) Remain 01:56:59 loss: 0.2427 Lr: 0.00350 [2024-11-25 17:34:51,044 INFO misc.py line 119 2586773] Train: [14/50][42/376] Data 0.003 (0.002) Batch 0.505 (0.506) Remain 01:56:59 loss: 0.2661 Lr: 0.00350 [2024-11-25 17:34:51,542 INFO misc.py line 119 2586773] Train: [14/50][43/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 01:56:55 loss: 0.2328 Lr: 0.00350 [2024-11-25 17:34:52,037 INFO misc.py line 119 2586773] Train: [14/50][44/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 01:56:51 loss: 0.2220 Lr: 0.00349 [2024-11-25 17:34:52,563 INFO misc.py line 119 2586773] Train: [14/50][45/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 01:56:57 loss: 0.3000 Lr: 0.00349 [2024-11-25 17:34:53,058 INFO misc.py line 119 2586773] Train: [14/50][46/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 01:56:53 loss: 0.2639 Lr: 0.00349 [2024-11-25 17:34:53,523 INFO misc.py line 119 2586773] Train: [14/50][47/376] Data 0.002 (0.002) Batch 0.465 (0.505) Remain 01:56:40 loss: 0.2581 Lr: 0.00349 [2024-11-25 17:34:54,040 INFO misc.py line 119 2586773] Train: [14/50][48/376] Data 0.002 (0.002) Batch 0.518 (0.505) Remain 01:56:43 loss: 0.2267 Lr: 0.00349 [2024-11-25 17:34:54,534 INFO misc.py line 119 2586773] Train: [14/50][49/376] Data 0.002 (0.002) Batch 0.494 (0.505) Remain 01:56:39 loss: 0.2466 Lr: 0.00349 [2024-11-25 17:34:55,062 INFO misc.py line 119 2586773] Train: [14/50][50/376] Data 0.002 (0.002) Batch 0.528 (0.505) Remain 01:56:46 loss: 0.2310 Lr: 0.00349 [2024-11-25 17:34:55,545 INFO misc.py line 119 2586773] Train: [14/50][51/376] Data 0.003 (0.002) Batch 0.483 (0.505) Remain 01:56:39 loss: 0.2328 Lr: 0.00349 [2024-11-25 17:34:56,049 INFO misc.py line 119 2586773] Train: [14/50][52/376] Data 0.002 (0.002) Batch 0.504 (0.505) Remain 01:56:38 loss: 0.2161 Lr: 0.00349 [2024-11-25 17:34:56,521 INFO misc.py line 119 2586773] Train: [14/50][53/376] Data 0.002 (0.002) Batch 0.473 (0.504) Remain 01:56:28 loss: 0.2310 Lr: 0.00349 [2024-11-25 17:34:57,012 INFO misc.py line 119 2586773] Train: [14/50][54/376] Data 0.002 (0.002) Batch 0.491 (0.504) Remain 01:56:24 loss: 0.3153 Lr: 0.00349 [2024-11-25 17:34:57,529 INFO misc.py line 119 2586773] Train: [14/50][55/376] Data 0.002 (0.002) Batch 0.516 (0.504) Remain 01:56:27 loss: 0.2133 Lr: 0.00349 [2024-11-25 17:34:58,033 INFO misc.py line 119 2586773] Train: [14/50][56/376] Data 0.002 (0.002) Batch 0.504 (0.504) Remain 01:56:27 loss: 0.1962 Lr: 0.00349 [2024-11-25 17:34:58,547 INFO misc.py line 119 2586773] Train: [14/50][57/376] Data 0.002 (0.002) Batch 0.514 (0.504) Remain 01:56:29 loss: 0.3093 Lr: 0.00349 [2024-11-25 17:34:59,048 INFO misc.py line 119 2586773] Train: [14/50][58/376] Data 0.002 (0.002) Batch 0.501 (0.504) Remain 01:56:27 loss: 0.2514 Lr: 0.00349 [2024-11-25 17:34:59,551 INFO misc.py line 119 2586773] Train: [14/50][59/376] Data 0.002 (0.002) Batch 0.503 (0.504) Remain 01:56:26 loss: 0.2974 Lr: 0.00349 [2024-11-25 17:35:00,098 INFO misc.py line 119 2586773] Train: [14/50][60/376] Data 0.002 (0.002) Batch 0.547 (0.505) Remain 01:56:36 loss: 0.2503 Lr: 0.00349 [2024-11-25 17:35:00,598 INFO misc.py line 119 2586773] Train: [14/50][61/376] Data 0.002 (0.002) Batch 0.499 (0.505) Remain 01:56:34 loss: 0.2154 Lr: 0.00349 [2024-11-25 17:35:01,138 INFO misc.py line 119 2586773] Train: [14/50][62/376] Data 0.002 (0.002) Batch 0.539 (0.506) Remain 01:56:42 loss: 0.2239 Lr: 0.00349 [2024-11-25 17:35:01,620 INFO misc.py line 119 2586773] Train: [14/50][63/376] Data 0.003 (0.002) Batch 0.483 (0.505) Remain 01:56:36 loss: 0.2198 Lr: 0.00349 [2024-11-25 17:35:02,106 INFO misc.py line 119 2586773] Train: [14/50][64/376] Data 0.002 (0.002) Batch 0.486 (0.505) Remain 01:56:31 loss: 0.2916 Lr: 0.00349 [2024-11-25 17:35:02,580 INFO misc.py line 119 2586773] Train: [14/50][65/376] Data 0.003 (0.002) Batch 0.474 (0.504) Remain 01:56:24 loss: 0.2514 Lr: 0.00349 [2024-11-25 17:35:03,064 INFO misc.py line 119 2586773] Train: [14/50][66/376] Data 0.002 (0.002) Batch 0.484 (0.504) Remain 01:56:19 loss: 0.2196 Lr: 0.00349 [2024-11-25 17:35:03,597 INFO misc.py line 119 2586773] Train: [14/50][67/376] Data 0.003 (0.002) Batch 0.532 (0.505) Remain 01:56:25 loss: 0.2798 Lr: 0.00349 [2024-11-25 17:35:04,100 INFO misc.py line 119 2586773] Train: [14/50][68/376] Data 0.002 (0.002) Batch 0.503 (0.505) Remain 01:56:24 loss: 0.2560 Lr: 0.00349 [2024-11-25 17:35:04,573 INFO misc.py line 119 2586773] Train: [14/50][69/376] Data 0.003 (0.002) Batch 0.473 (0.504) Remain 01:56:17 loss: 0.2566 Lr: 0.00349 [2024-11-25 17:35:05,084 INFO misc.py line 119 2586773] Train: [14/50][70/376] Data 0.002 (0.002) Batch 0.510 (0.504) Remain 01:56:18 loss: 0.2369 Lr: 0.00349 [2024-11-25 17:35:05,610 INFO misc.py line 119 2586773] Train: [14/50][71/376] Data 0.003 (0.002) Batch 0.527 (0.504) Remain 01:56:22 loss: 0.2302 Lr: 0.00349 [2024-11-25 17:35:06,103 INFO misc.py line 119 2586773] Train: [14/50][72/376] Data 0.003 (0.002) Batch 0.492 (0.504) Remain 01:56:19 loss: 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Batch 0.489 (0.508) Remain 01:54:57 loss: 0.2222 Lr: 0.00342 [2024-11-25 17:37:23,095 INFO misc.py line 119 2586773] Train: [14/50][341/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 01:54:57 loss: 0.2530 Lr: 0.00342 [2024-11-25 17:37:23,586 INFO misc.py line 119 2586773] Train: [14/50][342/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 01:54:56 loss: 0.2424 Lr: 0.00342 [2024-11-25 17:37:24,064 INFO misc.py line 119 2586773] Train: [14/50][343/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 01:54:54 loss: 0.2897 Lr: 0.00342 [2024-11-25 17:37:24,561 INFO misc.py line 119 2586773] Train: [14/50][344/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 01:54:53 loss: 0.2180 Lr: 0.00342 [2024-11-25 17:37:25,056 INFO misc.py line 119 2586773] Train: [14/50][345/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 01:54:52 loss: 0.2384 Lr: 0.00342 [2024-11-25 17:37:25,561 INFO misc.py line 119 2586773] Train: [14/50][346/376] Data 0.003 (0.002) Batch 0.505 (0.508) Remain 01:54:51 loss: 0.2643 Lr: 0.00342 [2024-11-25 17:37:26,067 INFO misc.py line 119 2586773] Train: [14/50][347/376] Data 0.002 (0.002) Batch 0.506 (0.508) Remain 01:54:51 loss: 0.2433 Lr: 0.00342 [2024-11-25 17:37:26,571 INFO misc.py line 119 2586773] Train: [14/50][348/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 01:54:50 loss: 0.2742 Lr: 0.00342 [2024-11-25 17:37:27,046 INFO misc.py line 119 2586773] Train: [14/50][349/376] Data 0.002 (0.002) Batch 0.475 (0.508) Remain 01:54:48 loss: 0.2865 Lr: 0.00342 [2024-11-25 17:37:27,527 INFO misc.py line 119 2586773] Train: [14/50][350/376] Data 0.002 (0.002) Batch 0.481 (0.508) Remain 01:54:47 loss: 0.2177 Lr: 0.00342 [2024-11-25 17:37:28,035 INFO misc.py line 119 2586773] Train: [14/50][351/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 01:54:46 loss: 0.2653 Lr: 0.00342 [2024-11-25 17:37:28,525 INFO misc.py line 119 2586773] Train: [14/50][352/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 01:54:45 loss: 0.2396 Lr: 0.00342 [2024-11-25 17:37:29,053 INFO misc.py line 119 2586773] Train: [14/50][353/376] Data 0.002 (0.002) Batch 0.528 (0.508) Remain 01:54:45 loss: 0.2872 Lr: 0.00342 [2024-11-25 17:37:29,564 INFO misc.py line 119 2586773] Train: [14/50][354/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 01:54:45 loss: 0.2518 Lr: 0.00342 [2024-11-25 17:37:30,099 INFO misc.py line 119 2586773] Train: [14/50][355/376] Data 0.003 (0.002) Batch 0.534 (0.508) Remain 01:54:45 loss: 0.2237 Lr: 0.00342 [2024-11-25 17:37:30,578 INFO misc.py line 119 2586773] Train: [14/50][356/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 01:54:44 loss: 0.2600 Lr: 0.00342 [2024-11-25 17:37:31,053 INFO misc.py line 119 2586773] Train: [14/50][357/376] Data 0.002 (0.002) Batch 0.475 (0.508) Remain 01:54:42 loss: 0.2031 Lr: 0.00342 [2024-11-25 17:37:31,510 INFO misc.py line 119 2586773] Train: [14/50][358/376] Data 0.002 (0.002) Batch 0.457 (0.508) Remain 01:54:40 loss: 0.2578 Lr: 0.00342 [2024-11-25 17:37:32,067 INFO misc.py line 119 2586773] Train: [14/50][359/376] Data 0.003 (0.002) Batch 0.557 (0.508) Remain 01:54:41 loss: 0.2898 Lr: 0.00342 [2024-11-25 17:37:32,587 INFO misc.py line 119 2586773] Train: [14/50][360/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 01:54:41 loss: 0.2031 Lr: 0.00342 [2024-11-25 17:37:33,120 INFO misc.py line 119 2586773] Train: [14/50][361/376] Data 0.002 (0.002) Batch 0.533 (0.508) Remain 01:54:41 loss: 0.3024 Lr: 0.00342 [2024-11-25 17:37:33,643 INFO misc.py line 119 2586773] Train: [14/50][362/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 01:54:42 loss: 0.2077 Lr: 0.00342 [2024-11-25 17:37:34,130 INFO misc.py line 119 2586773] Train: [14/50][363/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 01:54:40 loss: 0.2432 Lr: 0.00342 [2024-11-25 17:37:34,608 INFO misc.py line 119 2586773] Train: [14/50][364/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 01:54:39 loss: 0.1973 Lr: 0.00342 [2024-11-25 17:37:35,113 INFO misc.py line 119 2586773] Train: [14/50][365/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 01:54:38 loss: 0.2473 Lr: 0.00342 [2024-11-25 17:37:35,587 INFO misc.py line 119 2586773] Train: [14/50][366/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 01:54:36 loss: 0.2027 Lr: 0.00342 [2024-11-25 17:37:36,121 INFO misc.py line 119 2586773] Train: [14/50][367/376] Data 0.002 (0.002) Batch 0.533 (0.508) Remain 01:54:37 loss: 0.2201 Lr: 0.00342 [2024-11-25 17:37:36,617 INFO misc.py line 119 2586773] Train: [14/50][368/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 01:54:36 loss: 0.2660 Lr: 0.00342 [2024-11-25 17:37:37,119 INFO misc.py line 119 2586773] Train: [14/50][369/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 01:54:35 loss: 0.2312 Lr: 0.00342 [2024-11-25 17:37:37,656 INFO misc.py line 119 2586773] Train: [14/50][370/376] Data 0.002 (0.002) Batch 0.537 (0.508) Remain 01:54:36 loss: 0.2850 Lr: 0.00342 [2024-11-25 17:37:38,177 INFO misc.py line 119 2586773] Train: [14/50][371/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 01:54:36 loss: 0.3302 Lr: 0.00342 [2024-11-25 17:37:38,695 INFO misc.py line 119 2586773] Train: [14/50][372/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 01:54:35 loss: 0.2291 Lr: 0.00342 [2024-11-25 17:37:39,206 INFO misc.py line 119 2586773] Train: [14/50][373/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 01:54:35 loss: 0.2161 Lr: 0.00342 [2024-11-25 17:37:39,694 INFO misc.py line 119 2586773] Train: [14/50][374/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 01:54:34 loss: 0.2203 Lr: 0.00342 [2024-11-25 17:37:40,204 INFO misc.py line 119 2586773] Train: [14/50][375/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 01:54:33 loss: 0.2127 Lr: 0.00342 [2024-11-25 17:37:40,736 INFO misc.py line 119 2586773] Train: [14/50][376/376] Data 0.002 (0.002) Batch 0.532 (0.508) Remain 01:54:34 loss: 0.2918 Lr: 0.00342 [2024-11-25 17:37:40,737 INFO misc.py line 136 2586773] Train result: loss: 0.2439 [2024-11-25 17:37:40,737 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:37:51,747 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2317 [2024-11-25 17:37:52,003 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2360 [2024-11-25 17:37:52,265 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2994 [2024-11-25 17:37:52,489 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2375 [2024-11-25 17:37:52,755 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3273 [2024-11-25 17:37:53,023 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2332 [2024-11-25 17:37:53,247 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2457 [2024-11-25 17:37:53,517 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2419 [2024-11-25 17:37:53,743 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2996 [2024-11-25 17:37:54,003 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2979 [2024-11-25 17:37:54,233 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2538 [2024-11-25 17:37:54,505 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2701 [2024-11-25 17:37:54,769 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2877 [2024-11-25 17:37:55,031 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2748 [2024-11-25 17:37:55,264 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2691 [2024-11-25 17:37:55,501 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3219 [2024-11-25 17:37:55,767 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3198 [2024-11-25 17:37:56,019 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2566 [2024-11-25 17:37:56,251 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2658 [2024-11-25 17:37:56,510 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.3203 [2024-11-25 17:37:56,747 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2764 [2024-11-25 17:37:57,013 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2941 [2024-11-25 17:37:57,249 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2391 [2024-11-25 17:37:57,515 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2824 [2024-11-25 17:37:57,776 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2470 [2024-11-25 17:37:58,014 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2906 [2024-11-25 17:37:58,269 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3107 [2024-11-25 17:37:58,518 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2812 [2024-11-25 17:37:58,785 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3092 [2024-11-25 17:37:59,039 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3252 [2024-11-25 17:37:59,271 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2787 [2024-11-25 17:37:59,535 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2481 [2024-11-25 17:37:59,753 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2881 [2024-11-25 17:37:59,996 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2534 [2024-11-25 17:38:00,255 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2339 [2024-11-25 17:38:00,502 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2759 [2024-11-25 17:38:00,730 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2198 [2024-11-25 17:38:01,007 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2946 [2024-11-25 17:38:01,239 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2707 [2024-11-25 17:38:01,473 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2741 [2024-11-25 17:38:01,744 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2929 [2024-11-25 17:38:01,993 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3124 [2024-11-25 17:38:02,231 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2612 [2024-11-25 17:38:02,464 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2615 [2024-11-25 17:38:02,699 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2599 [2024-11-25 17:38:02,952 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2642 [2024-11-25 17:38:03,210 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2814 [2024-11-25 17:38:03,461 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3127 [2024-11-25 17:38:03,685 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2359 [2024-11-25 17:38:03,918 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2374 [2024-11-25 17:38:04,139 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2558 [2024-11-25 17:38:04,393 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2696 [2024-11-25 17:38:04,659 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2486 [2024-11-25 17:38:04,919 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3321 [2024-11-25 17:38:05,153 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2607 [2024-11-25 17:38:05,394 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2750 [2024-11-25 17:38:05,651 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3226 [2024-11-25 17:38:05,919 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2923 [2024-11-25 17:38:06,173 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2745 [2024-11-25 17:38:06,435 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2912 [2024-11-25 17:38:06,687 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2484 [2024-11-25 17:38:06,957 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2995 [2024-11-25 17:38:07,189 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2754 [2024-11-25 17:38:07,449 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2876 [2024-11-25 17:38:07,716 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3028 [2024-11-25 17:38:07,986 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2649 [2024-11-25 17:38:08,230 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2515 [2024-11-25 17:38:08,486 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3026 [2024-11-25 17:38:08,756 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2773 [2024-11-25 17:38:09,018 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2934 [2024-11-25 17:38:09,264 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2357 [2024-11-25 17:38:09,499 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2931 [2024-11-25 17:38:09,757 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2812 [2024-11-25 17:38:10,007 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.3078 [2024-11-25 17:38:10,225 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2809 [2024-11-25 17:38:10,446 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2115 [2024-11-25 17:38:10,716 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2625 [2024-11-25 17:38:10,954 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2318 [2024-11-25 17:38:11,216 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2499 [2024-11-25 17:38:11,472 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3092 [2024-11-25 17:38:11,713 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2490 [2024-11-25 17:38:11,975 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2926 [2024-11-25 17:38:12,223 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2282 [2024-11-25 17:38:12,470 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2820 [2024-11-25 17:38:12,741 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2702 [2024-11-25 17:38:12,980 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2896 [2024-11-25 17:38:13,247 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3091 [2024-11-25 17:38:13,508 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2775 [2024-11-25 17:38:13,763 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3071 [2024-11-25 17:38:14,009 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.3013 [2024-11-25 17:38:14,241 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2621 [2024-11-25 17:38:14,492 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2868 [2024-11-25 17:38:14,757 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2746 [2024-11-25 17:38:15,024 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2529 [2024-11-25 17:38:15,292 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2460 [2024-11-25 17:38:15,539 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2355 [2024-11-25 17:38:15,808 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2996 [2024-11-25 17:38:16,027 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3194 [2024-11-25 17:38:16,299 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2630 [2024-11-25 17:38:16,538 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2815 [2024-11-25 17:38:16,810 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2389 [2024-11-25 17:38:17,071 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2996 [2024-11-25 17:38:17,329 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2942 [2024-11-25 17:38:17,584 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3280 [2024-11-25 17:38:17,813 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2775 [2024-11-25 17:38:18,049 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2601 [2024-11-25 17:38:18,305 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2602 [2024-11-25 17:38:18,574 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2881 [2024-11-25 17:38:18,807 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3006 [2024-11-25 17:38:19,068 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2509 [2024-11-25 17:38:19,330 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2777 [2024-11-25 17:38:19,551 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2525 [2024-11-25 17:38:19,792 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2257 [2024-11-25 17:38:20,011 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2650 [2024-11-25 17:38:20,238 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2500 [2024-11-25 17:38:20,510 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3183 [2024-11-25 17:38:20,770 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2962 [2024-11-25 17:38:21,036 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2894 [2024-11-25 17:38:21,299 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2503 [2024-11-25 17:38:21,563 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3421 [2024-11-25 17:38:21,821 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2707 [2024-11-25 17:38:22,087 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2715 [2024-11-25 17:38:22,341 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2933 [2024-11-25 17:38:22,605 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3029 [2024-11-25 17:38:22,867 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2755 [2024-11-25 17:38:23,119 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3076 [2024-11-25 17:38:23,351 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2438 [2024-11-25 17:38:23,611 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3142 [2024-11-25 17:38:23,848 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2512 [2024-11-25 17:38:24,075 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2132 [2024-11-25 17:38:24,289 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2355 [2024-11-25 17:38:24,507 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2306 [2024-11-25 17:38:25,156 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7437/0.8168/0.9957. [2024-11-25 17:38:25,156 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9981 [2024-11-25 17:38:25,156 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4918/0.6355 [2024-11-25 17:38:25,157 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:38:25,157 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7469 [2024-11-25 17:38:25,158 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:38:28,002 INFO misc.py line 119 2586773] Train: [15/50][1/376] Data 0.096 (0.096) Batch 0.566 (0.566) Remain 02:07:35 loss: 0.2336 Lr: 0.00342 [2024-11-25 17:38:28,486 INFO misc.py line 119 2586773] Train: [15/50][2/376] Data 0.003 (0.003) Batch 0.484 (0.484) Remain 01:49:13 loss: 0.2469 Lr: 0.00342 [2024-11-25 17:38:28,957 INFO misc.py line 119 2586773] Train: [15/50][3/376] Data 0.002 (0.002) Batch 0.471 (0.471) Remain 01:46:15 loss: 0.2493 Lr: 0.00342 [2024-11-25 17:38:29,499 INFO misc.py line 119 2586773] Train: [15/50][4/376] Data 0.003 (0.003) Batch 0.541 (0.541) Remain 02:02:04 loss: 0.2273 Lr: 0.00341 [2024-11-25 17:38:30,002 INFO misc.py line 119 2586773] Train: [15/50][5/376] Data 0.002 (0.002) Batch 0.504 (0.523) Remain 01:57:50 loss: 0.2254 Lr: 0.00341 [2024-11-25 17:38:30,482 INFO misc.py line 119 2586773] Train: [15/50][6/376] Data 0.003 (0.002) Batch 0.480 (0.508) Remain 01:54:37 loss: 0.2116 Lr: 0.00341 [2024-11-25 17:38:30,974 INFO misc.py line 119 2586773] Train: [15/50][7/376] Data 0.002 (0.002) Batch 0.492 (0.504) Remain 01:53:42 loss: 0.2127 Lr: 0.00341 [2024-11-25 17:38:31,474 INFO misc.py line 119 2586773] Train: [15/50][8/376] Data 0.002 (0.002) Batch 0.500 (0.503) Remain 01:53:30 loss: 0.2397 Lr: 0.00341 [2024-11-25 17:38:32,007 INFO misc.py line 119 2586773] Train: [15/50][9/376] Data 0.003 (0.002) Batch 0.532 (0.508) Remain 01:54:34 loss: 0.2104 Lr: 0.00341 [2024-11-25 17:38:32,494 INFO misc.py line 119 2586773] Train: [15/50][10/376] Data 0.003 (0.002) Batch 0.488 (0.505) Remain 01:53:54 loss: 0.2312 Lr: 0.00341 [2024-11-25 17:38:33,025 INFO misc.py line 119 2586773] Train: [15/50][11/376] Data 0.003 (0.002) Batch 0.531 (0.509) Remain 01:54:37 loss: 0.2627 Lr: 0.00341 [2024-11-25 17:38:33,551 INFO misc.py line 119 2586773] Train: [15/50][12/376] Data 0.003 (0.003) Batch 0.526 (0.510) Remain 01:55:03 loss: 0.2299 Lr: 0.00341 [2024-11-25 17:38:34,024 INFO misc.py line 119 2586773] Train: [15/50][13/376] Data 0.002 (0.002) Batch 0.472 (0.507) Remain 01:54:11 loss: 0.2757 Lr: 0.00341 [2024-11-25 17:38:34,550 INFO misc.py line 119 2586773] Train: [15/50][14/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 01:54:35 loss: 0.2363 Lr: 0.00341 [2024-11-25 17:38:35,027 INFO misc.py line 119 2586773] Train: [15/50][15/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 01:53:59 loss: 0.2862 Lr: 0.00341 [2024-11-25 17:38:35,502 INFO misc.py line 119 2586773] Train: [15/50][16/376] Data 0.002 (0.002) Batch 0.475 (0.503) Remain 01:53:26 loss: 0.2902 Lr: 0.00341 [2024-11-25 17:38:35,996 INFO misc.py line 119 2586773] Train: [15/50][17/376] Data 0.002 (0.002) Batch 0.494 (0.503) Remain 01:53:17 loss: 0.1950 Lr: 0.00341 [2024-11-25 17:38:36,504 INFO misc.py line 119 2586773] Train: [15/50][18/376] Data 0.002 (0.002) Batch 0.508 (0.503) Remain 01:53:21 loss: 0.2101 Lr: 0.00341 [2024-11-25 17:38:37,055 INFO misc.py line 119 2586773] Train: [15/50][19/376] Data 0.002 (0.002) Batch 0.550 (0.506) Remain 01:54:01 loss: 0.2572 Lr: 0.00341 [2024-11-25 17:38:37,555 INFO misc.py line 119 2586773] Train: [15/50][20/376] Data 0.003 (0.002) Batch 0.500 (0.506) Remain 01:53:55 loss: 0.2110 Lr: 0.00341 [2024-11-25 17:38:38,041 INFO misc.py line 119 2586773] Train: [15/50][21/376] Data 0.002 (0.002) Batch 0.486 (0.505) Remain 01:53:40 loss: 0.2311 Lr: 0.00341 [2024-11-25 17:38:38,532 INFO misc.py line 119 2586773] Train: [15/50][22/376] Data 0.003 (0.002) Batch 0.491 (0.504) Remain 01:53:29 loss: 0.2152 Lr: 0.00341 [2024-11-25 17:38:39,035 INFO misc.py line 119 2586773] Train: [15/50][23/376] Data 0.002 (0.002) Batch 0.503 (0.504) Remain 01:53:28 loss: 0.2641 Lr: 0.00341 [2024-11-25 17:38:39,562 INFO misc.py line 119 2586773] Train: [15/50][24/376] Data 0.003 (0.002) Batch 0.528 (0.505) Remain 01:53:43 loss: 0.2432 Lr: 0.00341 [2024-11-25 17:38:40,044 INFO misc.py line 119 2586773] Train: [15/50][25/376] Data 0.003 (0.002) Batch 0.482 (0.504) Remain 01:53:28 loss: 0.2726 Lr: 0.00341 [2024-11-25 17:38:40,600 INFO misc.py line 119 2586773] Train: [15/50][26/376] Data 0.003 (0.002) Batch 0.555 (0.506) Remain 01:53:58 loss: 0.2852 Lr: 0.00341 [2024-11-25 17:38:41,093 INFO misc.py line 119 2586773] Train: [15/50][27/376] Data 0.003 (0.002) Batch 0.494 (0.506) Remain 01:53:51 loss: 0.2389 Lr: 0.00341 [2024-11-25 17:38:41,604 INFO misc.py line 119 2586773] Train: [15/50][28/376] Data 0.003 (0.002) Batch 0.511 (0.506) Remain 01:53:53 loss: 0.2245 Lr: 0.00341 [2024-11-25 17:38:42,113 INFO misc.py line 119 2586773] Train: [15/50][29/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 01:53:54 loss: 0.2216 Lr: 0.00341 [2024-11-25 17:38:42,613 INFO misc.py line 119 2586773] Train: [15/50][30/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 01:53:50 loss: 0.1881 Lr: 0.00341 [2024-11-25 17:38:43,153 INFO misc.py line 119 2586773] Train: [15/50][31/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 01:54:06 loss: 0.2284 Lr: 0.00341 [2024-11-25 17:38:43,676 INFO misc.py line 119 2586773] Train: [15/50][32/376] Data 0.003 (0.003) Batch 0.524 (0.508) Remain 01:54:13 loss: 0.2339 Lr: 0.00341 [2024-11-25 17:38:44,196 INFO misc.py line 119 2586773] Train: [15/50][33/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 01:54:19 loss: 0.2479 Lr: 0.00341 [2024-11-25 17:38:44,687 INFO misc.py line 119 2586773] Train: [15/50][34/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 01:54:10 loss: 0.3361 Lr: 0.00341 [2024-11-25 17:38:45,175 INFO misc.py line 119 2586773] Train: [15/50][35/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:54:02 loss: 0.2334 Lr: 0.00341 [2024-11-25 17:38:45,699 INFO misc.py line 119 2586773] Train: [15/50][36/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 01:54:08 loss: 0.2342 Lr: 0.00341 [2024-11-25 17:38:46,204 INFO misc.py line 119 2586773] Train: [15/50][37/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:54:07 loss: 0.2353 Lr: 0.00341 [2024-11-25 17:38:46,719 INFO misc.py line 119 2586773] Train: [15/50][38/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 01:54:10 loss: 0.2635 Lr: 0.00341 [2024-11-25 17:38:47,240 INFO misc.py line 119 2586773] Train: [15/50][39/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 01:54:14 loss: 0.2728 Lr: 0.00341 [2024-11-25 17:38:47,767 INFO misc.py line 119 2586773] Train: [15/50][40/376] Data 0.002 (0.002) Batch 0.527 (0.508) Remain 01:54:20 loss: 0.2741 Lr: 0.00341 [2024-11-25 17:38:48,297 INFO misc.py line 119 2586773] Train: [15/50][41/376] Data 0.002 (0.002) Batch 0.530 (0.509) Remain 01:54:28 loss: 0.2179 Lr: 0.00341 [2024-11-25 17:38:48,779 INFO misc.py line 119 2586773] Train: [15/50][42/376] Data 0.002 (0.002) Batch 0.482 (0.508) Remain 01:54:18 loss: 0.2139 Lr: 0.00341 [2024-11-25 17:38:49,271 INFO misc.py line 119 2586773] Train: [15/50][43/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 01:54:12 loss: 0.3028 Lr: 0.00341 [2024-11-25 17:38:49,766 INFO misc.py line 119 2586773] Train: [15/50][44/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 01:54:07 loss: 0.2065 Lr: 0.00340 [2024-11-25 17:38:50,235 INFO misc.py line 119 2586773] Train: [15/50][45/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 01:53:54 loss: 0.2176 Lr: 0.00340 [2024-11-25 17:38:50,720 INFO misc.py line 119 2586773] Train: [15/50][46/376] Data 0.002 (0.002) Batch 0.484 (0.506) Remain 01:53:46 loss: 0.2351 Lr: 0.00340 [2024-11-25 17:38:51,245 INFO misc.py line 119 2586773] Train: [15/50][47/376] Data 0.004 (0.002) Batch 0.527 (0.507) Remain 01:53:52 loss: 0.2844 Lr: 0.00340 [2024-11-25 17:38:51,733 INFO misc.py line 119 2586773] Train: [15/50][48/376] Data 0.002 (0.002) Batch 0.488 (0.506) Remain 01:53:46 loss: 0.2666 Lr: 0.00340 [2024-11-25 17:38:52,210 INFO misc.py line 119 2586773] Train: [15/50][49/376] Data 0.003 (0.002) Batch 0.476 (0.505) Remain 01:53:37 loss: 0.3855 Lr: 0.00340 [2024-11-25 17:38:52,754 INFO misc.py line 119 2586773] Train: [15/50][50/376] Data 0.003 (0.003) Batch 0.545 (0.506) Remain 01:53:48 loss: 0.2248 Lr: 0.00340 [2024-11-25 17:38:53,270 INFO misc.py line 119 2586773] Train: [15/50][51/376] Data 0.002 (0.003) Batch 0.515 (0.507) Remain 01:53:50 loss: 0.2233 Lr: 0.00340 [2024-11-25 17:38:53,763 INFO misc.py line 119 2586773] Train: [15/50][52/376] Data 0.002 (0.003) Batch 0.494 (0.506) Remain 01:53:46 loss: 0.2280 Lr: 0.00340 [2024-11-25 17:38:54,274 INFO misc.py line 119 2586773] Train: [15/50][53/376] Data 0.002 (0.003) Batch 0.510 (0.506) Remain 01:53:46 loss: 0.2391 Lr: 0.00340 [2024-11-25 17:38:54,773 INFO misc.py line 119 2586773] Train: [15/50][54/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 01:53:44 loss: 0.1855 Lr: 0.00340 [2024-11-25 17:38:55,253 INFO misc.py line 119 2586773] Train: [15/50][55/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 01:53:37 loss: 0.2198 Lr: 0.00340 [2024-11-25 17:38:55,739 INFO misc.py line 119 2586773] Train: [15/50][56/376] Data 0.002 (0.002) Batch 0.486 (0.505) Remain 01:53:31 loss: 0.2266 Lr: 0.00340 [2024-11-25 17:38:56,249 INFO misc.py line 119 2586773] Train: [15/50][57/376] Data 0.003 (0.002) Batch 0.510 (0.505) Remain 01:53:32 loss: 0.2526 Lr: 0.00340 [2024-11-25 17:38:56,740 INFO misc.py line 119 2586773] Train: [15/50][58/376] Data 0.003 (0.002) Batch 0.491 (0.505) Remain 01:53:28 loss: 0.2692 Lr: 0.00340 [2024-11-25 17:38:57,219 INFO misc.py line 119 2586773] Train: [15/50][59/376] Data 0.003 (0.002) Batch 0.478 (0.505) Remain 01:53:21 loss: 0.2234 Lr: 0.00340 [2024-11-25 17:38:57,699 INFO misc.py line 119 2586773] Train: [15/50][60/376] Data 0.002 (0.002) Batch 0.481 (0.504) Remain 01:53:15 loss: 0.3683 Lr: 0.00340 [2024-11-25 17:38:58,221 INFO misc.py line 119 2586773] Train: [15/50][61/376] Data 0.003 (0.003) Batch 0.522 (0.505) Remain 01:53:18 loss: 0.1928 Lr: 0.00340 [2024-11-25 17:38:58,792 INFO misc.py line 119 2586773] Train: [15/50][62/376] Data 0.002 (0.003) Batch 0.571 (0.506) Remain 01:53:33 loss: 0.2662 Lr: 0.00340 [2024-11-25 17:38:59,289 INFO misc.py line 119 2586773] Train: [15/50][63/376] Data 0.002 (0.002) Batch 0.497 (0.506) Remain 01:53:31 loss: 0.2254 Lr: 0.00340 [2024-11-25 17:38:59,789 INFO misc.py line 119 2586773] Train: [15/50][64/376] Data 0.002 (0.002) Batch 0.500 (0.505) Remain 01:53:29 loss: 0.2914 Lr: 0.00340 [2024-11-25 17:39:00,320 INFO misc.py line 119 2586773] Train: [15/50][65/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 01:53:34 loss: 0.2810 Lr: 0.00340 [2024-11-25 17:39:00,858 INFO misc.py line 119 2586773] Train: [15/50][66/376] Data 0.002 (0.002) Batch 0.538 (0.506) Remain 01:53:40 loss: 0.2334 Lr: 0.00340 [2024-11-25 17:39:01,328 INFO misc.py line 119 2586773] Train: [15/50][67/376] Data 0.002 (0.002) Batch 0.470 (0.506) Remain 01:53:32 loss: 0.2558 Lr: 0.00340 [2024-11-25 17:39:01,886 INFO misc.py line 119 2586773] Train: [15/50][68/376] Data 0.002 (0.002) Batch 0.557 (0.507) Remain 01:53:42 loss: 0.2999 Lr: 0.00340 [2024-11-25 17:39:02,391 INFO misc.py line 119 2586773] Train: [15/50][69/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:53:42 loss: 0.2718 Lr: 0.00340 [2024-11-25 17:39:02,867 INFO misc.py line 119 2586773] Train: [15/50][70/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 01:53:35 loss: 0.2389 Lr: 0.00340 [2024-11-25 17:39:03,359 INFO misc.py line 119 2586773] Train: [15/50][71/376] Data 0.002 (0.002) Batch 0.492 (0.506) Remain 01:53:32 loss: 0.2102 Lr: 0.00340 [2024-11-25 17:39:03,828 INFO misc.py line 119 2586773] Train: [15/50][72/376] Data 0.002 (0.002) Batch 0.469 (0.505) Remain 01:53:24 loss: 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INFO misc.py line 119 2586773] Train: [15/50][79/376] Data 0.002 (0.002) Batch 0.521 (0.505) Remain 01:53:16 loss: 0.1777 Lr: 0.00340 [2024-11-25 17:39:07,888 INFO misc.py line 119 2586773] Train: [15/50][80/376] Data 0.003 (0.002) Batch 0.546 (0.506) Remain 01:53:23 loss: 0.2973 Lr: 0.00340 [2024-11-25 17:39:08,372 INFO misc.py line 119 2586773] Train: [15/50][81/376] Data 0.003 (0.002) Batch 0.483 (0.505) Remain 01:53:18 loss: 0.2476 Lr: 0.00340 [2024-11-25 17:39:08,905 INFO misc.py line 119 2586773] Train: [15/50][82/376] Data 0.002 (0.002) Batch 0.533 (0.506) Remain 01:53:23 loss: 0.2690 Lr: 0.00340 [2024-11-25 17:39:09,384 INFO misc.py line 119 2586773] Train: [15/50][83/376] Data 0.002 (0.002) Batch 0.480 (0.505) Remain 01:53:18 loss: 0.2632 Lr: 0.00340 [2024-11-25 17:39:09,905 INFO misc.py line 119 2586773] Train: [15/50][84/376] Data 0.002 (0.002) Batch 0.520 (0.506) Remain 01:53:20 loss: 0.2092 Lr: 0.00339 [2024-11-25 17:39:10,419 INFO misc.py line 119 2586773] Train: 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line 119 2586773] Train: [15/50][272/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:52:04 loss: 0.2235 Lr: 0.00335 [2024-11-25 17:40:45,849 INFO misc.py line 119 2586773] Train: [15/50][273/376] Data 0.002 (0.002) Batch 0.511 (0.507) Remain 01:52:04 loss: 0.2175 Lr: 0.00335 [2024-11-25 17:40:46,367 INFO misc.py line 119 2586773] Train: [15/50][274/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:52:04 loss: 0.2081 Lr: 0.00335 [2024-11-25 17:40:46,858 INFO misc.py line 119 2586773] Train: [15/50][275/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:52:03 loss: 0.2604 Lr: 0.00335 [2024-11-25 17:40:47,342 INFO misc.py line 119 2586773] Train: [15/50][276/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 01:52:01 loss: 0.2556 Lr: 0.00335 [2024-11-25 17:40:47,860 INFO misc.py line 119 2586773] Train: [15/50][277/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:52:01 loss: 0.2818 Lr: 0.00335 [2024-11-25 17:40:48,392 INFO misc.py line 119 2586773] Train: 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Batch 0.499 (0.507) Remain 01:51:59 loss: 0.2328 Lr: 0.00334 [2024-11-25 17:40:51,964 INFO misc.py line 119 2586773] Train: [15/50][285/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:51:59 loss: 0.2622 Lr: 0.00334 [2024-11-25 17:40:52,482 INFO misc.py line 119 2586773] Train: [15/50][286/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:51:59 loss: 0.2450 Lr: 0.00334 [2024-11-25 17:40:52,965 INFO misc.py line 119 2586773] Train: [15/50][287/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 01:51:58 loss: 0.2202 Lr: 0.00334 [2024-11-25 17:40:53,480 INFO misc.py line 119 2586773] Train: [15/50][288/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:51:57 loss: 0.2423 Lr: 0.00334 [2024-11-25 17:40:53,961 INFO misc.py line 119 2586773] Train: [15/50][289/376] Data 0.003 (0.002) Batch 0.481 (0.507) Remain 01:51:56 loss: 0.2230 Lr: 0.00334 [2024-11-25 17:40:54,521 INFO misc.py line 119 2586773] Train: [15/50][290/376] Data 0.003 (0.002) Batch 0.561 (0.507) Remain 01:51:58 loss: 0.2668 Lr: 0.00334 [2024-11-25 17:40:55,030 INFO misc.py line 119 2586773] Train: [15/50][291/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:51:57 loss: 0.2733 Lr: 0.00334 [2024-11-25 17:40:55,549 INFO misc.py line 119 2586773] Train: [15/50][292/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 01:51:57 loss: 0.2197 Lr: 0.00334 [2024-11-25 17:40:56,069 INFO misc.py line 119 2586773] Train: [15/50][293/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 01:51:57 loss: 0.2400 Lr: 0.00334 [2024-11-25 17:40:56,590 INFO misc.py line 119 2586773] Train: [15/50][294/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 01:51:58 loss: 0.3015 Lr: 0.00334 [2024-11-25 17:40:57,096 INFO misc.py line 119 2586773] Train: [15/50][295/376] Data 0.003 (0.002) Batch 0.506 (0.507) Remain 01:51:57 loss: 0.2948 Lr: 0.00334 [2024-11-25 17:40:57,625 INFO misc.py line 119 2586773] Train: [15/50][296/376] Data 0.003 (0.002) Batch 0.529 (0.507) Remain 01:51:57 loss: 0.2169 Lr: 0.00334 [2024-11-25 17:40:58,107 INFO misc.py line 119 2586773] Train: [15/50][297/376] Data 0.003 (0.002) Batch 0.482 (0.507) Remain 01:51:56 loss: 0.2016 Lr: 0.00334 [2024-11-25 17:40:58,568 INFO misc.py line 119 2586773] Train: [15/50][298/376] Data 0.002 (0.002) Batch 0.461 (0.507) Remain 01:51:53 loss: 0.2385 Lr: 0.00334 [2024-11-25 17:40:59,058 INFO misc.py line 119 2586773] Train: [15/50][299/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:51:52 loss: 0.2184 Lr: 0.00334 [2024-11-25 17:40:59,575 INFO misc.py line 119 2586773] Train: [15/50][300/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:51:52 loss: 0.2549 Lr: 0.00334 [2024-11-25 17:41:00,081 INFO misc.py line 119 2586773] Train: [15/50][301/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:51:51 loss: 0.2294 Lr: 0.00334 [2024-11-25 17:41:00,623 INFO misc.py line 119 2586773] Train: [15/50][302/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 01:51:52 loss: 0.2584 Lr: 0.00334 [2024-11-25 17:41:01,130 INFO misc.py line 119 2586773] Train: [15/50][303/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:51:52 loss: 0.2257 Lr: 0.00334 [2024-11-25 17:41:01,622 INFO misc.py line 119 2586773] Train: [15/50][304/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 01:51:51 loss: 0.2626 Lr: 0.00334 [2024-11-25 17:41:02,093 INFO misc.py line 119 2586773] Train: [15/50][305/376] Data 0.003 (0.002) Batch 0.472 (0.507) Remain 01:51:49 loss: 0.2515 Lr: 0.00334 [2024-11-25 17:41:02,593 INFO misc.py line 119 2586773] Train: [15/50][306/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:51:48 loss: 0.2657 Lr: 0.00334 [2024-11-25 17:41:03,123 INFO misc.py line 119 2586773] Train: [15/50][307/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 01:51:48 loss: 0.2182 Lr: 0.00334 [2024-11-25 17:41:03,622 INFO misc.py line 119 2586773] Train: [15/50][308/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 01:51:47 loss: 0.2111 Lr: 0.00334 [2024-11-25 17:41:04,143 INFO misc.py line 119 2586773] Train: [15/50][309/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 01:51:47 loss: 0.2055 Lr: 0.00334 [2024-11-25 17:41:04,665 INFO misc.py line 119 2586773] Train: [15/50][310/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 01:51:48 loss: 0.2590 Lr: 0.00334 [2024-11-25 17:41:05,160 INFO misc.py line 119 2586773] Train: [15/50][311/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:51:47 loss: 0.2534 Lr: 0.00334 [2024-11-25 17:41:05,653 INFO misc.py line 119 2586773] Train: [15/50][312/376] Data 0.003 (0.002) Batch 0.493 (0.507) Remain 01:51:45 loss: 0.2637 Lr: 0.00334 [2024-11-25 17:41:06,174 INFO misc.py line 119 2586773] Train: [15/50][313/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 01:51:46 loss: 0.1953 Lr: 0.00334 [2024-11-25 17:41:06,659 INFO misc.py line 119 2586773] Train: [15/50][314/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:51:44 loss: 0.2098 Lr: 0.00334 [2024-11-25 17:41:07,154 INFO misc.py line 119 2586773] Train: [15/50][315/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:51:43 loss: 0.2438 Lr: 0.00334 [2024-11-25 17:41:07,688 INFO misc.py line 119 2586773] Train: [15/50][316/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 01:51:44 loss: 0.2976 Lr: 0.00334 [2024-11-25 17:41:08,161 INFO misc.py line 119 2586773] Train: [15/50][317/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 01:51:42 loss: 0.2397 Lr: 0.00334 [2024-11-25 17:41:08,678 INFO misc.py line 119 2586773] Train: [15/50][318/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:51:42 loss: 0.2769 Lr: 0.00334 [2024-11-25 17:41:09,168 INFO misc.py line 119 2586773] Train: [15/50][319/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:51:40 loss: 0.2738 Lr: 0.00334 [2024-11-25 17:41:09,655 INFO misc.py line 119 2586773] Train: [15/50][320/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 01:51:39 loss: 0.1947 Lr: 0.00333 [2024-11-25 17:41:10,161 INFO misc.py line 119 2586773] Train: [15/50][321/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 01:51:39 loss: 0.2586 Lr: 0.00333 [2024-11-25 17:41:10,655 INFO misc.py line 119 2586773] Train: [15/50][322/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:51:38 loss: 0.1986 Lr: 0.00333 [2024-11-25 17:41:11,140 INFO misc.py line 119 2586773] Train: [15/50][323/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:51:36 loss: 0.2443 Lr: 0.00333 [2024-11-25 17:41:11,634 INFO misc.py line 119 2586773] Train: [15/50][324/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:51:35 loss: 0.2457 Lr: 0.00333 [2024-11-25 17:41:12,128 INFO misc.py line 119 2586773] Train: [15/50][325/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:51:34 loss: 0.3308 Lr: 0.00333 [2024-11-25 17:41:12,655 INFO misc.py line 119 2586773] Train: [15/50][326/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 01:51:34 loss: 0.2809 Lr: 0.00333 [2024-11-25 17:41:13,156 INFO misc.py line 119 2586773] Train: [15/50][327/376] Data 0.003 (0.002) Batch 0.501 (0.507) Remain 01:51:34 loss: 0.2144 Lr: 0.00333 [2024-11-25 17:41:13,689 INFO misc.py line 119 2586773] Train: [15/50][328/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 01:51:34 loss: 0.2039 Lr: 0.00333 [2024-11-25 17:41:14,234 INFO misc.py line 119 2586773] Train: [15/50][329/376] Data 0.002 (0.002) Batch 0.545 (0.507) Remain 01:51:35 loss: 0.2595 Lr: 0.00333 [2024-11-25 17:41:14,750 INFO misc.py line 119 2586773] Train: [15/50][330/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:51:35 loss: 0.2221 Lr: 0.00333 [2024-11-25 17:41:15,210 INFO misc.py line 119 2586773] Train: [15/50][331/376] Data 0.002 (0.002) Batch 0.460 (0.507) Remain 01:51:33 loss: 0.2254 Lr: 0.00333 [2024-11-25 17:41:15,708 INFO misc.py line 119 2586773] Train: [15/50][332/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:51:32 loss: 0.2172 Lr: 0.00333 [2024-11-25 17:41:16,218 INFO misc.py line 119 2586773] Train: [15/50][333/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:51:31 loss: 0.2340 Lr: 0.00333 [2024-11-25 17:41:16,707 INFO misc.py line 119 2586773] Train: [15/50][334/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:51:30 loss: 0.2522 Lr: 0.00333 [2024-11-25 17:41:17,192 INFO misc.py line 119 2586773] Train: [15/50][335/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:51:29 loss: 0.2657 Lr: 0.00333 [2024-11-25 17:41:17,713 INFO misc.py line 119 2586773] Train: [15/50][336/376] Data 0.003 (0.002) Batch 0.521 (0.507) Remain 01:51:29 loss: 0.2079 Lr: 0.00333 [2024-11-25 17:41:18,247 INFO misc.py line 119 2586773] Train: [15/50][337/376] Data 0.003 (0.002) Batch 0.534 (0.507) Remain 01:51:29 loss: 0.2560 Lr: 0.00333 [2024-11-25 17:41:18,761 INFO misc.py line 119 2586773] Train: [15/50][338/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:51:29 loss: 0.2368 Lr: 0.00333 [2024-11-25 17:41:19,289 INFO misc.py line 119 2586773] Train: [15/50][339/376] Data 0.003 (0.002) Batch 0.528 (0.507) Remain 01:51:30 loss: 0.2360 Lr: 0.00333 [2024-11-25 17:41:19,790 INFO misc.py line 119 2586773] Train: [15/50][340/376] Data 0.003 (0.002) Batch 0.502 (0.507) Remain 01:51:29 loss: 0.2718 Lr: 0.00333 [2024-11-25 17:41:20,278 INFO misc.py line 119 2586773] Train: [15/50][341/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 01:51:28 loss: 0.2358 Lr: 0.00333 [2024-11-25 17:41:20,762 INFO misc.py line 119 2586773] Train: [15/50][342/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 01:51:26 loss: 0.2621 Lr: 0.00333 [2024-11-25 17:41:21,292 INFO misc.py line 119 2586773] Train: [15/50][343/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 01:51:27 loss: 0.2608 Lr: 0.00333 [2024-11-25 17:41:21,800 INFO misc.py line 119 2586773] Train: [15/50][344/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 01:51:26 loss: 0.2460 Lr: 0.00333 [2024-11-25 17:41:22,318 INFO misc.py line 119 2586773] Train: [15/50][345/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:51:26 loss: 0.2366 Lr: 0.00333 [2024-11-25 17:41:22,826 INFO misc.py line 119 2586773] Train: [15/50][346/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 01:51:26 loss: 0.2627 Lr: 0.00333 [2024-11-25 17:41:23,353 INFO misc.py line 119 2586773] Train: [15/50][347/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 01:51:26 loss: 0.2400 Lr: 0.00333 [2024-11-25 17:41:23,841 INFO misc.py line 119 2586773] Train: [15/50][348/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:51:25 loss: 0.3019 Lr: 0.00333 [2024-11-25 17:41:24,346 INFO misc.py line 119 2586773] Train: [15/50][349/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:51:24 loss: 0.2628 Lr: 0.00333 [2024-11-25 17:41:24,842 INFO misc.py line 119 2586773] Train: [15/50][350/376] Data 0.003 (0.002) Batch 0.496 (0.507) Remain 01:51:23 loss: 0.1885 Lr: 0.00333 [2024-11-25 17:41:25,367 INFO misc.py line 119 2586773] Train: [15/50][351/376] Data 0.003 (0.002) Batch 0.525 (0.507) Remain 01:51:23 loss: 0.2386 Lr: 0.00333 [2024-11-25 17:41:25,871 INFO misc.py line 119 2586773] Train: [15/50][352/376] Data 0.003 (0.002) Batch 0.504 (0.507) Remain 01:51:23 loss: 0.2591 Lr: 0.00333 [2024-11-25 17:41:26,370 INFO misc.py line 119 2586773] Train: [15/50][353/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:51:22 loss: 0.2796 Lr: 0.00333 [2024-11-25 17:41:26,899 INFO misc.py line 119 2586773] Train: [15/50][354/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 01:51:22 loss: 0.1966 Lr: 0.00333 [2024-11-25 17:41:27,448 INFO misc.py line 119 2586773] Train: [15/50][355/376] Data 0.003 (0.002) Batch 0.549 (0.507) Remain 01:51:23 loss: 0.1956 Lr: 0.00333 [2024-11-25 17:41:27,972 INFO misc.py line 119 2586773] Train: [15/50][356/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 01:51:23 loss: 0.1958 Lr: 0.00333 [2024-11-25 17:41:28,457 INFO misc.py line 119 2586773] Train: [15/50][357/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:51:22 loss: 0.2296 Lr: 0.00333 [2024-11-25 17:41:29,007 INFO misc.py line 119 2586773] Train: [15/50][358/376] Data 0.003 (0.002) Batch 0.550 (0.507) Remain 01:51:23 loss: 0.2788 Lr: 0.00333 [2024-11-25 17:41:29,525 INFO misc.py line 119 2586773] Train: [15/50][359/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:51:23 loss: 0.2092 Lr: 0.00332 [2024-11-25 17:41:30,005 INFO misc.py line 119 2586773] Train: [15/50][360/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 01:51:22 loss: 0.2408 Lr: 0.00332 [2024-11-25 17:41:30,494 INFO misc.py line 119 2586773] Train: [15/50][361/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:51:20 loss: 0.1985 Lr: 0.00332 [2024-11-25 17:41:31,040 INFO misc.py line 119 2586773] Train: [15/50][362/376] Data 0.002 (0.002) Batch 0.546 (0.507) Remain 01:51:21 loss: 0.2290 Lr: 0.00332 [2024-11-25 17:41:31,545 INFO misc.py line 119 2586773] Train: [15/50][363/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:51:21 loss: 0.2258 Lr: 0.00332 [2024-11-25 17:41:32,028 INFO misc.py line 119 2586773] Train: [15/50][364/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 01:51:19 loss: 0.2045 Lr: 0.00332 [2024-11-25 17:41:32,506 INFO misc.py line 119 2586773] Train: [15/50][365/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 01:51:18 loss: 0.2594 Lr: 0.00332 [2024-11-25 17:41:33,001 INFO misc.py line 119 2586773] Train: [15/50][366/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:51:17 loss: 0.2694 Lr: 0.00332 [2024-11-25 17:41:33,501 INFO misc.py line 119 2586773] Train: [15/50][367/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 01:51:16 loss: 0.2325 Lr: 0.00332 [2024-11-25 17:41:34,056 INFO misc.py line 119 2586773] Train: [15/50][368/376] Data 0.002 (0.002) Batch 0.555 (0.507) Remain 01:51:17 loss: 0.2074 Lr: 0.00332 [2024-11-25 17:41:34,542 INFO misc.py line 119 2586773] Train: [15/50][369/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:51:16 loss: 0.3083 Lr: 0.00332 [2024-11-25 17:41:35,041 INFO misc.py line 119 2586773] Train: [15/50][370/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 01:51:15 loss: 0.2396 Lr: 0.00332 [2024-11-25 17:41:35,588 INFO misc.py line 119 2586773] Train: [15/50][371/376] Data 0.002 (0.002) Batch 0.547 (0.507) Remain 01:51:16 loss: 0.2682 Lr: 0.00332 [2024-11-25 17:41:36,075 INFO misc.py line 119 2586773] Train: [15/50][372/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 01:51:15 loss: 0.1989 Lr: 0.00332 [2024-11-25 17:41:36,619 INFO misc.py line 119 2586773] Train: [15/50][373/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 01:51:16 loss: 0.2225 Lr: 0.00332 [2024-11-25 17:41:37,141 INFO misc.py line 119 2586773] Train: [15/50][374/376] Data 0.003 (0.002) Batch 0.522 (0.507) Remain 01:51:16 loss: 0.2187 Lr: 0.00332 [2024-11-25 17:41:37,711 INFO misc.py line 119 2586773] Train: [15/50][375/376] Data 0.002 (0.002) Batch 0.570 (0.507) Remain 01:51:17 loss: 0.2149 Lr: 0.00332 [2024-11-25 17:41:38,208 INFO misc.py line 119 2586773] Train: [15/50][376/376] Data 0.003 (0.002) Batch 0.497 (0.507) Remain 01:51:17 loss: 0.2084 Lr: 0.00332 [2024-11-25 17:41:38,208 INFO misc.py line 136 2586773] Train result: loss: 0.2395 [2024-11-25 17:41:38,208 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:41:49,284 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2015 [2024-11-25 17:41:49,535 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2233 [2024-11-25 17:41:49,799 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3920 [2024-11-25 17:41:50,022 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2173 [2024-11-25 17:41:50,285 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3087 [2024-11-25 17:41:50,560 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2115 [2024-11-25 17:41:50,782 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2063 [2024-11-25 17:41:51,050 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2167 [2024-11-25 17:41:51,274 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2660 [2024-11-25 17:41:51,535 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.3086 [2024-11-25 17:41:51,765 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2423 [2024-11-25 17:41:52,037 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2290 [2024-11-25 17:41:52,304 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2706 [2024-11-25 17:41:52,578 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2909 [2024-11-25 17:41:52,812 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2603 [2024-11-25 17:41:53,051 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3007 [2024-11-25 17:41:53,330 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3748 [2024-11-25 17:41:53,577 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2339 [2024-11-25 17:41:53,807 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2230 [2024-11-25 17:41:54,069 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2868 [2024-11-25 17:41:54,302 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2487 [2024-11-25 17:41:54,575 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2906 [2024-11-25 17:41:54,811 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2245 [2024-11-25 17:41:55,078 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2959 [2024-11-25 17:41:55,336 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2400 [2024-11-25 17:41:55,571 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2573 [2024-11-25 17:41:55,824 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3006 [2024-11-25 17:41:56,069 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2398 [2024-11-25 17:41:56,336 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3591 [2024-11-25 17:41:56,597 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3545 [2024-11-25 17:41:56,829 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.3165 [2024-11-25 17:41:57,095 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2341 [2024-11-25 17:41:57,321 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2500 [2024-11-25 17:41:57,559 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2087 [2024-11-25 17:41:57,819 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2156 [2024-11-25 17:41:58,070 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2652 [2024-11-25 17:41:58,297 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2096 [2024-11-25 17:41:58,575 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2583 [2024-11-25 17:41:58,807 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2704 [2024-11-25 17:41:59,053 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2792 [2024-11-25 17:41:59,323 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2708 [2024-11-25 17:41:59,573 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3161 [2024-11-25 17:41:59,811 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2851 [2024-11-25 17:42:00,043 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2510 [2024-11-25 17:42:00,281 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2439 [2024-11-25 17:42:00,538 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2629 [2024-11-25 17:42:00,804 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.3072 [2024-11-25 17:42:01,053 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3351 [2024-11-25 17:42:01,283 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2223 [2024-11-25 17:42:01,516 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2210 [2024-11-25 17:42:01,751 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2447 [2024-11-25 17:42:02,009 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2752 [2024-11-25 17:42:02,275 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2486 [2024-11-25 17:42:02,534 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3038 [2024-11-25 17:42:02,773 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2659 [2024-11-25 17:42:03,013 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2581 [2024-11-25 17:42:03,275 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2965 [2024-11-25 17:42:03,548 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2750 [2024-11-25 17:42:03,803 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2858 [2024-11-25 17:42:04,067 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.3243 [2024-11-25 17:42:04,336 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2678 [2024-11-25 17:42:04,612 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2709 [2024-11-25 17:42:04,843 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2378 [2024-11-25 17:42:05,121 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2982 [2024-11-25 17:42:05,389 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.3007 [2024-11-25 17:42:05,652 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2265 [2024-11-25 17:42:05,900 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2470 [2024-11-25 17:42:06,157 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3153 [2024-11-25 17:42:06,425 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2270 [2024-11-25 17:42:06,696 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3027 [2024-11-25 17:42:06,941 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2313 [2024-11-25 17:42:07,175 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2864 [2024-11-25 17:42:07,434 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2734 [2024-11-25 17:42:07,680 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2914 [2024-11-25 17:42:07,897 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2661 [2024-11-25 17:42:08,119 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2091 [2024-11-25 17:42:08,387 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2660 [2024-11-25 17:42:08,624 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2251 [2024-11-25 17:42:08,884 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2213 [2024-11-25 17:42:09,134 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3344 [2024-11-25 17:42:09,378 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2077 [2024-11-25 17:42:09,638 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2594 [2024-11-25 17:42:09,890 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2063 [2024-11-25 17:42:10,142 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2653 [2024-11-25 17:42:10,412 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2381 [2024-11-25 17:42:10,650 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2778 [2024-11-25 17:42:10,916 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3229 [2024-11-25 17:42:11,177 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2984 [2024-11-25 17:42:11,423 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2865 [2024-11-25 17:42:11,671 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2509 [2024-11-25 17:42:11,905 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2026 [2024-11-25 17:42:12,159 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2777 [2024-11-25 17:42:12,428 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2915 [2024-11-25 17:42:12,695 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2181 [2024-11-25 17:42:12,963 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2336 [2024-11-25 17:42:13,211 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2257 [2024-11-25 17:42:13,479 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2776 [2024-11-25 17:42:13,702 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3067 [2024-11-25 17:42:13,975 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2570 [2024-11-25 17:42:14,212 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2991 [2024-11-25 17:42:14,480 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2321 [2024-11-25 17:42:14,744 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3201 [2024-11-25 17:42:15,006 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2752 [2024-11-25 17:42:15,261 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3230 [2024-11-25 17:42:15,483 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2316 [2024-11-25 17:42:15,717 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2257 [2024-11-25 17:42:15,977 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2392 [2024-11-25 17:42:16,247 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.3055 [2024-11-25 17:42:16,480 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3055 [2024-11-25 17:42:16,743 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2825 [2024-11-25 17:42:17,006 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2232 [2024-11-25 17:42:17,228 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2357 [2024-11-25 17:42:17,464 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2293 [2024-11-25 17:42:17,685 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2479 [2024-11-25 17:42:17,910 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2435 [2024-11-25 17:42:18,182 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3616 [2024-11-25 17:42:18,442 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3293 [2024-11-25 17:42:18,709 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2937 [2024-11-25 17:42:18,975 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2509 [2024-11-25 17:42:19,235 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3802 [2024-11-25 17:42:19,495 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2812 [2024-11-25 17:42:19,759 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2076 [2024-11-25 17:42:20,014 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2897 [2024-11-25 17:42:20,279 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3017 [2024-11-25 17:42:20,541 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2548 [2024-11-25 17:42:20,791 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3119 [2024-11-25 17:42:21,022 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2416 [2024-11-25 17:42:21,283 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3073 [2024-11-25 17:42:21,520 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2594 [2024-11-25 17:42:21,747 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2108 [2024-11-25 17:42:21,958 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2550 [2024-11-25 17:42:22,174 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2111 [2024-11-25 17:42:22,873 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7438/0.8059/0.9958. [2024-11-25 17:42:22,873 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9958/0.9984 [2024-11-25 17:42:22,873 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.4918/0.6135 [2024-11-25 17:42:22,873 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:42:22,874 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7469 [2024-11-25 17:42:22,875 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:42:25,594 INFO misc.py line 119 2586773] Train: [16/50][1/376] Data 0.081 (0.081) Batch 0.606 (0.606) Remain 02:12:47 loss: 0.1928 Lr: 0.00332 [2024-11-25 17:42:26,118 INFO misc.py line 119 2586773] Train: [16/50][2/376] Data 0.002 (0.002) Batch 0.524 (0.524) Remain 01:54:51 loss: 0.2142 Lr: 0.00332 [2024-11-25 17:42:26,653 INFO misc.py line 119 2586773] Train: [16/50][3/376] Data 0.002 (0.002) Batch 0.535 (0.535) Remain 01:57:15 loss: 0.2380 Lr: 0.00332 [2024-11-25 17:42:27,168 INFO misc.py line 119 2586773] Train: [16/50][4/376] Data 0.002 (0.002) Batch 0.516 (0.516) Remain 01:53:03 loss: 0.2328 Lr: 0.00332 [2024-11-25 17:42:27,669 INFO misc.py line 119 2586773] Train: [16/50][5/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 01:51:22 loss: 0.2538 Lr: 0.00332 [2024-11-25 17:42:28,171 INFO misc.py line 119 2586773] Train: [16/50][6/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 01:50:58 loss: 0.2068 Lr: 0.00332 [2024-11-25 17:42:28,704 INFO misc.py line 119 2586773] Train: [16/50][7/376] Data 0.002 (0.002) Batch 0.533 (0.513) Remain 01:52:24 loss: 0.3145 Lr: 0.00332 [2024-11-25 17:42:29,199 INFO misc.py line 119 2586773] Train: [16/50][8/376] Data 0.002 (0.002) Batch 0.495 (0.509) Remain 01:51:36 loss: 0.2631 Lr: 0.00332 [2024-11-25 17:42:29,691 INFO misc.py line 119 2586773] Train: [16/50][9/376] Data 0.002 (0.002) Batch 0.492 (0.506) Remain 01:50:58 loss: 0.2063 Lr: 0.00332 [2024-11-25 17:42:30,211 INFO misc.py line 119 2586773] Train: [16/50][10/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 01:51:24 loss: 0.2186 Lr: 0.00332 [2024-11-25 17:42:30,712 INFO misc.py line 119 2586773] Train: [16/50][11/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:51:11 loss: 0.2438 Lr: 0.00332 [2024-11-25 17:42:31,228 INFO misc.py line 119 2586773] Train: [16/50][12/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 01:51:24 loss: 0.2382 Lr: 0.00332 [2024-11-25 17:42:31,769 INFO misc.py line 119 2586773] Train: [16/50][13/376] Data 0.002 (0.002) Batch 0.541 (0.512) Remain 01:52:06 loss: 0.2166 Lr: 0.00332 [2024-11-25 17:42:32,247 INFO misc.py line 119 2586773] Train: [16/50][14/376] Data 0.002 (0.002) Batch 0.478 (0.509) Remain 01:51:25 loss: 0.2058 Lr: 0.00332 [2024-11-25 17:42:32,752 INFO misc.py line 119 2586773] Train: [16/50][15/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 01:51:21 loss: 0.1917 Lr: 0.00332 [2024-11-25 17:42:33,261 INFO misc.py line 119 2586773] Train: [16/50][16/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 01:51:21 loss: 0.2061 Lr: 0.00332 [2024-11-25 17:42:33,756 INFO misc.py line 119 2586773] Train: [16/50][17/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:51:08 loss: 0.2452 Lr: 0.00332 [2024-11-25 17:42:34,250 INFO misc.py line 119 2586773] Train: [16/50][18/376] Data 0.003 (0.002) Batch 0.494 (0.506) Remain 01:50:56 loss: 0.2857 Lr: 0.00332 [2024-11-25 17:42:34,770 INFO misc.py line 119 2586773] Train: [16/50][19/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 01:51:07 loss: 0.2243 Lr: 0.00332 [2024-11-25 17:42:35,248 INFO misc.py line 119 2586773] Train: [16/50][20/376] Data 0.002 (0.002) Batch 0.478 (0.506) Remain 01:50:43 loss: 0.2466 Lr: 0.00332 [2024-11-25 17:42:35,717 INFO misc.py line 119 2586773] Train: [16/50][21/376] Data 0.002 (0.002) Batch 0.468 (0.504) Remain 01:50:16 loss: 0.2427 Lr: 0.00331 [2024-11-25 17:42:36,179 INFO misc.py line 119 2586773] Train: [16/50][22/376] Data 0.002 (0.002) Batch 0.462 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loss: 0.2532 Lr: 0.00324 [2024-11-25 17:44:52,748 INFO misc.py line 119 2586773] Train: [16/50][291/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:48:48 loss: 0.2250 Lr: 0.00324 [2024-11-25 17:44:53,230 INFO misc.py line 119 2586773] Train: [16/50][292/376] Data 0.003 (0.002) Batch 0.482 (0.507) Remain 01:48:46 loss: 0.2303 Lr: 0.00324 [2024-11-25 17:44:53,771 INFO misc.py line 119 2586773] Train: [16/50][293/376] Data 0.002 (0.002) Batch 0.541 (0.507) Remain 01:48:47 loss: 0.2622 Lr: 0.00324 [2024-11-25 17:44:54,288 INFO misc.py line 119 2586773] Train: [16/50][294/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 01:48:47 loss: 0.2321 Lr: 0.00324 [2024-11-25 17:44:54,782 INFO misc.py line 119 2586773] Train: [16/50][295/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:48:46 loss: 0.2391 Lr: 0.00324 [2024-11-25 17:44:55,252 INFO misc.py line 119 2586773] Train: [16/50][296/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 01:48:44 loss: 0.2105 Lr: 0.00324 [2024-11-25 17:44:55,789 INFO misc.py line 119 2586773] Train: [16/50][297/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 01:48:44 loss: 0.2510 Lr: 0.00324 [2024-11-25 17:44:56,289 INFO misc.py line 119 2586773] Train: [16/50][298/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 01:48:44 loss: 0.2353 Lr: 0.00324 [2024-11-25 17:44:56,777 INFO misc.py line 119 2586773] Train: [16/50][299/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:48:42 loss: 0.2292 Lr: 0.00324 [2024-11-25 17:44:57,329 INFO misc.py line 119 2586773] Train: [16/50][300/376] Data 0.002 (0.002) Batch 0.552 (0.507) Remain 01:48:44 loss: 0.2191 Lr: 0.00324 [2024-11-25 17:44:57,835 INFO misc.py line 119 2586773] Train: [16/50][301/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 01:48:43 loss: 0.2333 Lr: 0.00324 [2024-11-25 17:44:58,333 INFO misc.py line 119 2586773] Train: [16/50][302/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 01:48:42 loss: 0.2311 Lr: 0.00324 [2024-11-25 17:44:58,809 INFO misc.py line 119 2586773] Train: [16/50][303/376] Data 0.002 (0.002) Batch 0.476 (0.507) Remain 01:48:40 loss: 0.2334 Lr: 0.00324 [2024-11-25 17:44:59,347 INFO misc.py line 119 2586773] Train: [16/50][304/376] Data 0.003 (0.002) Batch 0.538 (0.507) Remain 01:48:41 loss: 0.2352 Lr: 0.00324 [2024-11-25 17:44:59,826 INFO misc.py line 119 2586773] Train: [16/50][305/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 01:48:39 loss: 0.2582 Lr: 0.00324 [2024-11-25 17:45:00,368 INFO misc.py line 119 2586773] Train: [16/50][306/376] Data 0.003 (0.002) Batch 0.542 (0.507) Remain 01:48:40 loss: 0.2422 Lr: 0.00324 [2024-11-25 17:45:00,882 INFO misc.py line 119 2586773] Train: [16/50][307/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:48:40 loss: 0.1944 Lr: 0.00324 [2024-11-25 17:45:01,397 INFO misc.py line 119 2586773] Train: [16/50][308/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:48:40 loss: 0.2348 Lr: 0.00324 [2024-11-25 17:45:01,894 INFO misc.py line 119 2586773] Train: [16/50][309/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:48:39 loss: 0.2718 Lr: 0.00324 [2024-11-25 17:45:02,408 INFO misc.py line 119 2586773] Train: [16/50][310/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:48:39 loss: 0.2291 Lr: 0.00324 [2024-11-25 17:45:02,898 INFO misc.py line 119 2586773] Train: [16/50][311/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:48:38 loss: 0.1911 Lr: 0.00324 [2024-11-25 17:45:03,407 INFO misc.py line 119 2586773] Train: [16/50][312/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:48:37 loss: 0.2252 Lr: 0.00324 [2024-11-25 17:45:03,868 INFO misc.py line 119 2586773] Train: [16/50][313/376] Data 0.002 (0.002) Batch 0.461 (0.507) Remain 01:48:35 loss: 0.2756 Lr: 0.00324 [2024-11-25 17:45:04,382 INFO misc.py line 119 2586773] Train: [16/50][314/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:48:35 loss: 0.2422 Lr: 0.00324 [2024-11-25 17:45:04,854 INFO misc.py line 119 2586773] Train: [16/50][315/376] Data 0.002 (0.002) Batch 0.472 (0.507) Remain 01:48:33 loss: 0.2517 Lr: 0.00324 [2024-11-25 17:45:05,392 INFO misc.py line 119 2586773] Train: [16/50][316/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 01:48:33 loss: 0.1909 Lr: 0.00324 [2024-11-25 17:45:05,900 INFO misc.py line 119 2586773] Train: [16/50][317/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 01:48:33 loss: 0.2026 Lr: 0.00324 [2024-11-25 17:45:06,418 INFO misc.py line 119 2586773] Train: [16/50][318/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:48:33 loss: 0.2485 Lr: 0.00324 [2024-11-25 17:45:06,924 INFO misc.py line 119 2586773] Train: [16/50][319/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 01:48:32 loss: 0.2319 Lr: 0.00324 [2024-11-25 17:45:07,449 INFO misc.py line 119 2586773] Train: [16/50][320/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 01:48:32 loss: 0.2453 Lr: 0.00323 [2024-11-25 17:45:07,999 INFO misc.py line 119 2586773] Train: [16/50][321/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 01:48:34 loss: 0.2662 Lr: 0.00323 [2024-11-25 17:45:08,539 INFO misc.py line 119 2586773] Train: [16/50][322/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 01:48:35 loss: 0.2134 Lr: 0.00323 [2024-11-25 17:45:09,046 INFO misc.py line 119 2586773] Train: [16/50][323/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:48:34 loss: 0.2331 Lr: 0.00323 [2024-11-25 17:45:09,550 INFO misc.py line 119 2586773] Train: [16/50][324/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:48:33 loss: 0.2051 Lr: 0.00323 [2024-11-25 17:45:10,054 INFO misc.py line 119 2586773] Train: [16/50][325/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:48:33 loss: 0.2112 Lr: 0.00323 [2024-11-25 17:45:10,550 INFO misc.py line 119 2586773] Train: [16/50][326/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:48:32 loss: 0.2683 Lr: 0.00323 [2024-11-25 17:45:11,054 INFO misc.py line 119 2586773] Train: [16/50][327/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:48:31 loss: 0.2235 Lr: 0.00323 [2024-11-25 17:45:11,567 INFO misc.py line 119 2586773] Train: [16/50][328/376] Data 0.003 (0.002) Batch 0.512 (0.507) Remain 01:48:31 loss: 0.2253 Lr: 0.00323 [2024-11-25 17:45:12,096 INFO misc.py line 119 2586773] Train: [16/50][329/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 01:48:31 loss: 0.3147 Lr: 0.00323 [2024-11-25 17:45:12,640 INFO misc.py line 119 2586773] Train: [16/50][330/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 01:48:32 loss: 0.2805 Lr: 0.00323 [2024-11-25 17:45:13,135 INFO misc.py line 119 2586773] Train: [16/50][331/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 01:48:31 loss: 0.2463 Lr: 0.00323 [2024-11-25 17:45:13,625 INFO misc.py line 119 2586773] Train: [16/50][332/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 01:48:30 loss: 0.2095 Lr: 0.00323 [2024-11-25 17:45:14,096 INFO misc.py line 119 2586773] Train: [16/50][333/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 01:48:28 loss: 0.2090 Lr: 0.00323 [2024-11-25 17:45:14,611 INFO misc.py line 119 2586773] Train: [16/50][334/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:48:28 loss: 0.2128 Lr: 0.00323 [2024-11-25 17:45:15,130 INFO misc.py line 119 2586773] Train: [16/50][335/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 01:48:28 loss: 0.2720 Lr: 0.00323 [2024-11-25 17:45:15,643 INFO misc.py line 119 2586773] Train: [16/50][336/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 01:48:27 loss: 0.2125 Lr: 0.00323 [2024-11-25 17:45:16,133 INFO misc.py line 119 2586773] Train: [16/50][337/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:48:26 loss: 0.2471 Lr: 0.00323 [2024-11-25 17:45:16,631 INFO misc.py line 119 2586773] Train: [16/50][338/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:48:25 loss: 0.2479 Lr: 0.00323 [2024-11-25 17:45:17,127 INFO misc.py line 119 2586773] Train: [16/50][339/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:48:24 loss: 0.2810 Lr: 0.00323 [2024-11-25 17:45:17,694 INFO misc.py line 119 2586773] Train: [16/50][340/376] Data 0.002 (0.002) Batch 0.567 (0.508) Remain 01:48:26 loss: 0.2159 Lr: 0.00323 [2024-11-25 17:45:18,229 INFO misc.py line 119 2586773] Train: [16/50][341/376] Data 0.003 (0.002) Batch 0.535 (0.508) Remain 01:48:27 loss: 0.2693 Lr: 0.00323 [2024-11-25 17:45:18,755 INFO misc.py line 119 2586773] Train: [16/50][342/376] Data 0.003 (0.002) Batch 0.526 (0.508) Remain 01:48:27 loss: 0.2462 Lr: 0.00323 [2024-11-25 17:45:19,266 INFO misc.py line 119 2586773] Train: [16/50][343/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 01:48:26 loss: 0.2038 Lr: 0.00323 [2024-11-25 17:45:19,735 INFO misc.py line 119 2586773] Train: [16/50][344/376] Data 0.002 (0.002) Batch 0.469 (0.508) Remain 01:48:25 loss: 0.2498 Lr: 0.00323 [2024-11-25 17:45:20,255 INFO misc.py line 119 2586773] Train: [16/50][345/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 01:48:24 loss: 0.2671 Lr: 0.00323 [2024-11-25 17:45:20,726 INFO misc.py line 119 2586773] Train: [16/50][346/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 01:48:23 loss: 0.2493 Lr: 0.00323 [2024-11-25 17:45:21,236 INFO misc.py line 119 2586773] Train: [16/50][347/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 01:48:22 loss: 0.2374 Lr: 0.00323 [2024-11-25 17:45:21,716 INFO misc.py line 119 2586773] Train: [16/50][348/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 01:48:21 loss: 0.2971 Lr: 0.00323 [2024-11-25 17:45:22,206 INFO misc.py line 119 2586773] Train: [16/50][349/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:48:19 loss: 0.1882 Lr: 0.00323 [2024-11-25 17:45:22,704 INFO misc.py line 119 2586773] Train: [16/50][350/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:48:19 loss: 0.2095 Lr: 0.00323 [2024-11-25 17:45:23,179 INFO misc.py line 119 2586773] Train: [16/50][351/376] Data 0.002 (0.002) Batch 0.475 (0.507) Remain 01:48:17 loss: 0.2105 Lr: 0.00323 [2024-11-25 17:45:23,716 INFO misc.py line 119 2586773] Train: [16/50][352/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 01:48:18 loss: 0.2468 Lr: 0.00323 [2024-11-25 17:45:24,240 INFO misc.py line 119 2586773] Train: [16/50][353/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 01:48:18 loss: 0.2717 Lr: 0.00323 [2024-11-25 17:45:24,741 INFO misc.py line 119 2586773] Train: [16/50][354/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:48:17 loss: 0.2407 Lr: 0.00323 [2024-11-25 17:45:25,236 INFO misc.py line 119 2586773] Train: [16/50][355/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:48:16 loss: 0.2419 Lr: 0.00323 [2024-11-25 17:45:25,706 INFO misc.py line 119 2586773] Train: [16/50][356/376] Data 0.003 (0.002) Batch 0.470 (0.507) Remain 01:48:14 loss: 0.2291 Lr: 0.00322 [2024-11-25 17:45:26,231 INFO misc.py line 119 2586773] Train: [16/50][357/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 01:48:14 loss: 0.2204 Lr: 0.00322 [2024-11-25 17:45:26,762 INFO misc.py line 119 2586773] Train: [16/50][358/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 01:48:15 loss: 0.2610 Lr: 0.00322 [2024-11-25 17:45:27,246 INFO misc.py line 119 2586773] Train: [16/50][359/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 01:48:13 loss: 0.2178 Lr: 0.00322 [2024-11-25 17:45:27,755 INFO misc.py line 119 2586773] Train: [16/50][360/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:48:13 loss: 0.1815 Lr: 0.00322 [2024-11-25 17:45:28,293 INFO misc.py line 119 2586773] Train: [16/50][361/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 01:48:13 loss: 0.2166 Lr: 0.00322 [2024-11-25 17:45:28,800 INFO misc.py line 119 2586773] Train: [16/50][362/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:48:13 loss: 0.2238 Lr: 0.00322 [2024-11-25 17:45:29,327 INFO misc.py line 119 2586773] Train: [16/50][363/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 01:48:13 loss: 0.3306 Lr: 0.00322 [2024-11-25 17:45:29,831 INFO misc.py line 119 2586773] Train: [16/50][364/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:48:12 loss: 0.2910 Lr: 0.00322 [2024-11-25 17:45:30,328 INFO misc.py line 119 2586773] Train: [16/50][365/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:48:12 loss: 0.2666 Lr: 0.00322 [2024-11-25 17:45:30,822 INFO misc.py line 119 2586773] Train: [16/50][366/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:48:11 loss: 0.2480 Lr: 0.00322 [2024-11-25 17:45:31,363 INFO misc.py line 119 2586773] Train: [16/50][367/376] Data 0.002 (0.002) Batch 0.541 (0.507) Remain 01:48:11 loss: 0.2750 Lr: 0.00322 [2024-11-25 17:45:31,852 INFO misc.py line 119 2586773] Train: [16/50][368/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:48:10 loss: 0.2287 Lr: 0.00322 [2024-11-25 17:45:32,361 INFO misc.py line 119 2586773] Train: [16/50][369/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:48:10 loss: 0.1781 Lr: 0.00322 [2024-11-25 17:45:32,849 INFO misc.py line 119 2586773] Train: [16/50][370/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:48:08 loss: 0.2276 Lr: 0.00322 [2024-11-25 17:45:33,370 INFO misc.py line 119 2586773] Train: [16/50][371/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 01:48:08 loss: 0.1737 Lr: 0.00322 [2024-11-25 17:45:33,894 INFO misc.py line 119 2586773] Train: [16/50][372/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 01:48:08 loss: 0.2306 Lr: 0.00322 [2024-11-25 17:45:34,446 INFO misc.py line 119 2586773] Train: [16/50][373/376] Data 0.002 (0.002) Batch 0.551 (0.508) Remain 01:48:10 loss: 0.2593 Lr: 0.00322 [2024-11-25 17:45:34,984 INFO misc.py line 119 2586773] Train: [16/50][374/376] Data 0.002 (0.002) Batch 0.538 (0.508) Remain 01:48:10 loss: 0.3046 Lr: 0.00322 [2024-11-25 17:45:35,475 INFO misc.py line 119 2586773] Train: [16/50][375/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 01:48:09 loss: 0.2556 Lr: 0.00322 [2024-11-25 17:45:36,010 INFO misc.py line 119 2586773] Train: [16/50][376/376] Data 0.002 (0.002) Batch 0.535 (0.508) Remain 01:48:09 loss: 0.2548 Lr: 0.00322 [2024-11-25 17:45:36,010 INFO misc.py line 136 2586773] Train result: loss: 0.2411 [2024-11-25 17:45:36,011 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:45:46,929 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2097 [2024-11-25 17:45:47,186 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2347 [2024-11-25 17:45:47,450 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2719 [2024-11-25 17:45:47,675 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2219 [2024-11-25 17:45:47,939 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3111 [2024-11-25 17:45:48,207 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2179 [2024-11-25 17:45:48,432 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2282 [2024-11-25 17:45:48,703 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2319 [2024-11-25 17:45:48,927 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2564 [2024-11-25 17:45:49,186 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2867 [2024-11-25 17:45:49,417 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2292 [2024-11-25 17:45:49,689 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2028 [2024-11-25 17:45:49,960 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2603 [2024-11-25 17:45:50,222 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2592 [2024-11-25 17:45:50,454 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2426 [2024-11-25 17:45:50,692 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2705 [2024-11-25 17:45:50,959 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3081 [2024-11-25 17:45:51,207 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2287 [2024-11-25 17:45:51,439 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2142 [2024-11-25 17:45:51,700 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2835 [2024-11-25 17:45:51,934 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2222 [2024-11-25 17:45:52,203 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2801 [2024-11-25 17:45:52,440 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2223 [2024-11-25 17:45:52,708 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2826 [2024-11-25 17:45:52,970 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2528 [2024-11-25 17:45:53,205 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2508 [2024-11-25 17:45:53,454 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3029 [2024-11-25 17:45:53,701 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2598 [2024-11-25 17:45:53,968 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2966 [2024-11-25 17:45:54,219 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3256 [2024-11-25 17:45:54,454 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2926 [2024-11-25 17:45:54,719 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2337 [2024-11-25 17:45:54,937 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2265 [2024-11-25 17:45:55,179 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2110 [2024-11-25 17:45:55,440 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2373 [2024-11-25 17:45:55,685 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2496 [2024-11-25 17:45:55,910 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2124 [2024-11-25 17:45:56,180 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2547 [2024-11-25 17:45:56,411 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2693 [2024-11-25 17:45:56,646 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2726 [2024-11-25 17:45:56,917 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2931 [2024-11-25 17:45:57,169 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2980 [2024-11-25 17:45:57,406 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2714 [2024-11-25 17:45:57,640 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2606 [2024-11-25 17:45:57,877 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2474 [2024-11-25 17:45:58,129 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2624 [2024-11-25 17:45:58,386 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2746 [2024-11-25 17:45:58,635 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3052 [2024-11-25 17:45:58,859 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2303 [2024-11-25 17:45:59,094 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2236 [2024-11-25 17:45:59,314 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2513 [2024-11-25 17:45:59,566 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2727 [2024-11-25 17:45:59,831 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2507 [2024-11-25 17:46:00,092 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3114 [2024-11-25 17:46:00,327 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2424 [2024-11-25 17:46:00,567 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2498 [2024-11-25 17:46:00,823 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3151 [2024-11-25 17:46:01,094 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2671 [2024-11-25 17:46:01,352 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2801 [2024-11-25 17:46:01,613 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2761 [2024-11-25 17:46:01,865 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2364 [2024-11-25 17:46:02,133 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2489 [2024-11-25 17:46:02,362 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2284 [2024-11-25 17:46:02,622 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2696 [2024-11-25 17:46:02,887 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2891 [2024-11-25 17:46:03,153 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2384 [2024-11-25 17:46:03,401 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2236 [2024-11-25 17:46:03,659 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3134 [2024-11-25 17:46:03,927 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2420 [2024-11-25 17:46:04,190 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2871 [2024-11-25 17:46:04,432 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2145 [2024-11-25 17:46:04,667 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2768 [2024-11-25 17:46:04,922 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2827 [2024-11-25 17:46:05,164 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2785 [2024-11-25 17:46:05,382 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2620 [2024-11-25 17:46:05,602 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2318 [2024-11-25 17:46:05,872 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2592 [2024-11-25 17:46:06,109 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2391 [2024-11-25 17:46:06,366 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2363 [2024-11-25 17:46:06,616 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3247 [2024-11-25 17:46:06,857 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2187 [2024-11-25 17:46:07,120 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2606 [2024-11-25 17:46:07,368 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2066 [2024-11-25 17:46:07,615 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2727 [2024-11-25 17:46:07,887 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2441 [2024-11-25 17:46:08,126 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2677 [2024-11-25 17:46:08,388 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2977 [2024-11-25 17:46:08,647 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2699 [2024-11-25 17:46:08,896 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2926 [2024-11-25 17:46:09,144 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2459 [2024-11-25 17:46:09,378 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2239 [2024-11-25 17:46:09,633 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2765 [2024-11-25 17:46:09,901 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2656 [2024-11-25 17:46:10,168 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2212 [2024-11-25 17:46:10,432 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2375 [2024-11-25 17:46:10,681 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2357 [2024-11-25 17:46:10,950 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2787 [2024-11-25 17:46:11,168 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3121 [2024-11-25 17:46:11,438 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2598 [2024-11-25 17:46:11,675 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2805 [2024-11-25 17:46:11,947 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2202 [2024-11-25 17:46:12,211 INFO evaluator.py line 159 2586773] Test: 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[2024-11-25 17:46:15,145 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2413 [2024-11-25 17:46:15,370 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2400 [2024-11-25 17:46:15,639 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3291 [2024-11-25 17:46:15,898 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2825 [2024-11-25 17:46:16,166 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2869 [2024-11-25 17:46:16,431 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2426 [2024-11-25 17:46:16,694 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3269 [2024-11-25 17:46:16,954 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2908 [2024-11-25 17:46:17,219 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2366 [2024-11-25 17:46:17,476 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2765 [2024-11-25 17:46:17,738 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2809 [2024-11-25 17:46:18,001 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2498 [2024-11-25 17:46:18,251 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3089 [2024-11-25 17:46:18,482 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2148 [2024-11-25 17:46:18,741 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2975 [2024-11-25 17:46:18,976 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2832 [2024-11-25 17:46:19,203 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2061 [2024-11-25 17:46:19,414 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2584 [2024-11-25 17:46:19,634 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2097 [2024-11-25 17:46:20,304 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7529/0.8154/0.9960. [2024-11-25 17:46:20,305 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9984 [2024-11-25 17:46:20,305 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5098/0.6325 [2024-11-25 17:46:20,305 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:46:20,306 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7529 [2024-11-25 17:46:20,306 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7529 [2024-11-25 17:46:20,306 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:46:24,871 INFO misc.py line 119 2586773] Train: [17/50][1/376] Data 0.120 (0.120) Batch 0.649 (0.649) Remain 02:18:13 loss: 0.2358 Lr: 0.00322 [2024-11-25 17:46:25,435 INFO misc.py line 119 2586773] Train: [17/50][2/376] Data 0.003 (0.003) Batch 0.563 (0.563) Remain 02:00:01 loss: 0.2386 Lr: 0.00322 [2024-11-25 17:46:25,892 INFO misc.py line 119 2586773] Train: [17/50][3/376] Data 0.003 (0.003) Batch 0.457 (0.457) Remain 01:37:22 loss: 0.2486 Lr: 0.00322 [2024-11-25 17:46:26,366 INFO misc.py line 119 2586773] Train: [17/50][4/376] Data 0.002 (0.002) Batch 0.475 (0.475) Remain 01:41:04 loss: 0.2090 Lr: 0.00322 [2024-11-25 17:46:26,850 INFO misc.py line 119 2586773] Train: [17/50][5/376] Data 0.002 (0.002) Batch 0.483 (0.479) Remain 01:41:59 loss: 0.2093 Lr: 0.00322 [2024-11-25 17:46:27,374 INFO misc.py line 119 2586773] Train: [17/50][6/376] Data 0.003 (0.002) Batch 0.525 (0.494) Remain 01:45:14 loss: 0.2359 Lr: 0.00322 [2024-11-25 17:46:27,862 INFO misc.py line 119 2586773] Train: [17/50][7/376] Data 0.003 (0.002) Batch 0.488 (0.493) Remain 01:44:53 loss: 0.3563 Lr: 0.00322 [2024-11-25 17:46:28,384 INFO misc.py line 119 2586773] Train: [17/50][8/376] Data 0.003 (0.002) Batch 0.522 (0.498) Remain 01:46:08 loss: 0.2512 Lr: 0.00322 [2024-11-25 17:46:28,889 INFO misc.py line 119 2586773] Train: [17/50][9/376] Data 0.003 (0.002) Batch 0.505 (0.500) Remain 01:46:22 loss: 0.2261 Lr: 0.00322 [2024-11-25 17:46:29,409 INFO misc.py line 119 2586773] Train: [17/50][10/376] Data 0.002 (0.002) Batch 0.519 (0.502) Remain 01:46:58 loss: 0.2242 Lr: 0.00322 [2024-11-25 17:46:29,940 INFO misc.py line 119 2586773] Train: [17/50][11/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 01:47:43 loss: 0.2806 Lr: 0.00322 [2024-11-25 17:46:30,421 INFO misc.py line 119 2586773] Train: [17/50][12/376] Data 0.003 (0.002) Batch 0.481 (0.503) Remain 01:47:07 loss: 0.2245 Lr: 0.00322 [2024-11-25 17:46:30,970 INFO misc.py line 119 2586773] Train: [17/50][13/376] Data 0.003 (0.002) Batch 0.549 (0.508) Remain 01:48:06 loss: 0.2472 Lr: 0.00322 [2024-11-25 17:46:31,515 INFO misc.py line 119 2586773] Train: [17/50][14/376] Data 0.003 (0.002) Batch 0.544 (0.511) Remain 01:48:47 loss: 0.2326 Lr: 0.00322 [2024-11-25 17:46:31,990 INFO misc.py line 119 2586773] Train: [17/50][15/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 01:48:09 loss: 0.2192 Lr: 0.00322 [2024-11-25 17:46:32,501 INFO misc.py line 119 2586773] Train: [17/50][16/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 01:48:11 loss: 0.1998 Lr: 0.00321 [2024-11-25 17:46:32,986 INFO misc.py line 119 2586773] Train: [17/50][17/376] Data 0.003 (0.002) Batch 0.485 (0.507) Remain 01:47:49 loss: 0.2322 Lr: 0.00321 [2024-11-25 17:46:33,518 INFO misc.py line 119 2586773] Train: [17/50][18/376] Data 0.003 (0.002) Batch 0.532 (0.508) Remain 01:48:10 loss: 0.2223 Lr: 0.00321 [2024-11-25 17:46:34,061 INFO misc.py line 119 2586773] Train: [17/50][19/376] Data 0.002 (0.002) Batch 0.543 (0.511) Remain 01:48:37 loss: 0.2281 Lr: 0.00321 [2024-11-25 17:46:34,560 INFO misc.py line 119 2586773] Train: [17/50][20/376] Data 0.003 (0.002) Batch 0.499 (0.510) Remain 01:48:28 loss: 0.2392 Lr: 0.00321 [2024-11-25 17:46:35,029 INFO misc.py line 119 2586773] Train: [17/50][21/376] Data 0.003 (0.002) Batch 0.469 (0.508) Remain 01:47:58 loss: 0.2884 Lr: 0.00321 [2024-11-25 17:46:35,577 INFO misc.py line 119 2586773] Train: [17/50][22/376] Data 0.003 (0.002) Batch 0.548 (0.510) Remain 01:48:25 loss: 0.3520 Lr: 0.00321 [2024-11-25 17:46:36,083 INFO misc.py line 119 2586773] Train: [17/50][23/376] Data 0.003 (0.002) Batch 0.507 (0.510) Remain 01:48:22 loss: 0.2182 Lr: 0.00321 [2024-11-25 17:46:36,565 INFO misc.py line 119 2586773] Train: [17/50][24/376] Data 0.002 (0.002) Batch 0.482 (0.508) Remain 01:48:05 loss: 0.2141 Lr: 0.00321 [2024-11-25 17:46:37,051 INFO misc.py line 119 2586773] Train: [17/50][25/376] Data 0.003 (0.003) Batch 0.486 (0.507) Remain 01:47:51 loss: 0.2173 Lr: 0.00321 [2024-11-25 17:46:37,618 INFO misc.py line 119 2586773] Train: [17/50][26/376] Data 0.003 (0.003) Batch 0.567 (0.510) Remain 01:48:24 loss: 0.2942 Lr: 0.00321 [2024-11-25 17:46:38,139 INFO misc.py line 119 2586773] Train: [17/50][27/376] Data 0.003 (0.003) Batch 0.521 (0.510) Remain 01:48:29 loss: 0.2379 Lr: 0.00321 [2024-11-25 17:46:38,645 INFO misc.py line 119 2586773] Train: [17/50][28/376] Data 0.003 (0.003) Batch 0.506 (0.510) Remain 01:48:27 loss: 0.2040 Lr: 0.00321 [2024-11-25 17:46:39,164 INFO misc.py line 119 2586773] Train: [17/50][29/376] Data 0.002 (0.003) Batch 0.519 (0.510) Remain 01:48:31 loss: 0.2585 Lr: 0.00321 [2024-11-25 17:46:39,678 INFO misc.py line 119 2586773] Train: [17/50][30/376] Data 0.002 (0.003) Batch 0.514 (0.511) Remain 01:48:32 loss: 0.2406 Lr: 0.00321 [2024-11-25 17:46:40,144 INFO misc.py line 119 2586773] Train: [17/50][31/376] Data 0.003 (0.003) Batch 0.466 (0.509) Remain 01:48:11 loss: 0.3272 Lr: 0.00321 [2024-11-25 17:46:40,634 INFO misc.py line 119 2586773] Train: [17/50][32/376] Data 0.002 (0.003) Batch 0.491 (0.508) Remain 01:48:02 loss: 0.3343 Lr: 0.00321 [2024-11-25 17:46:41,128 INFO misc.py line 119 2586773] Train: [17/50][33/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 01:47:55 loss: 0.2257 Lr: 0.00321 [2024-11-25 17:46:41,646 INFO misc.py line 119 2586773] Train: [17/50][34/376] Data 0.003 (0.003) Batch 0.518 (0.508) Remain 01:47:59 loss: 0.2833 Lr: 0.00321 [2024-11-25 17:46:42,143 INFO misc.py line 119 2586773] Train: [17/50][35/376] Data 0.002 (0.003) Batch 0.497 (0.508) Remain 01:47:54 loss: 0.2469 Lr: 0.00321 [2024-11-25 17:46:42,624 INFO misc.py line 119 2586773] Train: [17/50][36/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 01:47:43 loss: 0.2299 Lr: 0.00321 [2024-11-25 17:46:43,125 INFO misc.py line 119 2586773] Train: [17/50][37/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 01:47:41 loss: 0.2699 Lr: 0.00321 [2024-11-25 17:46:43,621 INFO misc.py line 119 2586773] Train: [17/50][38/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:47:36 loss: 0.2431 Lr: 0.00321 [2024-11-25 17:46:44,153 INFO misc.py line 119 2586773] Train: [17/50][39/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 01:47:44 loss: 0.2527 Lr: 0.00321 [2024-11-25 17:46:44,644 INFO misc.py line 119 2586773] Train: [17/50][40/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 01:47:38 loss: 0.2333 Lr: 0.00321 [2024-11-25 17:46:45,131 INFO misc.py line 119 2586773] Train: [17/50][41/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 01:47:31 loss: 0.1971 Lr: 0.00321 [2024-11-25 17:46:45,683 INFO misc.py line 119 2586773] Train: [17/50][42/376] Data 0.002 (0.002) Batch 0.552 (0.507) Remain 01:47:46 loss: 0.2583 Lr: 0.00321 [2024-11-25 17:46:46,200 INFO misc.py line 119 2586773] Train: [17/50][43/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 01:47:48 loss: 0.2596 Lr: 0.00321 [2024-11-25 17:46:46,701 INFO misc.py line 119 2586773] Train: [17/50][44/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 01:47:45 loss: 0.2196 Lr: 0.00321 [2024-11-25 17:46:47,226 INFO misc.py line 119 2586773] Train: [17/50][45/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 01:47:50 loss: 0.2270 Lr: 0.00321 [2024-11-25 17:46:47,736 INFO misc.py line 119 2586773] Train: [17/50][46/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 01:47:51 loss: 0.2049 Lr: 0.00321 [2024-11-25 17:46:48,239 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Batch 0.477 (0.505) Remain 01:45:46 loss: 0.1968 Lr: 0.00316 [2024-11-25 17:48:13,469 INFO misc.py line 119 2586773] Train: [17/50][216/376] Data 0.002 (0.002) Batch 0.536 (0.505) Remain 01:45:47 loss: 0.3315 Lr: 0.00316 [2024-11-25 17:48:13,947 INFO misc.py line 119 2586773] Train: [17/50][217/376] Data 0.002 (0.002) Batch 0.478 (0.505) Remain 01:45:45 loss: 0.2431 Lr: 0.00316 [2024-11-25 17:48:14,436 INFO misc.py line 119 2586773] Train: [17/50][218/376] Data 0.003 (0.002) Batch 0.489 (0.505) Remain 01:45:44 loss: 0.2110 Lr: 0.00316 [2024-11-25 17:48:14,915 INFO misc.py line 119 2586773] Train: [17/50][219/376] Data 0.003 (0.002) Batch 0.479 (0.505) Remain 01:45:42 loss: 0.2400 Lr: 0.00316 [2024-11-25 17:48:15,465 INFO misc.py line 119 2586773] Train: [17/50][220/376] Data 0.003 (0.002) Batch 0.550 (0.505) Remain 01:45:44 loss: 0.1932 Lr: 0.00316 [2024-11-25 17:48:15,996 INFO misc.py line 119 2586773] Train: [17/50][221/376] Data 0.002 (0.002) Batch 0.531 (0.505) Remain 01:45:45 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line 119 2586773] Train: [17/50][315/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 01:45:04 loss: 0.2277 Lr: 0.00313 [2024-11-25 17:49:04,153 INFO misc.py line 119 2586773] Train: [17/50][316/376] Data 0.003 (0.002) Batch 0.507 (0.506) Remain 01:45:04 loss: 0.2094 Lr: 0.00313 [2024-11-25 17:49:04,692 INFO misc.py line 119 2586773] Train: [17/50][317/376] Data 0.002 (0.002) Batch 0.539 (0.506) Remain 01:45:04 loss: 0.2241 Lr: 0.00313 [2024-11-25 17:49:05,219 INFO misc.py line 119 2586773] Train: [17/50][318/376] Data 0.002 (0.002) Batch 0.527 (0.506) Remain 01:45:05 loss: 0.2293 Lr: 0.00313 [2024-11-25 17:49:05,718 INFO misc.py line 119 2586773] Train: [17/50][319/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 01:45:04 loss: 0.1702 Lr: 0.00313 [2024-11-25 17:49:06,195 INFO misc.py line 119 2586773] Train: [17/50][320/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 01:45:02 loss: 0.2494 Lr: 0.00313 [2024-11-25 17:49:06,713 INFO misc.py line 119 2586773] Train: 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Batch 0.533 (0.506) Remain 01:44:58 loss: 0.2134 Lr: 0.00313 [2024-11-25 17:49:10,233 INFO misc.py line 119 2586773] Train: [17/50][328/376] Data 0.003 (0.002) Batch 0.513 (0.506) Remain 01:44:58 loss: 0.1996 Lr: 0.00313 [2024-11-25 17:49:10,739 INFO misc.py line 119 2586773] Train: [17/50][329/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 01:44:58 loss: 0.2082 Lr: 0.00313 [2024-11-25 17:49:11,219 INFO misc.py line 119 2586773] Train: [17/50][330/376] Data 0.003 (0.002) Batch 0.480 (0.506) Remain 01:44:56 loss: 0.2272 Lr: 0.00313 [2024-11-25 17:49:11,731 INFO misc.py line 119 2586773] Train: [17/50][331/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 01:44:56 loss: 0.2656 Lr: 0.00313 [2024-11-25 17:49:12,206 INFO misc.py line 119 2586773] Train: [17/50][332/376] Data 0.003 (0.002) Batch 0.475 (0.506) Remain 01:44:54 loss: 0.2423 Lr: 0.00313 [2024-11-25 17:49:12,689 INFO misc.py line 119 2586773] Train: [17/50][333/376] Data 0.003 (0.002) Batch 0.483 (0.505) Remain 01:44:53 loss: 0.3729 Lr: 0.00313 [2024-11-25 17:49:13,235 INFO misc.py line 119 2586773] Train: [17/50][334/376] Data 0.002 (0.002) Batch 0.546 (0.506) Remain 01:44:54 loss: 0.2159 Lr: 0.00313 [2024-11-25 17:49:13,746 INFO misc.py line 119 2586773] Train: [17/50][335/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 01:44:54 loss: 0.2540 Lr: 0.00313 [2024-11-25 17:49:14,252 INFO misc.py line 119 2586773] Train: [17/50][336/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 01:44:53 loss: 0.2172 Lr: 0.00313 [2024-11-25 17:49:14,809 INFO misc.py line 119 2586773] Train: [17/50][337/376] Data 0.002 (0.002) Batch 0.557 (0.506) Remain 01:44:54 loss: 0.2178 Lr: 0.00312 [2024-11-25 17:49:15,300 INFO misc.py line 119 2586773] Train: [17/50][338/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 01:44:53 loss: 0.2612 Lr: 0.00312 [2024-11-25 17:49:15,822 INFO misc.py line 119 2586773] Train: [17/50][339/376] Data 0.002 (0.002) Batch 0.522 (0.506) Remain 01:44:53 loss: 0.2587 Lr: 0.00312 [2024-11-25 17:49:16,360 INFO misc.py line 119 2586773] Train: [17/50][340/376] Data 0.002 (0.002) Batch 0.538 (0.506) Remain 01:44:54 loss: 0.2343 Lr: 0.00312 [2024-11-25 17:49:16,855 INFO misc.py line 119 2586773] Train: [17/50][341/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 01:44:53 loss: 0.2226 Lr: 0.00312 [2024-11-25 17:49:17,355 INFO misc.py line 119 2586773] Train: [17/50][342/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 01:44:53 loss: 0.1975 Lr: 0.00312 [2024-11-25 17:49:17,853 INFO misc.py line 119 2586773] Train: [17/50][343/376] Data 0.002 (0.002) Batch 0.497 (0.506) Remain 01:44:52 loss: 0.1905 Lr: 0.00312 [2024-11-25 17:49:18,329 INFO misc.py line 119 2586773] Train: [17/50][344/376] Data 0.003 (0.002) Batch 0.477 (0.506) Remain 01:44:50 loss: 0.2793 Lr: 0.00312 [2024-11-25 17:49:18,828 INFO misc.py line 119 2586773] Train: [17/50][345/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 01:44:49 loss: 0.2829 Lr: 0.00312 [2024-11-25 17:49:19,335 INFO misc.py line 119 2586773] Train: [17/50][346/376] Data 0.002 (0.002) Batch 0.507 (0.506) Remain 01:44:49 loss: 0.2269 Lr: 0.00312 [2024-11-25 17:49:19,874 INFO misc.py line 119 2586773] Train: [17/50][347/376] Data 0.002 (0.002) Batch 0.540 (0.506) Remain 01:44:50 loss: 0.2510 Lr: 0.00312 [2024-11-25 17:49:20,375 INFO misc.py line 119 2586773] Train: [17/50][348/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 01:44:49 loss: 0.2472 Lr: 0.00312 [2024-11-25 17:49:20,851 INFO misc.py line 119 2586773] Train: [17/50][349/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 01:44:47 loss: 0.3451 Lr: 0.00312 [2024-11-25 17:49:21,358 INFO misc.py line 119 2586773] Train: [17/50][350/376] Data 0.002 (0.002) Batch 0.507 (0.506) Remain 01:44:47 loss: 0.2018 Lr: 0.00312 [2024-11-25 17:49:21,877 INFO misc.py line 119 2586773] Train: [17/50][351/376] Data 0.002 (0.002) Batch 0.519 (0.506) Remain 01:44:47 loss: 0.2101 Lr: 0.00312 [2024-11-25 17:49:22,393 INFO misc.py line 119 2586773] Train: [17/50][352/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 01:44:47 loss: 0.2230 Lr: 0.00312 [2024-11-25 17:49:22,901 INFO misc.py line 119 2586773] Train: [17/50][353/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 01:44:46 loss: 0.2050 Lr: 0.00312 [2024-11-25 17:49:23,386 INFO misc.py line 119 2586773] Train: [17/50][354/376] Data 0.002 (0.002) Batch 0.485 (0.506) Remain 01:44:45 loss: 0.2490 Lr: 0.00312 [2024-11-25 17:49:23,866 INFO misc.py line 119 2586773] Train: [17/50][355/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 01:44:44 loss: 0.2333 Lr: 0.00312 [2024-11-25 17:49:24,379 INFO misc.py line 119 2586773] Train: [17/50][356/376] Data 0.002 (0.002) Batch 0.513 (0.506) Remain 01:44:43 loss: 0.2460 Lr: 0.00312 [2024-11-25 17:49:24,925 INFO misc.py line 119 2586773] Train: [17/50][357/376] Data 0.002 (0.002) Batch 0.546 (0.506) Remain 01:44:44 loss: 0.2585 Lr: 0.00312 [2024-11-25 17:49:25,394 INFO misc.py line 119 2586773] Train: [17/50][358/376] Data 0.002 (0.002) Batch 0.470 (0.506) Remain 01:44:43 loss: 0.2350 Lr: 0.00312 [2024-11-25 17:49:25,912 INFO misc.py line 119 2586773] Train: [17/50][359/376] Data 0.003 (0.002) Batch 0.518 (0.506) Remain 01:44:43 loss: 0.2000 Lr: 0.00312 [2024-11-25 17:49:26,450 INFO misc.py line 119 2586773] Train: [17/50][360/376] Data 0.002 (0.002) Batch 0.538 (0.506) Remain 01:44:43 loss: 0.2469 Lr: 0.00312 [2024-11-25 17:49:26,959 INFO misc.py line 119 2586773] Train: [17/50][361/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 01:44:43 loss: 0.2541 Lr: 0.00312 [2024-11-25 17:49:27,436 INFO misc.py line 119 2586773] Train: [17/50][362/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 01:44:41 loss: 0.2073 Lr: 0.00312 [2024-11-25 17:49:27,952 INFO misc.py line 119 2586773] Train: [17/50][363/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 01:44:41 loss: 0.2173 Lr: 0.00312 [2024-11-25 17:49:28,439 INFO misc.py line 119 2586773] Train: [17/50][364/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 01:44:40 loss: 0.1798 Lr: 0.00312 [2024-11-25 17:49:28,929 INFO misc.py line 119 2586773] Train: [17/50][365/376] Data 0.002 (0.002) Batch 0.490 (0.506) Remain 01:44:39 loss: 0.2399 Lr: 0.00312 [2024-11-25 17:49:29,424 INFO misc.py line 119 2586773] Train: [17/50][366/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 01:44:38 loss: 0.1923 Lr: 0.00312 [2024-11-25 17:49:29,954 INFO misc.py line 119 2586773] Train: [17/50][367/376] Data 0.002 (0.002) Batch 0.530 (0.506) Remain 01:44:38 loss: 0.2557 Lr: 0.00312 [2024-11-25 17:49:30,490 INFO misc.py line 119 2586773] Train: [17/50][368/376] Data 0.002 (0.002) Batch 0.536 (0.506) Remain 01:44:39 loss: 0.3209 Lr: 0.00312 [2024-11-25 17:49:30,976 INFO misc.py line 119 2586773] Train: [17/50][369/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 01:44:38 loss: 0.2320 Lr: 0.00312 [2024-11-25 17:49:31,469 INFO misc.py line 119 2586773] Train: [17/50][370/376] Data 0.002 (0.002) Batch 0.492 (0.506) Remain 01:44:37 loss: 0.2673 Lr: 0.00312 [2024-11-25 17:49:32,001 INFO misc.py line 119 2586773] Train: [17/50][371/376] Data 0.002 (0.002) Batch 0.532 (0.506) Remain 01:44:37 loss: 0.2000 Lr: 0.00311 [2024-11-25 17:49:32,490 INFO misc.py line 119 2586773] Train: [17/50][372/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 01:44:36 loss: 0.2165 Lr: 0.00311 [2024-11-25 17:49:32,975 INFO misc.py line 119 2586773] Train: [17/50][373/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 01:44:35 loss: 0.2386 Lr: 0.00311 [2024-11-25 17:49:33,464 INFO misc.py line 119 2586773] Train: [17/50][374/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 01:44:34 loss: 0.2134 Lr: 0.00311 [2024-11-25 17:49:33,962 INFO misc.py line 119 2586773] Train: [17/50][375/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 01:44:33 loss: 0.2372 Lr: 0.00311 [2024-11-25 17:49:34,476 INFO misc.py line 119 2586773] Train: [17/50][376/376] Data 0.002 (0.002) Batch 0.514 (0.506) Remain 01:44:33 loss: 0.1951 Lr: 0.00311 [2024-11-25 17:49:34,476 INFO misc.py line 136 2586773] Train result: loss: 0.2402 [2024-11-25 17:49:34,477 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:49:45,327 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1911 [2024-11-25 17:49:45,590 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2262 [2024-11-25 17:49:45,854 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3006 [2024-11-25 17:49:46,081 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2091 [2024-11-25 17:49:46,345 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3061 [2024-11-25 17:49:46,613 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2193 [2024-11-25 17:49:46,844 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2272 [2024-11-25 17:49:47,115 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2329 [2024-11-25 17:49:47,342 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2528 [2024-11-25 17:49:47,603 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2812 [2024-11-25 17:49:47,843 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2190 [2024-11-25 17:49:48,115 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2322 [2024-11-25 17:49:48,381 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2474 [2024-11-25 17:49:48,644 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2670 [2024-11-25 17:49:48,877 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2494 [2024-11-25 17:49:49,117 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3039 [2024-11-25 17:49:49,386 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3333 [2024-11-25 17:49:49,640 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2304 [2024-11-25 17:49:49,871 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2145 [2024-11-25 17:49:50,133 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2537 [2024-11-25 17:49:50,366 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2603 [2024-11-25 17:49:50,635 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2864 [2024-11-25 17:49:50,880 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2243 [2024-11-25 17:49:51,147 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2490 [2024-11-25 17:49:51,422 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2364 [2024-11-25 17:49:51,655 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2807 [2024-11-25 17:49:51,913 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2812 [2024-11-25 17:49:52,159 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2550 [2024-11-25 17:49:52,425 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3188 [2024-11-25 17:49:52,678 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3445 [2024-11-25 17:49:52,922 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2755 [2024-11-25 17:49:53,195 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2244 [2024-11-25 17:49:53,419 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2624 [2024-11-25 17:49:53,661 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2186 [2024-11-25 17:49:53,921 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2442 [2024-11-25 17:49:54,168 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2441 [2024-11-25 17:49:54,394 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2074 [2024-11-25 17:49:54,668 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2543 [2024-11-25 17:49:54,900 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2685 [2024-11-25 17:49:55,143 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2713 [2024-11-25 17:49:55,412 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2975 [2024-11-25 17:49:55,670 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3034 [2024-11-25 17:49:55,908 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2521 [2024-11-25 17:49:56,140 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2461 [2024-11-25 17:49:56,377 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2335 [2024-11-25 17:49:56,624 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2565 [2024-11-25 17:49:56,888 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2667 [2024-11-25 17:49:57,140 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3188 [2024-11-25 17:49:57,370 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2196 [2024-11-25 17:49:57,604 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2185 [2024-11-25 17:49:57,829 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2533 [2024-11-25 17:49:58,088 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2608 [2024-11-25 17:49:58,352 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2398 [2024-11-25 17:49:58,613 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3001 [2024-11-25 17:49:58,851 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2428 [2024-11-25 17:49:59,095 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2382 [2024-11-25 17:49:59,360 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2924 [2024-11-25 17:49:59,635 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2609 [2024-11-25 17:49:59,891 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2896 [2024-11-25 17:50:00,151 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2685 [2024-11-25 17:50:00,401 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2410 [2024-11-25 17:50:00,671 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2466 [2024-11-25 17:50:00,901 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2357 [2024-11-25 17:50:01,161 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.3020 [2024-11-25 17:50:01,428 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2925 [2024-11-25 17:50:01,691 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2413 [2024-11-25 17:50:01,938 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2090 [2024-11-25 17:50:02,195 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.3016 [2024-11-25 17:50:02,463 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2246 [2024-11-25 17:50:02,724 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3179 [2024-11-25 17:50:02,968 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2197 [2024-11-25 17:50:03,202 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2599 [2024-11-25 17:50:03,460 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2697 [2024-11-25 17:50:03,702 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2822 [2024-11-25 17:50:03,918 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2561 [2024-11-25 17:50:04,138 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2229 [2024-11-25 17:50:04,409 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2540 [2024-11-25 17:50:04,646 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2396 [2024-11-25 17:50:04,903 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2343 [2024-11-25 17:50:05,154 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3162 [2024-11-25 17:50:05,397 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2359 [2024-11-25 17:50:05,657 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2581 [2024-11-25 17:50:05,907 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1996 [2024-11-25 17:50:06,158 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2696 [2024-11-25 17:50:06,431 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2408 [2024-11-25 17:50:06,666 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2649 [2024-11-25 17:50:06,932 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.3232 [2024-11-25 17:50:07,193 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2669 [2024-11-25 17:50:07,441 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2851 [2024-11-25 17:50:07,689 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2425 [2024-11-25 17:50:07,923 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2399 [2024-11-25 17:50:08,175 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2708 [2024-11-25 17:50:08,440 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2672 [2024-11-25 17:50:08,706 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2041 [2024-11-25 17:50:08,973 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2314 [2024-11-25 17:50:09,221 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2163 [2024-11-25 17:50:09,488 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2529 [2024-11-25 17:50:09,708 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3056 [2024-11-25 17:50:09,983 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2577 [2024-11-25 17:50:10,225 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2800 [2024-11-25 17:50:10,493 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2227 [2024-11-25 17:50:10,753 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.3064 [2024-11-25 17:50:11,014 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2670 [2024-11-25 17:50:11,268 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3239 [2024-11-25 17:50:11,489 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2441 [2024-11-25 17:50:11,724 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2230 [2024-11-25 17:50:11,978 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2316 [2024-11-25 17:50:12,246 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2840 [2024-11-25 17:50:12,480 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2934 [2024-11-25 17:50:12,741 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2475 [2024-11-25 17:50:13,004 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2246 [2024-11-25 17:50:13,226 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2478 [2024-11-25 17:50:13,460 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2139 [2024-11-25 17:50:13,681 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2284 [2024-11-25 17:50:13,907 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2161 [2024-11-25 17:50:14,181 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3542 [2024-11-25 17:50:14,441 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2956 [2024-11-25 17:50:14,708 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2881 [2024-11-25 17:50:14,973 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2419 [2024-11-25 17:50:15,236 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3317 [2024-11-25 17:50:15,495 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2859 [2024-11-25 17:50:15,758 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2186 [2024-11-25 17:50:16,013 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2892 [2024-11-25 17:50:16,278 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2799 [2024-11-25 17:50:16,539 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2562 [2024-11-25 17:50:16,789 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2848 [2024-11-25 17:50:17,020 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2188 [2024-11-25 17:50:17,280 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2866 [2024-11-25 17:50:17,518 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2729 [2024-11-25 17:50:17,743 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2020 [2024-11-25 17:50:17,956 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2432 [2024-11-25 17:50:18,171 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2175 [2024-11-25 17:50:18,907 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7558/0.8215/0.9960. [2024-11-25 17:50:18,907 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9984 [2024-11-25 17:50:18,907 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5157/0.6446 [2024-11-25 17:50:18,908 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:50:18,909 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7558 [2024-11-25 17:50:18,909 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7558 [2024-11-25 17:50:18,909 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:50:23,404 INFO misc.py line 119 2586773] Train: [18/50][1/376] Data 0.121 (0.121) Batch 0.608 (0.608) Remain 02:05:38 loss: 0.2186 Lr: 0.00311 [2024-11-25 17:50:23,949 INFO misc.py line 119 2586773] Train: [18/50][2/376] Data 0.002 (0.002) Batch 0.546 (0.546) Remain 01:52:50 loss: 0.2007 Lr: 0.00311 [2024-11-25 17:50:24,513 INFO misc.py line 119 2586773] Train: [18/50][3/376] Data 0.003 (0.003) Batch 0.563 (0.563) Remain 01:56:27 loss: 0.2245 Lr: 0.00311 [2024-11-25 17:50:24,985 INFO misc.py line 119 2586773] Train: [18/50][4/376] Data 0.002 (0.002) Batch 0.473 (0.473) Remain 01:37:41 loss: 0.2220 Lr: 0.00311 [2024-11-25 17:50:25,482 INFO misc.py line 119 2586773] Train: [18/50][5/376] Data 0.002 (0.002) Batch 0.497 (0.485) Remain 01:40:13 loss: 0.2177 Lr: 0.00311 [2024-11-25 17:50:25,971 INFO misc.py line 119 2586773] Train: [18/50][6/376] Data 0.002 (0.002) Batch 0.489 (0.486) Remain 01:40:29 loss: 0.2297 Lr: 0.00311 [2024-11-25 17:50:26,481 INFO misc.py line 119 2586773] Train: [18/50][7/376] Data 0.003 (0.002) Batch 0.510 (0.492) Remain 01:41:42 loss: 0.2247 Lr: 0.00311 [2024-11-25 17:50:27,015 INFO misc.py line 119 2586773] Train: [18/50][8/376] Data 0.003 (0.002) Batch 0.534 (0.500) Remain 01:43:24 loss: 0.2444 Lr: 0.00311 [2024-11-25 17:50:27,546 INFO misc.py line 119 2586773] Train: [18/50][9/376] Data 0.002 (0.002) Batch 0.531 (0.505) Remain 01:44:27 loss: 0.2619 Lr: 0.00311 [2024-11-25 17:50:28,008 INFO misc.py line 119 2586773] Train: [18/50][10/376] Data 0.003 (0.002) Batch 0.463 (0.499) Remain 01:43:11 loss: 0.2521 Lr: 0.00311 [2024-11-25 17:50:28,497 INFO misc.py line 119 2586773] Train: [18/50][11/376] Data 0.003 (0.002) Batch 0.489 (0.498) Remain 01:42:54 loss: 0.2766 Lr: 0.00311 [2024-11-25 17:50:28,988 INFO misc.py line 119 2586773] Train: [18/50][12/376] Data 0.002 (0.002) Batch 0.491 (0.497) Remain 01:42:44 loss: 0.2125 Lr: 0.00311 [2024-11-25 17:50:29,464 INFO misc.py line 119 2586773] Train: [18/50][13/376] Data 0.003 (0.003) Batch 0.476 (0.495) Remain 01:42:16 loss: 0.2485 Lr: 0.00311 [2024-11-25 17:50:29,990 INFO misc.py line 119 2586773] Train: [18/50][14/376] Data 0.002 (0.002) Batch 0.526 (0.498) Remain 01:42:51 loss: 0.2277 Lr: 0.00311 [2024-11-25 17:50:30,475 INFO misc.py line 119 2586773] Train: [18/50][15/376] Data 0.003 (0.003) Batch 0.485 (0.497) Remain 01:42:37 loss: 0.2432 Lr: 0.00311 [2024-11-25 17:50:30,977 INFO misc.py line 119 2586773] Train: [18/50][16/376] Data 0.003 (0.003) Batch 0.502 (0.497) Remain 01:42:41 loss: 0.1933 Lr: 0.00311 [2024-11-25 17:50:31,497 INFO misc.py line 119 2586773] Train: [18/50][17/376] Data 0.003 (0.003) Batch 0.520 (0.499) Remain 01:43:01 loss: 0.2777 Lr: 0.00311 [2024-11-25 17:50:31,999 INFO misc.py line 119 2586773] Train: [18/50][18/376] Data 0.002 (0.003) Batch 0.502 (0.499) Remain 01:43:03 loss: 0.1978 Lr: 0.00311 [2024-11-25 17:50:32,521 INFO misc.py line 119 2586773] Train: [18/50][19/376] Data 0.003 (0.003) Batch 0.522 (0.501) Remain 01:43:21 loss: 0.2086 Lr: 0.00311 [2024-11-25 17:50:33,044 INFO misc.py line 119 2586773] Train: [18/50][20/376] Data 0.003 (0.003) Batch 0.523 (0.502) Remain 01:43:36 loss: 0.2378 Lr: 0.00311 [2024-11-25 17:50:33,568 INFO misc.py line 119 2586773] Train: [18/50][21/376] Data 0.003 (0.003) Batch 0.524 (0.503) Remain 01:43:51 loss: 0.2203 Lr: 0.00311 [2024-11-25 17:50:34,091 INFO misc.py line 119 2586773] Train: [18/50][22/376] Data 0.002 (0.003) Batch 0.523 (0.504) Remain 01:44:04 loss: 0.1847 Lr: 0.00311 [2024-11-25 17:50:34,597 INFO misc.py line 119 2586773] Train: [18/50][23/376] Data 0.003 (0.003) Batch 0.506 (0.504) Remain 01:44:04 loss: 0.2587 Lr: 0.00311 [2024-11-25 17:50:35,116 INFO misc.py line 119 2586773] Train: [18/50][24/376] Data 0.003 (0.003) Batch 0.519 (0.505) Remain 01:44:12 loss: 0.3089 Lr: 0.00311 [2024-11-25 17:50:35,606 INFO misc.py line 119 2586773] Train: [18/50][25/376] Data 0.003 (0.003) Batch 0.490 (0.504) Remain 01:44:03 loss: 0.2103 Lr: 0.00311 [2024-11-25 17:50:36,089 INFO misc.py line 119 2586773] Train: [18/50][26/376] Data 0.003 (0.003) Batch 0.483 (0.503) Remain 01:43:52 loss: 0.2430 Lr: 0.00311 [2024-11-25 17:50:36,652 INFO misc.py line 119 2586773] Train: [18/50][27/376] Data 0.003 (0.003) Batch 0.563 (0.506) Remain 01:44:22 loss: 0.2172 Lr: 0.00311 [2024-11-25 17:50:37,136 INFO misc.py line 119 2586773] Train: [18/50][28/376] Data 0.003 (0.003) Batch 0.484 (0.505) Remain 01:44:11 loss: 0.2655 Lr: 0.00311 [2024-11-25 17:50:37,651 INFO misc.py line 119 2586773] Train: [18/50][29/376] Data 0.003 (0.003) Batch 0.515 (0.505) Remain 01:44:15 loss: 0.1883 Lr: 0.00311 [2024-11-25 17:50:38,156 INFO misc.py line 119 2586773] Train: [18/50][30/376] Data 0.003 (0.003) Batch 0.505 (0.505) Remain 01:44:14 loss: 0.2723 Lr: 0.00310 [2024-11-25 17:50:38,676 INFO misc.py line 119 2586773] Train: [18/50][31/376] Data 0.003 (0.003) Batch 0.520 (0.506) Remain 01:44:20 loss: 0.2569 Lr: 0.00310 [2024-11-25 17:50:39,216 INFO misc.py line 119 2586773] Train: [18/50][32/376] Data 0.002 (0.003) Batch 0.540 (0.507) Remain 01:44:34 loss: 0.2275 Lr: 0.00310 [2024-11-25 17:50:39,720 INFO misc.py line 119 2586773] Train: [18/50][33/376] Data 0.003 (0.003) Batch 0.504 (0.507) Remain 01:44:33 loss: 0.2043 Lr: 0.00310 [2024-11-25 17:50:40,249 INFO misc.py line 119 2586773] Train: [18/50][34/376] Data 0.002 (0.003) Batch 0.529 (0.508) Remain 01:44:41 loss: 0.2115 Lr: 0.00310 [2024-11-25 17:50:40,739 INFO misc.py line 119 2586773] Train: [18/50][35/376] Data 0.003 (0.003) Batch 0.489 (0.507) Remain 01:44:33 loss: 0.2518 Lr: 0.00310 [2024-11-25 17:50:41,266 INFO misc.py line 119 2586773] Train: [18/50][36/376] Data 0.002 (0.003) Batch 0.527 (0.508) Remain 01:44:40 loss: 0.1965 Lr: 0.00310 [2024-11-25 17:50:41,796 INFO misc.py line 119 2586773] Train: [18/50][37/376] Data 0.003 (0.003) Batch 0.530 (0.508) Remain 01:44:48 loss: 0.1860 Lr: 0.00310 [2024-11-25 17:50:42,318 INFO misc.py line 119 2586773] Train: [18/50][38/376] Data 0.003 (0.003) Batch 0.521 (0.509) Remain 01:44:52 loss: 0.2000 Lr: 0.00310 [2024-11-25 17:50:42,793 INFO misc.py line 119 2586773] Train: [18/50][39/376] Data 0.002 (0.003) Batch 0.475 (0.508) Remain 01:44:40 loss: 0.2450 Lr: 0.00310 [2024-11-25 17:50:43,261 INFO misc.py line 119 2586773] Train: [18/50][40/376] Data 0.002 (0.003) Batch 0.468 (0.507) Remain 01:44:26 loss: 0.2317 Lr: 0.00310 [2024-11-25 17:50:43,734 INFO misc.py line 119 2586773] Train: [18/50][41/376] Data 0.002 (0.003) Batch 0.473 (0.506) Remain 01:44:15 loss: 0.2280 Lr: 0.00310 [2024-11-25 17:50:44,217 INFO misc.py line 119 2586773] Train: [18/50][42/376] Data 0.002 (0.003) Batch 0.483 (0.505) Remain 01:44:07 loss: 0.2063 Lr: 0.00310 [2024-11-25 17:50:44,718 INFO misc.py line 119 2586773] Train: [18/50][43/376] Data 0.002 (0.003) Batch 0.500 (0.505) Remain 01:44:05 loss: 0.1962 Lr: 0.00310 [2024-11-25 17:50:45,228 INFO misc.py line 119 2586773] Train: [18/50][44/376] Data 0.003 (0.003) Batch 0.510 (0.505) Remain 01:44:06 loss: 0.2245 Lr: 0.00310 [2024-11-25 17:50:45,770 INFO misc.py line 119 2586773] Train: [18/50][45/376] Data 0.003 (0.003) Batch 0.543 (0.506) Remain 01:44:17 loss: 0.2112 Lr: 0.00310 [2024-11-25 17:50:46,323 INFO misc.py line 119 2586773] Train: [18/50][46/376] Data 0.002 (0.003) Batch 0.553 (0.507) Remain 01:44:30 loss: 0.2880 Lr: 0.00310 [2024-11-25 17:50:46,844 INFO misc.py line 119 2586773] Train: [18/50][47/376] Data 0.003 (0.003) Batch 0.521 (0.508) Remain 01:44:33 loss: 0.2420 Lr: 0.00310 [2024-11-25 17:50:47,333 INFO misc.py line 119 2586773] Train: [18/50][48/376] Data 0.003 (0.003) Batch 0.489 (0.507) Remain 01:44:28 loss: 0.2159 Lr: 0.00310 [2024-11-25 17:50:47,847 INFO misc.py line 119 2586773] Train: [18/50][49/376] Data 0.002 (0.003) Batch 0.514 (0.507) Remain 01:44:29 loss: 0.2015 Lr: 0.00310 [2024-11-25 17:50:48,341 INFO misc.py line 119 2586773] Train: [18/50][50/376] Data 0.002 (0.003) Batch 0.493 (0.507) Remain 01:44:25 loss: 0.2216 Lr: 0.00310 [2024-11-25 17:50:48,857 INFO misc.py line 119 2586773] Train: [18/50][51/376] Data 0.002 (0.003) Batch 0.516 (0.507) Remain 01:44:27 loss: 0.2182 Lr: 0.00310 [2024-11-25 17:50:49,372 INFO misc.py line 119 2586773] Train: [18/50][52/376] Data 0.003 (0.003) Batch 0.515 (0.507) Remain 01:44:28 loss: 0.2076 Lr: 0.00310 [2024-11-25 17:50:49,907 INFO misc.py line 119 2586773] Train: [18/50][53/376] Data 0.003 (0.003) Batch 0.535 (0.508) Remain 01:44:34 loss: 0.2144 Lr: 0.00310 [2024-11-25 17:50:50,418 INFO misc.py line 119 2586773] Train: [18/50][54/376] Data 0.002 (0.003) Batch 0.511 (0.508) Remain 01:44:35 loss: 0.2350 Lr: 0.00310 [2024-11-25 17:50:50,915 INFO misc.py line 119 2586773] Train: [18/50][55/376] Data 0.002 (0.003) Batch 0.497 (0.508) Remain 01:44:32 loss: 0.2152 Lr: 0.00310 [2024-11-25 17:50:51,438 INFO misc.py line 119 2586773] Train: [18/50][56/376] Data 0.003 (0.003) Batch 0.523 (0.508) Remain 01:44:35 loss: 0.2380 Lr: 0.00310 [2024-11-25 17:50:51,994 INFO misc.py line 119 2586773] Train: [18/50][57/376] Data 0.003 (0.003) Batch 0.556 (0.509) Remain 01:44:45 loss: 0.1997 Lr: 0.00310 [2024-11-25 17:50:52,527 INFO misc.py line 119 2586773] Train: [18/50][58/376] Data 0.002 (0.003) Batch 0.533 (0.509) Remain 01:44:50 loss: 0.2898 Lr: 0.00310 [2024-11-25 17:50:53,061 INFO misc.py line 119 2586773] Train: [18/50][59/376] Data 0.003 (0.003) Batch 0.534 (0.510) Remain 01:44:55 loss: 0.2244 Lr: 0.00310 [2024-11-25 17:50:53,588 INFO misc.py line 119 2586773] Train: [18/50][60/376] Data 0.003 (0.003) Batch 0.527 (0.510) Remain 01:44:58 loss: 0.3096 Lr: 0.00310 [2024-11-25 17:50:54,113 INFO misc.py line 119 2586773] Train: [18/50][61/376] Data 0.002 (0.003) Batch 0.525 (0.510) Remain 01:45:01 loss: 0.2552 Lr: 0.00310 [2024-11-25 17:50:54,606 INFO misc.py line 119 2586773] Train: [18/50][62/376] Data 0.003 (0.003) Batch 0.493 (0.510) Remain 01:44:57 loss: 0.2283 Lr: 0.00310 [2024-11-25 17:50:55,129 INFO misc.py line 119 2586773] Train: [18/50][63/376] Data 0.003 (0.003) Batch 0.523 (0.510) Remain 01:44:59 loss: 0.2464 Lr: 0.00310 [2024-11-25 17:50:55,632 INFO misc.py line 119 2586773] Train: [18/50][64/376] Data 0.002 (0.003) Batch 0.504 (0.510) Remain 01:44:57 loss: 0.2429 Lr: 0.00309 [2024-11-25 17:50:56,099 INFO misc.py line 119 2586773] Train: [18/50][65/376] Data 0.003 (0.003) Batch 0.467 (0.509) Remain 01:44:48 loss: 0.2757 Lr: 0.00309 [2024-11-25 17:50:56,606 INFO misc.py line 119 2586773] Train: [18/50][66/376] Data 0.003 (0.003) Batch 0.507 (0.509) Remain 01:44:47 loss: 0.2053 Lr: 0.00309 [2024-11-25 17:50:57,124 INFO misc.py line 119 2586773] Train: [18/50][67/376] Data 0.002 (0.003) Batch 0.518 (0.510) Remain 01:44:48 loss: 0.2206 Lr: 0.00309 [2024-11-25 17:50:57,631 INFO misc.py line 119 2586773] Train: [18/50][68/376] Data 0.003 (0.003) Batch 0.507 (0.510) Remain 01:44:47 loss: 0.2489 Lr: 0.00309 [2024-11-25 17:50:58,112 INFO misc.py line 119 2586773] Train: [18/50][69/376] Data 0.002 (0.003) Batch 0.481 (0.509) Remain 01:44:41 loss: 0.2184 Lr: 0.00309 [2024-11-25 17:50:58,619 INFO misc.py line 119 2586773] Train: [18/50][70/376] Data 0.002 (0.003) Batch 0.507 (0.509) Remain 01:44:40 loss: 0.2221 Lr: 0.00309 [2024-11-25 17:50:59,114 INFO misc.py line 119 2586773] Train: [18/50][71/376] Data 0.003 (0.003) Batch 0.495 (0.509) Remain 01:44:37 loss: 0.2572 Lr: 0.00309 [2024-11-25 17:50:59,576 INFO misc.py 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Batch 0.504 (0.508) Remain 01:42:20 loss: 0.2309 Lr: 0.00302 [2024-11-25 17:53:09,697 INFO misc.py line 119 2586773] Train: [18/50][328/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 01:42:19 loss: 0.2207 Lr: 0.00302 [2024-11-25 17:53:10,210 INFO misc.py line 119 2586773] Train: [18/50][329/376] Data 0.003 (0.002) Batch 0.513 (0.508) Remain 01:42:19 loss: 0.2084 Lr: 0.00302 [2024-11-25 17:53:10,682 INFO misc.py line 119 2586773] Train: [18/50][330/376] Data 0.003 (0.002) Batch 0.472 (0.508) Remain 01:42:17 loss: 0.1968 Lr: 0.00302 [2024-11-25 17:53:11,140 INFO misc.py line 119 2586773] Train: [18/50][331/376] Data 0.002 (0.002) Batch 0.458 (0.508) Remain 01:42:15 loss: 0.2277 Lr: 0.00302 [2024-11-25 17:53:11,648 INFO misc.py line 119 2586773] Train: [18/50][332/376] Data 0.003 (0.002) Batch 0.508 (0.508) Remain 01:42:14 loss: 0.2236 Lr: 0.00302 [2024-11-25 17:53:12,164 INFO misc.py line 119 2586773] Train: [18/50][333/376] Data 0.003 (0.002) Batch 0.516 (0.508) Remain 01:42:14 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17:53:15,744 INFO misc.py line 119 2586773] Train: [18/50][340/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 01:42:11 loss: 0.2042 Lr: 0.00301 [2024-11-25 17:53:16,201 INFO misc.py line 119 2586773] Train: [18/50][341/376] Data 0.002 (0.002) Batch 0.457 (0.508) Remain 01:42:09 loss: 0.2101 Lr: 0.00301 [2024-11-25 17:53:16,701 INFO misc.py line 119 2586773] Train: [18/50][342/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 01:42:08 loss: 0.2115 Lr: 0.00301 [2024-11-25 17:53:17,175 INFO misc.py line 119 2586773] Train: [18/50][343/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 01:42:06 loss: 0.2895 Lr: 0.00301 [2024-11-25 17:53:17,691 INFO misc.py line 119 2586773] Train: [18/50][344/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 01:42:06 loss: 0.1884 Lr: 0.00301 [2024-11-25 17:53:18,183 INFO misc.py line 119 2586773] Train: [18/50][345/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 01:42:05 loss: 0.1984 Lr: 0.00301 [2024-11-25 17:53:18,731 INFO misc.py line 119 2586773] Train: [18/50][346/376] Data 0.002 (0.002) Batch 0.549 (0.508) Remain 01:42:06 loss: 0.2543 Lr: 0.00301 [2024-11-25 17:53:19,230 INFO misc.py line 119 2586773] Train: [18/50][347/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 01:42:05 loss: 0.2249 Lr: 0.00301 [2024-11-25 17:53:19,742 INFO misc.py line 119 2586773] Train: [18/50][348/376] Data 0.003 (0.002) Batch 0.512 (0.508) Remain 01:42:05 loss: 0.2647 Lr: 0.00301 [2024-11-25 17:53:20,267 INFO misc.py line 119 2586773] Train: [18/50][349/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 01:42:05 loss: 0.2213 Lr: 0.00301 [2024-11-25 17:53:20,791 INFO misc.py line 119 2586773] Train: [18/50][350/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 01:42:05 loss: 0.2654 Lr: 0.00301 [2024-11-25 17:53:21,274 INFO misc.py line 119 2586773] Train: [18/50][351/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 01:42:04 loss: 0.2129 Lr: 0.00301 [2024-11-25 17:53:21,797 INFO misc.py line 119 2586773] Train: [18/50][352/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 01:42:04 loss: 0.2316 Lr: 0.00301 [2024-11-25 17:53:22,315 INFO misc.py line 119 2586773] Train: [18/50][353/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 01:42:03 loss: 0.1904 Lr: 0.00301 [2024-11-25 17:53:22,819 INFO misc.py line 119 2586773] Train: [18/50][354/376] Data 0.003 (0.002) Batch 0.504 (0.508) Remain 01:42:03 loss: 0.2375 Lr: 0.00301 [2024-11-25 17:53:23,276 INFO misc.py line 119 2586773] Train: [18/50][355/376] Data 0.003 (0.002) Batch 0.457 (0.508) Remain 01:42:01 loss: 0.2508 Lr: 0.00301 [2024-11-25 17:53:23,784 INFO misc.py line 119 2586773] Train: [18/50][356/376] Data 0.003 (0.002) Batch 0.508 (0.508) Remain 01:42:00 loss: 0.2317 Lr: 0.00301 [2024-11-25 17:53:24,257 INFO misc.py line 119 2586773] Train: [18/50][357/376] Data 0.005 (0.002) Batch 0.472 (0.508) Remain 01:41:58 loss: 0.2068 Lr: 0.00301 [2024-11-25 17:53:24,741 INFO misc.py line 119 2586773] Train: [18/50][358/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 01:41:57 loss: 0.1934 Lr: 0.00301 [2024-11-25 17:53:25,258 INFO misc.py line 119 2586773] Train: [18/50][359/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 01:41:57 loss: 0.2504 Lr: 0.00301 [2024-11-25 17:53:25,801 INFO misc.py line 119 2586773] Train: [18/50][360/376] Data 0.003 (0.002) Batch 0.544 (0.508) Remain 01:41:58 loss: 0.2764 Lr: 0.00301 [2024-11-25 17:53:26,310 INFO misc.py line 119 2586773] Train: [18/50][361/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 01:41:57 loss: 0.2645 Lr: 0.00301 [2024-11-25 17:53:26,811 INFO misc.py line 119 2586773] Train: [18/50][362/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 01:41:56 loss: 0.2788 Lr: 0.00301 [2024-11-25 17:53:27,284 INFO misc.py line 119 2586773] Train: [18/50][363/376] Data 0.002 (0.002) Batch 0.472 (0.508) Remain 01:41:55 loss: 0.2143 Lr: 0.00301 [2024-11-25 17:53:27,769 INFO misc.py line 119 2586773] Train: [18/50][364/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 01:41:53 loss: 0.2586 Lr: 0.00301 [2024-11-25 17:53:28,255 INFO misc.py line 119 2586773] Train: [18/50][365/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 01:41:52 loss: 0.2380 Lr: 0.00301 [2024-11-25 17:53:28,804 INFO misc.py line 119 2586773] Train: [18/50][366/376] Data 0.002 (0.002) Batch 0.549 (0.508) Remain 01:41:53 loss: 0.2226 Lr: 0.00301 [2024-11-25 17:53:29,341 INFO misc.py line 119 2586773] Train: [18/50][367/376] Data 0.002 (0.002) Batch 0.537 (0.508) Remain 01:41:54 loss: 0.2154 Lr: 0.00301 [2024-11-25 17:53:29,866 INFO misc.py line 119 2586773] Train: [18/50][368/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 01:41:54 loss: 0.2330 Lr: 0.00300 [2024-11-25 17:53:30,349 INFO misc.py line 119 2586773] Train: [18/50][369/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 01:41:52 loss: 0.2441 Lr: 0.00300 [2024-11-25 17:53:30,866 INFO misc.py line 119 2586773] Train: [18/50][370/376] Data 0.003 (0.002) Batch 0.517 (0.508) Remain 01:41:52 loss: 0.2567 Lr: 0.00300 [2024-11-25 17:53:31,332 INFO misc.py line 119 2586773] Train: [18/50][371/376] Data 0.002 (0.002) Batch 0.466 (0.508) Remain 01:41:50 loss: 0.2287 Lr: 0.00300 [2024-11-25 17:53:31,874 INFO misc.py line 119 2586773] Train: [18/50][372/376] Data 0.002 (0.002) Batch 0.541 (0.508) Remain 01:41:51 loss: 0.2638 Lr: 0.00300 [2024-11-25 17:53:32,385 INFO misc.py line 119 2586773] Train: [18/50][373/376] Data 0.003 (0.002) Batch 0.511 (0.508) Remain 01:41:50 loss: 0.2483 Lr: 0.00300 [2024-11-25 17:53:32,871 INFO misc.py line 119 2586773] Train: [18/50][374/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 01:41:49 loss: 0.2229 Lr: 0.00300 [2024-11-25 17:53:33,424 INFO misc.py line 119 2586773] Train: [18/50][375/376] Data 0.002 (0.002) Batch 0.552 (0.508) Remain 01:41:50 loss: 0.2240 Lr: 0.00300 [2024-11-25 17:53:33,929 INFO misc.py line 119 2586773] Train: [18/50][376/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 01:41:50 loss: 0.2455 Lr: 0.00300 [2024-11-25 17:53:33,929 INFO misc.py line 136 2586773] Train result: loss: 0.2351 [2024-11-25 17:53:33,930 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:53:44,838 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2020 [2024-11-25 17:53:45,095 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2426 [2024-11-25 17:53:45,357 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2608 [2024-11-25 17:53:45,579 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1892 [2024-11-25 17:53:45,843 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2894 [2024-11-25 17:53:46,110 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1943 [2024-11-25 17:53:46,341 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1994 [2024-11-25 17:53:46,610 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2226 [2024-11-25 17:53:46,834 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2928 [2024-11-25 17:53:47,094 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2620 [2024-11-25 17:53:47,325 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2108 [2024-11-25 17:53:47,597 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2349 [2024-11-25 17:53:47,862 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2569 [2024-11-25 17:53:48,125 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2492 [2024-11-25 17:53:48,358 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2436 [2024-11-25 17:53:48,597 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2670 [2024-11-25 17:53:48,866 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2923 [2024-11-25 17:53:49,112 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2083 [2024-11-25 17:53:49,345 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2258 [2024-11-25 17:53:49,605 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2461 [2024-11-25 17:53:49,838 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2406 [2024-11-25 17:53:50,106 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2788 [2024-11-25 17:53:50,340 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2135 [2024-11-25 17:53:50,608 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2835 [2024-11-25 17:53:50,869 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2390 [2024-11-25 17:53:51,105 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2448 [2024-11-25 17:53:51,363 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2780 [2024-11-25 17:53:51,610 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2424 [2024-11-25 17:53:51,878 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2866 [2024-11-25 17:53:52,132 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3018 [2024-11-25 17:53:52,364 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2501 [2024-11-25 17:53:52,627 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2333 [2024-11-25 17:53:52,848 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2672 [2024-11-25 17:53:53,086 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2563 [2024-11-25 17:53:53,350 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2367 [2024-11-25 17:53:53,596 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2566 [2024-11-25 17:53:53,826 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2028 [2024-11-25 17:53:54,094 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2403 [2024-11-25 17:53:54,324 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2667 [2024-11-25 17:53:54,559 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2544 [2024-11-25 17:53:54,828 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2867 [2024-11-25 17:53:55,077 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2988 [2024-11-25 17:53:55,314 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2658 [2024-11-25 17:53:55,546 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2477 [2024-11-25 17:53:55,784 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2492 [2024-11-25 17:53:56,032 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2514 [2024-11-25 17:53:56,294 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2468 [2024-11-25 17:53:56,546 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3161 [2024-11-25 17:53:56,773 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2186 [2024-11-25 17:53:57,007 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2122 [2024-11-25 17:53:57,227 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2322 [2024-11-25 17:53:57,480 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2647 [2024-11-25 17:53:57,747 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2307 [2024-11-25 17:53:58,008 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3053 [2024-11-25 17:53:58,238 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2502 [2024-11-25 17:53:58,477 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2356 [2024-11-25 17:53:58,735 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2727 [2024-11-25 17:53:59,003 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2673 [2024-11-25 17:53:59,257 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2684 [2024-11-25 17:53:59,524 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2295 [2024-11-25 17:53:59,775 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2351 [2024-11-25 17:54:00,045 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2603 [2024-11-25 17:54:00,274 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2493 [2024-11-25 17:54:00,532 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2769 [2024-11-25 17:54:00,808 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2630 [2024-11-25 17:54:01,075 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2049 [2024-11-25 17:54:01,326 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2000 [2024-11-25 17:54:01,581 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2885 [2024-11-25 17:54:01,849 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2413 [2024-11-25 17:54:02,112 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3067 [2024-11-25 17:54:02,355 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2210 [2024-11-25 17:54:02,588 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2752 [2024-11-25 17:54:02,843 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2543 [2024-11-25 17:54:03,085 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2777 [2024-11-25 17:54:03,301 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2647 [2024-11-25 17:54:03,521 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2233 [2024-11-25 17:54:03,795 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2409 [2024-11-25 17:54:04,034 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2237 [2024-11-25 17:54:04,292 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2181 [2024-11-25 17:54:04,553 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3030 [2024-11-25 17:54:04,793 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2396 [2024-11-25 17:54:05,053 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2648 [2024-11-25 17:54:05,304 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1933 [2024-11-25 17:54:05,553 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2498 [2024-11-25 17:54:05,823 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2489 [2024-11-25 17:54:06,058 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2638 [2024-11-25 17:54:06,325 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2802 [2024-11-25 17:54:06,587 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2368 [2024-11-25 17:54:06,834 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2776 [2024-11-25 17:54:07,083 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2652 [2024-11-25 17:54:07,315 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2534 [2024-11-25 17:54:07,568 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2709 [2024-11-25 17:54:07,844 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2766 [2024-11-25 17:54:08,111 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2060 [2024-11-25 17:54:08,376 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2293 [2024-11-25 17:54:08,628 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2134 [2024-11-25 17:54:08,902 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2622 [2024-11-25 17:54:09,123 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2942 [2024-11-25 17:54:09,394 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2702 [2024-11-25 17:54:09,631 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2607 [2024-11-25 17:54:09,899 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2046 [2024-11-25 17:54:10,160 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2679 [2024-11-25 17:54:10,418 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2777 [2024-11-25 17:54:10,669 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2799 [2024-11-25 17:54:10,892 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2326 [2024-11-25 17:54:11,127 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2347 [2024-11-25 17:54:11,386 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2273 [2024-11-25 17:54:11,655 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2493 [2024-11-25 17:54:11,889 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2585 [2024-11-25 17:54:12,149 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2426 [2024-11-25 17:54:12,412 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2342 [2024-11-25 17:54:12,633 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2300 [2024-11-25 17:54:12,868 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2320 [2024-11-25 17:54:13,086 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2324 [2024-11-25 17:54:13,310 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2293 [2024-11-25 17:54:13,584 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2934 [2024-11-25 17:54:13,842 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2858 [2024-11-25 17:54:14,111 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2465 [2024-11-25 17:54:14,376 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2328 [2024-11-25 17:54:14,638 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2829 [2024-11-25 17:54:14,895 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2795 [2024-11-25 17:54:15,160 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2330 [2024-11-25 17:54:15,416 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2817 [2024-11-25 17:54:15,678 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2658 [2024-11-25 17:54:15,940 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2571 [2024-11-25 17:54:16,191 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2862 [2024-11-25 17:54:16,423 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2199 [2024-11-25 17:54:16,681 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2672 [2024-11-25 17:54:16,916 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2550 [2024-11-25 17:54:17,144 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1929 [2024-11-25 17:54:17,357 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2394 [2024-11-25 17:54:17,575 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2110 [2024-11-25 17:54:18,155 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7651/0.8527/0.9960. [2024-11-25 17:54:18,155 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9959/0.9979 [2024-11-25 17:54:18,155 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5342/0.7076 [2024-11-25 17:54:18,156 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:54:18,157 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7651 [2024-11-25 17:54:18,157 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7651 [2024-11-25 17:54:18,157 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:54:22,599 INFO misc.py line 119 2586773] Train: [19/50][1/376] Data 0.119 (0.119) Batch 0.551 (0.551) Remain 01:50:30 loss: 0.2748 Lr: 0.00300 [2024-11-25 17:54:23,099 INFO misc.py line 119 2586773] Train: [19/50][2/376] Data 0.003 (0.003) Batch 0.499 (0.499) Remain 01:40:05 loss: 0.2471 Lr: 0.00300 [2024-11-25 17:54:23,592 INFO misc.py line 119 2586773] Train: [19/50][3/376] Data 0.002 (0.002) Batch 0.494 (0.494) Remain 01:38:58 loss: 0.3262 Lr: 0.00300 [2024-11-25 17:54:24,090 INFO misc.py line 119 2586773] Train: [19/50][4/376] Data 0.002 (0.002) Batch 0.498 (0.498) Remain 01:39:47 loss: 0.2103 Lr: 0.00300 [2024-11-25 17:54:24,584 INFO misc.py line 119 2586773] Train: [19/50][5/376] Data 0.002 (0.002) Batch 0.494 (0.496) Remain 01:39:24 loss: 0.2829 Lr: 0.00300 [2024-11-25 17:54:25,098 INFO misc.py line 119 2586773] Train: [19/50][6/376] Data 0.002 (0.002) Batch 0.513 (0.502) Remain 01:40:34 loss: 0.2390 Lr: 0.00300 [2024-11-25 17:54:25,594 INFO misc.py line 119 2586773] Train: [19/50][7/376] Data 0.003 (0.003) Batch 0.496 (0.500) Remain 01:40:18 loss: 0.2396 Lr: 0.00300 [2024-11-25 17:54:26,098 INFO misc.py line 119 2586773] Train: [19/50][8/376] Data 0.002 (0.003) Batch 0.504 (0.501) Remain 01:40:25 loss: 0.2906 Lr: 0.00300 [2024-11-25 17:54:26,572 INFO misc.py line 119 2586773] Train: [19/50][9/376] Data 0.002 (0.002) Batch 0.474 (0.497) Remain 01:39:29 loss: 0.2801 Lr: 0.00300 [2024-11-25 17:54:27,091 INFO misc.py line 119 2586773] Train: [19/50][10/376] Data 0.002 (0.002) Batch 0.520 (0.500) Remain 01:40:09 loss: 0.2793 Lr: 0.00300 [2024-11-25 17:54:27,644 INFO misc.py line 119 2586773] Train: [19/50][11/376] Data 0.002 (0.002) Batch 0.553 (0.506) Remain 01:41:28 loss: 0.1913 Lr: 0.00300 [2024-11-25 17:54:28,134 INFO misc.py line 119 2586773] Train: [19/50][12/376] Data 0.003 (0.002) Batch 0.490 (0.505) Remain 01:41:05 loss: 0.2295 Lr: 0.00300 [2024-11-25 17:54:28,626 INFO misc.py line 119 2586773] Train: [19/50][13/376] Data 0.003 (0.003) Batch 0.492 (0.503) Remain 01:40:50 loss: 0.2392 Lr: 0.00300 [2024-11-25 17:54:29,123 INFO misc.py line 119 2586773] Train: [19/50][14/376] Data 0.002 (0.002) Batch 0.497 (0.503) Remain 01:40:42 loss: 0.2479 Lr: 0.00300 [2024-11-25 17:54:29,660 INFO misc.py line 119 2586773] Train: [19/50][15/376] Data 0.002 (0.002) Batch 0.537 (0.506) Remain 01:41:16 loss: 0.2707 Lr: 0.00300 [2024-11-25 17:54:30,150 INFO misc.py line 119 2586773] Train: [19/50][16/376] Data 0.003 (0.002) Batch 0.490 (0.504) Remain 01:41:01 loss: 0.2158 Lr: 0.00300 [2024-11-25 17:54:30,671 INFO misc.py line 119 2586773] Train: [19/50][17/376] Data 0.003 (0.003) Batch 0.521 (0.506) Remain 01:41:15 loss: 0.2553 Lr: 0.00300 [2024-11-25 17:54:31,182 INFO misc.py line 119 2586773] Train: [19/50][18/376] Data 0.003 (0.003) Batch 0.511 (0.506) Remain 01:41:19 loss: 0.2906 Lr: 0.00300 [2024-11-25 17:54:31,670 INFO misc.py line 119 2586773] Train: [19/50][19/376] Data 0.002 (0.003) Batch 0.487 (0.505) Remain 01:41:04 loss: 0.2328 Lr: 0.00300 [2024-11-25 17:54:32,192 INFO misc.py line 119 2586773] Train: [19/50][20/376] Data 0.002 (0.003) Batch 0.523 (0.506) Remain 01:41:16 loss: 0.1939 Lr: 0.00300 [2024-11-25 17:54:32,661 INFO misc.py line 119 2586773] Train: [19/50][21/376] Data 0.003 (0.003) Batch 0.469 (0.504) Remain 01:40:51 loss: 0.2722 Lr: 0.00300 [2024-11-25 17:54:33,126 INFO misc.py line 119 2586773] Train: [19/50][22/376] Data 0.002 (0.002) Batch 0.464 (0.502) Remain 01:40:26 loss: 0.2098 Lr: 0.00300 [2024-11-25 17:54:33,603 INFO misc.py line 119 2586773] Train: [19/50][23/376] Data 0.002 (0.002) Batch 0.478 (0.501) Remain 01:40:11 loss: 0.2378 Lr: 0.00300 [2024-11-25 17:54:34,143 INFO misc.py line 119 2586773] Train: [19/50][24/376] Data 0.002 (0.002) Batch 0.540 (0.502) Remain 01:40:33 loss: 0.2032 Lr: 0.00300 [2024-11-25 17:54:34,635 INFO misc.py line 119 2586773] Train: [19/50][25/376] Data 0.003 (0.002) Batch 0.491 (0.502) Remain 01:40:26 loss: 0.2053 Lr: 0.00299 [2024-11-25 17:54:35,152 INFO misc.py line 119 2586773] Train: [19/50][26/376] Data 0.003 (0.003) Batch 0.518 (0.503) Remain 01:40:34 loss: 0.1875 Lr: 0.00299 [2024-11-25 17:54:35,645 INFO misc.py line 119 2586773] Train: [19/50][27/376] Data 0.003 (0.003) Batch 0.493 (0.502) Remain 01:40:28 loss: 0.2443 Lr: 0.00299 [2024-11-25 17:54:36,122 INFO misc.py line 119 2586773] Train: [19/50][28/376] Data 0.003 (0.003) Batch 0.478 (0.501) Remain 01:40:16 loss: 0.2028 Lr: 0.00299 [2024-11-25 17:54:36,640 INFO misc.py line 119 2586773] Train: [19/50][29/376] Data 0.003 (0.003) Batch 0.517 (0.502) Remain 01:40:23 loss: 0.3001 Lr: 0.00299 [2024-11-25 17:54:37,139 INFO misc.py line 119 2586773] Train: [19/50][30/376] Data 0.002 (0.003) Batch 0.499 (0.502) Remain 01:40:21 loss: 0.2058 Lr: 0.00299 [2024-11-25 17:54:37,652 INFO misc.py line 119 2586773] Train: [19/50][31/376] Data 0.003 (0.003) Batch 0.514 (0.502) Remain 01:40:26 loss: 0.2611 Lr: 0.00299 [2024-11-25 17:54:38,139 INFO misc.py line 119 2586773] Train: [19/50][32/376] Data 0.002 (0.003) Batch 0.487 (0.502) Remain 01:40:19 loss: 0.2320 Lr: 0.00299 [2024-11-25 17:54:38,629 INFO misc.py line 119 2586773] Train: [19/50][33/376] Data 0.002 (0.003) Batch 0.490 (0.501) Remain 01:40:14 loss: 0.1994 Lr: 0.00299 [2024-11-25 17:54:39,119 INFO misc.py line 119 2586773] Train: [19/50][34/376] Data 0.003 (0.003) Batch 0.490 (0.501) Remain 01:40:09 loss: 0.1860 Lr: 0.00299 [2024-11-25 17:54:39,592 INFO misc.py line 119 2586773] Train: [19/50][35/376] Data 0.002 (0.003) Batch 0.473 (0.500) Remain 01:39:58 loss: 0.2579 Lr: 0.00299 [2024-11-25 17:54:40,089 INFO misc.py line 119 2586773] Train: [19/50][36/376] Data 0.002 (0.003) Batch 0.497 (0.500) Remain 01:39:56 loss: 0.2613 Lr: 0.00299 [2024-11-25 17:54:40,618 INFO misc.py line 119 2586773] Train: [19/50][37/376] Data 0.002 (0.003) Batch 0.529 (0.501) Remain 01:40:06 loss: 0.2706 Lr: 0.00299 [2024-11-25 17:54:41,123 INFO misc.py line 119 2586773] Train: [19/50][38/376] Data 0.003 (0.003) Batch 0.505 (0.501) Remain 01:40:07 loss: 0.2160 Lr: 0.00299 [2024-11-25 17:54:41,661 INFO misc.py line 119 2586773] Train: [19/50][39/376] Data 0.002 (0.003) Batch 0.538 (0.502) Remain 01:40:19 loss: 0.2079 Lr: 0.00299 [2024-11-25 17:54:42,174 INFO misc.py line 119 2586773] Train: [19/50][40/376] Data 0.002 (0.002) Batch 0.513 (0.502) Remain 01:40:22 loss: 0.2249 Lr: 0.00299 [2024-11-25 17:54:42,679 INFO misc.py line 119 2586773] Train: [19/50][41/376] Data 0.002 (0.002) Batch 0.505 (0.502) Remain 01:40:23 loss: 0.3108 Lr: 0.00299 [2024-11-25 17:54:43,205 INFO misc.py line 119 2586773] Train: [19/50][42/376] Data 0.002 (0.002) Batch 0.525 (0.503) Remain 01:40:29 loss: 0.2233 Lr: 0.00299 [2024-11-25 17:54:43,684 INFO misc.py line 119 2586773] Train: [19/50][43/376] Data 0.002 (0.002) Batch 0.479 (0.502) Remain 01:40:21 loss: 0.2408 Lr: 0.00299 [2024-11-25 17:54:44,182 INFO misc.py line 119 2586773] Train: [19/50][44/376] Data 0.003 (0.002) Batch 0.498 (0.502) Remain 01:40:20 loss: 0.2170 Lr: 0.00299 [2024-11-25 17:54:44,669 INFO misc.py line 119 2586773] Train: [19/50][45/376] Data 0.002 (0.002) Batch 0.487 (0.502) Remain 01:40:15 loss: 0.2277 Lr: 0.00299 [2024-11-25 17:54:45,204 INFO misc.py line 119 2586773] Train: [19/50][46/376] Data 0.002 (0.002) Batch 0.535 (0.503) Remain 01:40:24 loss: 0.2426 Lr: 0.00299 [2024-11-25 17:54:45,711 INFO misc.py line 119 2586773] Train: [19/50][47/376] Data 0.002 (0.002) Batch 0.508 (0.503) Remain 01:40:24 loss: 0.1910 Lr: 0.00299 [2024-11-25 17:54:46,252 INFO misc.py line 119 2586773] Train: [19/50][48/376] Data 0.002 (0.002) Batch 0.540 (0.504) Remain 01:40:34 loss: 0.2907 Lr: 0.00299 [2024-11-25 17:54:46,751 INFO misc.py line 119 2586773] Train: [19/50][49/376] Data 0.002 (0.002) Batch 0.500 (0.503) Remain 01:40:32 loss: 0.2376 Lr: 0.00299 [2024-11-25 17:54:47,247 INFO misc.py line 119 2586773] Train: [19/50][50/376] Data 0.002 (0.002) Batch 0.496 (0.503) Remain 01:40:30 loss: 0.2219 Lr: 0.00299 [2024-11-25 17:54:47,732 INFO misc.py line 119 2586773] Train: [19/50][51/376] Data 0.002 (0.002) Batch 0.484 (0.503) Remain 01:40:25 loss: 0.2266 Lr: 0.00299 [2024-11-25 17:54:48,209 INFO misc.py line 119 2586773] Train: [19/50][52/376] Data 0.003 (0.002) Batch 0.477 (0.502) Remain 01:40:18 loss: 0.3371 Lr: 0.00299 [2024-11-25 17:54:48,721 INFO misc.py line 119 2586773] Train: [19/50][53/376] Data 0.002 (0.002) Batch 0.511 (0.503) Remain 01:40:20 loss: 0.2187 Lr: 0.00299 [2024-11-25 17:54:49,207 INFO misc.py line 119 2586773] Train: [19/50][54/376] Data 0.002 (0.002) Batch 0.487 (0.502) Remain 01:40:15 loss: 0.2771 Lr: 0.00299 [2024-11-25 17:54:49,684 INFO misc.py line 119 2586773] Train: [19/50][55/376] Data 0.002 (0.002) Batch 0.477 (0.502) Remain 01:40:09 loss: 0.1933 Lr: 0.00299 [2024-11-25 17:54:50,210 INFO misc.py line 119 2586773] Train: [19/50][56/376] Data 0.002 (0.002) Batch 0.526 (0.502) Remain 01:40:14 loss: 0.1839 Lr: 0.00299 [2024-11-25 17:54:50,751 INFO misc.py line 119 2586773] Train: [19/50][57/376] Data 0.002 (0.002) Batch 0.540 (0.503) Remain 01:40:22 loss: 0.2420 Lr: 0.00299 [2024-11-25 17:54:51,267 INFO misc.py line 119 2586773] Train: [19/50][58/376] Data 0.002 (0.002) Batch 0.516 (0.503) Remain 01:40:25 loss: 0.2423 Lr: 0.00298 [2024-11-25 17:54:51,759 INFO misc.py line 119 2586773] Train: [19/50][59/376] Data 0.002 (0.002) Batch 0.492 (0.503) Remain 01:40:22 loss: 0.2121 Lr: 0.00298 [2024-11-25 17:54:52,252 INFO misc.py line 119 2586773] Train: [19/50][60/376] Data 0.002 (0.002) Batch 0.493 (0.503) Remain 01:40:19 loss: 0.2276 Lr: 0.00298 [2024-11-25 17:54:52,756 INFO misc.py line 119 2586773] Train: [19/50][61/376] Data 0.003 (0.002) Batch 0.504 (0.503) Remain 01:40:19 loss: 0.2031 Lr: 0.00298 [2024-11-25 17:54:53,259 INFO misc.py line 119 2586773] Train: [19/50][62/376] Data 0.002 (0.002) Batch 0.503 (0.503) Remain 01:40:18 loss: 0.2530 Lr: 0.00298 [2024-11-25 17:54:53,749 INFO misc.py line 119 2586773] Train: [19/50][63/376] Data 0.002 (0.002) Batch 0.490 (0.503) Remain 01:40:15 loss: 0.2428 Lr: 0.00298 [2024-11-25 17:54:54,240 INFO misc.py line 119 2586773] Train: [19/50][64/376] Data 0.002 (0.002) Batch 0.490 (0.502) Remain 01:40:12 loss: 0.2595 Lr: 0.00298 [2024-11-25 17:54:54,791 INFO misc.py line 119 2586773] Train: [19/50][65/376] Data 0.002 (0.002) Batch 0.552 (0.503) Remain 01:40:21 loss: 0.2345 Lr: 0.00298 [2024-11-25 17:54:55,304 INFO misc.py line 119 2586773] Train: [19/50][66/376] Data 0.003 (0.002) Batch 0.513 (0.503) Remain 01:40:23 loss: 0.2639 Lr: 0.00298 [2024-11-25 17:54:55,860 INFO misc.py line 119 2586773] Train: [19/50][67/376] Data 0.002 (0.002) Batch 0.555 (0.504) Remain 01:40:32 loss: 0.2066 Lr: 0.00298 [2024-11-25 17:54:56,349 INFO misc.py line 119 2586773] Train: [19/50][68/376] Data 0.002 (0.002) Batch 0.489 (0.504) Remain 01:40:29 loss: 0.2652 Lr: 0.00298 [2024-11-25 17:54:56,862 INFO misc.py line 119 2586773] Train: [19/50][69/376] Data 0.002 (0.002) Batch 0.513 (0.504) Remain 01:40:30 loss: 0.2599 Lr: 0.00298 [2024-11-25 17:54:57,375 INFO misc.py line 119 2586773] Train: [19/50][70/376] Data 0.003 (0.002) Batch 0.513 (0.504) Remain 01:40:31 loss: 0.2144 Lr: 0.00298 [2024-11-25 17:54:57,860 INFO misc.py line 119 2586773] Train: [19/50][71/376] Data 0.002 (0.002) Batch 0.485 (0.504) Remain 01:40:27 loss: 0.2193 Lr: 0.00298 [2024-11-25 17:54:58,381 INFO misc.py line 119 2586773] Train: [19/50][72/376] Data 0.002 (0.002) Batch 0.521 (0.504) Remain 01:40:30 loss: 0.2771 Lr: 0.00298 [2024-11-25 17:54:58,932 INFO misc.py line 119 2586773] Train: [19/50][73/376] Data 0.002 (0.002) Batch 0.551 (0.505) Remain 01:40:37 loss: 0.2012 Lr: 0.00298 [2024-11-25 17:54:59,470 INFO misc.py line 119 2586773] Train: [19/50][74/376] Data 0.003 (0.002) Batch 0.538 (0.505) Remain 01:40:42 loss: 0.1838 Lr: 0.00298 [2024-11-25 17:54:59,989 INFO misc.py line 119 2586773] Train: [19/50][75/376] Data 0.002 (0.002) Batch 0.519 (0.506) Remain 01:40:44 loss: 0.2814 Lr: 0.00298 [2024-11-25 17:55:00,494 INFO misc.py line 119 2586773] Train: [19/50][76/376] Data 0.003 (0.002) Batch 0.505 (0.506) Remain 01:40:43 loss: 0.2160 Lr: 0.00298 [2024-11-25 17:55:00,997 INFO misc.py line 119 2586773] Train: [19/50][77/376] Data 0.002 (0.002) Batch 0.503 (0.505) Remain 01:40:42 loss: 0.2246 Lr: 0.00298 [2024-11-25 17:55:01,514 INFO misc.py line 119 2586773] Train: [19/50][78/376] Data 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01:40:49 loss: 0.2867 Lr: 0.00298 [2024-11-25 17:55:05,155 INFO misc.py line 119 2586773] Train: [19/50][85/376] Data 0.005 (0.003) Batch 0.548 (0.507) Remain 01:40:55 loss: 0.2838 Lr: 0.00298 [2024-11-25 17:55:05,660 INFO misc.py line 119 2586773] Train: [19/50][86/376] Data 0.005 (0.003) Batch 0.507 (0.507) Remain 01:40:54 loss: 0.2321 Lr: 0.00298 [2024-11-25 17:55:06,197 INFO misc.py line 119 2586773] Train: [19/50][87/376] Data 0.003 (0.003) Batch 0.537 (0.507) Remain 01:40:58 loss: 0.2145 Lr: 0.00298 [2024-11-25 17:55:06,722 INFO misc.py line 119 2586773] Train: [19/50][88/376] Data 0.003 (0.003) Batch 0.524 (0.507) Remain 01:41:00 loss: 0.2596 Lr: 0.00298 [2024-11-25 17:55:07,217 INFO misc.py line 119 2586773] Train: [19/50][89/376] Data 0.003 (0.003) Batch 0.495 (0.507) Remain 01:40:58 loss: 0.2145 Lr: 0.00298 [2024-11-25 17:55:07,785 INFO misc.py line 119 2586773] Train: [19/50][90/376] Data 0.003 (0.003) Batch 0.568 (0.508) Remain 01:41:06 loss: 0.1839 Lr: 0.00298 [2024-11-25 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Batch 0.542 (0.510) Remain 01:40:49 loss: 0.2478 Lr: 0.00295 [2024-11-25 17:55:43,656 INFO misc.py line 119 2586773] Train: [19/50][160/376] Data 0.003 (0.003) Batch 0.579 (0.510) Remain 01:40:54 loss: 0.2395 Lr: 0.00295 [2024-11-25 17:55:44,148 INFO misc.py line 119 2586773] Train: [19/50][161/376] Data 0.003 (0.003) Batch 0.492 (0.510) Remain 01:40:52 loss: 0.2007 Lr: 0.00295 [2024-11-25 17:55:44,691 INFO misc.py line 119 2586773] Train: [19/50][162/376] Data 0.003 (0.003) Batch 0.542 (0.510) Remain 01:40:54 loss: 0.2694 Lr: 0.00295 [2024-11-25 17:55:45,187 INFO misc.py line 119 2586773] Train: [19/50][163/376] Data 0.003 (0.003) Batch 0.496 (0.510) Remain 01:40:52 loss: 0.2293 Lr: 0.00295 [2024-11-25 17:55:45,662 INFO misc.py line 119 2586773] Train: [19/50][164/376] Data 0.003 (0.003) Batch 0.476 (0.510) Remain 01:40:49 loss: 0.2365 Lr: 0.00295 [2024-11-25 17:55:46,195 INFO misc.py line 119 2586773] Train: [19/50][165/376] Data 0.002 (0.003) Batch 0.533 (0.510) Remain 01:40:50 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01:39:32 loss: 0.2097 Lr: 0.00289 [2024-11-25 17:57:25,672 INFO misc.py line 119 2586773] Train: [19/50][359/376] Data 0.003 (0.003) Batch 0.465 (0.511) Remain 01:39:30 loss: 0.2473 Lr: 0.00289 [2024-11-25 17:57:26,181 INFO misc.py line 119 2586773] Train: [19/50][360/376] Data 0.002 (0.003) Batch 0.508 (0.511) Remain 01:39:29 loss: 0.2946 Lr: 0.00289 [2024-11-25 17:57:26,713 INFO misc.py line 119 2586773] Train: [19/50][361/376] Data 0.003 (0.003) Batch 0.533 (0.512) Remain 01:39:29 loss: 0.2537 Lr: 0.00289 [2024-11-25 17:57:27,220 INFO misc.py line 119 2586773] Train: [19/50][362/376] Data 0.003 (0.003) Batch 0.507 (0.511) Remain 01:39:29 loss: 0.2010 Lr: 0.00289 [2024-11-25 17:57:27,667 INFO misc.py line 119 2586773] Train: [19/50][363/376] Data 0.003 (0.003) Batch 0.447 (0.511) Remain 01:39:26 loss: 0.2402 Lr: 0.00289 [2024-11-25 17:57:28,206 INFO misc.py line 119 2586773] Train: [19/50][364/376] Data 0.003 (0.003) Batch 0.539 (0.511) Remain 01:39:26 loss: 0.1900 Lr: 0.00289 [2024-11-25 17:57:28,719 INFO misc.py line 119 2586773] Train: [19/50][365/376] Data 0.003 (0.003) Batch 0.513 (0.511) Remain 01:39:26 loss: 0.1875 Lr: 0.00289 [2024-11-25 17:57:29,238 INFO misc.py line 119 2586773] Train: [19/50][366/376] Data 0.002 (0.003) Batch 0.520 (0.511) Remain 01:39:26 loss: 0.2062 Lr: 0.00289 [2024-11-25 17:57:29,719 INFO misc.py line 119 2586773] Train: [19/50][367/376] Data 0.003 (0.003) Batch 0.481 (0.511) Remain 01:39:24 loss: 0.2243 Lr: 0.00289 [2024-11-25 17:57:30,236 INFO misc.py line 119 2586773] Train: [19/50][368/376] Data 0.003 (0.003) Batch 0.517 (0.511) Remain 01:39:24 loss: 0.2728 Lr: 0.00289 [2024-11-25 17:57:30,796 INFO misc.py line 119 2586773] Train: [19/50][369/376] Data 0.003 (0.003) Batch 0.561 (0.511) Remain 01:39:25 loss: 0.2441 Lr: 0.00289 [2024-11-25 17:57:31,305 INFO misc.py line 119 2586773] Train: [19/50][370/376] Data 0.002 (0.003) Batch 0.509 (0.511) Remain 01:39:24 loss: 0.1986 Lr: 0.00289 [2024-11-25 17:57:31,820 INFO misc.py line 119 2586773] Train: [19/50][371/376] Data 0.002 (0.003) Batch 0.514 (0.511) Remain 01:39:24 loss: 0.2257 Lr: 0.00289 [2024-11-25 17:57:32,308 INFO misc.py line 119 2586773] Train: [19/50][372/376] Data 0.004 (0.003) Batch 0.488 (0.511) Remain 01:39:23 loss: 0.2419 Lr: 0.00289 [2024-11-25 17:57:32,824 INFO misc.py line 119 2586773] Train: [19/50][373/376] Data 0.003 (0.003) Batch 0.517 (0.511) Remain 01:39:22 loss: 0.2281 Lr: 0.00289 [2024-11-25 17:57:33,334 INFO misc.py line 119 2586773] Train: [19/50][374/376] Data 0.003 (0.003) Batch 0.510 (0.511) Remain 01:39:22 loss: 0.1915 Lr: 0.00289 [2024-11-25 17:57:33,830 INFO misc.py line 119 2586773] Train: [19/50][375/376] Data 0.002 (0.003) Batch 0.496 (0.511) Remain 01:39:21 loss: 0.2232 Lr: 0.00289 [2024-11-25 17:57:34,374 INFO misc.py line 119 2586773] Train: [19/50][376/376] Data 0.002 (0.003) Batch 0.543 (0.511) Remain 01:39:21 loss: 0.1919 Lr: 0.00289 [2024-11-25 17:57:34,374 INFO misc.py line 136 2586773] Train result: loss: 0.2344 [2024-11-25 17:57:34,375 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 17:57:47,500 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2021 [2024-11-25 17:57:47,755 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2213 [2024-11-25 17:57:48,022 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2695 [2024-11-25 17:57:48,246 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2216 [2024-11-25 17:57:48,512 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3192 [2024-11-25 17:57:48,787 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2343 [2024-11-25 17:57:49,009 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2406 [2024-11-25 17:57:49,281 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2251 [2024-11-25 17:57:49,508 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2526 [2024-11-25 17:57:49,771 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2623 [2024-11-25 17:57:50,005 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2454 [2024-11-25 17:57:50,280 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2269 [2024-11-25 17:57:50,549 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2462 [2024-11-25 17:57:50,812 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2538 [2024-11-25 17:57:51,046 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2507 [2024-11-25 17:57:51,289 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2910 [2024-11-25 17:57:51,556 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3116 [2024-11-25 17:57:51,807 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2449 [2024-11-25 17:57:52,038 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2198 [2024-11-25 17:57:52,300 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2703 [2024-11-25 17:57:52,532 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2257 [2024-11-25 17:57:52,797 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.3102 [2024-11-25 17:57:53,040 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2463 [2024-11-25 17:57:53,309 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2787 [2024-11-25 17:57:53,571 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2540 [2024-11-25 17:57:53,811 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2403 [2024-11-25 17:57:54,064 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.3095 [2024-11-25 17:57:54,310 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2786 [2024-11-25 17:57:54,577 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3158 [2024-11-25 17:57:54,831 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3249 [2024-11-25 17:57:55,067 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2840 [2024-11-25 17:57:55,333 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2353 [2024-11-25 17:57:55,553 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2647 [2024-11-25 17:57:55,794 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2433 [2024-11-25 17:57:56,055 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2462 [2024-11-25 17:57:56,302 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2590 [2024-11-25 17:57:56,538 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2057 [2024-11-25 17:57:56,808 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2857 [2024-11-25 17:57:57,039 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2814 [2024-11-25 17:57:57,286 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2634 [2024-11-25 17:57:57,561 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2911 [2024-11-25 17:57:57,812 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3147 [2024-11-25 17:57:58,053 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2785 [2024-11-25 17:57:58,285 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2436 [2024-11-25 17:57:58,523 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2435 [2024-11-25 17:57:58,772 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2718 [2024-11-25 17:57:59,033 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2650 [2024-11-25 17:57:59,287 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3261 [2024-11-25 17:57:59,516 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2355 [2024-11-25 17:57:59,751 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2240 [2024-11-25 17:57:59,974 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2484 [2024-11-25 17:58:00,239 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2712 [2024-11-25 17:58:00,509 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2587 [2024-11-25 17:58:00,772 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2828 [2024-11-25 17:58:01,004 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2416 [2024-11-25 17:58:01,247 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2463 [2024-11-25 17:58:01,526 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3058 [2024-11-25 17:58:01,801 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2491 [2024-11-25 17:58:02,058 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2786 [2024-11-25 17:58:02,326 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2783 [2024-11-25 17:58:02,591 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2333 [2024-11-25 17:58:02,869 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2745 [2024-11-25 17:58:03,098 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2468 [2024-11-25 17:58:03,365 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2831 [2024-11-25 17:58:03,632 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2620 [2024-11-25 17:58:03,908 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2388 [2024-11-25 17:58:04,161 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2314 [2024-11-25 17:58:04,422 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2927 [2024-11-25 17:58:04,691 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2460 [2024-11-25 17:58:04,954 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3066 [2024-11-25 17:58:05,200 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2193 [2024-11-25 17:58:05,435 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2927 [2024-11-25 17:58:05,697 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2715 [2024-11-25 17:58:05,942 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2954 [2024-11-25 17:58:06,160 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2677 [2024-11-25 17:58:06,382 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2143 [2024-11-25 17:58:06,649 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2768 [2024-11-25 17:58:06,887 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2421 [2024-11-25 17:58:07,147 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2340 [2024-11-25 17:58:07,406 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3083 [2024-11-25 17:58:07,648 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2550 [2024-11-25 17:58:07,912 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2768 [2024-11-25 17:58:08,163 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2153 [2024-11-25 17:58:08,412 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2701 [2024-11-25 17:58:08,682 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2456 [2024-11-25 17:58:08,917 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2659 [2024-11-25 17:58:09,186 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2835 [2024-11-25 17:58:09,447 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2661 [2024-11-25 17:58:09,695 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2917 [2024-11-25 17:58:09,949 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2563 [2024-11-25 17:58:10,184 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2450 [2024-11-25 17:58:10,440 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2873 [2024-11-25 17:58:10,710 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2854 [2024-11-25 17:58:10,980 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2309 [2024-11-25 17:58:11,249 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2284 [2024-11-25 17:58:11,498 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2400 [2024-11-25 17:58:11,769 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2890 [2024-11-25 17:58:11,989 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2818 [2024-11-25 17:58:12,262 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2640 [2024-11-25 17:58:12,501 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2677 [2024-11-25 17:58:12,773 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2394 [2024-11-25 17:58:13,045 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2989 [2024-11-25 17:58:13,309 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2690 [2024-11-25 17:58:13,561 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3162 [2024-11-25 17:58:13,785 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2502 [2024-11-25 17:58:14,023 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2361 [2024-11-25 17:58:14,282 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2540 [2024-11-25 17:58:14,557 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2777 [2024-11-25 17:58:14,796 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2713 [2024-11-25 17:58:15,057 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2587 [2024-11-25 17:58:15,321 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2358 [2024-11-25 17:58:15,544 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2527 [2024-11-25 17:58:15,781 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2473 [2024-11-25 17:58:16,003 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2366 [2024-11-25 17:58:16,229 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2315 [2024-11-25 17:58:16,501 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2990 [2024-11-25 17:58:16,763 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2923 [2024-11-25 17:58:17,036 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2965 [2024-11-25 17:58:17,314 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2402 [2024-11-25 17:58:17,576 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3057 [2024-11-25 17:58:17,837 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2869 [2024-11-25 17:58:18,103 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2321 [2024-11-25 17:58:18,361 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2983 [2024-11-25 17:58:18,625 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2555 [2024-11-25 17:58:18,891 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2522 [2024-11-25 17:58:19,144 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3013 [2024-11-25 17:58:19,376 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2322 [2024-11-25 17:58:19,636 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2821 [2024-11-25 17:58:19,875 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2647 [2024-11-25 17:58:20,102 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2072 [2024-11-25 17:58:20,313 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2494 [2024-11-25 17:58:20,533 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2202 [2024-11-25 17:58:21,440 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7531/0.8291/0.9958. [2024-11-25 17:58:21,440 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9958/0.9981 [2024-11-25 17:58:21,440 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5103/0.6601 [2024-11-25 17:58:21,441 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 17:58:21,442 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7651 [2024-11-25 17:58:21,442 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 17:58:24,347 INFO misc.py line 119 2586773] Train: [20/50][1/376] Data 0.141 (0.141) Batch 0.638 (0.638) Remain 02:03:58 loss: 0.2545 Lr: 0.00289 [2024-11-25 17:58:24,823 INFO misc.py line 119 2586773] Train: [20/50][2/376] Data 0.003 (0.003) Batch 0.476 (0.476) Remain 01:32:30 loss: 0.2112 Lr: 0.00289 [2024-11-25 17:58:25,352 INFO misc.py line 119 2586773] Train: [20/50][3/376] Data 0.003 (0.003) Batch 0.529 (0.529) Remain 01:42:47 loss: 0.2250 Lr: 0.00289 [2024-11-25 17:58:25,864 INFO misc.py line 119 2586773] Train: [20/50][4/376] Data 0.003 (0.003) Batch 0.512 (0.512) Remain 01:39:20 loss: 0.2184 Lr: 0.00289 [2024-11-25 17:58:26,386 INFO misc.py line 119 2586773] Train: [20/50][5/376] Data 0.003 (0.003) Batch 0.523 (0.517) Remain 01:40:26 loss: 0.2218 Lr: 0.00289 [2024-11-25 17:58:26,951 INFO misc.py line 119 2586773] Train: [20/50][6/376] Data 0.003 (0.003) Batch 0.564 (0.533) Remain 01:43:27 loss: 0.2490 Lr: 0.00289 [2024-11-25 17:58:27,477 INFO misc.py line 119 2586773] Train: [20/50][7/376] Data 0.003 (0.003) Batch 0.526 (0.531) Remain 01:43:06 loss: 0.2689 Lr: 0.00289 [2024-11-25 17:58:28,012 INFO misc.py line 119 2586773] Train: [20/50][8/376] Data 0.004 (0.003) Batch 0.535 (0.532) Remain 01:43:14 loss: 0.2668 Lr: 0.00288 [2024-11-25 17:58:28,518 INFO misc.py line 119 2586773] Train: [20/50][9/376] Data 0.003 (0.003) Batch 0.507 (0.528) Remain 01:42:25 loss: 0.2500 Lr: 0.00288 [2024-11-25 17:58:28,997 INFO misc.py line 119 2586773] Train: [20/50][10/376] Data 0.003 (0.003) Batch 0.479 (0.521) Remain 01:41:04 loss: 0.2606 Lr: 0.00288 [2024-11-25 17:58:29,486 INFO misc.py line 119 2586773] Train: [20/50][11/376] Data 0.003 (0.003) Batch 0.489 (0.517) Remain 01:40:17 loss: 0.2347 Lr: 0.00288 [2024-11-25 17:58:29,977 INFO misc.py line 119 2586773] Train: [20/50][12/376] Data 0.003 (0.003) Batch 0.491 (0.514) Remain 01:39:43 loss: 0.1818 Lr: 0.00288 [2024-11-25 17:58:30,534 INFO misc.py line 119 2586773] Train: [20/50][13/376] Data 0.003 (0.003) Batch 0.557 (0.518) Remain 01:40:32 loss: 0.2178 Lr: 0.00288 [2024-11-25 17:58:31,065 INFO misc.py line 119 2586773] Train: [20/50][14/376] Data 0.003 (0.003) Batch 0.531 (0.519) Remain 01:40:45 loss: 0.2204 Lr: 0.00288 [2024-11-25 17:58:31,553 INFO misc.py line 119 2586773] Train: [20/50][15/376] Data 0.004 (0.003) Batch 0.488 (0.517) Remain 01:40:14 loss: 0.2569 Lr: 0.00288 [2024-11-25 17:58:32,095 INFO misc.py line 119 2586773] Train: 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Batch 0.478 (0.512) Remain 01:37:59 loss: 0.2602 Lr: 0.00283 [2024-11-25 17:59:52,424 INFO misc.py line 119 2586773] Train: [20/50][173/376] Data 0.002 (0.003) Batch 0.546 (0.512) Remain 01:38:01 loss: 0.2128 Lr: 0.00283 [2024-11-25 17:59:52,943 INFO misc.py line 119 2586773] Train: [20/50][174/376] Data 0.003 (0.003) Batch 0.519 (0.512) Remain 01:38:01 loss: 0.2435 Lr: 0.00283 [2024-11-25 17:59:53,442 INFO misc.py line 119 2586773] Train: [20/50][175/376] Data 0.002 (0.003) Batch 0.499 (0.512) Remain 01:38:00 loss: 0.2265 Lr: 0.00283 [2024-11-25 17:59:53,916 INFO misc.py line 119 2586773] Train: [20/50][176/376] Data 0.002 (0.003) Batch 0.474 (0.512) Remain 01:37:56 loss: 0.2241 Lr: 0.00283 [2024-11-25 17:59:54,453 INFO misc.py line 119 2586773] Train: [20/50][177/376] Data 0.002 (0.003) Batch 0.537 (0.512) Remain 01:37:58 loss: 0.2547 Lr: 0.00283 [2024-11-25 17:59:54,926 INFO misc.py line 119 2586773] Train: [20/50][178/376] Data 0.002 (0.003) Batch 0.473 (0.512) Remain 01:37:55 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Batch 0.550 (0.509) Remain 01:36:32 loss: 0.2787 Lr: 0.00280 [2024-11-25 18:00:48,996 INFO misc.py line 119 2586773] Train: [20/50][285/376] Data 0.002 (0.003) Batch 0.505 (0.509) Remain 01:36:32 loss: 0.1987 Lr: 0.00280 [2024-11-25 18:00:49,521 INFO misc.py line 119 2586773] Train: [20/50][286/376] Data 0.002 (0.003) Batch 0.525 (0.509) Remain 01:36:32 loss: 0.2637 Lr: 0.00280 [2024-11-25 18:00:50,013 INFO misc.py line 119 2586773] Train: [20/50][287/376] Data 0.002 (0.003) Batch 0.491 (0.509) Remain 01:36:30 loss: 0.2093 Lr: 0.00280 [2024-11-25 18:00:50,477 INFO misc.py line 119 2586773] Train: [20/50][288/376] Data 0.002 (0.003) Batch 0.464 (0.509) Remain 01:36:28 loss: 0.2212 Lr: 0.00280 [2024-11-25 18:00:50,985 INFO misc.py line 119 2586773] Train: [20/50][289/376] Data 0.002 (0.003) Batch 0.508 (0.509) Remain 01:36:28 loss: 0.2065 Lr: 0.00280 [2024-11-25 18:00:51,459 INFO misc.py line 119 2586773] Train: [20/50][290/376] Data 0.003 (0.003) Batch 0.475 (0.509) Remain 01:36:26 loss: 0.2886 Lr: 0.00280 [2024-11-25 18:00:52,002 INFO misc.py line 119 2586773] Train: [20/50][291/376] Data 0.002 (0.003) Batch 0.543 (0.509) Remain 01:36:27 loss: 0.2097 Lr: 0.00280 [2024-11-25 18:00:52,524 INFO misc.py line 119 2586773] Train: [20/50][292/376] Data 0.002 (0.003) Batch 0.522 (0.509) Remain 01:36:27 loss: 0.2274 Lr: 0.00280 [2024-11-25 18:00:53,034 INFO misc.py line 119 2586773] Train: [20/50][293/376] Data 0.003 (0.003) Batch 0.510 (0.509) Remain 01:36:26 loss: 0.2205 Lr: 0.00279 [2024-11-25 18:00:53,587 INFO misc.py line 119 2586773] Train: [20/50][294/376] Data 0.002 (0.003) Batch 0.553 (0.509) Remain 01:36:27 loss: 0.2404 Lr: 0.00279 [2024-11-25 18:00:54,092 INFO misc.py line 119 2586773] Train: [20/50][295/376] Data 0.002 (0.003) Batch 0.505 (0.509) Remain 01:36:27 loss: 0.1863 Lr: 0.00279 [2024-11-25 18:00:54,655 INFO misc.py line 119 2586773] Train: [20/50][296/376] Data 0.002 (0.003) Batch 0.562 (0.510) Remain 01:36:28 loss: 0.2370 Lr: 0.00279 [2024-11-25 18:00:55,179 INFO misc.py line 119 2586773] Train: [20/50][297/376] Data 0.003 (0.003) Batch 0.525 (0.510) Remain 01:36:28 loss: 0.2235 Lr: 0.00279 [2024-11-25 18:00:55,655 INFO misc.py line 119 2586773] Train: [20/50][298/376] Data 0.003 (0.003) Batch 0.476 (0.510) Remain 01:36:26 loss: 0.2111 Lr: 0.00279 [2024-11-25 18:00:56,172 INFO misc.py line 119 2586773] Train: [20/50][299/376] Data 0.002 (0.003) Batch 0.517 (0.510) Remain 01:36:26 loss: 0.1967 Lr: 0.00279 [2024-11-25 18:00:56,666 INFO misc.py line 119 2586773] Train: [20/50][300/376] Data 0.002 (0.003) Batch 0.494 (0.509) Remain 01:36:25 loss: 0.1844 Lr: 0.00279 [2024-11-25 18:00:57,158 INFO misc.py line 119 2586773] Train: [20/50][301/376] Data 0.002 (0.003) Batch 0.492 (0.509) Remain 01:36:24 loss: 0.2325 Lr: 0.00279 [2024-11-25 18:00:57,676 INFO misc.py line 119 2586773] Train: [20/50][302/376] Data 0.003 (0.003) Batch 0.518 (0.509) Remain 01:36:24 loss: 0.2500 Lr: 0.00279 [2024-11-25 18:00:58,192 INFO misc.py line 119 2586773] Train: [20/50][303/376] Data 0.002 (0.003) Batch 0.516 (0.509) Remain 01:36:23 loss: 0.2159 Lr: 0.00279 [2024-11-25 18:00:58,705 INFO misc.py line 119 2586773] Train: [20/50][304/376] Data 0.002 (0.003) Batch 0.513 (0.509) Remain 01:36:23 loss: 0.2946 Lr: 0.00279 [2024-11-25 18:00:59,178 INFO misc.py line 119 2586773] Train: [20/50][305/376] Data 0.003 (0.003) Batch 0.473 (0.509) Remain 01:36:21 loss: 0.2198 Lr: 0.00279 [2024-11-25 18:00:59,752 INFO misc.py line 119 2586773] Train: [20/50][306/376] Data 0.002 (0.003) Batch 0.574 (0.510) Remain 01:36:23 loss: 0.2388 Lr: 0.00279 [2024-11-25 18:01:00,246 INFO misc.py line 119 2586773] Train: [20/50][307/376] Data 0.002 (0.003) Batch 0.494 (0.510) Remain 01:36:22 loss: 0.2459 Lr: 0.00279 [2024-11-25 18:01:00,712 INFO misc.py line 119 2586773] Train: [20/50][308/376] Data 0.003 (0.003) Batch 0.466 (0.509) Remain 01:36:20 loss: 0.2122 Lr: 0.00279 [2024-11-25 18:01:01,233 INFO misc.py line 119 2586773] Train: [20/50][309/376] Data 0.002 (0.003) Batch 0.522 (0.509) Remain 01:36:20 loss: 0.2363 Lr: 0.00279 [2024-11-25 18:01:01,747 INFO misc.py line 119 2586773] Train: [20/50][310/376] Data 0.002 (0.003) Batch 0.514 (0.509) Remain 01:36:19 loss: 0.1859 Lr: 0.00279 [2024-11-25 18:01:02,259 INFO misc.py line 119 2586773] Train: [20/50][311/376] Data 0.002 (0.003) Batch 0.512 (0.509) Remain 01:36:19 loss: 0.2349 Lr: 0.00279 [2024-11-25 18:01:02,779 INFO misc.py line 119 2586773] Train: [20/50][312/376] Data 0.003 (0.003) Batch 0.520 (0.509) Remain 01:36:19 loss: 0.2108 Lr: 0.00279 [2024-11-25 18:01:03,305 INFO misc.py line 119 2586773] Train: [20/50][313/376] Data 0.003 (0.003) Batch 0.525 (0.510) Remain 01:36:19 loss: 0.1970 Lr: 0.00279 [2024-11-25 18:01:03,818 INFO misc.py line 119 2586773] Train: [20/50][314/376] Data 0.003 (0.003) Batch 0.514 (0.510) Remain 01:36:19 loss: 0.2080 Lr: 0.00279 [2024-11-25 18:01:04,328 INFO misc.py line 119 2586773] Train: [20/50][315/376] Data 0.003 (0.003) Batch 0.509 (0.510) Remain 01:36:18 loss: 0.2060 Lr: 0.00279 [2024-11-25 18:01:04,819 INFO misc.py line 119 2586773] Train: [20/50][316/376] Data 0.002 (0.003) Batch 0.491 (0.509) Remain 01:36:17 loss: 0.2631 Lr: 0.00279 [2024-11-25 18:01:05,306 INFO misc.py line 119 2586773] Train: [20/50][317/376] Data 0.003 (0.003) Batch 0.488 (0.509) Remain 01:36:16 loss: 0.1912 Lr: 0.00279 [2024-11-25 18:01:05,818 INFO misc.py line 119 2586773] Train: [20/50][318/376] Data 0.002 (0.003) Batch 0.512 (0.509) Remain 01:36:15 loss: 0.2251 Lr: 0.00279 [2024-11-25 18:01:06,327 INFO misc.py line 119 2586773] Train: [20/50][319/376] Data 0.002 (0.003) Batch 0.508 (0.509) Remain 01:36:15 loss: 0.2452 Lr: 0.00279 [2024-11-25 18:01:06,855 INFO misc.py line 119 2586773] Train: [20/50][320/376] Data 0.002 (0.003) Batch 0.529 (0.509) Remain 01:36:15 loss: 0.1745 Lr: 0.00279 [2024-11-25 18:01:07,414 INFO misc.py line 119 2586773] Train: [20/50][321/376] Data 0.002 (0.003) Batch 0.558 (0.510) Remain 01:36:16 loss: 0.2663 Lr: 0.00279 [2024-11-25 18:01:07,972 INFO misc.py line 119 2586773] Train: [20/50][322/376] Data 0.002 (0.003) Batch 0.558 (0.510) Remain 01:36:17 loss: 0.1835 Lr: 0.00279 [2024-11-25 18:01:08,520 INFO misc.py line 119 2586773] Train: [20/50][323/376] Data 0.002 (0.003) Batch 0.547 (0.510) Remain 01:36:18 loss: 0.2538 Lr: 0.00279 [2024-11-25 18:01:09,052 INFO misc.py line 119 2586773] Train: [20/50][324/376] Data 0.002 (0.003) Batch 0.532 (0.510) Remain 01:36:18 loss: 0.2714 Lr: 0.00278 [2024-11-25 18:01:09,549 INFO misc.py line 119 2586773] Train: [20/50][325/376] Data 0.002 (0.003) Batch 0.496 (0.510) Remain 01:36:17 loss: 0.3283 Lr: 0.00278 [2024-11-25 18:01:10,047 INFO misc.py line 119 2586773] Train: [20/50][326/376] Data 0.004 (0.003) Batch 0.499 (0.510) Remain 01:36:17 loss: 0.2215 Lr: 0.00278 [2024-11-25 18:01:10,533 INFO misc.py line 119 2586773] Train: [20/50][327/376] Data 0.002 (0.003) Batch 0.486 (0.510) Remain 01:36:15 loss: 0.2033 Lr: 0.00278 [2024-11-25 18:01:11,048 INFO misc.py line 119 2586773] Train: [20/50][328/376] Data 0.002 (0.003) Batch 0.515 (0.510) Remain 01:36:15 loss: 0.1956 Lr: 0.00278 [2024-11-25 18:01:11,561 INFO misc.py line 119 2586773] Train: [20/50][329/376] Data 0.003 (0.003) Batch 0.513 (0.510) Remain 01:36:14 loss: 0.2244 Lr: 0.00278 [2024-11-25 18:01:12,071 INFO misc.py line 119 2586773] Train: [20/50][330/376] Data 0.002 (0.003) Batch 0.510 (0.510) Remain 01:36:14 loss: 0.1950 Lr: 0.00278 [2024-11-25 18:01:12,573 INFO misc.py line 119 2586773] Train: [20/50][331/376] Data 0.003 (0.003) Batch 0.502 (0.510) Remain 01:36:13 loss: 0.2089 Lr: 0.00278 [2024-11-25 18:01:13,071 INFO misc.py line 119 2586773] Train: [20/50][332/376] Data 0.002 (0.003) Batch 0.497 (0.510) Remain 01:36:12 loss: 0.2685 Lr: 0.00278 [2024-11-25 18:01:13,603 INFO misc.py line 119 2586773] Train: [20/50][333/376] Data 0.002 (0.003) Batch 0.532 (0.510) Remain 01:36:13 loss: 0.2488 Lr: 0.00278 [2024-11-25 18:01:14,142 INFO misc.py line 119 2586773] Train: [20/50][334/376] Data 0.002 (0.003) Batch 0.539 (0.510) Remain 01:36:13 loss: 0.2662 Lr: 0.00278 [2024-11-25 18:01:14,681 INFO misc.py line 119 2586773] Train: [20/50][335/376] Data 0.002 (0.003) Batch 0.540 (0.510) Remain 01:36:14 loss: 0.2992 Lr: 0.00278 [2024-11-25 18:01:15,194 INFO misc.py line 119 2586773] Train: [20/50][336/376] Data 0.002 (0.003) Batch 0.513 (0.510) Remain 01:36:13 loss: 0.1944 Lr: 0.00278 [2024-11-25 18:01:15,676 INFO misc.py line 119 2586773] Train: [20/50][337/376] Data 0.002 (0.003) Batch 0.482 (0.510) Remain 01:36:12 loss: 0.2801 Lr: 0.00278 [2024-11-25 18:01:16,166 INFO misc.py line 119 2586773] Train: [20/50][338/376] Data 0.002 (0.003) Batch 0.489 (0.510) Remain 01:36:10 loss: 0.2450 Lr: 0.00278 [2024-11-25 18:01:16,682 INFO misc.py line 119 2586773] Train: [20/50][339/376] Data 0.002 (0.003) Batch 0.517 (0.510) Remain 01:36:10 loss: 0.2660 Lr: 0.00278 [2024-11-25 18:01:17,184 INFO misc.py line 119 2586773] Train: [20/50][340/376] Data 0.002 (0.003) Batch 0.501 (0.510) Remain 01:36:09 loss: 0.2813 Lr: 0.00278 [2024-11-25 18:01:17,682 INFO misc.py line 119 2586773] Train: [20/50][341/376] Data 0.003 (0.003) Batch 0.498 (0.510) Remain 01:36:08 loss: 0.2239 Lr: 0.00278 [2024-11-25 18:01:18,150 INFO misc.py line 119 2586773] Train: [20/50][342/376] Data 0.002 (0.003) Batch 0.469 (0.510) Remain 01:36:07 loss: 0.2265 Lr: 0.00278 [2024-11-25 18:01:18,666 INFO misc.py line 119 2586773] Train: [20/50][343/376] Data 0.003 (0.003) Batch 0.516 (0.510) Remain 01:36:06 loss: 0.2706 Lr: 0.00278 [2024-11-25 18:01:19,179 INFO misc.py line 119 2586773] Train: [20/50][344/376] Data 0.002 (0.003) Batch 0.513 (0.510) Remain 01:36:06 loss: 0.2467 Lr: 0.00278 [2024-11-25 18:01:19,675 INFO misc.py line 119 2586773] Train: [20/50][345/376] Data 0.002 (0.003) Batch 0.496 (0.510) Remain 01:36:05 loss: 0.1910 Lr: 0.00278 [2024-11-25 18:01:20,176 INFO misc.py line 119 2586773] Train: [20/50][346/376] Data 0.002 (0.003) Batch 0.501 (0.510) Remain 01:36:04 loss: 0.1977 Lr: 0.00278 [2024-11-25 18:01:20,662 INFO misc.py line 119 2586773] Train: [20/50][347/376] Data 0.002 (0.003) Batch 0.486 (0.510) Remain 01:36:03 loss: 0.2214 Lr: 0.00278 [2024-11-25 18:01:21,189 INFO misc.py line 119 2586773] Train: [20/50][348/376] Data 0.002 (0.003) Batch 0.527 (0.510) Remain 01:36:03 loss: 0.2245 Lr: 0.00278 [2024-11-25 18:01:21,656 INFO misc.py line 119 2586773] Train: [20/50][349/376] Data 0.002 (0.003) Batch 0.467 (0.510) Remain 01:36:01 loss: 0.2455 Lr: 0.00278 [2024-11-25 18:01:22,125 INFO misc.py line 119 2586773] Train: [20/50][350/376] Data 0.002 (0.003) Batch 0.469 (0.509) Remain 01:35:59 loss: 0.1949 Lr: 0.00278 [2024-11-25 18:01:22,677 INFO misc.py line 119 2586773] Train: [20/50][351/376] Data 0.003 (0.003) Batch 0.552 (0.510) Remain 01:36:00 loss: 0.2326 Lr: 0.00278 [2024-11-25 18:01:23,184 INFO misc.py line 119 2586773] Train: [20/50][352/376] Data 0.002 (0.003) Batch 0.507 (0.510) Remain 01:35:59 loss: 0.2607 Lr: 0.00278 [2024-11-25 18:01:23,722 INFO misc.py line 119 2586773] Train: [20/50][353/376] Data 0.002 (0.003) Batch 0.538 (0.510) Remain 01:36:00 loss: 0.2692 Lr: 0.00278 [2024-11-25 18:01:24,217 INFO misc.py line 119 2586773] Train: [20/50][354/376] Data 0.002 (0.003) Batch 0.494 (0.510) Remain 01:35:59 loss: 0.3283 Lr: 0.00278 [2024-11-25 18:01:24,728 INFO misc.py line 119 2586773] Train: [20/50][355/376] Data 0.002 (0.003) Batch 0.512 (0.510) Remain 01:35:58 loss: 0.2589 Lr: 0.00277 [2024-11-25 18:01:25,266 INFO misc.py line 119 2586773] Train: [20/50][356/376] Data 0.002 (0.003) Batch 0.538 (0.510) Remain 01:35:59 loss: 0.2695 Lr: 0.00277 [2024-11-25 18:01:25,776 INFO misc.py line 119 2586773] Train: [20/50][357/376] Data 0.002 (0.003) Batch 0.510 (0.510) Remain 01:35:58 loss: 0.2293 Lr: 0.00277 [2024-11-25 18:01:26,296 INFO misc.py line 119 2586773] Train: [20/50][358/376] Data 0.002 (0.003) Batch 0.520 (0.510) Remain 01:35:58 loss: 0.2057 Lr: 0.00277 [2024-11-25 18:01:26,766 INFO misc.py line 119 2586773] Train: [20/50][359/376] Data 0.002 (0.003) Batch 0.470 (0.510) Remain 01:35:56 loss: 0.2282 Lr: 0.00277 [2024-11-25 18:01:27,258 INFO misc.py line 119 2586773] Train: [20/50][360/376] Data 0.002 (0.003) Batch 0.492 (0.510) Remain 01:35:55 loss: 0.2489 Lr: 0.00277 [2024-11-25 18:01:27,754 INFO misc.py line 119 2586773] Train: [20/50][361/376] Data 0.002 (0.003) Batch 0.496 (0.510) Remain 01:35:54 loss: 0.2542 Lr: 0.00277 [2024-11-25 18:01:28,261 INFO misc.py line 119 2586773] Train: [20/50][362/376] Data 0.002 (0.003) Batch 0.507 (0.509) Remain 01:35:54 loss: 0.1761 Lr: 0.00277 [2024-11-25 18:01:28,780 INFO misc.py line 119 2586773] Train: [20/50][363/376] Data 0.002 (0.003) Batch 0.519 (0.510) Remain 01:35:54 loss: 0.2325 Lr: 0.00277 [2024-11-25 18:01:29,264 INFO misc.py line 119 2586773] Train: [20/50][364/376] Data 0.002 (0.003) Batch 0.484 (0.509) Remain 01:35:52 loss: 0.2098 Lr: 0.00277 [2024-11-25 18:01:29,748 INFO misc.py line 119 2586773] Train: [20/50][365/376] Data 0.002 (0.003) Batch 0.484 (0.509) Remain 01:35:51 loss: 0.2774 Lr: 0.00277 [2024-11-25 18:01:30,276 INFO misc.py line 119 2586773] Train: [20/50][366/376] Data 0.002 (0.003) Batch 0.528 (0.509) Remain 01:35:51 loss: 0.2516 Lr: 0.00277 [2024-11-25 18:01:30,755 INFO misc.py line 119 2586773] Train: [20/50][367/376] Data 0.002 (0.003) Batch 0.479 (0.509) Remain 01:35:50 loss: 0.2014 Lr: 0.00277 [2024-11-25 18:01:31,258 INFO misc.py line 119 2586773] Train: [20/50][368/376] Data 0.002 (0.003) Batch 0.503 (0.509) Remain 01:35:49 loss: 0.2123 Lr: 0.00277 [2024-11-25 18:01:31,787 INFO misc.py line 119 2586773] Train: [20/50][369/376] Data 0.002 (0.003) Batch 0.529 (0.509) Remain 01:35:49 loss: 0.2143 Lr: 0.00277 [2024-11-25 18:01:32,261 INFO misc.py line 119 2586773] Train: [20/50][370/376] Data 0.003 (0.003) Batch 0.474 (0.509) Remain 01:35:47 loss: 0.2055 Lr: 0.00277 [2024-11-25 18:01:32,768 INFO misc.py line 119 2586773] Train: [20/50][371/376] Data 0.002 (0.003) Batch 0.507 (0.509) Remain 01:35:47 loss: 0.2299 Lr: 0.00277 [2024-11-25 18:01:33,245 INFO misc.py line 119 2586773] Train: [20/50][372/376] Data 0.002 (0.003) Batch 0.477 (0.509) Remain 01:35:45 loss: 0.2115 Lr: 0.00277 [2024-11-25 18:01:33,742 INFO misc.py line 119 2586773] Train: [20/50][373/376] Data 0.002 (0.003) Batch 0.497 (0.509) Remain 01:35:44 loss: 0.2599 Lr: 0.00277 [2024-11-25 18:01:34,247 INFO misc.py line 119 2586773] Train: [20/50][374/376] Data 0.002 (0.003) Batch 0.505 (0.509) Remain 01:35:44 loss: 0.2947 Lr: 0.00277 [2024-11-25 18:01:34,750 INFO misc.py line 119 2586773] Train: [20/50][375/376] Data 0.002 (0.003) Batch 0.503 (0.509) Remain 01:35:43 loss: 0.2194 Lr: 0.00277 [2024-11-25 18:01:35,250 INFO misc.py line 119 2586773] Train: [20/50][376/376] Data 0.002 (0.003) Batch 0.500 (0.509) Remain 01:35:42 loss: 0.1921 Lr: 0.00277 [2024-11-25 18:01:35,251 INFO misc.py line 136 2586773] Train result: loss: 0.2309 [2024-11-25 18:01:35,251 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:01:46,073 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1855 [2024-11-25 18:01:46,336 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2362 [2024-11-25 18:01:46,601 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3285 [2024-11-25 18:01:46,824 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2138 [2024-11-25 18:01:47,101 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3145 [2024-11-25 18:01:47,375 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2232 [2024-11-25 18:01:47,597 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2321 [2024-11-25 18:01:47,867 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2297 [2024-11-25 18:01:48,090 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2879 [2024-11-25 18:01:48,351 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2758 [2024-11-25 18:01:48,582 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2089 [2024-11-25 18:01:48,853 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2524 [2024-11-25 18:01:49,117 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2647 [2024-11-25 18:01:49,385 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2521 [2024-11-25 18:01:49,618 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2511 [2024-11-25 18:01:49,859 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3172 [2024-11-25 18:01:50,123 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3061 [2024-11-25 18:01:50,370 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2188 [2024-11-25 18:01:50,600 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2255 [2024-11-25 18:01:50,861 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2627 [2024-11-25 18:01:51,094 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2558 [2024-11-25 18:01:51,360 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2821 [2024-11-25 18:01:51,597 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2171 [2024-11-25 18:01:51,863 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2609 [2024-11-25 18:01:52,125 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2376 [2024-11-25 18:01:52,363 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.3194 [2024-11-25 18:01:52,618 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2906 [2024-11-25 18:01:52,864 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2637 [2024-11-25 18:01:53,130 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.3022 [2024-11-25 18:01:53,382 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3192 [2024-11-25 18:01:53,617 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2908 [2024-11-25 18:01:53,884 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2431 [2024-11-25 18:01:54,102 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2646 [2024-11-25 18:01:54,339 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2444 [2024-11-25 18:01:54,600 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2371 [2024-11-25 18:01:54,850 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2587 [2024-11-25 18:01:55,084 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2191 [2024-11-25 18:01:55,360 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2513 [2024-11-25 18:01:55,593 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2741 [2024-11-25 18:01:55,831 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2740 [2024-11-25 18:01:56,104 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2988 [2024-11-25 18:01:56,357 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3013 [2024-11-25 18:01:56,596 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2815 [2024-11-25 18:01:56,832 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2389 [2024-11-25 18:01:57,071 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2463 [2024-11-25 18:01:57,323 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2506 [2024-11-25 18:01:57,587 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2690 [2024-11-25 18:01:57,844 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3331 [2024-11-25 18:01:58,074 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2315 [2024-11-25 18:01:58,319 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2246 [2024-11-25 18:01:58,543 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2551 [2024-11-25 18:01:58,799 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2876 [2024-11-25 18:01:59,069 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2424 [2024-11-25 18:01:59,332 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3420 [2024-11-25 18:01:59,565 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2552 [2024-11-25 18:01:59,804 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2408 [2024-11-25 18:02:00,063 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3140 [2024-11-25 18:02:00,331 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2923 [2024-11-25 18:02:00,588 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2910 [2024-11-25 18:02:00,851 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2926 [2024-11-25 18:02:01,106 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2295 [2024-11-25 18:02:01,384 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2508 [2024-11-25 18:02:01,625 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2513 [2024-11-25 18:02:01,888 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2663 [2024-11-25 18:02:02,160 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2706 [2024-11-25 18:02:02,433 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2289 [2024-11-25 18:02:02,680 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2283 [2024-11-25 18:02:02,942 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2809 [2024-11-25 18:02:03,211 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2489 [2024-11-25 18:02:03,473 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.3127 [2024-11-25 18:02:03,721 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2090 [2024-11-25 18:02:03,959 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2811 [2024-11-25 18:02:04,216 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2902 [2024-11-25 18:02:04,464 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2832 [2024-11-25 18:02:04,680 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2754 [2024-11-25 18:02:04,904 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2149 [2024-11-25 18:02:05,173 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2729 [2024-11-25 18:02:05,410 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2387 [2024-11-25 18:02:05,672 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2466 [2024-11-25 18:02:05,924 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2994 [2024-11-25 18:02:06,166 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2626 [2024-11-25 18:02:06,429 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.3109 [2024-11-25 18:02:06,681 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2211 [2024-11-25 18:02:06,929 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2625 [2024-11-25 18:02:07,203 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2641 [2024-11-25 18:02:07,443 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2776 [2024-11-25 18:02:07,711 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2683 [2024-11-25 18:02:07,975 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2745 [2024-11-25 18:02:08,225 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2961 [2024-11-25 18:02:08,477 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2877 [2024-11-25 18:02:08,713 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2511 [2024-11-25 18:02:08,965 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2660 [2024-11-25 18:02:09,236 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2848 [2024-11-25 18:02:09,507 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2233 [2024-11-25 18:02:09,772 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2320 [2024-11-25 18:02:10,025 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2250 [2024-11-25 18:02:10,295 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2883 [2024-11-25 18:02:10,516 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3243 [2024-11-25 18:02:10,791 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2755 [2024-11-25 18:02:11,031 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2765 [2024-11-25 18:02:11,304 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2196 [2024-11-25 18:02:11,566 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2740 [2024-11-25 18:02:11,827 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.3028 [2024-11-25 18:02:12,080 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3071 [2024-11-25 18:02:12,309 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2570 [2024-11-25 18:02:12,544 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2507 [2024-11-25 18:02:12,805 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2339 [2024-11-25 18:02:13,078 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2617 [2024-11-25 18:02:13,314 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2772 [2024-11-25 18:02:13,579 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2458 [2024-11-25 18:02:13,852 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2292 [2024-11-25 18:02:14,075 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2702 [2024-11-25 18:02:14,316 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2369 [2024-11-25 18:02:14,536 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2312 [2024-11-25 18:02:14,763 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2308 [2024-11-25 18:02:15,038 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3315 [2024-11-25 18:02:15,299 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2949 [2024-11-25 18:02:15,566 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2539 [2024-11-25 18:02:15,833 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2468 [2024-11-25 18:02:16,097 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3639 [2024-11-25 18:02:16,359 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2882 [2024-11-25 18:02:16,629 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2180 [2024-11-25 18:02:16,887 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2716 [2024-11-25 18:02:17,153 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2621 [2024-11-25 18:02:17,420 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2642 [2024-11-25 18:02:17,671 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2735 [2024-11-25 18:02:17,909 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2229 [2024-11-25 18:02:18,173 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2866 [2024-11-25 18:02:18,410 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2699 [2024-11-25 18:02:18,639 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2069 [2024-11-25 18:02:18,851 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2495 [2024-11-25 18:02:19,077 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2083 [2024-11-25 18:02:19,767 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7506/0.8111/0.9960. [2024-11-25 18:02:19,767 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9985 [2024-11-25 18:02:19,768 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5053/0.6237 [2024-11-25 18:02:19,768 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:02:19,768 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7651 [2024-11-25 18:02:19,769 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:02:22,739 INFO misc.py line 119 2586773] Train: [21/50][1/376] Data 0.096 (0.096) Batch 0.600 (0.600) Remain 01:52:52 loss: 0.1983 Lr: 0.00277 [2024-11-25 18:02:23,225 INFO misc.py line 119 2586773] Train: [21/50][2/376] Data 0.003 (0.003) Batch 0.486 (0.486) Remain 01:31:20 loss: 0.2529 Lr: 0.00277 [2024-11-25 18:02:23,776 INFO misc.py line 119 2586773] Train: [21/50][3/376] Data 0.003 (0.003) Batch 0.551 (0.551) Remain 01:43:35 loss: 0.2429 Lr: 0.00277 [2024-11-25 18:02:24,292 INFO misc.py line 119 2586773] Train: [21/50][4/376] Data 0.003 (0.003) Batch 0.516 (0.516) Remain 01:37:03 loss: 0.2041 Lr: 0.00277 [2024-11-25 18:02:24,762 INFO misc.py line 119 2586773] Train: [21/50][5/376] Data 0.003 (0.003) Batch 0.470 (0.493) Remain 01:32:40 loss: 0.2500 Lr: 0.00277 [2024-11-25 18:02:25,255 INFO misc.py line 119 2586773] Train: [21/50][6/376] Data 0.002 (0.002) Batch 0.493 (0.493) Remain 01:32:40 loss: 0.1920 Lr: 0.00277 [2024-11-25 18:02:25,789 INFO misc.py line 119 2586773] Train: [21/50][7/376] Data 0.003 (0.002) Batch 0.534 (0.503) Remain 01:34:35 loss: 0.2425 Lr: 0.00277 [2024-11-25 18:02:26,298 INFO misc.py line 119 2586773] Train: [21/50][8/376] Data 0.002 (0.002) Batch 0.508 (0.504) Remain 01:34:44 loss: 0.2032 Lr: 0.00277 [2024-11-25 18:02:26,895 INFO misc.py line 119 2586773] Train: [21/50][9/376] Data 0.003 (0.003) Batch 0.597 (0.520) Remain 01:37:37 loss: 0.2601 Lr: 0.00277 [2024-11-25 18:02:27,416 INFO misc.py line 119 2586773] Train: [21/50][10/376] Data 0.004 (0.003) Batch 0.521 (0.520) Remain 01:37:39 loss: 0.2006 Lr: 0.00276 [2024-11-25 18:02:27,927 INFO misc.py line 119 2586773] Train: [21/50][11/376] Data 0.003 (0.003) Batch 0.512 (0.519) Remain 01:37:27 loss: 0.2013 Lr: 0.00276 [2024-11-25 18:02:28,434 INFO misc.py line 119 2586773] Train: [21/50][12/376] Data 0.003 (0.003) Batch 0.506 (0.517) Remain 01:37:10 loss: 0.1833 Lr: 0.00276 [2024-11-25 18:02:28,934 INFO misc.py line 119 2586773] Train: [21/50][13/376] Data 0.004 (0.003) Batch 0.501 (0.516) Remain 01:36:50 loss: 0.2542 Lr: 0.00276 [2024-11-25 18:02:29,467 INFO misc.py line 119 2586773] Train: [21/50][14/376] Data 0.004 (0.003) Batch 0.533 (0.517) Remain 01:37:07 loss: 0.2064 Lr: 0.00276 [2024-11-25 18:02:29,961 INFO misc.py line 119 2586773] Train: [21/50][15/376] Data 0.003 (0.003) Batch 0.495 (0.515) Remain 01:36:46 loss: 0.2082 Lr: 0.00276 [2024-11-25 18:02:30,497 INFO misc.py line 119 2586773] Train: [21/50][16/376] Data 0.002 (0.003) Batch 0.536 (0.517) Remain 01:37:03 loss: 0.2135 Lr: 0.00276 [2024-11-25 18:02:31,000 INFO misc.py line 119 2586773] Train: [21/50][17/376] Data 0.003 (0.003) Batch 0.503 (0.516) Remain 01:36:51 loss: 0.2402 Lr: 0.00276 [2024-11-25 18:02:31,532 INFO misc.py line 119 2586773] Train: [21/50][18/376] Data 0.003 (0.003) Batch 0.532 (0.517) Remain 01:37:03 loss: 0.2493 Lr: 0.00276 [2024-11-25 18:02:32,043 INFO misc.py line 119 2586773] Train: [21/50][19/376] Data 0.002 (0.003) Batch 0.510 (0.517) Remain 01:36:58 loss: 0.2050 Lr: 0.00276 [2024-11-25 18:02:32,526 INFO misc.py line 119 2586773] Train: [21/50][20/376] Data 0.003 (0.003) Batch 0.484 (0.515) Remain 01:36:35 loss: 0.2434 Lr: 0.00276 [2024-11-25 18:02:33,065 INFO misc.py line 119 2586773] Train: [21/50][21/376] Data 0.002 (0.003) Batch 0.539 (0.516) Remain 01:36:50 loss: 0.2248 Lr: 0.00276 [2024-11-25 18:02:33,597 INFO misc.py line 119 2586773] Train: [21/50][22/376] Data 0.003 (0.003) Batch 0.531 (0.517) Remain 01:36:59 loss: 0.2602 Lr: 0.00276 [2024-11-25 18:02:34,110 INFO misc.py line 119 2586773] Train: [21/50][23/376] Data 0.003 (0.003) Batch 0.514 (0.517) Remain 01:36:56 loss: 0.2317 Lr: 0.00276 [2024-11-25 18:02:34,614 INFO misc.py line 119 2586773] Train: [21/50][24/376] Data 0.003 (0.003) Batch 0.504 (0.516) Remain 01:36:49 loss: 0.1790 Lr: 0.00276 [2024-11-25 18:02:35,133 INFO misc.py line 119 2586773] Train: [21/50][25/376] Data 0.003 (0.003) Batch 0.519 (0.516) Remain 01:36:50 loss: 0.2791 Lr: 0.00276 [2024-11-25 18:02:35,648 INFO misc.py line 119 2586773] Train: [21/50][26/376] Data 0.003 (0.003) Batch 0.515 (0.516) Remain 01:36:49 loss: 0.2919 Lr: 0.00276 [2024-11-25 18:02:36,145 INFO misc.py line 119 2586773] Train: [21/50][27/376] Data 0.003 (0.003) Batch 0.498 (0.515) Remain 01:36:39 loss: 0.2694 Lr: 0.00276 [2024-11-25 18:02:36,624 INFO misc.py line 119 2586773] Train: [21/50][28/376] Data 0.003 (0.003) Batch 0.479 (0.514) Remain 01:36:22 loss: 0.2269 Lr: 0.00276 [2024-11-25 18:02:37,106 INFO misc.py line 119 2586773] Train: [21/50][29/376] Data 0.003 (0.003) Batch 0.482 (0.513) Remain 01:36:08 loss: 0.2146 Lr: 0.00276 [2024-11-25 18:02:37,648 INFO misc.py line 119 2586773] Train: [21/50][30/376] Data 0.003 (0.003) Batch 0.542 (0.514) Remain 01:36:20 loss: 0.2693 Lr: 0.00276 [2024-11-25 18:02:38,159 INFO misc.py line 119 2586773] Train: [21/50][31/376] Data 0.003 (0.003) Batch 0.511 (0.514) Remain 01:36:18 loss: 0.2438 Lr: 0.00276 [2024-11-25 18:02:38,624 INFO misc.py line 119 2586773] Train: [21/50][32/376] Data 0.003 (0.003) Batch 0.466 (0.512) Remain 01:35:59 loss: 0.2094 Lr: 0.00276 [2024-11-25 18:02:39,105 INFO misc.py line 119 2586773] Train: [21/50][33/376] Data 0.002 (0.003) Batch 0.481 (0.511) Remain 01:35:47 loss: 0.1799 Lr: 0.00276 [2024-11-25 18:02:39,639 INFO misc.py line 119 2586773] Train: [21/50][34/376] Data 0.002 (0.003) Batch 0.533 (0.512) Remain 01:35:54 loss: 0.2412 Lr: 0.00276 [2024-11-25 18:02:40,167 INFO misc.py 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Batch 0.497 (0.518) Remain 01:36:25 loss: 0.2463 Lr: 0.00273 [2024-11-25 18:03:22,896 INFO misc.py line 119 2586773] Train: [21/50][117/376] Data 0.002 (0.003) Batch 0.558 (0.519) Remain 01:36:29 loss: 0.2334 Lr: 0.00273 [2024-11-25 18:03:23,390 INFO misc.py line 119 2586773] Train: [21/50][118/376] Data 0.003 (0.003) Batch 0.494 (0.518) Remain 01:36:26 loss: 0.2274 Lr: 0.00273 [2024-11-25 18:03:23,856 INFO misc.py line 119 2586773] Train: [21/50][119/376] Data 0.003 (0.003) Batch 0.466 (0.518) Remain 01:36:20 loss: 0.2563 Lr: 0.00273 [2024-11-25 18:03:24,356 INFO misc.py line 119 2586773] Train: [21/50][120/376] Data 0.003 (0.003) Batch 0.501 (0.518) Remain 01:36:18 loss: 0.2106 Lr: 0.00273 [2024-11-25 18:03:24,843 INFO misc.py line 119 2586773] Train: [21/50][121/376] Data 0.003 (0.003) Batch 0.487 (0.518) Remain 01:36:14 loss: 0.2334 Lr: 0.00273 [2024-11-25 18:03:25,325 INFO misc.py line 119 2586773] Train: [21/50][122/376] Data 0.003 (0.003) Batch 0.482 (0.517) Remain 01:36:11 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Batch 0.494 (0.517) Remain 01:35:16 loss: 0.1906 Lr: 0.00269 [2024-11-25 18:04:20,704 INFO misc.py line 119 2586773] Train: [21/50][229/376] Data 0.003 (0.003) Batch 0.553 (0.517) Remain 01:35:17 loss: 0.2204 Lr: 0.00269 [2024-11-25 18:04:21,206 INFO misc.py line 119 2586773] Train: [21/50][230/376] Data 0.003 (0.003) Batch 0.501 (0.517) Remain 01:35:16 loss: 0.2366 Lr: 0.00269 [2024-11-25 18:04:21,741 INFO misc.py line 119 2586773] Train: [21/50][231/376] Data 0.003 (0.003) Batch 0.535 (0.517) Remain 01:35:16 loss: 0.2327 Lr: 0.00269 [2024-11-25 18:04:22,289 INFO misc.py line 119 2586773] Train: [21/50][232/376] Data 0.003 (0.003) Batch 0.548 (0.518) Remain 01:35:17 loss: 0.1725 Lr: 0.00269 [2024-11-25 18:04:22,775 INFO misc.py line 119 2586773] Train: [21/50][233/376] Data 0.003 (0.003) Batch 0.486 (0.517) Remain 01:35:15 loss: 0.2302 Lr: 0.00269 [2024-11-25 18:04:23,287 INFO misc.py line 119 2586773] Train: [21/50][234/376] Data 0.003 (0.003) Batch 0.512 (0.517) Remain 01:35:14 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18:04:26,849 INFO misc.py line 119 2586773] Train: [21/50][241/376] Data 0.003 (0.003) Batch 0.507 (0.517) Remain 01:35:08 loss: 0.1993 Lr: 0.00269 [2024-11-25 18:04:27,349 INFO misc.py line 119 2586773] Train: [21/50][242/376] Data 0.003 (0.003) Batch 0.500 (0.517) Remain 01:35:07 loss: 0.2082 Lr: 0.00269 [2024-11-25 18:04:27,895 INFO misc.py line 119 2586773] Train: [21/50][243/376] Data 0.003 (0.003) Batch 0.546 (0.517) Remain 01:35:07 loss: 0.2375 Lr: 0.00269 [2024-11-25 18:04:28,463 INFO misc.py line 119 2586773] Train: [21/50][244/376] Data 0.003 (0.003) Batch 0.568 (0.517) Remain 01:35:09 loss: 0.2387 Lr: 0.00269 [2024-11-25 18:04:28,952 INFO misc.py line 119 2586773] Train: [21/50][245/376] Data 0.003 (0.003) Batch 0.489 (0.517) Remain 01:35:07 loss: 0.2201 Lr: 0.00269 [2024-11-25 18:04:29,492 INFO misc.py line 119 2586773] Train: [21/50][246/376] Data 0.003 (0.003) Batch 0.540 (0.517) Remain 01:35:08 loss: 0.2564 Lr: 0.00269 [2024-11-25 18:04:29,989 INFO misc.py line 119 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Batch 0.528 (0.516) Remain 01:34:34 loss: 0.2806 Lr: 0.00268 [2024-11-25 18:04:49,351 INFO misc.py line 119 2586773] Train: [21/50][285/376] Data 0.003 (0.003) Batch 0.555 (0.516) Remain 01:34:35 loss: 0.2761 Lr: 0.00268 [2024-11-25 18:04:49,841 INFO misc.py line 119 2586773] Train: [21/50][286/376] Data 0.003 (0.003) Batch 0.490 (0.516) Remain 01:34:34 loss: 0.2057 Lr: 0.00268 [2024-11-25 18:04:50,350 INFO misc.py line 119 2586773] Train: [21/50][287/376] Data 0.003 (0.003) Batch 0.509 (0.516) Remain 01:34:33 loss: 0.1895 Lr: 0.00268 [2024-11-25 18:04:50,865 INFO misc.py line 119 2586773] Train: [21/50][288/376] Data 0.003 (0.003) Batch 0.514 (0.516) Remain 01:34:32 loss: 0.2025 Lr: 0.00267 [2024-11-25 18:04:51,436 INFO misc.py line 119 2586773] Train: [21/50][289/376] Data 0.003 (0.003) Batch 0.572 (0.516) Remain 01:34:34 loss: 0.2519 Lr: 0.00267 [2024-11-25 18:04:51,945 INFO misc.py line 119 2586773] Train: [21/50][290/376] Data 0.003 (0.003) Batch 0.508 (0.516) Remain 01:34:33 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2586773] Train: [21/50][303/376] Data 0.003 (0.003) Batch 0.601 (0.517) Remain 01:34:30 loss: 0.2311 Lr: 0.00267 [2024-11-25 18:04:59,328 INFO misc.py line 119 2586773] Train: [21/50][304/376] Data 0.004 (0.003) Batch 0.569 (0.517) Remain 01:34:32 loss: 0.1984 Lr: 0.00267 [2024-11-25 18:04:59,899 INFO misc.py line 119 2586773] Train: [21/50][305/376] Data 0.005 (0.003) Batch 0.572 (0.517) Remain 01:34:33 loss: 0.1917 Lr: 0.00267 [2024-11-25 18:05:00,456 INFO misc.py line 119 2586773] Train: [21/50][306/376] Data 0.003 (0.003) Batch 0.556 (0.517) Remain 01:34:34 loss: 0.2034 Lr: 0.00267 [2024-11-25 18:05:01,027 INFO misc.py line 119 2586773] Train: [21/50][307/376] Data 0.006 (0.003) Batch 0.572 (0.517) Remain 01:34:36 loss: 0.1996 Lr: 0.00267 [2024-11-25 18:05:01,527 INFO misc.py line 119 2586773] Train: [21/50][308/376] Data 0.003 (0.003) Batch 0.500 (0.517) Remain 01:34:34 loss: 0.2273 Lr: 0.00267 [2024-11-25 18:05:02,073 INFO misc.py line 119 2586773] Train: [21/50][309/376] Data 0.003 (0.003) Batch 0.546 (0.517) Remain 01:34:35 loss: 0.2469 Lr: 0.00267 [2024-11-25 18:05:02,559 INFO misc.py line 119 2586773] Train: [21/50][310/376] Data 0.003 (0.003) Batch 0.485 (0.517) Remain 01:34:33 loss: 0.1983 Lr: 0.00267 [2024-11-25 18:05:03,031 INFO misc.py line 119 2586773] Train: [21/50][311/376] Data 0.003 (0.003) Batch 0.472 (0.517) Remain 01:34:31 loss: 0.1981 Lr: 0.00267 [2024-11-25 18:05:03,500 INFO misc.py line 119 2586773] Train: [21/50][312/376] Data 0.002 (0.003) Batch 0.470 (0.517) Remain 01:34:29 loss: 0.2537 Lr: 0.00267 [2024-11-25 18:05:04,017 INFO misc.py line 119 2586773] Train: [21/50][313/376] Data 0.002 (0.003) Batch 0.517 (0.517) Remain 01:34:28 loss: 0.2951 Lr: 0.00267 [2024-11-25 18:05:04,549 INFO misc.py line 119 2586773] Train: [21/50][314/376] Data 0.002 (0.003) Batch 0.532 (0.517) Remain 01:34:28 loss: 0.2157 Lr: 0.00267 [2024-11-25 18:05:05,063 INFO misc.py line 119 2586773] Train: [21/50][315/376] Data 0.002 (0.003) Batch 0.514 (0.517) Remain 01:34:28 loss: 0.2401 Lr: 0.00267 [2024-11-25 18:05:05,635 INFO misc.py line 119 2586773] Train: [21/50][316/376] Data 0.003 (0.003) Batch 0.572 (0.517) Remain 01:34:29 loss: 0.2635 Lr: 0.00267 [2024-11-25 18:05:06,134 INFO misc.py line 119 2586773] Train: [21/50][317/376] Data 0.003 (0.003) Batch 0.499 (0.517) Remain 01:34:28 loss: 0.2297 Lr: 0.00267 [2024-11-25 18:05:06,679 INFO misc.py line 119 2586773] Train: [21/50][318/376] Data 0.003 (0.003) Batch 0.545 (0.517) Remain 01:34:29 loss: 0.2861 Lr: 0.00266 [2024-11-25 18:05:07,136 INFO misc.py line 119 2586773] Train: [21/50][319/376] Data 0.002 (0.003) Batch 0.457 (0.517) Remain 01:34:26 loss: 0.2833 Lr: 0.00266 [2024-11-25 18:05:07,638 INFO misc.py line 119 2586773] Train: [21/50][320/376] Data 0.003 (0.003) Batch 0.503 (0.517) Remain 01:34:25 loss: 0.2426 Lr: 0.00266 [2024-11-25 18:05:08,163 INFO misc.py line 119 2586773] Train: [21/50][321/376] Data 0.003 (0.003) Batch 0.525 (0.517) Remain 01:34:25 loss: 0.1996 Lr: 0.00266 [2024-11-25 18:05:08,640 INFO misc.py line 119 2586773] Train: [21/50][322/376] Data 0.003 (0.003) Batch 0.476 (0.517) Remain 01:34:23 loss: 0.1975 Lr: 0.00266 [2024-11-25 18:05:09,154 INFO misc.py line 119 2586773] Train: [21/50][323/376] Data 0.002 (0.003) Batch 0.514 (0.517) Remain 01:34:22 loss: 0.2689 Lr: 0.00266 [2024-11-25 18:05:09,689 INFO misc.py line 119 2586773] Train: [21/50][324/376] Data 0.003 (0.003) Batch 0.535 (0.517) Remain 01:34:22 loss: 0.2270 Lr: 0.00266 [2024-11-25 18:05:10,179 INFO misc.py line 119 2586773] Train: [21/50][325/376] Data 0.003 (0.003) Batch 0.490 (0.517) Remain 01:34:21 loss: 0.2456 Lr: 0.00266 [2024-11-25 18:05:10,709 INFO misc.py line 119 2586773] Train: [21/50][326/376] Data 0.002 (0.003) Batch 0.530 (0.517) Remain 01:34:21 loss: 0.2660 Lr: 0.00266 [2024-11-25 18:05:11,235 INFO misc.py line 119 2586773] Train: [21/50][327/376] Data 0.003 (0.003) Batch 0.526 (0.517) Remain 01:34:21 loss: 0.2048 Lr: 0.00266 [2024-11-25 18:05:11,740 INFO misc.py line 119 2586773] Train: [21/50][328/376] Data 0.002 (0.003) Batch 0.505 (0.517) Remain 01:34:20 loss: 0.2062 Lr: 0.00266 [2024-11-25 18:05:12,285 INFO misc.py line 119 2586773] Train: [21/50][329/376] Data 0.003 (0.003) Batch 0.545 (0.517) Remain 01:34:20 loss: 0.2207 Lr: 0.00266 [2024-11-25 18:05:12,804 INFO misc.py line 119 2586773] Train: [21/50][330/376] Data 0.002 (0.003) Batch 0.519 (0.517) Remain 01:34:20 loss: 0.1621 Lr: 0.00266 [2024-11-25 18:05:13,338 INFO misc.py line 119 2586773] Train: [21/50][331/376] Data 0.002 (0.003) Batch 0.535 (0.517) Remain 01:34:20 loss: 0.1819 Lr: 0.00266 [2024-11-25 18:05:13,871 INFO misc.py line 119 2586773] Train: [21/50][332/376] Data 0.002 (0.003) Batch 0.533 (0.517) Remain 01:34:20 loss: 0.2027 Lr: 0.00266 [2024-11-25 18:05:14,402 INFO misc.py line 119 2586773] Train: [21/50][333/376] Data 0.003 (0.003) Batch 0.531 (0.517) Remain 01:34:20 loss: 0.1855 Lr: 0.00266 [2024-11-25 18:05:14,916 INFO misc.py line 119 2586773] Train: [21/50][334/376] Data 0.003 (0.003) Batch 0.514 (0.517) Remain 01:34:19 loss: 0.2166 Lr: 0.00266 [2024-11-25 18:05:15,455 INFO misc.py line 119 2586773] Train: [21/50][335/376] Data 0.003 (0.003) Batch 0.539 (0.517) Remain 01:34:19 loss: 0.2637 Lr: 0.00266 [2024-11-25 18:05:15,962 INFO misc.py line 119 2586773] Train: [21/50][336/376] Data 0.003 (0.003) Batch 0.506 (0.517) Remain 01:34:18 loss: 0.1844 Lr: 0.00266 [2024-11-25 18:05:16,441 INFO misc.py line 119 2586773] Train: [21/50][337/376] Data 0.003 (0.003) Batch 0.480 (0.517) Remain 01:34:17 loss: 0.2107 Lr: 0.00266 [2024-11-25 18:05:16,941 INFO misc.py line 119 2586773] Train: [21/50][338/376] Data 0.003 (0.003) Batch 0.500 (0.517) Remain 01:34:16 loss: 0.2151 Lr: 0.00266 [2024-11-25 18:05:17,439 INFO misc.py line 119 2586773] Train: [21/50][339/376] Data 0.002 (0.003) Batch 0.498 (0.517) Remain 01:34:14 loss: 0.2324 Lr: 0.00266 [2024-11-25 18:05:17,956 INFO misc.py line 119 2586773] Train: [21/50][340/376] Data 0.003 (0.003) Batch 0.518 (0.517) Remain 01:34:14 loss: 0.2008 Lr: 0.00266 [2024-11-25 18:05:18,492 INFO misc.py line 119 2586773] Train: [21/50][341/376] Data 0.003 (0.003) Batch 0.535 (0.517) Remain 01:34:14 loss: 0.2292 Lr: 0.00266 [2024-11-25 18:05:18,998 INFO misc.py line 119 2586773] Train: [21/50][342/376] Data 0.003 (0.003) Batch 0.506 (0.517) Remain 01:34:13 loss: 0.2129 Lr: 0.00266 [2024-11-25 18:05:19,495 INFO misc.py line 119 2586773] Train: [21/50][343/376] Data 0.002 (0.003) Batch 0.497 (0.517) Remain 01:34:12 loss: 0.2379 Lr: 0.00266 [2024-11-25 18:05:20,045 INFO misc.py line 119 2586773] Train: [21/50][344/376] Data 0.003 (0.003) Batch 0.550 (0.517) Remain 01:34:13 loss: 0.2802 Lr: 0.00266 [2024-11-25 18:05:20,522 INFO misc.py line 119 2586773] Train: [21/50][345/376] Data 0.003 (0.003) Batch 0.477 (0.517) Remain 01:34:11 loss: 0.2702 Lr: 0.00266 [2024-11-25 18:05:21,068 INFO misc.py line 119 2586773] Train: [21/50][346/376] Data 0.003 (0.003) Batch 0.545 (0.517) Remain 01:34:11 loss: 0.2316 Lr: 0.00266 [2024-11-25 18:05:21,539 INFO misc.py line 119 2586773] Train: [21/50][347/376] Data 0.003 (0.003) Batch 0.471 (0.517) Remain 01:34:09 loss: 0.3251 Lr: 0.00266 [2024-11-25 18:05:22,081 INFO misc.py line 119 2586773] Train: [21/50][348/376] Data 0.003 (0.003) Batch 0.541 (0.517) Remain 01:34:09 loss: 0.2416 Lr: 0.00266 [2024-11-25 18:05:22,582 INFO misc.py line 119 2586773] Train: [21/50][349/376] Data 0.003 (0.003) Batch 0.502 (0.517) Remain 01:34:08 loss: 0.2032 Lr: 0.00265 [2024-11-25 18:05:23,095 INFO misc.py line 119 2586773] Train: [21/50][350/376] Data 0.002 (0.003) Batch 0.513 (0.517) Remain 01:34:08 loss: 0.2278 Lr: 0.00265 [2024-11-25 18:05:23,613 INFO misc.py line 119 2586773] Train: [21/50][351/376] Data 0.003 (0.003) Batch 0.517 (0.517) Remain 01:34:07 loss: 0.2149 Lr: 0.00265 [2024-11-25 18:05:24,141 INFO misc.py line 119 2586773] Train: [21/50][352/376] Data 0.004 (0.003) Batch 0.529 (0.517) Remain 01:34:07 loss: 0.1915 Lr: 0.00265 [2024-11-25 18:05:24,701 INFO misc.py line 119 2586773] Train: [21/50][353/376] Data 0.003 (0.003) Batch 0.560 (0.517) Remain 01:34:08 loss: 0.2407 Lr: 0.00265 [2024-11-25 18:05:25,196 INFO misc.py line 119 2586773] Train: [21/50][354/376] Data 0.003 (0.003) Batch 0.495 (0.517) Remain 01:34:07 loss: 0.2863 Lr: 0.00265 [2024-11-25 18:05:25,728 INFO misc.py line 119 2586773] Train: [21/50][355/376] Data 0.002 (0.003) Batch 0.532 (0.517) Remain 01:34:07 loss: 0.2592 Lr: 0.00265 [2024-11-25 18:05:26,272 INFO misc.py line 119 2586773] Train: [21/50][356/376] Data 0.002 (0.003) Batch 0.544 (0.517) Remain 01:34:07 loss: 0.2666 Lr: 0.00265 [2024-11-25 18:05:26,747 INFO misc.py line 119 2586773] Train: [21/50][357/376] Data 0.002 (0.003) Batch 0.475 (0.517) Remain 01:34:05 loss: 0.2524 Lr: 0.00265 [2024-11-25 18:05:27,253 INFO misc.py line 119 2586773] Train: [21/50][358/376] Data 0.003 (0.003) Batch 0.506 (0.517) Remain 01:34:04 loss: 0.2136 Lr: 0.00265 [2024-11-25 18:05:27,767 INFO misc.py line 119 2586773] Train: [21/50][359/376] Data 0.003 (0.003) Batch 0.514 (0.517) Remain 01:34:04 loss: 0.2350 Lr: 0.00265 [2024-11-25 18:05:28,257 INFO misc.py line 119 2586773] Train: [21/50][360/376] Data 0.003 (0.003) Batch 0.490 (0.517) Remain 01:34:02 loss: 0.2811 Lr: 0.00265 [2024-11-25 18:05:28,720 INFO misc.py line 119 2586773] Train: [21/50][361/376] Data 0.002 (0.003) Batch 0.464 (0.517) Remain 01:34:00 loss: 0.2441 Lr: 0.00265 [2024-11-25 18:05:29,212 INFO misc.py line 119 2586773] Train: [21/50][362/376] Data 0.002 (0.003) Batch 0.491 (0.517) Remain 01:33:59 loss: 0.2485 Lr: 0.00265 [2024-11-25 18:05:29,765 INFO misc.py line 119 2586773] Train: [21/50][363/376] Data 0.002 (0.003) Batch 0.554 (0.517) Remain 01:34:00 loss: 0.2138 Lr: 0.00265 [2024-11-25 18:05:30,280 INFO misc.py line 119 2586773] Train: [21/50][364/376] Data 0.002 (0.003) Batch 0.515 (0.517) Remain 01:33:59 loss: 0.2379 Lr: 0.00265 [2024-11-25 18:05:30,797 INFO misc.py line 119 2586773] Train: [21/50][365/376] Data 0.002 (0.003) Batch 0.517 (0.517) Remain 01:33:59 loss: 0.2416 Lr: 0.00265 [2024-11-25 18:05:31,277 INFO misc.py line 119 2586773] Train: [21/50][366/376] Data 0.002 (0.003) Batch 0.480 (0.517) Remain 01:33:57 loss: 0.2300 Lr: 0.00265 [2024-11-25 18:05:31,790 INFO misc.py line 119 2586773] Train: [21/50][367/376] Data 0.002 (0.003) Batch 0.513 (0.517) Remain 01:33:56 loss: 0.2648 Lr: 0.00265 [2024-11-25 18:05:32,295 INFO misc.py line 119 2586773] Train: [21/50][368/376] Data 0.002 (0.003) Batch 0.505 (0.516) Remain 01:33:55 loss: 0.1795 Lr: 0.00265 [2024-11-25 18:05:32,776 INFO misc.py line 119 2586773] Train: [21/50][369/376] Data 0.002 (0.003) Batch 0.482 (0.516) Remain 01:33:54 loss: 0.2202 Lr: 0.00265 [2024-11-25 18:05:33,316 INFO misc.py line 119 2586773] Train: [21/50][370/376] Data 0.003 (0.003) Batch 0.540 (0.516) Remain 01:33:54 loss: 0.2548 Lr: 0.00265 [2024-11-25 18:05:33,839 INFO misc.py line 119 2586773] Train: [21/50][371/376] Data 0.002 (0.003) Batch 0.523 (0.516) Remain 01:33:54 loss: 0.2282 Lr: 0.00265 [2024-11-25 18:05:34,369 INFO misc.py line 119 2586773] Train: [21/50][372/376] Data 0.002 (0.003) Batch 0.529 (0.517) Remain 01:33:54 loss: 0.2590 Lr: 0.00265 [2024-11-25 18:05:34,861 INFO misc.py line 119 2586773] Train: [21/50][373/376] Data 0.003 (0.003) Batch 0.493 (0.516) Remain 01:33:52 loss: 0.2212 Lr: 0.00265 [2024-11-25 18:05:35,363 INFO misc.py line 119 2586773] Train: [21/50][374/376] Data 0.002 (0.003) Batch 0.502 (0.516) Remain 01:33:51 loss: 0.2067 Lr: 0.00265 [2024-11-25 18:05:35,834 INFO misc.py line 119 2586773] Train: [21/50][375/376] Data 0.002 (0.003) Batch 0.471 (0.516) Remain 01:33:50 loss: 0.2458 Lr: 0.00265 [2024-11-25 18:05:36,351 INFO misc.py line 119 2586773] Train: [21/50][376/376] Data 0.002 (0.003) Batch 0.516 (0.516) Remain 01:33:49 loss: 0.2268 Lr: 0.00265 [2024-11-25 18:05:36,351 INFO misc.py line 136 2586773] Train result: loss: 0.2309 [2024-11-25 18:05:36,352 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:05:47,700 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1920 [2024-11-25 18:05:48,172 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2155 [2024-11-25 18:05:48,487 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2520 [2024-11-25 18:05:48,714 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2145 [2024-11-25 18:05:48,979 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2839 [2024-11-25 18:05:49,256 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2073 [2024-11-25 18:05:49,479 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2057 [2024-11-25 18:05:49,751 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2140 [2024-11-25 18:05:49,980 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2566 [2024-11-25 18:05:50,246 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2392 [2024-11-25 18:05:50,479 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.1960 [2024-11-25 18:05:50,751 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.1956 [2024-11-25 18:05:51,020 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2443 [2024-11-25 18:05:51,281 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2436 [2024-11-25 18:05:51,516 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2414 [2024-11-25 18:05:51,755 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2903 [2024-11-25 18:05:52,038 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3009 [2024-11-25 18:05:52,287 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2122 [2024-11-25 18:05:52,519 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2045 [2024-11-25 18:05:52,784 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2375 [2024-11-25 18:05:53,019 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2361 [2024-11-25 18:05:53,287 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2656 [2024-11-25 18:05:53,526 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2118 [2024-11-25 18:05:53,799 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2575 [2024-11-25 18:05:54,061 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2243 [2024-11-25 18:05:54,295 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2364 [2024-11-25 18:05:54,546 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2632 [2024-11-25 18:05:54,798 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2519 [2024-11-25 18:05:55,065 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2710 [2024-11-25 18:05:55,330 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3037 [2024-11-25 18:05:55,566 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2638 [2024-11-25 18:05:55,831 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2317 [2024-11-25 18:05:56,052 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2486 [2024-11-25 18:05:56,293 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2137 [2024-11-25 18:05:56,556 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2167 [2024-11-25 18:05:56,804 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2410 [2024-11-25 18:05:57,033 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2029 [2024-11-25 18:05:57,304 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2592 [2024-11-25 18:05:57,544 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2666 [2024-11-25 18:05:57,779 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2422 [2024-11-25 18:05:58,051 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2888 [2024-11-25 18:05:58,304 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2825 [2024-11-25 18:05:58,542 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2594 [2024-11-25 18:05:58,781 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2261 [2024-11-25 18:05:59,017 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2286 [2024-11-25 18:05:59,266 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2381 [2024-11-25 18:05:59,528 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2632 [2024-11-25 18:05:59,780 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2881 [2024-11-25 18:06:00,005 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2148 [2024-11-25 18:06:00,242 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2041 [2024-11-25 18:06:00,464 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2486 [2024-11-25 18:06:00,719 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2447 [2024-11-25 18:06:00,988 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2348 [2024-11-25 18:06:01,250 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2988 [2024-11-25 18:06:01,481 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2271 [2024-11-25 18:06:01,721 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2271 [2024-11-25 18:06:01,981 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2582 [2024-11-25 18:06:02,252 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2795 [2024-11-25 18:06:02,510 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2583 [2024-11-25 18:06:02,774 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2593 [2024-11-25 18:06:03,028 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2126 [2024-11-25 18:06:03,299 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2523 [2024-11-25 18:06:03,528 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2321 [2024-11-25 18:06:03,794 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2612 [2024-11-25 18:06:04,061 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2589 [2024-11-25 18:06:04,326 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2262 [2024-11-25 18:06:04,573 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2123 [2024-11-25 18:06:04,830 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2884 [2024-11-25 18:06:05,098 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2256 [2024-11-25 18:06:05,362 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2751 [2024-11-25 18:06:05,606 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2052 [2024-11-25 18:06:05,840 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2674 [2024-11-25 18:06:06,096 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2685 [2024-11-25 18:06:06,344 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2639 [2024-11-25 18:06:06,564 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2708 [2024-11-25 18:06:06,787 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2169 [2024-11-25 18:06:07,057 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2406 [2024-11-25 18:06:07,294 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2234 [2024-11-25 18:06:07,553 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2207 [2024-11-25 18:06:07,804 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2854 [2024-11-25 18:06:08,047 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2248 [2024-11-25 18:06:08,308 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2480 [2024-11-25 18:06:08,563 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2076 [2024-11-25 18:06:08,813 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2456 [2024-11-25 18:06:09,083 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2341 [2024-11-25 18:06:09,321 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2522 [2024-11-25 18:06:09,586 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2843 [2024-11-25 18:06:09,849 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2512 [2024-11-25 18:06:10,097 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2667 [2024-11-25 18:06:10,346 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2356 [2024-11-25 18:06:10,585 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2318 [2024-11-25 18:06:10,841 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2546 [2024-11-25 18:06:11,112 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2580 [2024-11-25 18:06:11,379 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2103 [2024-11-25 18:06:11,648 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2389 [2024-11-25 18:06:11,897 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2263 [2024-11-25 18:06:12,163 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2614 [2024-11-25 18:06:12,383 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2807 [2024-11-25 18:06:12,657 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2554 [2024-11-25 18:06:12,894 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2524 [2024-11-25 18:06:13,168 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2051 [2024-11-25 18:06:13,432 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2637 [2024-11-25 18:06:13,691 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2728 [2024-11-25 18:06:13,945 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2976 [2024-11-25 18:06:14,173 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2279 [2024-11-25 18:06:14,410 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2202 [2024-11-25 18:06:14,665 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2308 [2024-11-25 18:06:14,936 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2201 [2024-11-25 18:06:15,174 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2644 [2024-11-25 18:06:15,441 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2223 [2024-11-25 18:06:15,711 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2171 [2024-11-25 18:06:15,934 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2376 [2024-11-25 18:06:16,172 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2073 [2024-11-25 18:06:16,393 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2093 [2024-11-25 18:06:16,620 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2076 [2024-11-25 18:06:16,891 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2842 [2024-11-25 18:06:17,152 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2807 [2024-11-25 18:06:17,419 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2442 [2024-11-25 18:06:17,689 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2445 [2024-11-25 18:06:17,954 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3254 [2024-11-25 18:06:18,216 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2837 [2024-11-25 18:06:18,482 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1959 [2024-11-25 18:06:18,743 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2695 [2024-11-25 18:06:19,010 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2517 [2024-11-25 18:06:19,271 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2565 [2024-11-25 18:06:19,524 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2811 [2024-11-25 18:06:19,755 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2094 [2024-11-25 18:06:20,016 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2533 [2024-11-25 18:06:20,252 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2668 [2024-11-25 18:06:20,478 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1927 [2024-11-25 18:06:20,691 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2318 [2024-11-25 18:06:20,912 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2035 [2024-11-25 18:06:21,759 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7712/0.8524/0.9962. [2024-11-25 18:06:21,759 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9961/0.9981 [2024-11-25 18:06:21,759 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5462/0.7068 [2024-11-25 18:06:21,760 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:06:21,760 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7712 [2024-11-25 18:06:21,761 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7712 [2024-11-25 18:06:21,761 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:06:26,200 INFO misc.py line 119 2586773] Train: [22/50][1/376] Data 0.082 (0.082) Batch 0.520 (0.520) Remain 01:34:32 loss: 0.2072 Lr: 0.00265 [2024-11-25 18:06:26,682 INFO misc.py line 119 2586773] Train: [22/50][2/376] Data 0.003 (0.003) Batch 0.481 (0.481) Remain 01:27:28 loss: 0.1986 Lr: 0.00265 [2024-11-25 18:06:27,174 INFO misc.py line 119 2586773] Train: [22/50][3/376] Data 0.003 (0.003) Batch 0.492 (0.492) Remain 01:29:23 loss: 0.2791 Lr: 0.00264 [2024-11-25 18:06:27,717 INFO misc.py line 119 2586773] Train: [22/50][4/376] Data 0.003 (0.003) Batch 0.543 (0.543) Remain 01:38:41 loss: 0.2115 Lr: 0.00264 [2024-11-25 18:06:28,237 INFO misc.py line 119 2586773] Train: [22/50][5/376] Data 0.003 (0.003) Batch 0.520 (0.532) Remain 01:36:33 loss: 0.1696 Lr: 0.00264 [2024-11-25 18:06:28,734 INFO misc.py line 119 2586773] Train: [22/50][6/376] Data 0.003 (0.003) Batch 0.497 (0.520) Remain 01:34:25 loss: 0.2127 Lr: 0.00264 [2024-11-25 18:06:29,226 INFO misc.py line 119 2586773] Train: [22/50][7/376] Data 0.004 (0.003) Batch 0.493 (0.513) Remain 01:33:11 loss: 0.2332 Lr: 0.00264 [2024-11-25 18:06:29,713 INFO misc.py line 119 2586773] Train: [22/50][8/376] Data 0.003 (0.003) Batch 0.487 (0.508) Remain 01:32:13 loss: 0.1985 Lr: 0.00264 [2024-11-25 18:06:30,239 INFO misc.py line 119 2586773] Train: [22/50][9/376] Data 0.003 (0.003) Batch 0.526 (0.511) Remain 01:32:46 loss: 0.2539 Lr: 0.00264 [2024-11-25 18:06:30,779 INFO misc.py line 119 2586773] Train: [22/50][10/376] Data 0.003 (0.003) Batch 0.540 (0.515) Remain 01:33:30 loss: 0.2037 Lr: 0.00264 [2024-11-25 18:06:31,269 INFO misc.py line 119 2586773] Train: [22/50][11/376] Data 0.003 (0.003) Batch 0.490 (0.512) Remain 01:32:56 loss: 0.2821 Lr: 0.00264 [2024-11-25 18:06:31,834 INFO misc.py line 119 2586773] Train: [22/50][12/376] Data 0.003 (0.003) Batch 0.565 (0.518) Remain 01:33:59 loss: 0.2286 Lr: 0.00264 [2024-11-25 18:06:32,319 INFO misc.py line 119 2586773] Train: [22/50][13/376] Data 0.003 (0.003) Batch 0.486 (0.515) Remain 01:33:24 loss: 0.2775 Lr: 0.00264 [2024-11-25 18:06:32,811 INFO misc.py line 119 2586773] Train: [22/50][14/376] Data 0.003 (0.003) Batch 0.492 (0.513) Remain 01:33:01 loss: 0.2234 Lr: 0.00264 [2024-11-25 18:06:33,358 INFO misc.py line 119 2586773] Train: [22/50][15/376] Data 0.003 (0.003) Batch 0.546 (0.515) Remain 01:33:31 loss: 0.2571 Lr: 0.00264 [2024-11-25 18:06:33,860 INFO misc.py line 119 2586773] Train: [22/50][16/376] Data 0.003 (0.003) Batch 0.502 (0.514) Remain 01:33:20 loss: 0.2171 Lr: 0.00264 [2024-11-25 18:06:34,349 INFO misc.py line 119 2586773] Train: [22/50][17/376] Data 0.003 (0.003) Batch 0.489 (0.513) Remain 01:32:59 loss: 0.2221 Lr: 0.00264 [2024-11-25 18:06:34,903 INFO misc.py line 119 2586773] Train: [22/50][18/376] Data 0.003 (0.003) Batch 0.554 (0.515) Remain 01:33:29 loss: 0.2454 Lr: 0.00264 [2024-11-25 18:06:35,469 INFO misc.py line 119 2586773] Train: [22/50][19/376] Data 0.003 (0.003) Batch 0.565 (0.518) Remain 01:34:03 loss: 0.2472 Lr: 0.00264 [2024-11-25 18:06:36,035 INFO misc.py line 119 2586773] Train: [22/50][20/376] Data 0.003 (0.003) Batch 0.566 (0.521) Remain 01:34:33 loss: 0.2282 Lr: 0.00264 [2024-11-25 18:06:36,535 INFO misc.py line 119 2586773] Train: [22/50][21/376] Data 0.003 (0.003) Batch 0.500 (0.520) Remain 01:34:19 loss: 0.2317 Lr: 0.00264 [2024-11-25 18:06:37,061 INFO misc.py line 119 2586773] Train: [22/50][22/376] Data 0.003 (0.003) Batch 0.526 (0.520) Remain 01:34:22 loss: 0.1991 Lr: 0.00264 [2024-11-25 18:06:37,621 INFO misc.py line 119 2586773] Train: [22/50][23/376] Data 0.003 (0.003) Batch 0.560 (0.522) Remain 01:34:43 loss: 0.2409 Lr: 0.00264 [2024-11-25 18:06:38,134 INFO misc.py line 119 2586773] Train: [22/50][24/376] Data 0.003 (0.003) Batch 0.513 (0.522) Remain 01:34:38 loss: 0.2428 Lr: 0.00264 [2024-11-25 18:06:38,682 INFO misc.py line 119 2586773] Train: [22/50][25/376] Data 0.003 (0.003) Batch 0.548 (0.523) Remain 01:34:50 loss: 0.2758 Lr: 0.00264 [2024-11-25 18:06:39,187 INFO misc.py line 119 2586773] Train: [22/50][26/376] Data 0.003 (0.003) Batch 0.505 (0.522) Remain 01:34:41 loss: 0.2113 Lr: 0.00264 [2024-11-25 18:06:39,742 INFO misc.py line 119 2586773] Train: [22/50][27/376] Data 0.003 (0.003) Batch 0.555 (0.524) Remain 01:34:55 loss: 0.2388 Lr: 0.00264 [2024-11-25 18:06:40,260 INFO misc.py line 119 2586773] Train: [22/50][28/376] Data 0.003 (0.003) Batch 0.518 (0.523) Remain 01:34:52 loss: 0.2733 Lr: 0.00264 [2024-11-25 18:06:40,729 INFO misc.py line 119 2586773] Train: [22/50][29/376] Data 0.003 (0.003) Batch 0.469 (0.521) Remain 01:34:29 loss: 0.1860 Lr: 0.00264 [2024-11-25 18:06:41,255 INFO misc.py line 119 2586773] Train: [22/50][30/376] Data 0.003 (0.003) Batch 0.526 (0.522) Remain 01:34:31 loss: 0.2464 Lr: 0.00264 [2024-11-25 18:06:41,775 INFO misc.py line 119 2586773] Train: [22/50][31/376] Data 0.003 (0.003) Batch 0.519 (0.521) Remain 01:34:29 loss: 0.2723 Lr: 0.00264 [2024-11-25 18:06:42,276 INFO misc.py line 119 2586773] Train: [22/50][32/376] Data 0.003 (0.003) Batch 0.501 (0.521) Remain 01:34:21 loss: 0.2302 Lr: 0.00264 [2024-11-25 18:06:42,792 INFO misc.py line 119 2586773] Train: [22/50][33/376] Data 0.003 (0.003) Batch 0.516 (0.521) Remain 01:34:19 loss: 0.1932 Lr: 0.00264 [2024-11-25 18:06:43,257 INFO misc.py line 119 2586773] Train: [22/50][34/376] Data 0.003 (0.003) Batch 0.466 (0.519) Remain 01:33:59 loss: 0.2583 Lr: 0.00263 [2024-11-25 18:06:43,781 INFO misc.py line 119 2586773] Train: [22/50][35/376] Data 0.003 (0.003) Batch 0.525 (0.519) Remain 01:34:00 loss: 0.2422 Lr: 0.00263 [2024-11-25 18:06:44,254 INFO misc.py line 119 2586773] Train: [22/50][36/376] Data 0.002 (0.003) Batch 0.473 (0.518) Remain 01:33:45 loss: 0.2144 Lr: 0.00263 [2024-11-25 18:06:44,750 INFO misc.py line 119 2586773] Train: [22/50][37/376] Data 0.003 (0.003) Batch 0.496 (0.517) Remain 01:33:37 loss: 0.2249 Lr: 0.00263 [2024-11-25 18:06:45,305 INFO misc.py line 119 2586773] Train: [22/50][38/376] Data 0.002 (0.003) Batch 0.554 (0.518) Remain 01:33:48 loss: 0.2686 Lr: 0.00263 [2024-11-25 18:06:45,765 INFO misc.py line 119 2586773] Train: [22/50][39/376] Data 0.003 (0.003) Batch 0.460 (0.516) Remain 01:33:30 loss: 0.2024 Lr: 0.00263 [2024-11-25 18:06:46,282 INFO misc.py line 119 2586773] Train: [22/50][40/376] Data 0.003 (0.003) Batch 0.517 (0.516) Remain 01:33:30 loss: 0.2459 Lr: 0.00263 [2024-11-25 18:06:46,803 INFO misc.py line 119 2586773] Train: [22/50][41/376] Data 0.003 (0.003) Batch 0.521 (0.517) Remain 01:33:31 loss: 0.3061 Lr: 0.00263 [2024-11-25 18:06:47,334 INFO misc.py line 119 2586773] Train: [22/50][42/376] Data 0.003 (0.003) Batch 0.531 (0.517) Remain 01:33:34 loss: 0.2667 Lr: 0.00263 [2024-11-25 18:06:47,866 INFO misc.py line 119 2586773] Train: [22/50][43/376] Data 0.003 (0.003) Batch 0.532 (0.517) Remain 01:33:38 loss: 0.2249 Lr: 0.00263 [2024-11-25 18:06:48,375 INFO misc.py line 119 2586773] Train: [22/50][44/376] Data 0.003 (0.003) Batch 0.509 (0.517) Remain 01:33:35 loss: 0.2309 Lr: 0.00263 [2024-11-25 18:06:48,880 INFO misc.py line 119 2586773] Train: [22/50][45/376] Data 0.003 (0.003) Batch 0.505 (0.517) Remain 01:33:31 loss: 0.2267 Lr: 0.00263 [2024-11-25 18:06:49,403 INFO misc.py line 119 2586773] Train: [22/50][46/376] Data 0.002 (0.003) Batch 0.524 (0.517) Remain 01:33:33 loss: 0.2811 Lr: 0.00263 [2024-11-25 18:06:49,903 INFO misc.py line 119 2586773] Train: [22/50][47/376] Data 0.003 (0.003) Batch 0.500 (0.517) Remain 01:33:28 loss: 0.2234 Lr: 0.00263 [2024-11-25 18:06:50,397 INFO misc.py line 119 2586773] Train: [22/50][48/376] Data 0.002 (0.003) Batch 0.494 (0.516) Remain 01:33:22 loss: 0.2704 Lr: 0.00263 [2024-11-25 18:06:50,892 INFO misc.py line 119 2586773] Train: [22/50][49/376] Data 0.003 (0.003) Batch 0.495 (0.516) Remain 01:33:16 loss: 0.2129 Lr: 0.00263 [2024-11-25 18:06:51,378 INFO misc.py line 119 2586773] Train: [22/50][50/376] Data 0.003 (0.003) Batch 0.486 (0.515) Remain 01:33:09 loss: 0.2297 Lr: 0.00263 [2024-11-25 18:06:51,908 INFO misc.py line 119 2586773] Train: [22/50][51/376] Data 0.003 (0.003) Batch 0.530 (0.515) Remain 01:33:12 loss: 0.2491 Lr: 0.00263 [2024-11-25 18:06:52,473 INFO misc.py line 119 2586773] Train: [22/50][52/376] Data 0.002 (0.003) Batch 0.566 (0.516) Remain 01:33:23 loss: 0.2630 Lr: 0.00263 [2024-11-25 18:06:53,010 INFO misc.py line 119 2586773] Train: 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Batch 0.543 (0.511) Remain 01:31:31 loss: 0.2196 Lr: 0.00259 [2024-11-25 18:07:47,418 INFO misc.py line 119 2586773] Train: [22/50][160/376] Data 0.002 (0.003) Batch 0.512 (0.511) Remain 01:31:31 loss: 0.2054 Lr: 0.00259 [2024-11-25 18:07:47,911 INFO misc.py line 119 2586773] Train: [22/50][161/376] Data 0.003 (0.003) Batch 0.494 (0.511) Remain 01:31:29 loss: 0.2406 Lr: 0.00259 [2024-11-25 18:07:48,422 INFO misc.py line 119 2586773] Train: [22/50][162/376] Data 0.002 (0.003) Batch 0.510 (0.511) Remain 01:31:29 loss: 0.2117 Lr: 0.00259 [2024-11-25 18:07:48,954 INFO misc.py line 119 2586773] Train: [22/50][163/376] Data 0.002 (0.003) Batch 0.533 (0.511) Remain 01:31:30 loss: 0.2301 Lr: 0.00259 [2024-11-25 18:07:49,489 INFO misc.py line 119 2586773] Train: [22/50][164/376] Data 0.002 (0.003) Batch 0.535 (0.511) Remain 01:31:31 loss: 0.2322 Lr: 0.00259 [2024-11-25 18:07:49,963 INFO misc.py line 119 2586773] Train: [22/50][165/376] Data 0.002 (0.003) Batch 0.474 (0.511) Remain 01:31:28 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18:07:53,461 INFO misc.py line 119 2586773] Train: [22/50][172/376] Data 0.002 (0.003) Batch 0.523 (0.511) Remain 01:31:19 loss: 0.2444 Lr: 0.00259 [2024-11-25 18:07:53,977 INFO misc.py line 119 2586773] Train: [22/50][173/376] Data 0.003 (0.003) Batch 0.516 (0.511) Remain 01:31:19 loss: 0.2385 Lr: 0.00259 [2024-11-25 18:07:54,485 INFO misc.py line 119 2586773] Train: [22/50][174/376] Data 0.002 (0.003) Batch 0.508 (0.511) Remain 01:31:18 loss: 0.2081 Lr: 0.00259 [2024-11-25 18:07:55,033 INFO misc.py line 119 2586773] Train: [22/50][175/376] Data 0.002 (0.003) Batch 0.549 (0.511) Remain 01:31:20 loss: 0.2396 Lr: 0.00259 [2024-11-25 18:07:55,577 INFO misc.py line 119 2586773] Train: [22/50][176/376] Data 0.002 (0.003) Batch 0.544 (0.511) Remain 01:31:22 loss: 0.1803 Lr: 0.00259 [2024-11-25 18:07:56,133 INFO misc.py line 119 2586773] Train: [22/50][177/376] Data 0.002 (0.003) Batch 0.556 (0.511) Remain 01:31:24 loss: 0.2183 Lr: 0.00259 [2024-11-25 18:07:56,628 INFO misc.py line 119 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Batch 0.518 (0.511) Remain 01:31:07 loss: 0.3089 Lr: 0.00257 [2024-11-25 18:08:16,135 INFO misc.py line 119 2586773] Train: [22/50][216/376] Data 0.003 (0.003) Batch 0.525 (0.512) Remain 01:31:07 loss: 0.1955 Lr: 0.00257 [2024-11-25 18:08:16,620 INFO misc.py line 119 2586773] Train: [22/50][217/376] Data 0.002 (0.003) Batch 0.485 (0.511) Remain 01:31:05 loss: 0.1993 Lr: 0.00257 [2024-11-25 18:08:17,127 INFO misc.py line 119 2586773] Train: [22/50][218/376] Data 0.002 (0.003) Batch 0.508 (0.511) Remain 01:31:04 loss: 0.1932 Lr: 0.00257 [2024-11-25 18:08:17,641 INFO misc.py line 119 2586773] Train: [22/50][219/376] Data 0.002 (0.003) Batch 0.514 (0.511) Remain 01:31:04 loss: 0.2200 Lr: 0.00257 [2024-11-25 18:08:18,115 INFO misc.py line 119 2586773] Train: [22/50][220/376] Data 0.002 (0.003) Batch 0.474 (0.511) Remain 01:31:02 loss: 0.2024 Lr: 0.00257 [2024-11-25 18:08:18,607 INFO misc.py line 119 2586773] Train: [22/50][221/376] Data 0.002 (0.003) Batch 0.492 (0.511) Remain 01:31:00 loss: 0.2211 Lr: 0.00257 [2024-11-25 18:08:19,115 INFO misc.py line 119 2586773] Train: [22/50][222/376] Data 0.003 (0.003) Batch 0.508 (0.511) Remain 01:31:00 loss: 0.2233 Lr: 0.00257 [2024-11-25 18:08:19,628 INFO misc.py line 119 2586773] Train: [22/50][223/376] Data 0.002 (0.003) Batch 0.513 (0.511) Remain 01:30:59 loss: 0.2343 Lr: 0.00257 [2024-11-25 18:08:20,115 INFO misc.py line 119 2586773] Train: [22/50][224/376] Data 0.002 (0.003) Batch 0.487 (0.511) Remain 01:30:57 loss: 0.2738 Lr: 0.00257 [2024-11-25 18:08:20,655 INFO misc.py line 119 2586773] Train: [22/50][225/376] Data 0.002 (0.003) Batch 0.540 (0.511) Remain 01:30:58 loss: 0.2376 Lr: 0.00257 [2024-11-25 18:08:21,139 INFO misc.py line 119 2586773] Train: [22/50][226/376] Data 0.002 (0.003) Batch 0.484 (0.511) Remain 01:30:57 loss: 0.2139 Lr: 0.00257 [2024-11-25 18:08:21,672 INFO misc.py line 119 2586773] Train: [22/50][227/376] Data 0.002 (0.003) Batch 0.533 (0.511) Remain 01:30:57 loss: 0.2514 Lr: 0.00257 [2024-11-25 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Batch 0.479 (0.511) Remain 01:30:29 loss: 0.2373 Lr: 0.00256 [2024-11-25 18:08:44,514 INFO misc.py line 119 2586773] Train: [22/50][272/376] Data 0.002 (0.003) Batch 0.491 (0.511) Remain 01:30:28 loss: 0.2355 Lr: 0.00256 [2024-11-25 18:08:45,030 INFO misc.py line 119 2586773] Train: [22/50][273/376] Data 0.003 (0.003) Batch 0.517 (0.511) Remain 01:30:27 loss: 0.2756 Lr: 0.00256 [2024-11-25 18:08:45,562 INFO misc.py line 119 2586773] Train: [22/50][274/376] Data 0.002 (0.003) Batch 0.532 (0.511) Remain 01:30:28 loss: 0.2689 Lr: 0.00255 [2024-11-25 18:08:46,037 INFO misc.py line 119 2586773] Train: [22/50][275/376] Data 0.002 (0.003) Batch 0.475 (0.511) Remain 01:30:26 loss: 0.1874 Lr: 0.00255 [2024-11-25 18:08:46,549 INFO misc.py line 119 2586773] Train: [22/50][276/376] Data 0.002 (0.003) Batch 0.512 (0.511) Remain 01:30:25 loss: 0.2704 Lr: 0.00255 [2024-11-25 18:08:47,038 INFO misc.py line 119 2586773] Train: [22/50][277/376] Data 0.002 (0.003) Batch 0.490 (0.510) Remain 01:30:24 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2586773] Train: [22/50][290/376] Data 0.003 (0.003) Batch 0.502 (0.510) Remain 01:30:11 loss: 0.1608 Lr: 0.00255 [2024-11-25 18:08:53,985 INFO misc.py line 119 2586773] Train: [22/50][291/376] Data 0.003 (0.003) Batch 0.491 (0.510) Remain 01:30:10 loss: 0.2742 Lr: 0.00255 [2024-11-25 18:08:54,502 INFO misc.py line 119 2586773] Train: [22/50][292/376] Data 0.003 (0.003) Batch 0.517 (0.510) Remain 01:30:09 loss: 0.2096 Lr: 0.00255 [2024-11-25 18:08:55,045 INFO misc.py line 119 2586773] Train: [22/50][293/376] Data 0.003 (0.003) Batch 0.543 (0.510) Remain 01:30:10 loss: 0.2580 Lr: 0.00255 [2024-11-25 18:08:55,555 INFO misc.py line 119 2586773] Train: [22/50][294/376] Data 0.003 (0.003) Batch 0.510 (0.510) Remain 01:30:10 loss: 0.2308 Lr: 0.00255 [2024-11-25 18:08:56,015 INFO misc.py line 119 2586773] Train: [22/50][295/376] Data 0.003 (0.003) Batch 0.460 (0.510) Remain 01:30:07 loss: 0.1880 Lr: 0.00255 [2024-11-25 18:08:56,515 INFO misc.py line 119 2586773] Train: [22/50][296/376] Data 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[2024-11-25 18:09:03,049 INFO misc.py line 119 2586773] Train: [22/50][309/376] Data 0.002 (0.003) Batch 0.520 (0.509) Remain 01:29:57 loss: 0.2266 Lr: 0.00254 [2024-11-25 18:09:03,543 INFO misc.py line 119 2586773] Train: [22/50][310/376] Data 0.003 (0.003) Batch 0.494 (0.509) Remain 01:29:55 loss: 0.1821 Lr: 0.00254 [2024-11-25 18:09:04,065 INFO misc.py line 119 2586773] Train: [22/50][311/376] Data 0.003 (0.003) Batch 0.522 (0.509) Remain 01:29:55 loss: 0.2114 Lr: 0.00254 [2024-11-25 18:09:04,573 INFO misc.py line 119 2586773] Train: [22/50][312/376] Data 0.003 (0.003) Batch 0.508 (0.509) Remain 01:29:55 loss: 0.2161 Lr: 0.00254 [2024-11-25 18:09:05,060 INFO misc.py line 119 2586773] Train: [22/50][313/376] Data 0.002 (0.003) Batch 0.487 (0.509) Remain 01:29:54 loss: 0.2197 Lr: 0.00254 [2024-11-25 18:09:05,562 INFO misc.py line 119 2586773] Train: [22/50][314/376] Data 0.002 (0.003) Batch 0.502 (0.509) Remain 01:29:53 loss: 0.1744 Lr: 0.00254 [2024-11-25 18:09:06,041 INFO misc.py 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Batch 0.517 (0.509) Remain 01:29:42 loss: 0.2289 Lr: 0.00254 [2024-11-25 18:09:12,517 INFO misc.py line 119 2586773] Train: [22/50][328/376] Data 0.003 (0.003) Batch 0.478 (0.509) Remain 01:29:40 loss: 0.2054 Lr: 0.00254 [2024-11-25 18:09:12,996 INFO misc.py line 119 2586773] Train: [22/50][329/376] Data 0.002 (0.003) Batch 0.479 (0.509) Remain 01:29:39 loss: 0.1854 Lr: 0.00254 [2024-11-25 18:09:13,511 INFO misc.py line 119 2586773] Train: [22/50][330/376] Data 0.002 (0.003) Batch 0.514 (0.509) Remain 01:29:38 loss: 0.2305 Lr: 0.00254 [2024-11-25 18:09:14,047 INFO misc.py line 119 2586773] Train: [22/50][331/376] Data 0.003 (0.003) Batch 0.537 (0.509) Remain 01:29:39 loss: 0.2597 Lr: 0.00254 [2024-11-25 18:09:14,576 INFO misc.py line 119 2586773] Train: [22/50][332/376] Data 0.002 (0.003) Batch 0.528 (0.509) Remain 01:29:39 loss: 0.1864 Lr: 0.00254 [2024-11-25 18:09:15,078 INFO misc.py line 119 2586773] Train: [22/50][333/376] Data 0.003 (0.003) Batch 0.502 (0.509) Remain 01:29:38 loss: 0.1746 Lr: 0.00254 [2024-11-25 18:09:15,559 INFO misc.py line 119 2586773] Train: [22/50][334/376] Data 0.003 (0.003) Batch 0.481 (0.509) Remain 01:29:37 loss: 0.2354 Lr: 0.00253 [2024-11-25 18:09:16,074 INFO misc.py line 119 2586773] Train: [22/50][335/376] Data 0.003 (0.003) Batch 0.514 (0.509) Remain 01:29:36 loss: 0.2062 Lr: 0.00253 [2024-11-25 18:09:16,563 INFO misc.py line 119 2586773] Train: [22/50][336/376] Data 0.003 (0.003) Batch 0.490 (0.509) Remain 01:29:35 loss: 0.2712 Lr: 0.00253 [2024-11-25 18:09:17,063 INFO misc.py line 119 2586773] Train: [22/50][337/376] Data 0.002 (0.003) Batch 0.499 (0.509) Remain 01:29:34 loss: 0.2150 Lr: 0.00253 [2024-11-25 18:09:17,587 INFO misc.py line 119 2586773] Train: [22/50][338/376] Data 0.003 (0.003) Batch 0.524 (0.509) Remain 01:29:34 loss: 0.2076 Lr: 0.00253 [2024-11-25 18:09:18,094 INFO misc.py line 119 2586773] Train: [22/50][339/376] Data 0.003 (0.003) Batch 0.507 (0.509) Remain 01:29:34 loss: 0.2050 Lr: 0.00253 [2024-11-25 18:09:18,602 INFO misc.py line 119 2586773] Train: [22/50][340/376] Data 0.003 (0.003) Batch 0.508 (0.509) Remain 01:29:33 loss: 0.2541 Lr: 0.00253 [2024-11-25 18:09:19,138 INFO misc.py line 119 2586773] Train: [22/50][341/376] Data 0.003 (0.003) Batch 0.536 (0.509) Remain 01:29:34 loss: 0.2498 Lr: 0.00253 [2024-11-25 18:09:19,623 INFO misc.py line 119 2586773] Train: [22/50][342/376] Data 0.003 (0.003) Batch 0.485 (0.509) Remain 01:29:32 loss: 0.1940 Lr: 0.00253 [2024-11-25 18:09:20,102 INFO misc.py line 119 2586773] Train: [22/50][343/376] Data 0.003 (0.003) Batch 0.479 (0.509) Remain 01:29:31 loss: 0.2839 Lr: 0.00253 [2024-11-25 18:09:20,583 INFO misc.py line 119 2586773] Train: [22/50][344/376] Data 0.003 (0.003) Batch 0.481 (0.509) Remain 01:29:30 loss: 0.3045 Lr: 0.00253 [2024-11-25 18:09:21,081 INFO misc.py line 119 2586773] Train: [22/50][345/376] Data 0.003 (0.003) Batch 0.499 (0.509) Remain 01:29:29 loss: 0.2318 Lr: 0.00253 [2024-11-25 18:09:21,598 INFO misc.py line 119 2586773] Train: [22/50][346/376] Data 0.002 (0.003) Batch 0.517 (0.509) Remain 01:29:29 loss: 0.2261 Lr: 0.00253 [2024-11-25 18:09:22,102 INFO misc.py line 119 2586773] Train: [22/50][347/376] Data 0.003 (0.003) Batch 0.504 (0.509) Remain 01:29:28 loss: 0.2167 Lr: 0.00253 [2024-11-25 18:09:22,598 INFO misc.py line 119 2586773] Train: [22/50][348/376] Data 0.003 (0.003) Batch 0.496 (0.508) Remain 01:29:27 loss: 0.2107 Lr: 0.00253 [2024-11-25 18:09:23,040 INFO misc.py line 119 2586773] Train: [22/50][349/376] Data 0.003 (0.003) Batch 0.442 (0.508) Remain 01:29:24 loss: 0.3672 Lr: 0.00253 [2024-11-25 18:09:23,534 INFO misc.py line 119 2586773] Train: [22/50][350/376] Data 0.003 (0.003) Batch 0.495 (0.508) Remain 01:29:23 loss: 0.2153 Lr: 0.00253 [2024-11-25 18:09:24,064 INFO misc.py line 119 2586773] Train: [22/50][351/376] Data 0.003 (0.003) Batch 0.530 (0.508) Remain 01:29:24 loss: 0.1907 Lr: 0.00253 [2024-11-25 18:09:24,617 INFO misc.py line 119 2586773] Train: [22/50][352/376] Data 0.002 (0.003) Batch 0.553 (0.508) Remain 01:29:24 loss: 0.2813 Lr: 0.00253 [2024-11-25 18:09:25,105 INFO misc.py line 119 2586773] Train: [22/50][353/376] Data 0.003 (0.003) Batch 0.489 (0.508) Remain 01:29:23 loss: 0.2600 Lr: 0.00253 [2024-11-25 18:09:25,612 INFO misc.py line 119 2586773] Train: [22/50][354/376] Data 0.003 (0.003) Batch 0.507 (0.508) Remain 01:29:23 loss: 0.1870 Lr: 0.00253 [2024-11-25 18:09:26,127 INFO misc.py line 119 2586773] Train: [22/50][355/376] Data 0.002 (0.003) Batch 0.515 (0.508) Remain 01:29:22 loss: 0.2063 Lr: 0.00253 [2024-11-25 18:09:26,628 INFO misc.py line 119 2586773] Train: [22/50][356/376] Data 0.002 (0.003) Batch 0.501 (0.508) Remain 01:29:22 loss: 0.2058 Lr: 0.00253 [2024-11-25 18:09:27,117 INFO misc.py line 119 2586773] Train: [22/50][357/376] Data 0.002 (0.003) Batch 0.489 (0.508) Remain 01:29:21 loss: 0.2230 Lr: 0.00253 [2024-11-25 18:09:27,645 INFO misc.py line 119 2586773] Train: [22/50][358/376] Data 0.002 (0.003) Batch 0.528 (0.508) Remain 01:29:21 loss: 0.2407 Lr: 0.00253 [2024-11-25 18:09:28,128 INFO misc.py line 119 2586773] Train: [22/50][359/376] Data 0.002 (0.003) Batch 0.483 (0.508) Remain 01:29:19 loss: 0.2208 Lr: 0.00253 [2024-11-25 18:09:28,624 INFO misc.py line 119 2586773] Train: [22/50][360/376] Data 0.002 (0.003) Batch 0.496 (0.508) Remain 01:29:19 loss: 0.2353 Lr: 0.00253 [2024-11-25 18:09:29,108 INFO misc.py line 119 2586773] Train: [22/50][361/376] Data 0.003 (0.003) Batch 0.484 (0.508) Remain 01:29:17 loss: 0.1918 Lr: 0.00253 [2024-11-25 18:09:29,612 INFO misc.py line 119 2586773] Train: [22/50][362/376] Data 0.002 (0.003) Batch 0.503 (0.508) Remain 01:29:17 loss: 0.2401 Lr: 0.00253 [2024-11-25 18:09:30,110 INFO misc.py line 119 2586773] Train: [22/50][363/376] Data 0.002 (0.003) Batch 0.499 (0.508) Remain 01:29:16 loss: 0.2659 Lr: 0.00253 [2024-11-25 18:09:30,643 INFO misc.py line 119 2586773] Train: [22/50][364/376] Data 0.002 (0.003) Batch 0.533 (0.508) Remain 01:29:16 loss: 0.2567 Lr: 0.00252 [2024-11-25 18:09:31,104 INFO misc.py line 119 2586773] Train: [22/50][365/376] Data 0.002 (0.003) Batch 0.461 (0.508) Remain 01:29:14 loss: 0.2408 Lr: 0.00252 [2024-11-25 18:09:31,600 INFO misc.py line 119 2586773] Train: [22/50][366/376] Data 0.002 (0.003) Batch 0.496 (0.508) Remain 01:29:13 loss: 0.1831 Lr: 0.00252 [2024-11-25 18:09:32,082 INFO misc.py line 119 2586773] Train: [22/50][367/376] Data 0.002 (0.002) Batch 0.482 (0.508) Remain 01:29:12 loss: 0.2079 Lr: 0.00252 [2024-11-25 18:09:32,641 INFO misc.py line 119 2586773] Train: [22/50][368/376] Data 0.002 (0.002) Batch 0.559 (0.508) Remain 01:29:13 loss: 0.2517 Lr: 0.00252 [2024-11-25 18:09:33,128 INFO misc.py line 119 2586773] Train: [22/50][369/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 01:29:12 loss: 0.3759 Lr: 0.00252 [2024-11-25 18:09:33,617 INFO misc.py line 119 2586773] Train: [22/50][370/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 01:29:11 loss: 0.2184 Lr: 0.00252 [2024-11-25 18:09:34,129 INFO misc.py line 119 2586773] Train: [22/50][371/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 01:29:11 loss: 0.2581 Lr: 0.00252 [2024-11-25 18:09:34,623 INFO misc.py line 119 2586773] Train: [22/50][372/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 01:29:10 loss: 0.2033 Lr: 0.00252 [2024-11-25 18:09:35,149 INFO misc.py line 119 2586773] Train: [22/50][373/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 01:29:10 loss: 0.2485 Lr: 0.00252 [2024-11-25 18:09:35,672 INFO misc.py line 119 2586773] Train: [22/50][374/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 01:29:10 loss: 0.2306 Lr: 0.00252 [2024-11-25 18:09:36,165 INFO misc.py line 119 2586773] Train: [22/50][375/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 01:29:09 loss: 0.2029 Lr: 0.00252 [2024-11-25 18:09:36,665 INFO misc.py line 119 2586773] Train: [22/50][376/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 01:29:08 loss: 0.1951 Lr: 0.00252 [2024-11-25 18:09:36,665 INFO misc.py line 136 2586773] Train result: loss: 0.2294 [2024-11-25 18:09:36,665 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:09:47,660 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1994 [2024-11-25 18:09:47,921 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2554 [2024-11-25 18:09:48,194 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2580 [2024-11-25 18:09:48,418 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2157 [2024-11-25 18:09:48,688 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3115 [2024-11-25 18:09:48,961 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2215 [2024-11-25 18:09:49,185 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2495 [2024-11-25 18:09:49,453 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2711 [2024-11-25 18:09:49,684 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2884 [2024-11-25 18:09:49,945 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2876 [2024-11-25 18:09:50,178 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2184 [2024-11-25 18:09:50,450 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2688 [2024-11-25 18:09:50,716 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2851 [2024-11-25 18:09:50,980 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2462 [2024-11-25 18:09:51,212 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2368 [2024-11-25 18:09:51,453 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3079 [2024-11-25 18:09:51,722 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2786 [2024-11-25 18:09:51,972 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2481 [2024-11-25 18:09:52,205 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2592 [2024-11-25 18:09:52,465 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2571 [2024-11-25 18:09:52,699 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2690 [2024-11-25 18:09:52,966 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2785 [2024-11-25 18:09:53,203 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2211 [2024-11-25 18:09:53,470 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2907 [2024-11-25 18:09:53,733 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2612 [2024-11-25 18:09:53,971 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2765 [2024-11-25 18:09:54,222 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2878 [2024-11-25 18:09:54,468 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2529 [2024-11-25 18:09:54,735 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2985 [2024-11-25 18:09:54,988 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3204 [2024-11-25 18:09:55,221 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2799 [2024-11-25 18:09:55,485 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2204 [2024-11-25 18:09:55,703 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2951 [2024-11-25 18:09:55,945 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2493 [2024-11-25 18:09:56,205 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2383 [2024-11-25 18:09:56,451 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2687 [2024-11-25 18:09:56,676 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2088 [2024-11-25 18:09:56,948 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2692 [2024-11-25 18:09:57,178 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2813 [2024-11-25 18:09:57,413 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2700 [2024-11-25 18:09:57,691 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3213 [2024-11-25 18:09:57,941 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2914 [2024-11-25 18:09:58,177 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2590 [2024-11-25 18:09:58,411 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2469 [2024-11-25 18:09:58,646 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2570 [2024-11-25 18:09:58,894 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2534 [2024-11-25 18:09:59,154 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2622 [2024-11-25 18:09:59,404 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2977 [2024-11-25 18:09:59,628 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2387 [2024-11-25 18:09:59,860 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2257 [2024-11-25 18:10:00,080 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2734 [2024-11-25 18:10:00,333 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2644 [2024-11-25 18:10:00,600 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2319 [2024-11-25 18:10:00,859 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3275 [2024-11-25 18:10:01,092 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2586 [2024-11-25 18:10:01,331 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2436 [2024-11-25 18:10:01,589 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.3241 [2024-11-25 18:10:01,856 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2934 [2024-11-25 18:10:02,114 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2604 [2024-11-25 18:10:02,375 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2601 [2024-11-25 18:10:02,628 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2413 [2024-11-25 18:10:02,900 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2600 [2024-11-25 18:10:03,130 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2644 [2024-11-25 18:10:03,388 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2739 [2024-11-25 18:10:03,654 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2828 [2024-11-25 18:10:03,921 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2658 [2024-11-25 18:10:04,164 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2238 [2024-11-25 18:10:04,421 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2897 [2024-11-25 18:10:04,692 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2902 [2024-11-25 18:10:04,955 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2909 [2024-11-25 18:10:05,198 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2100 [2024-11-25 18:10:05,437 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2979 [2024-11-25 18:10:05,696 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2762 [2024-11-25 18:10:05,939 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2888 [2024-11-25 18:10:06,156 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2860 [2024-11-25 18:10:06,379 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2290 [2024-11-25 18:10:06,648 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2770 [2024-11-25 18:10:06,885 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2443 [2024-11-25 18:10:07,143 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2585 [2024-11-25 18:10:07,394 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3117 [2024-11-25 18:10:07,636 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2822 [2024-11-25 18:10:07,900 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2809 [2024-11-25 18:10:08,150 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2095 [2024-11-25 18:10:08,396 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.3168 [2024-11-25 18:10:08,667 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2592 [2024-11-25 18:10:08,906 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2861 [2024-11-25 18:10:09,167 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2867 [2024-11-25 18:10:09,427 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2540 [2024-11-25 18:10:09,674 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.3052 [2024-11-25 18:10:09,922 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2843 [2024-11-25 18:10:10,155 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2809 [2024-11-25 18:10:10,410 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2968 [2024-11-25 18:10:10,681 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2626 [2024-11-25 18:10:10,946 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2320 [2024-11-25 18:10:11,213 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2508 [2024-11-25 18:10:11,470 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2189 [2024-11-25 18:10:11,738 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2687 [2024-11-25 18:10:11,958 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3177 [2024-11-25 18:10:12,230 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2604 [2024-11-25 18:10:12,467 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2684 [2024-11-25 18:10:12,737 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2425 [2024-11-25 18:10:12,997 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2926 [2024-11-25 18:10:13,255 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2628 [2024-11-25 18:10:13,507 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3016 [2024-11-25 18:10:13,730 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2892 [2024-11-25 18:10:13,969 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2444 [2024-11-25 18:10:14,227 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2337 [2024-11-25 18:10:14,497 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2802 [2024-11-25 18:10:14,732 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2893 [2024-11-25 18:10:14,995 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2514 [2024-11-25 18:10:15,259 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2814 [2024-11-25 18:10:15,489 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2463 [2024-11-25 18:10:15,727 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2220 [2024-11-25 18:10:15,946 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2209 [2024-11-25 18:10:16,172 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2254 [2024-11-25 18:10:16,443 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2844 [2024-11-25 18:10:16,704 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2941 [2024-11-25 18:10:16,971 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2956 [2024-11-25 18:10:17,236 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2468 [2024-11-25 18:10:17,502 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2780 [2024-11-25 18:10:17,762 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2904 [2024-11-25 18:10:18,027 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2588 [2024-11-25 18:10:18,285 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2879 [2024-11-25 18:10:18,547 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2849 [2024-11-25 18:10:18,811 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2700 [2024-11-25 18:10:19,063 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.3088 [2024-11-25 18:10:19,294 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2571 [2024-11-25 18:10:19,554 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.3149 [2024-11-25 18:10:19,790 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2757 [2024-11-25 18:10:20,016 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2027 [2024-11-25 18:10:20,228 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2496 [2024-11-25 18:10:20,445 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2272 [2024-11-25 18:10:21,147 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7485/0.8238/0.9958. [2024-11-25 18:10:21,148 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9957/0.9980 [2024-11-25 18:10:21,148 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5013/0.6496 [2024-11-25 18:10:21,148 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:10:21,149 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7712 [2024-11-25 18:10:21,149 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:10:24,572 INFO misc.py line 119 2586773] Train: [23/50][1/376] Data 0.086 (0.086) Batch 0.508 (0.508) Remain 01:29:12 loss: 0.2204 Lr: 0.00252 [2024-11-25 18:10:25,108 INFO misc.py line 119 2586773] Train: [23/50][2/376] Data 0.002 (0.002) Batch 0.536 (0.536) Remain 01:34:01 loss: 0.2664 Lr: 0.00252 [2024-11-25 18:10:25,582 INFO misc.py line 119 2586773] Train: [23/50][3/376] Data 0.002 (0.002) Batch 0.475 (0.475) Remain 01:23:16 loss: 0.2142 Lr: 0.00252 [2024-11-25 18:10:26,083 INFO misc.py line 119 2586773] Train: [23/50][4/376] Data 0.003 (0.003) Batch 0.501 (0.501) Remain 01:27:48 loss: 0.1845 Lr: 0.00252 [2024-11-25 18:10:26,618 INFO misc.py line 119 2586773] Train: [23/50][5/376] Data 0.002 (0.002) Batch 0.535 (0.518) Remain 01:30:46 loss: 0.2173 Lr: 0.00252 [2024-11-25 18:10:27,114 INFO misc.py line 119 2586773] Train: [23/50][6/376] Data 0.003 (0.003) Batch 0.496 (0.510) Remain 01:29:30 loss: 0.1995 Lr: 0.00252 [2024-11-25 18:10:27,640 INFO misc.py line 119 2586773] Train: [23/50][7/376] Data 0.002 (0.002) Batch 0.526 (0.514) Remain 01:30:10 loss: 0.2235 Lr: 0.00252 [2024-11-25 18:10:28,157 INFO misc.py line 119 2586773] Train: [23/50][8/376] Data 0.003 (0.003) Batch 0.518 (0.515) Remain 01:30:17 loss: 0.2353 Lr: 0.00252 [2024-11-25 18:10:28,726 INFO misc.py line 119 2586773] Train: [23/50][9/376] Data 0.002 (0.002) Batch 0.569 (0.524) Remain 01:31:51 loss: 0.2433 Lr: 0.00252 [2024-11-25 18:10:29,251 INFO misc.py line 119 2586773] Train: [23/50][10/376] Data 0.003 (0.003) Batch 0.525 (0.524) Remain 01:31:52 loss: 0.2225 Lr: 0.00252 [2024-11-25 18:10:29,775 INFO misc.py line 119 2586773] Train: [23/50][11/376] Data 0.002 (0.003) Batch 0.524 (0.524) Remain 01:31:51 loss: 0.1904 Lr: 0.00252 [2024-11-25 18:10:30,264 INFO misc.py line 119 2586773] Train: [23/50][12/376] Data 0.002 (0.002) Batch 0.490 (0.520) Remain 01:31:10 loss: 0.1983 Lr: 0.00252 [2024-11-25 18:10:30,779 INFO misc.py line 119 2586773] Train: [23/50][13/376] Data 0.002 (0.002) Batch 0.515 (0.520) Remain 01:31:04 loss: 0.2387 Lr: 0.00252 [2024-11-25 18:10:31,306 INFO misc.py line 119 2586773] Train: [23/50][14/376] Data 0.003 (0.002) Batch 0.527 (0.520) Remain 01:31:10 loss: 0.2073 Lr: 0.00252 [2024-11-25 18:10:31,789 INFO misc.py line 119 2586773] Train: [23/50][15/376] Data 0.002 (0.002) Batch 0.483 (0.517) Remain 01:30:37 loss: 0.1895 Lr: 0.00252 [2024-11-25 18:10:32,303 INFO misc.py line 119 2586773] Train: [23/50][16/376] Data 0.002 (0.002) Batch 0.513 (0.517) Remain 01:30:33 loss: 0.2197 Lr: 0.00252 [2024-11-25 18:10:32,784 INFO misc.py line 119 2586773] Train: [23/50][17/376] Data 0.003 (0.002) Batch 0.482 (0.514) Remain 01:30:06 loss: 0.2561 Lr: 0.00252 [2024-11-25 18:10:33,273 INFO misc.py line 119 2586773] Train: [23/50][18/376] Data 0.003 (0.002) Batch 0.489 (0.513) Remain 01:29:48 loss: 0.2036 Lr: 0.00251 [2024-11-25 18:10:33,786 INFO misc.py line 119 2586773] Train: [23/50][19/376] Data 0.002 (0.002) Batch 0.512 (0.513) Remain 01:29:48 loss: 0.2074 Lr: 0.00251 [2024-11-25 18:10:34,307 INFO misc.py line 119 2586773] Train: [23/50][20/376] Data 0.002 (0.002) Batch 0.521 (0.513) Remain 01:29:52 loss: 0.2057 Lr: 0.00251 [2024-11-25 18:10:34,813 INFO misc.py line 119 2586773] Train: [23/50][21/376] Data 0.002 (0.002) Batch 0.506 (0.513) Remain 01:29:47 loss: 0.2507 Lr: 0.00251 [2024-11-25 18:10:35,356 INFO misc.py line 119 2586773] Train: [23/50][22/376] Data 0.003 (0.002) Batch 0.543 (0.514) Remain 01:30:04 loss: 0.2553 Lr: 0.00251 [2024-11-25 18:10:35,826 INFO misc.py line 119 2586773] Train: [23/50][23/376] Data 0.002 (0.002) Batch 0.470 (0.512) Remain 01:29:40 loss: 0.2133 Lr: 0.00251 [2024-11-25 18:10:36,345 INFO misc.py line 119 2586773] Train: [23/50][24/376] Data 0.002 (0.002) Batch 0.518 (0.513) Remain 01:29:43 loss: 0.2378 Lr: 0.00251 [2024-11-25 18:10:36,860 INFO misc.py line 119 2586773] Train: [23/50][25/376] Data 0.003 (0.002) Batch 0.515 (0.513) Remain 01:29:43 loss: 0.2475 Lr: 0.00251 [2024-11-25 18:10:37,390 INFO misc.py line 119 2586773] Train: [23/50][26/376] Data 0.003 (0.002) Batch 0.530 (0.513) Remain 01:29:51 loss: 0.2674 Lr: 0.00251 [2024-11-25 18:10:37,903 INFO misc.py line 119 2586773] Train: [23/50][27/376] Data 0.003 (0.002) Batch 0.513 (0.513) Remain 01:29:50 loss: 0.2748 Lr: 0.00251 [2024-11-25 18:10:38,398 INFO misc.py line 119 2586773] Train: [23/50][28/376] Data 0.003 (0.002) Batch 0.495 (0.513) Remain 01:29:42 loss: 0.2582 Lr: 0.00251 [2024-11-25 18:10:38,894 INFO misc.py line 119 2586773] Train: [23/50][29/376] Data 0.003 (0.002) Batch 0.496 (0.512) Remain 01:29:35 loss: 0.2050 Lr: 0.00251 [2024-11-25 18:10:39,381 INFO misc.py line 119 2586773] Train: [23/50][30/376] Data 0.003 (0.003) Batch 0.487 (0.511) Remain 01:29:24 loss: 0.1973 Lr: 0.00251 [2024-11-25 18:10:39,913 INFO misc.py line 119 2586773] Train: [23/50][31/376] Data 0.002 (0.002) Batch 0.532 (0.512) Remain 01:29:32 loss: 0.2800 Lr: 0.00251 [2024-11-25 18:10:40,437 INFO misc.py line 119 2586773] Train: [23/50][32/376] Data 0.002 (0.002) Batch 0.524 (0.512) Remain 01:29:36 loss: 0.2407 Lr: 0.00251 [2024-11-25 18:10:40,939 INFO misc.py line 119 2586773] Train: [23/50][33/376] Data 0.002 (0.002) Batch 0.502 (0.512) Remain 01:29:32 loss: 0.2136 Lr: 0.00251 [2024-11-25 18:10:41,468 INFO misc.py line 119 2586773] Train: [23/50][34/376] Data 0.002 (0.002) Batch 0.529 (0.512) Remain 01:29:37 loss: 0.1846 Lr: 0.00251 [2024-11-25 18:10:41,946 INFO misc.py line 119 2586773] Train: [23/50][35/376] Data 0.002 (0.002) Batch 0.478 (0.511) Remain 01:29:25 loss: 0.2055 Lr: 0.00251 [2024-11-25 18:10:42,454 INFO misc.py line 119 2586773] Train: [23/50][36/376] Data 0.002 (0.002) Batch 0.508 (0.511) Remain 01:29:24 loss: 0.3628 Lr: 0.00251 [2024-11-25 18:10:42,971 INFO misc.py line 119 2586773] Train: [23/50][37/376] Data 0.003 (0.002) Batch 0.517 (0.511) Remain 01:29:25 loss: 0.2365 Lr: 0.00251 [2024-11-25 18:10:43,533 INFO misc.py line 119 2586773] Train: [23/50][38/376] Data 0.002 (0.002) Batch 0.562 (0.513) Remain 01:29:40 loss: 0.2587 Lr: 0.00251 [2024-11-25 18:10:44,011 INFO misc.py line 119 2586773] Train: [23/50][39/376] Data 0.002 (0.002) Batch 0.478 (0.512) Remain 01:29:29 loss: 0.2232 Lr: 0.00251 [2024-11-25 18:10:44,506 INFO misc.py line 119 2586773] Train: [23/50][40/376] Data 0.002 (0.002) Batch 0.494 (0.511) Remain 01:29:23 loss: 0.2014 Lr: 0.00251 [2024-11-25 18:10:45,013 INFO misc.py line 119 2586773] Train: [23/50][41/376] Data 0.002 (0.002) Batch 0.508 (0.511) Remain 01:29:22 loss: 0.1720 Lr: 0.00251 [2024-11-25 18:10:45,526 INFO misc.py line 119 2586773] Train: [23/50][42/376] Data 0.002 (0.002) Batch 0.513 (0.511) Remain 01:29:22 loss: 0.2128 Lr: 0.00251 [2024-11-25 18:10:46,016 INFO misc.py line 119 2586773] Train: [23/50][43/376] Data 0.002 (0.002) Batch 0.490 (0.511) Remain 01:29:16 loss: 0.2099 Lr: 0.00251 [2024-11-25 18:10:46,582 INFO misc.py line 119 2586773] Train: [23/50][44/376] Data 0.003 (0.002) Batch 0.566 (0.512) Remain 01:29:29 loss: 0.2245 Lr: 0.00251 [2024-11-25 18:10:47,076 INFO misc.py line 119 2586773] Train: [23/50][45/376] Data 0.002 (0.002) Batch 0.494 (0.512) Remain 01:29:24 loss: 0.2787 Lr: 0.00251 [2024-11-25 18:10:47,600 INFO misc.py line 119 2586773] Train: [23/50][46/376] Data 0.002 (0.002) Batch 0.525 (0.512) Remain 01:29:27 loss: 0.2135 Lr: 0.00251 [2024-11-25 18:10:48,121 INFO misc.py line 119 2586773] Train: [23/50][47/376] Data 0.003 (0.002) Batch 0.520 (0.512) Remain 01:29:28 loss: 0.2157 Lr: 0.00250 [2024-11-25 18:10:48,654 INFO misc.py line 119 2586773] Train: [23/50][48/376] Data 0.002 (0.002) Batch 0.533 (0.513) Remain 01:29:33 loss: 0.2552 Lr: 0.00250 [2024-11-25 18:10:49,141 INFO misc.py line 119 2586773] Train: [23/50][49/376] Data 0.003 (0.002) Batch 0.487 (0.512) Remain 01:29:26 loss: 0.2368 Lr: 0.00250 [2024-11-25 18:10:49,707 INFO misc.py line 119 2586773] Train: [23/50][50/376] Data 0.002 (0.002) Batch 0.566 (0.513) Remain 01:29:38 loss: 0.1952 Lr: 0.00250 [2024-11-25 18:10:50,202 INFO misc.py line 119 2586773] Train: [23/50][51/376] Data 0.002 (0.002) Batch 0.495 (0.513) Remain 01:29:33 loss: 0.2437 Lr: 0.00250 [2024-11-25 18:10:50,709 INFO misc.py line 119 2586773] Train: [23/50][52/376] Data 0.002 (0.002) Batch 0.507 (0.513) Remain 01:29:31 loss: 0.2307 Lr: 0.00250 [2024-11-25 18:10:51,227 INFO misc.py line 119 2586773] Train: [23/50][53/376] Data 0.002 (0.002) Batch 0.519 (0.513) Remain 01:29:32 loss: 0.1830 Lr: 0.00250 [2024-11-25 18:10:51,718 INFO misc.py line 119 2586773] Train: [23/50][54/376] Data 0.003 (0.002) Batch 0.490 (0.512) Remain 01:29:27 loss: 0.2068 Lr: 0.00250 [2024-11-25 18:10:52,257 INFO misc.py line 119 2586773] Train: [23/50][55/376] Data 0.002 (0.002) Batch 0.539 (0.513) Remain 01:29:32 loss: 0.2670 Lr: 0.00250 [2024-11-25 18:10:52,796 INFO misc.py line 119 2586773] Train: [23/50][56/376] Data 0.003 (0.002) Batch 0.539 (0.513) Remain 01:29:36 loss: 0.2245 Lr: 0.00250 [2024-11-25 18:10:53,300 INFO misc.py line 119 2586773] Train: [23/50][57/376] Data 0.003 (0.002) Batch 0.505 (0.513) Remain 01:29:34 loss: 0.2225 Lr: 0.00250 [2024-11-25 18:10:53,792 INFO misc.py line 119 2586773] Train: [23/50][58/376] Data 0.002 (0.002) Batch 0.492 (0.513) Remain 01:29:30 loss: 0.2253 Lr: 0.00250 [2024-11-25 18:10:54,260 INFO misc.py line 119 2586773] Train: [23/50][59/376] Data 0.002 (0.002) Batch 0.467 (0.512) Remain 01:29:21 loss: 0.2713 Lr: 0.00250 [2024-11-25 18:10:54,786 INFO misc.py line 119 2586773] Train: [23/50][60/376] Data 0.002 (0.002) Batch 0.527 (0.512) Remain 01:29:23 loss: 0.2359 Lr: 0.00250 [2024-11-25 18:10:55,266 INFO misc.py line 119 2586773] Train: [23/50][61/376] Data 0.003 (0.002) Batch 0.479 (0.512) Remain 01:29:16 loss: 0.1868 Lr: 0.00250 [2024-11-25 18:10:55,781 INFO misc.py line 119 2586773] Train: [23/50][62/376] Data 0.003 (0.002) Batch 0.516 (0.512) Remain 01:29:16 loss: 0.1803 Lr: 0.00250 [2024-11-25 18:10:56,308 INFO misc.py line 119 2586773] Train: [23/50][63/376] Data 0.002 (0.002) Batch 0.527 (0.512) Remain 01:29:19 loss: 0.2040 Lr: 0.00250 [2024-11-25 18:10:56,823 INFO misc.py line 119 2586773] Train: [23/50][64/376] Data 0.002 (0.002) Batch 0.515 (0.512) Remain 01:29:19 loss: 0.1842 Lr: 0.00250 [2024-11-25 18:10:57,324 INFO misc.py line 119 2586773] Train: [23/50][65/376] Data 0.002 (0.002) Batch 0.501 (0.512) Remain 01:29:16 loss: 0.1873 Lr: 0.00250 [2024-11-25 18:10:57,838 INFO misc.py line 119 2586773] Train: [23/50][66/376] Data 0.002 (0.002) Batch 0.514 (0.512) Remain 01:29:16 loss: 0.2442 Lr: 0.00250 [2024-11-25 18:10:58,345 INFO misc.py line 119 2586773] Train: [23/50][67/376] Data 0.002 (0.002) Batch 0.506 (0.512) Remain 01:29:15 loss: 0.2023 Lr: 0.00250 [2024-11-25 18:10:58,903 INFO misc.py line 119 2586773] Train: [23/50][68/376] Data 0.002 (0.002) Batch 0.558 (0.513) Remain 01:29:21 loss: 0.2056 Lr: 0.00250 [2024-11-25 18:10:59,398 INFO misc.py line 119 2586773] Train: [23/50][69/376] Data 0.003 (0.002) Batch 0.495 (0.512) Remain 01:29:18 loss: 0.2341 Lr: 0.00250 [2024-11-25 18:10:59,887 INFO misc.py line 119 2586773] Train: [23/50][70/376] Data 0.002 (0.002) Batch 0.489 (0.512) Remain 01:29:14 loss: 0.2209 Lr: 0.00250 [2024-11-25 18:11:00,437 INFO misc.py line 119 2586773] Train: [23/50][71/376] Data 0.003 (0.002) Batch 0.549 (0.513) Remain 01:29:19 loss: 0.2089 Lr: 0.00250 [2024-11-25 18:11:00,914 INFO misc.py line 119 2586773] Train: [23/50][72/376] Data 0.002 (0.002) Batch 0.477 (0.512) Remain 01:29:13 loss: 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INFO misc.py line 119 2586773] Train: [23/50][79/376] Data 0.002 (0.002) Batch 0.523 (0.511) Remain 01:28:58 loss: 0.2344 Lr: 0.00249 [2024-11-25 18:11:04,901 INFO misc.py line 119 2586773] Train: [23/50][80/376] Data 0.002 (0.002) Batch 0.487 (0.511) Remain 01:28:55 loss: 0.2559 Lr: 0.00249 [2024-11-25 18:11:05,383 INFO misc.py line 119 2586773] Train: [23/50][81/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 01:28:50 loss: 0.1908 Lr: 0.00249 [2024-11-25 18:11:05,890 INFO misc.py line 119 2586773] Train: [23/50][82/376] Data 0.002 (0.002) Batch 0.507 (0.510) Remain 01:28:49 loss: 0.2088 Lr: 0.00249 [2024-11-25 18:11:06,407 INFO misc.py line 119 2586773] Train: [23/50][83/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 01:28:50 loss: 0.1918 Lr: 0.00249 [2024-11-25 18:11:06,931 INFO misc.py line 119 2586773] Train: [23/50][84/376] Data 0.002 (0.002) Batch 0.523 (0.510) Remain 01:28:51 loss: 0.1814 Lr: 0.00249 [2024-11-25 18:11:07,426 INFO misc.py line 119 2586773] Train: 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line 119 2586773] Train: [23/50][216/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 01:27:23 loss: 0.2120 Lr: 0.00245 [2024-11-25 18:12:14,375 INFO misc.py line 119 2586773] Train: [23/50][217/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 01:27:21 loss: 0.2538 Lr: 0.00245 [2024-11-25 18:12:14,872 INFO misc.py line 119 2586773] Train: [23/50][218/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 01:27:20 loss: 0.2408 Lr: 0.00245 [2024-11-25 18:12:15,356 INFO misc.py line 119 2586773] Train: [23/50][219/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 01:27:19 loss: 0.2488 Lr: 0.00245 [2024-11-25 18:12:15,908 INFO misc.py line 119 2586773] Train: [23/50][220/376] Data 0.002 (0.002) Batch 0.552 (0.508) Remain 01:27:20 loss: 0.2081 Lr: 0.00245 [2024-11-25 18:12:16,391 INFO misc.py line 119 2586773] Train: [23/50][221/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 01:27:19 loss: 0.2620 Lr: 0.00245 [2024-11-25 18:12:16,877 INFO misc.py line 119 2586773] Train: 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Batch 0.513 (0.508) Remain 01:27:12 loss: 0.2208 Lr: 0.00244 [2024-11-25 18:12:20,362 INFO misc.py line 119 2586773] Train: [23/50][229/376] Data 0.002 (0.002) Batch 0.477 (0.508) Remain 01:27:10 loss: 0.2175 Lr: 0.00244 [2024-11-25 18:12:20,835 INFO misc.py line 119 2586773] Train: [23/50][230/376] Data 0.002 (0.002) Batch 0.473 (0.508) Remain 01:27:08 loss: 0.3333 Lr: 0.00244 [2024-11-25 18:12:21,356 INFO misc.py line 119 2586773] Train: [23/50][231/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 01:27:08 loss: 0.2332 Lr: 0.00244 [2024-11-25 18:12:21,828 INFO misc.py line 119 2586773] Train: [23/50][232/376] Data 0.002 (0.002) Batch 0.472 (0.508) Remain 01:27:06 loss: 0.2020 Lr: 0.00244 [2024-11-25 18:12:22,328 INFO misc.py line 119 2586773] Train: [23/50][233/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 01:27:05 loss: 0.2223 Lr: 0.00244 [2024-11-25 18:12:22,841 INFO misc.py line 119 2586773] Train: [23/50][234/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 01:27:05 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Batch 0.506 (0.508) Remain 01:26:11 loss: 0.2253 Lr: 0.00241 [2024-11-25 18:13:17,138 INFO misc.py line 119 2586773] Train: [23/50][341/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 01:26:10 loss: 0.2122 Lr: 0.00241 [2024-11-25 18:13:17,625 INFO misc.py line 119 2586773] Train: [23/50][342/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:26:09 loss: 0.2048 Lr: 0.00241 [2024-11-25 18:13:18,107 INFO misc.py line 119 2586773] Train: [23/50][343/376] Data 0.003 (0.002) Batch 0.482 (0.507) Remain 01:26:08 loss: 0.2049 Lr: 0.00241 [2024-11-25 18:13:18,586 INFO misc.py line 119 2586773] Train: [23/50][344/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 01:26:06 loss: 0.2587 Lr: 0.00240 [2024-11-25 18:13:19,126 INFO misc.py line 119 2586773] Train: [23/50][345/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 01:26:07 loss: 0.2439 Lr: 0.00240 [2024-11-25 18:13:19,624 INFO misc.py line 119 2586773] Train: [23/50][346/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:26:06 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18:13:23,146 INFO misc.py line 119 2586773] Train: [23/50][353/376] Data 0.003 (0.002) Batch 0.507 (0.507) Remain 01:26:02 loss: 0.2375 Lr: 0.00240 [2024-11-25 18:13:23,640 INFO misc.py line 119 2586773] Train: [23/50][354/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:26:01 loss: 0.1794 Lr: 0.00240 [2024-11-25 18:13:24,183 INFO misc.py line 119 2586773] Train: [23/50][355/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 01:26:01 loss: 0.2349 Lr: 0.00240 [2024-11-25 18:13:24,686 INFO misc.py line 119 2586773] Train: [23/50][356/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 01:26:01 loss: 0.2894 Lr: 0.00240 [2024-11-25 18:13:25,134 INFO misc.py line 119 2586773] Train: [23/50][357/376] Data 0.002 (0.002) Batch 0.448 (0.507) Remain 01:25:58 loss: 0.1974 Lr: 0.00240 [2024-11-25 18:13:25,599 INFO misc.py line 119 2586773] Train: [23/50][358/376] Data 0.002 (0.002) Batch 0.465 (0.507) Remain 01:25:57 loss: 0.2125 Lr: 0.00240 [2024-11-25 18:13:26,119 INFO misc.py line 119 2586773] Train: [23/50][359/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 01:25:56 loss: 0.1935 Lr: 0.00240 [2024-11-25 18:13:26,585 INFO misc.py line 119 2586773] Train: [23/50][360/376] Data 0.002 (0.002) Batch 0.465 (0.507) Remain 01:25:55 loss: 0.2359 Lr: 0.00240 [2024-11-25 18:13:27,120 INFO misc.py line 119 2586773] Train: [23/50][361/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 01:25:55 loss: 0.2699 Lr: 0.00240 [2024-11-25 18:13:27,616 INFO misc.py line 119 2586773] Train: [23/50][362/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:25:54 loss: 0.2206 Lr: 0.00240 [2024-11-25 18:13:28,157 INFO misc.py line 119 2586773] Train: [23/50][363/376] Data 0.003 (0.002) Batch 0.541 (0.507) Remain 01:25:55 loss: 0.2238 Lr: 0.00240 [2024-11-25 18:13:28,650 INFO misc.py line 119 2586773] Train: [23/50][364/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 01:25:54 loss: 0.1816 Lr: 0.00240 [2024-11-25 18:13:29,150 INFO misc.py line 119 2586773] Train: [23/50][365/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:25:53 loss: 0.2395 Lr: 0.00240 [2024-11-25 18:13:29,633 INFO misc.py line 119 2586773] Train: [23/50][366/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 01:25:52 loss: 0.1629 Lr: 0.00240 [2024-11-25 18:13:30,183 INFO misc.py line 119 2586773] Train: [23/50][367/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 01:25:53 loss: 0.1981 Lr: 0.00240 [2024-11-25 18:13:30,693 INFO misc.py line 119 2586773] Train: [23/50][368/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:25:52 loss: 0.2746 Lr: 0.00240 [2024-11-25 18:13:31,197 INFO misc.py line 119 2586773] Train: [23/50][369/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 01:25:52 loss: 0.2587 Lr: 0.00240 [2024-11-25 18:13:31,676 INFO misc.py line 119 2586773] Train: [23/50][370/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 01:25:50 loss: 0.1919 Lr: 0.00240 [2024-11-25 18:13:32,201 INFO misc.py line 119 2586773] Train: [23/50][371/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 01:25:50 loss: 0.1672 Lr: 0.00240 [2024-11-25 18:13:32,745 INFO misc.py line 119 2586773] Train: [23/50][372/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 01:25:51 loss: 0.2648 Lr: 0.00240 [2024-11-25 18:13:33,234 INFO misc.py line 119 2586773] Train: [23/50][373/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:25:50 loss: 0.2437 Lr: 0.00239 [2024-11-25 18:13:33,778 INFO misc.py line 119 2586773] Train: [23/50][374/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 01:25:50 loss: 0.2432 Lr: 0.00239 [2024-11-25 18:13:34,293 INFO misc.py line 119 2586773] Train: [23/50][375/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:25:50 loss: 0.2354 Lr: 0.00239 [2024-11-25 18:13:34,790 INFO misc.py line 119 2586773] Train: [23/50][376/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 01:25:49 loss: 0.1531 Lr: 0.00239 [2024-11-25 18:13:34,791 INFO misc.py line 136 2586773] Train result: loss: 0.2264 [2024-11-25 18:13:34,791 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:13:45,879 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1898 [2024-11-25 18:13:46,137 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2314 [2024-11-25 18:13:46,403 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2985 [2024-11-25 18:13:46,627 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2072 [2024-11-25 18:13:46,891 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2941 [2024-11-25 18:13:47,164 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2040 [2024-11-25 18:13:47,388 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2422 [2024-11-25 18:13:47,658 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2350 [2024-11-25 18:13:47,882 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2979 [2024-11-25 18:13:48,144 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2646 [2024-11-25 18:13:48,375 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2207 [2024-11-25 18:13:48,648 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2553 [2024-11-25 18:13:48,913 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2594 [2024-11-25 18:13:49,179 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2556 [2024-11-25 18:13:49,411 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2279 [2024-11-25 18:13:49,654 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2957 [2024-11-25 18:13:49,919 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2987 [2024-11-25 18:13:50,165 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2722 [2024-11-25 18:13:50,399 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2345 [2024-11-25 18:13:50,657 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2247 [2024-11-25 18:13:50,891 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2633 [2024-11-25 18:13:51,159 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2895 [2024-11-25 18:13:51,397 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2270 [2024-11-25 18:13:51,663 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2714 [2024-11-25 18:13:51,924 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2321 [2024-11-25 18:13:52,158 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2643 [2024-11-25 18:13:52,413 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2642 [2024-11-25 18:13:52,661 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2440 [2024-11-25 18:13:52,928 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2918 [2024-11-25 18:13:53,184 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3128 [2024-11-25 18:13:53,420 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2683 [2024-11-25 18:13:53,684 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2269 [2024-11-25 18:13:53,907 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2821 [2024-11-25 18:13:54,147 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2580 [2024-11-25 18:13:54,410 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2494 [2024-11-25 18:13:54,657 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2466 [2024-11-25 18:13:54,882 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2316 [2024-11-25 18:13:55,152 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2340 [2024-11-25 18:13:55,384 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2708 [2024-11-25 18:13:55,618 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2393 [2024-11-25 18:13:55,888 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3373 [2024-11-25 18:13:56,148 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.3021 [2024-11-25 18:13:56,387 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2743 [2024-11-25 18:13:56,620 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2463 [2024-11-25 18:13:56,857 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2498 [2024-11-25 18:13:57,107 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2517 [2024-11-25 18:13:57,365 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2450 [2024-11-25 18:13:57,615 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2929 [2024-11-25 18:13:57,842 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2259 [2024-11-25 18:13:58,075 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2189 [2024-11-25 18:13:58,295 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2759 [2024-11-25 18:13:58,549 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2500 [2024-11-25 18:13:58,817 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2699 [2024-11-25 18:13:59,077 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3270 [2024-11-25 18:13:59,309 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2314 [2024-11-25 18:13:59,551 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2359 [2024-11-25 18:13:59,808 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2725 [2024-11-25 18:14:00,089 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2943 [2024-11-25 18:14:00,345 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2687 [2024-11-25 18:14:00,605 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2474 [2024-11-25 18:14:00,858 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2299 [2024-11-25 18:14:01,131 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2375 [2024-11-25 18:14:01,360 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2227 [2024-11-25 18:14:01,622 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2677 [2024-11-25 18:14:01,887 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2600 [2024-11-25 18:14:02,162 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2165 [2024-11-25 18:14:02,409 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2222 [2024-11-25 18:14:02,668 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2780 [2024-11-25 18:14:02,938 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2457 [2024-11-25 18:14:03,200 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2969 [2024-11-25 18:14:03,445 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.1996 [2024-11-25 18:14:03,678 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2775 [2024-11-25 18:14:03,936 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2558 [2024-11-25 18:14:04,180 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2894 [2024-11-25 18:14:04,396 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2884 [2024-11-25 18:14:04,616 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2234 [2024-11-25 18:14:04,883 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2669 [2024-11-25 18:14:05,118 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2539 [2024-11-25 18:14:05,378 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2514 [2024-11-25 18:14:05,627 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3024 [2024-11-25 18:14:05,868 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2596 [2024-11-25 18:14:06,132 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2641 [2024-11-25 18:14:06,382 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2070 [2024-11-25 18:14:06,628 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2742 [2024-11-25 18:14:06,896 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2323 [2024-11-25 18:14:07,138 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2663 [2024-11-25 18:14:07,400 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2588 [2024-11-25 18:14:07,659 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2342 [2024-11-25 18:14:07,908 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2921 [2024-11-25 18:14:08,166 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2401 [2024-11-25 18:14:08,402 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2705 [2024-11-25 18:14:08,655 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2678 [2024-11-25 18:14:08,923 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2662 [2024-11-25 18:14:09,188 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2490 [2024-11-25 18:14:09,455 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2582 [2024-11-25 18:14:09,704 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2255 [2024-11-25 18:14:09,972 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2334 [2024-11-25 18:14:10,201 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3152 [2024-11-25 18:14:10,471 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2530 [2024-11-25 18:14:10,714 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2726 [2024-11-25 18:14:10,995 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2137 [2024-11-25 18:14:11,267 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2901 [2024-11-25 18:14:11,528 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2594 [2024-11-25 18:14:11,781 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.3028 [2024-11-25 18:14:12,014 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2685 [2024-11-25 18:14:12,261 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2558 [2024-11-25 18:14:12,517 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2425 [2024-11-25 18:14:12,798 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2673 [2024-11-25 18:14:13,034 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2766 [2024-11-25 18:14:13,306 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2487 [2024-11-25 18:14:13,582 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2449 [2024-11-25 18:14:13,815 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2309 [2024-11-25 18:14:14,061 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2129 [2024-11-25 18:14:14,289 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2234 [2024-11-25 18:14:14,513 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2279 [2024-11-25 18:14:14,785 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2935 [2024-11-25 18:14:15,048 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2749 [2024-11-25 18:14:15,314 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2504 [2024-11-25 18:14:15,585 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2413 [2024-11-25 18:14:15,848 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2725 [2024-11-25 18:14:16,111 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2962 [2024-11-25 18:14:16,380 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2370 [2024-11-25 18:14:16,637 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2733 [2024-11-25 18:14:16,908 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2469 [2024-11-25 18:14:17,175 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2921 [2024-11-25 18:14:17,426 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2799 [2024-11-25 18:14:17,657 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2263 [2024-11-25 18:14:17,924 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2586 [2024-11-25 18:14:18,162 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2741 [2024-11-25 18:14:18,396 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2015 [2024-11-25 18:14:18,613 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2448 [2024-11-25 18:14:18,834 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2084 [2024-11-25 18:14:19,433 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7578/0.8369/0.9959. [2024-11-25 18:14:19,433 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9959/0.9980 [2024-11-25 18:14:19,433 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5197/0.6757 [2024-11-25 18:14:19,434 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:14:19,435 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7712 [2024-11-25 18:14:19,435 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:14:22,165 INFO misc.py line 119 2586773] Train: [24/50][1/376] Data 0.080 (0.080) Batch 0.590 (0.590) Remain 01:39:46 loss: 0.1957 Lr: 0.00239 [2024-11-25 18:14:22,679 INFO misc.py line 119 2586773] Train: [24/50][2/376] Data 0.002 (0.002) Batch 0.514 (0.514) Remain 01:26:57 loss: 0.2409 Lr: 0.00239 [2024-11-25 18:14:23,166 INFO misc.py line 119 2586773] Train: [24/50][3/376] Data 0.002 (0.002) Batch 0.486 (0.486) Remain 01:22:14 loss: 0.2059 Lr: 0.00239 [2024-11-25 18:14:23,674 INFO misc.py line 119 2586773] Train: [24/50][4/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 01:25:54 loss: 0.1885 Lr: 0.00239 [2024-11-25 18:14:24,166 INFO misc.py line 119 2586773] Train: [24/50][5/376] Data 0.002 (0.002) Batch 0.492 (0.500) Remain 01:24:34 loss: 0.2608 Lr: 0.00239 [2024-11-25 18:14:24,696 INFO misc.py line 119 2586773] Train: [24/50][6/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 01:26:15 loss: 0.1922 Lr: 0.00239 [2024-11-25 18:14:25,187 INFO misc.py line 119 2586773] Train: [24/50][7/376] Data 0.002 (0.002) Batch 0.491 (0.505) Remain 01:25:25 loss: 0.2361 Lr: 0.00239 [2024-11-25 18:14:25,696 INFO misc.py line 119 2586773] Train: [24/50][8/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 01:25:32 loss: 0.1813 Lr: 0.00239 [2024-11-25 18:14:26,206 INFO misc.py line 119 2586773] Train: [24/50][9/376] Data 0.003 (0.002) Batch 0.511 (0.507) Remain 01:25:40 loss: 0.2337 Lr: 0.00239 [2024-11-25 18:14:26,736 INFO misc.py line 119 2586773] Train: [24/50][10/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 01:26:12 loss: 0.3024 Lr: 0.00239 [2024-11-25 18:14:27,245 INFO misc.py line 119 2586773] Train: [24/50][11/376] Data 0.003 (0.002) Batch 0.509 (0.510) Remain 01:26:10 loss: 0.2704 Lr: 0.00239 [2024-11-25 18:14:27,731 INFO misc.py line 119 2586773] Train: [24/50][12/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 01:25:43 loss: 0.2006 Lr: 0.00239 [2024-11-25 18:14:28,222 INFO misc.py line 119 2586773] Train: [24/50][13/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 01:25:26 loss: 0.2051 Lr: 0.00239 [2024-11-25 18:14:28,735 INFO misc.py line 119 2586773] Train: [24/50][14/376] Data 0.002 (0.002) Batch 0.513 (0.506) Remain 01:25:32 loss: 0.1901 Lr: 0.00239 [2024-11-25 18:14:29,227 INFO misc.py line 119 2586773] Train: [24/50][15/376] Data 0.002 (0.002) Batch 0.492 (0.505) Remain 01:25:20 loss: 0.2097 Lr: 0.00239 [2024-11-25 18:14:29,721 INFO misc.py line 119 2586773] Train: 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[2024-11-25 18:16:37,225 INFO misc.py line 119 2586773] Train: [24/50][266/376] Data 0.002 (0.002) Batch 0.543 (0.510) Remain 01:23:59 loss: 0.2378 Lr: 0.00230 [2024-11-25 18:16:37,714 INFO misc.py line 119 2586773] Train: [24/50][267/376] Data 0.002 (0.002) Batch 0.489 (0.510) Remain 01:23:57 loss: 0.1697 Lr: 0.00230 [2024-11-25 18:16:38,243 INFO misc.py line 119 2586773] Train: [24/50][268/376] Data 0.003 (0.002) Batch 0.529 (0.510) Remain 01:23:58 loss: 0.2690 Lr: 0.00230 [2024-11-25 18:16:38,766 INFO misc.py line 119 2586773] Train: [24/50][269/376] Data 0.002 (0.002) Batch 0.524 (0.510) Remain 01:23:58 loss: 0.2303 Lr: 0.00230 [2024-11-25 18:16:39,288 INFO misc.py line 119 2586773] Train: [24/50][270/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 01:23:58 loss: 0.2047 Lr: 0.00230 [2024-11-25 18:16:39,793 INFO misc.py line 119 2586773] Train: [24/50][271/376] Data 0.002 (0.002) Batch 0.505 (0.510) Remain 01:23:57 loss: 0.2004 Lr: 0.00230 [2024-11-25 18:16:40,355 INFO misc.py 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Batch 0.493 (0.510) Remain 01:23:53 loss: 0.2319 Lr: 0.00230 [2024-11-25 18:16:47,062 INFO misc.py line 119 2586773] Train: [24/50][285/376] Data 0.002 (0.002) Batch 0.552 (0.510) Remain 01:23:54 loss: 0.2327 Lr: 0.00230 [2024-11-25 18:16:47,577 INFO misc.py line 119 2586773] Train: [24/50][286/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 01:23:54 loss: 0.2302 Lr: 0.00230 [2024-11-25 18:16:48,111 INFO misc.py line 119 2586773] Train: [24/50][287/376] Data 0.002 (0.002) Batch 0.534 (0.510) Remain 01:23:54 loss: 0.1960 Lr: 0.00230 [2024-11-25 18:16:48,671 INFO misc.py line 119 2586773] Train: [24/50][288/376] Data 0.003 (0.002) Batch 0.559 (0.511) Remain 01:23:55 loss: 0.2006 Lr: 0.00230 [2024-11-25 18:16:49,160 INFO misc.py line 119 2586773] Train: [24/50][289/376] Data 0.002 (0.002) Batch 0.489 (0.510) Remain 01:23:54 loss: 0.1680 Lr: 0.00229 [2024-11-25 18:16:49,728 INFO misc.py line 119 2586773] Train: [24/50][290/376] Data 0.002 (0.002) Batch 0.569 (0.511) Remain 01:23:56 loss: 0.1920 Lr: 0.00229 [2024-11-25 18:16:50,232 INFO misc.py line 119 2586773] Train: [24/50][291/376] Data 0.002 (0.002) Batch 0.504 (0.511) Remain 01:23:55 loss: 0.2081 Lr: 0.00229 [2024-11-25 18:16:50,769 INFO misc.py line 119 2586773] Train: [24/50][292/376] Data 0.003 (0.002) Batch 0.537 (0.511) Remain 01:23:55 loss: 0.2304 Lr: 0.00229 [2024-11-25 18:16:51,252 INFO misc.py line 119 2586773] Train: [24/50][293/376] Data 0.002 (0.002) Batch 0.484 (0.511) Remain 01:23:54 loss: 0.1933 Lr: 0.00229 [2024-11-25 18:16:51,742 INFO misc.py line 119 2586773] Train: [24/50][294/376] Data 0.002 (0.002) Batch 0.490 (0.511) Remain 01:23:53 loss: 0.1943 Lr: 0.00229 [2024-11-25 18:16:52,235 INFO misc.py line 119 2586773] Train: [24/50][295/376] Data 0.003 (0.002) Batch 0.493 (0.511) Remain 01:23:52 loss: 0.1850 Lr: 0.00229 [2024-11-25 18:16:52,745 INFO misc.py line 119 2586773] Train: [24/50][296/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 01:23:51 loss: 0.2213 Lr: 0.00229 [2024-11-25 18:16:53,295 INFO misc.py line 119 2586773] Train: [24/50][297/376] Data 0.002 (0.002) Batch 0.550 (0.511) Remain 01:23:52 loss: 0.2186 Lr: 0.00229 [2024-11-25 18:16:53,802 INFO misc.py line 119 2586773] Train: [24/50][298/376] Data 0.002 (0.002) Batch 0.507 (0.511) Remain 01:23:51 loss: 0.2258 Lr: 0.00229 [2024-11-25 18:16:54,295 INFO misc.py line 119 2586773] Train: [24/50][299/376] Data 0.002 (0.002) Batch 0.493 (0.511) Remain 01:23:50 loss: 0.1936 Lr: 0.00229 [2024-11-25 18:16:54,793 INFO misc.py line 119 2586773] Train: [24/50][300/376] Data 0.003 (0.002) Batch 0.498 (0.511) Remain 01:23:49 loss: 0.2540 Lr: 0.00229 [2024-11-25 18:16:55,343 INFO misc.py line 119 2586773] Train: [24/50][301/376] Data 0.002 (0.002) Batch 0.550 (0.511) Remain 01:23:50 loss: 0.2620 Lr: 0.00229 [2024-11-25 18:16:55,855 INFO misc.py line 119 2586773] Train: [24/50][302/376] Data 0.003 (0.002) Batch 0.512 (0.511) Remain 01:23:50 loss: 0.2294 Lr: 0.00229 [2024-11-25 18:16:56,376 INFO misc.py line 119 2586773] Train: [24/50][303/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 01:23:49 loss: 0.2190 Lr: 0.00229 [2024-11-25 18:16:56,850 INFO misc.py line 119 2586773] Train: [24/50][304/376] Data 0.002 (0.002) Batch 0.474 (0.511) Remain 01:23:48 loss: 0.1866 Lr: 0.00229 [2024-11-25 18:16:57,345 INFO misc.py line 119 2586773] Train: [24/50][305/376] Data 0.002 (0.002) Batch 0.495 (0.511) Remain 01:23:47 loss: 0.2101 Lr: 0.00229 [2024-11-25 18:16:57,851 INFO misc.py line 119 2586773] Train: [24/50][306/376] Data 0.002 (0.002) Batch 0.506 (0.511) Remain 01:23:46 loss: 0.2036 Lr: 0.00229 [2024-11-25 18:16:58,332 INFO misc.py line 119 2586773] Train: [24/50][307/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 01:23:45 loss: 0.3331 Lr: 0.00229 [2024-11-25 18:16:58,869 INFO misc.py line 119 2586773] Train: [24/50][308/376] Data 0.002 (0.002) Batch 0.536 (0.511) Remain 01:23:45 loss: 0.2262 Lr: 0.00229 [2024-11-25 18:16:59,347 INFO misc.py line 119 2586773] Train: [24/50][309/376] Data 0.002 (0.002) Batch 0.478 (0.510) Remain 01:23:43 loss: 0.2136 Lr: 0.00229 [2024-11-25 18:16:59,851 INFO misc.py line 119 2586773] Train: [24/50][310/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 01:23:43 loss: 0.2081 Lr: 0.00229 [2024-11-25 18:17:00,345 INFO misc.py line 119 2586773] Train: [24/50][311/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 01:23:42 loss: 0.2297 Lr: 0.00229 [2024-11-25 18:17:00,885 INFO misc.py line 119 2586773] Train: [24/50][312/376] Data 0.002 (0.002) Batch 0.540 (0.510) Remain 01:23:42 loss: 0.2147 Lr: 0.00229 [2024-11-25 18:17:01,406 INFO misc.py line 119 2586773] Train: [24/50][313/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 01:23:42 loss: 0.2664 Lr: 0.00229 [2024-11-25 18:17:01,921 INFO misc.py line 119 2586773] Train: [24/50][314/376] Data 0.002 (0.002) Batch 0.514 (0.510) Remain 01:23:41 loss: 0.1980 Lr: 0.00229 [2024-11-25 18:17:02,477 INFO misc.py line 119 2586773] Train: [24/50][315/376] Data 0.002 (0.002) Batch 0.557 (0.511) Remain 01:23:42 loss: 0.2282 Lr: 0.00229 [2024-11-25 18:17:02,971 INFO misc.py line 119 2586773] Train: [24/50][316/376] Data 0.002 (0.002) Batch 0.494 (0.511) Remain 01:23:41 loss: 0.2224 Lr: 0.00229 [2024-11-25 18:17:03,514 INFO misc.py line 119 2586773] Train: [24/50][317/376] Data 0.002 (0.002) Batch 0.543 (0.511) Remain 01:23:42 loss: 0.2535 Lr: 0.00229 [2024-11-25 18:17:04,015 INFO misc.py line 119 2586773] Train: [24/50][318/376] Data 0.002 (0.002) Batch 0.501 (0.511) Remain 01:23:41 loss: 0.2146 Lr: 0.00228 [2024-11-25 18:17:04,544 INFO misc.py line 119 2586773] Train: [24/50][319/376] Data 0.002 (0.002) Batch 0.529 (0.511) Remain 01:23:41 loss: 0.2171 Lr: 0.00228 [2024-11-25 18:17:05,080 INFO misc.py line 119 2586773] Train: [24/50][320/376] Data 0.002 (0.002) Batch 0.536 (0.511) Remain 01:23:41 loss: 0.2678 Lr: 0.00228 [2024-11-25 18:17:05,579 INFO misc.py line 119 2586773] Train: [24/50][321/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 01:23:41 loss: 0.2306 Lr: 0.00228 [2024-11-25 18:17:06,107 INFO misc.py line 119 2586773] Train: [24/50][322/376] Data 0.002 (0.002) Batch 0.528 (0.511) Remain 01:23:41 loss: 0.2820 Lr: 0.00228 [2024-11-25 18:17:06,632 INFO misc.py line 119 2586773] Train: [24/50][323/376] Data 0.002 (0.002) Batch 0.525 (0.511) Remain 01:23:40 loss: 0.3067 Lr: 0.00228 [2024-11-25 18:17:07,148 INFO misc.py line 119 2586773] Train: [24/50][324/376] Data 0.002 (0.002) Batch 0.516 (0.511) Remain 01:23:40 loss: 0.1899 Lr: 0.00228 [2024-11-25 18:17:07,678 INFO misc.py line 119 2586773] Train: [24/50][325/376] Data 0.002 (0.002) Batch 0.530 (0.511) Remain 01:23:40 loss: 0.2024 Lr: 0.00228 [2024-11-25 18:17:08,214 INFO misc.py line 119 2586773] Train: [24/50][326/376] Data 0.002 (0.002) Batch 0.535 (0.511) Remain 01:23:40 loss: 0.2036 Lr: 0.00228 [2024-11-25 18:17:08,695 INFO misc.py line 119 2586773] Train: [24/50][327/376] Data 0.003 (0.002) Batch 0.481 (0.511) Remain 01:23:39 loss: 0.2143 Lr: 0.00228 [2024-11-25 18:17:09,198 INFO misc.py line 119 2586773] Train: [24/50][328/376] Data 0.002 (0.002) Batch 0.503 (0.511) Remain 01:23:38 loss: 0.2295 Lr: 0.00228 [2024-11-25 18:17:09,736 INFO misc.py line 119 2586773] Train: [24/50][329/376] Data 0.002 (0.002) Batch 0.538 (0.511) Remain 01:23:39 loss: 0.2222 Lr: 0.00228 [2024-11-25 18:17:10,221 INFO misc.py line 119 2586773] Train: [24/50][330/376] Data 0.002 (0.002) Batch 0.486 (0.511) Remain 01:23:37 loss: 0.2524 Lr: 0.00228 [2024-11-25 18:17:10,684 INFO misc.py line 119 2586773] Train: [24/50][331/376] Data 0.002 (0.002) Batch 0.463 (0.511) Remain 01:23:35 loss: 0.2451 Lr: 0.00228 [2024-11-25 18:17:11,211 INFO misc.py line 119 2586773] Train: [24/50][332/376] Data 0.003 (0.002) Batch 0.527 (0.511) Remain 01:23:35 loss: 0.1983 Lr: 0.00228 [2024-11-25 18:17:11,716 INFO misc.py line 119 2586773] Train: [24/50][333/376] Data 0.002 (0.002) Batch 0.504 (0.511) Remain 01:23:35 loss: 0.2910 Lr: 0.00228 [2024-11-25 18:17:12,242 INFO misc.py line 119 2586773] Train: [24/50][334/376] Data 0.003 (0.002) Batch 0.527 (0.511) Remain 01:23:35 loss: 0.1930 Lr: 0.00228 [2024-11-25 18:17:12,791 INFO misc.py line 119 2586773] Train: [24/50][335/376] Data 0.002 (0.002) Batch 0.548 (0.511) Remain 01:23:35 loss: 0.2055 Lr: 0.00228 [2024-11-25 18:17:13,316 INFO misc.py line 119 2586773] Train: [24/50][336/376] Data 0.003 (0.002) Batch 0.526 (0.511) Remain 01:23:35 loss: 0.2601 Lr: 0.00228 [2024-11-25 18:17:13,833 INFO misc.py line 119 2586773] Train: [24/50][337/376] Data 0.002 (0.002) Batch 0.517 (0.511) Remain 01:23:35 loss: 0.1772 Lr: 0.00228 [2024-11-25 18:17:14,366 INFO misc.py line 119 2586773] Train: [24/50][338/376] Data 0.002 (0.002) Batch 0.533 (0.511) Remain 01:23:35 loss: 0.2110 Lr: 0.00228 [2024-11-25 18:17:14,848 INFO misc.py line 119 2586773] Train: [24/50][339/376] Data 0.002 (0.002) Batch 0.482 (0.511) Remain 01:23:34 loss: 0.2185 Lr: 0.00228 [2024-11-25 18:17:15,374 INFO misc.py line 119 2586773] Train: [24/50][340/376] Data 0.002 (0.002) Batch 0.526 (0.511) Remain 01:23:33 loss: 0.2018 Lr: 0.00228 [2024-11-25 18:17:15,892 INFO misc.py line 119 2586773] Train: [24/50][341/376] Data 0.002 (0.002) Batch 0.518 (0.511) Remain 01:23:33 loss: 0.2447 Lr: 0.00228 [2024-11-25 18:17:16,434 INFO misc.py line 119 2586773] Train: [24/50][342/376] Data 0.002 (0.002) Batch 0.542 (0.511) Remain 01:23:34 loss: 0.2382 Lr: 0.00228 [2024-11-25 18:17:16,877 INFO misc.py line 119 2586773] Train: [24/50][343/376] Data 0.002 (0.002) Batch 0.443 (0.511) Remain 01:23:31 loss: 0.2380 Lr: 0.00228 [2024-11-25 18:17:17,359 INFO misc.py line 119 2586773] Train: [24/50][344/376] Data 0.002 (0.002) Batch 0.482 (0.511) Remain 01:23:30 loss: 0.1784 Lr: 0.00228 [2024-11-25 18:17:17,820 INFO misc.py line 119 2586773] Train: [24/50][345/376] Data 0.002 (0.002) Batch 0.461 (0.511) Remain 01:23:28 loss: 0.2262 Lr: 0.00228 [2024-11-25 18:17:18,330 INFO misc.py line 119 2586773] Train: [24/50][346/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 01:23:27 loss: 0.2043 Lr: 0.00228 [2024-11-25 18:17:18,828 INFO misc.py line 119 2586773] Train: [24/50][347/376] Data 0.002 (0.002) Batch 0.498 (0.511) Remain 01:23:26 loss: 0.2058 Lr: 0.00227 [2024-11-25 18:17:19,342 INFO misc.py line 119 2586773] Train: [24/50][348/376] Data 0.002 (0.002) Batch 0.514 (0.511) Remain 01:23:26 loss: 0.2583 Lr: 0.00227 [2024-11-25 18:17:19,824 INFO misc.py line 119 2586773] Train: [24/50][349/376] Data 0.002 (0.002) Batch 0.482 (0.511) Remain 01:23:25 loss: 0.1848 Lr: 0.00227 [2024-11-25 18:17:20,376 INFO misc.py line 119 2586773] Train: [24/50][350/376] Data 0.002 (0.002) Batch 0.553 (0.511) Remain 01:23:25 loss: 0.2275 Lr: 0.00227 [2024-11-25 18:17:20,875 INFO misc.py line 119 2586773] Train: [24/50][351/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 01:23:24 loss: 0.2110 Lr: 0.00227 [2024-11-25 18:17:21,356 INFO misc.py line 119 2586773] Train: [24/50][352/376] Data 0.002 (0.002) Batch 0.481 (0.511) Remain 01:23:23 loss: 0.2224 Lr: 0.00227 [2024-11-25 18:17:21,894 INFO misc.py line 119 2586773] Train: [24/50][353/376] Data 0.002 (0.002) Batch 0.538 (0.511) Remain 01:23:23 loss: 0.2752 Lr: 0.00227 [2024-11-25 18:17:22,438 INFO misc.py line 119 2586773] Train: [24/50][354/376] Data 0.002 (0.002) Batch 0.544 (0.511) Remain 01:23:24 loss: 0.2825 Lr: 0.00227 [2024-11-25 18:17:22,910 INFO misc.py line 119 2586773] Train: [24/50][355/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 01:23:22 loss: 0.2607 Lr: 0.00227 [2024-11-25 18:17:23,438 INFO misc.py line 119 2586773] Train: [24/50][356/376] Data 0.002 (0.002) Batch 0.529 (0.511) Remain 01:23:22 loss: 0.2774 Lr: 0.00227 [2024-11-25 18:17:23,980 INFO misc.py line 119 2586773] Train: [24/50][357/376] Data 0.002 (0.002) Batch 0.541 (0.511) Remain 01:23:23 loss: 0.1974 Lr: 0.00227 [2024-11-25 18:17:24,475 INFO misc.py line 119 2586773] Train: [24/50][358/376] Data 0.002 (0.002) Batch 0.495 (0.511) Remain 01:23:22 loss: 0.2047 Lr: 0.00227 [2024-11-25 18:17:24,977 INFO misc.py line 119 2586773] Train: [24/50][359/376] Data 0.002 (0.002) Batch 0.502 (0.511) Remain 01:23:21 loss: 0.1898 Lr: 0.00227 [2024-11-25 18:17:25,489 INFO misc.py line 119 2586773] Train: [24/50][360/376] Data 0.002 (0.002) Batch 0.512 (0.511) Remain 01:23:20 loss: 0.1910 Lr: 0.00227 [2024-11-25 18:17:26,000 INFO misc.py line 119 2586773] Train: [24/50][361/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 01:23:20 loss: 0.2428 Lr: 0.00227 [2024-11-25 18:17:26,498 INFO misc.py line 119 2586773] Train: [24/50][362/376] Data 0.002 (0.002) Batch 0.498 (0.511) Remain 01:23:19 loss: 0.1956 Lr: 0.00227 [2024-11-25 18:17:26,997 INFO misc.py line 119 2586773] Train: [24/50][363/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 01:23:18 loss: 0.1959 Lr: 0.00227 [2024-11-25 18:17:27,530 INFO misc.py line 119 2586773] Train: [24/50][364/376] Data 0.002 (0.002) Batch 0.533 (0.511) Remain 01:23:18 loss: 0.1788 Lr: 0.00227 [2024-11-25 18:17:28,065 INFO misc.py line 119 2586773] Train: [24/50][365/376] Data 0.002 (0.002) Batch 0.534 (0.511) Remain 01:23:18 loss: 0.1913 Lr: 0.00227 [2024-11-25 18:17:28,553 INFO misc.py line 119 2586773] Train: [24/50][366/376] Data 0.002 (0.002) Batch 0.488 (0.511) Remain 01:23:17 loss: 0.1880 Lr: 0.00227 [2024-11-25 18:17:29,036 INFO misc.py line 119 2586773] Train: [24/50][367/376] Data 0.002 (0.002) Batch 0.483 (0.511) Remain 01:23:16 loss: 0.2212 Lr: 0.00227 [2024-11-25 18:17:29,492 INFO misc.py line 119 2586773] Train: [24/50][368/376] Data 0.002 (0.002) Batch 0.456 (0.510) Remain 01:23:14 loss: 0.2495 Lr: 0.00227 [2024-11-25 18:17:30,018 INFO misc.py line 119 2586773] Train: [24/50][369/376] Data 0.002 (0.002) Batch 0.526 (0.511) Remain 01:23:14 loss: 0.2353 Lr: 0.00227 [2024-11-25 18:17:30,500 INFO misc.py line 119 2586773] Train: [24/50][370/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 01:23:13 loss: 0.1753 Lr: 0.00227 [2024-11-25 18:17:31,032 INFO misc.py line 119 2586773] Train: [24/50][371/376] Data 0.002 (0.002) Batch 0.532 (0.511) Remain 01:23:13 loss: 0.2328 Lr: 0.00227 [2024-11-25 18:17:31,517 INFO misc.py line 119 2586773] Train: [24/50][372/376] Data 0.002 (0.002) Batch 0.485 (0.510) Remain 01:23:12 loss: 0.2175 Lr: 0.00227 [2024-11-25 18:17:32,009 INFO misc.py line 119 2586773] Train: [24/50][373/376] Data 0.002 (0.002) Batch 0.492 (0.510) Remain 01:23:11 loss: 0.1924 Lr: 0.00227 [2024-11-25 18:17:32,551 INFO misc.py line 119 2586773] Train: [24/50][374/376] Data 0.002 (0.002) Batch 0.542 (0.510) Remain 01:23:11 loss: 0.1981 Lr: 0.00227 [2024-11-25 18:17:33,053 INFO misc.py line 119 2586773] Train: [24/50][375/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 01:23:10 loss: 0.2194 Lr: 0.00227 [2024-11-25 18:17:33,601 INFO misc.py line 119 2586773] Train: [24/50][376/376] Data 0.002 (0.002) Batch 0.548 (0.511) Remain 01:23:11 loss: 0.2390 Lr: 0.00226 [2024-11-25 18:17:33,602 INFO misc.py line 136 2586773] Train result: loss: 0.2210 [2024-11-25 18:17:33,602 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:17:44,621 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2092 [2024-11-25 18:17:44,889 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2438 [2024-11-25 18:17:45,150 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2304 [2024-11-25 18:17:45,374 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2058 [2024-11-25 18:17:45,637 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2981 [2024-11-25 18:17:45,911 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2099 [2024-11-25 18:17:46,133 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2329 [2024-11-25 18:17:46,403 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2443 [2024-11-25 18:17:46,628 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2926 [2024-11-25 18:17:46,897 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2582 [2024-11-25 18:17:47,130 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2064 [2024-11-25 18:17:47,401 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2633 [2024-11-25 18:17:47,667 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2837 [2024-11-25 18:17:47,931 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2456 [2024-11-25 18:17:48,166 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2304 [2024-11-25 18:17:48,405 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3226 [2024-11-25 18:17:48,670 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2743 [2024-11-25 18:17:48,925 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2208 [2024-11-25 18:17:49,158 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2677 [2024-11-25 18:17:49,418 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2308 [2024-11-25 18:17:49,658 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2542 [2024-11-25 18:17:49,927 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2732 [2024-11-25 18:17:50,163 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2125 [2024-11-25 18:17:50,430 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2471 [2024-11-25 18:17:50,692 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2229 [2024-11-25 18:17:50,932 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2617 [2024-11-25 18:17:51,186 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2766 [2024-11-25 18:17:51,433 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2355 [2024-11-25 18:17:51,698 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2904 [2024-11-25 18:17:51,951 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2971 [2024-11-25 18:17:52,185 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2630 [2024-11-25 18:17:52,447 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2244 [2024-11-25 18:17:52,677 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2709 [2024-11-25 18:17:52,914 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2347 [2024-11-25 18:17:53,174 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2331 [2024-11-25 18:17:53,419 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2892 [2024-11-25 18:17:53,647 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2099 [2024-11-25 18:17:53,918 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2275 [2024-11-25 18:17:54,150 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2644 [2024-11-25 18:17:54,386 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2460 [2024-11-25 18:17:54,658 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3027 [2024-11-25 18:17:54,910 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2863 [2024-11-25 18:17:55,147 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2638 [2024-11-25 18:17:55,383 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2364 [2024-11-25 18:17:55,619 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2520 [2024-11-25 18:17:55,866 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2395 [2024-11-25 18:17:56,123 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2338 [2024-11-25 18:17:56,377 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2909 [2024-11-25 18:17:56,601 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2101 [2024-11-25 18:17:56,837 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2078 [2024-11-25 18:17:57,058 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2558 [2024-11-25 18:17:57,311 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2454 [2024-11-25 18:17:57,578 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2263 [2024-11-25 18:17:57,840 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3165 [2024-11-25 18:17:58,072 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2301 [2024-11-25 18:17:58,313 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2386 [2024-11-25 18:17:58,572 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2712 [2024-11-25 18:17:58,839 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2807 [2024-11-25 18:17:59,094 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2515 [2024-11-25 18:17:59,356 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2533 [2024-11-25 18:17:59,608 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2221 [2024-11-25 18:17:59,881 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2371 [2024-11-25 18:18:00,112 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2647 [2024-11-25 18:18:00,371 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2560 [2024-11-25 18:18:00,638 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2628 [2024-11-25 18:18:00,906 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2051 [2024-11-25 18:18:01,153 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2061 [2024-11-25 18:18:01,409 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2698 [2024-11-25 18:18:01,678 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2599 [2024-11-25 18:18:01,942 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2795 [2024-11-25 18:18:02,187 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2292 [2024-11-25 18:18:02,421 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2841 [2024-11-25 18:18:02,679 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2465 [2024-11-25 18:18:02,922 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2812 [2024-11-25 18:18:03,139 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2742 [2024-11-25 18:18:03,361 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2100 [2024-11-25 18:18:03,633 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2566 [2024-11-25 18:18:03,870 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2510 [2024-11-25 18:18:04,130 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2495 [2024-11-25 18:18:04,380 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3070 [2024-11-25 18:18:04,621 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2614 [2024-11-25 18:18:04,884 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2726 [2024-11-25 18:18:05,136 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2402 [2024-11-25 18:18:05,384 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2573 [2024-11-25 18:18:05,654 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2508 [2024-11-25 18:18:05,897 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2802 [2024-11-25 18:18:06,158 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2486 [2024-11-25 18:18:06,416 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2382 [2024-11-25 18:18:06,664 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2812 [2024-11-25 18:18:06,912 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2754 [2024-11-25 18:18:07,146 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2610 [2024-11-25 18:18:07,399 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2654 [2024-11-25 18:18:07,666 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2482 [2024-11-25 18:18:07,935 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2013 [2024-11-25 18:18:08,202 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2447 [2024-11-25 18:18:08,450 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2064 [2024-11-25 18:18:08,717 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2359 [2024-11-25 18:18:08,937 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3103 [2024-11-25 18:18:09,212 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2368 [2024-11-25 18:18:09,449 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2737 [2024-11-25 18:18:09,719 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2238 [2024-11-25 18:18:09,978 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2476 [2024-11-25 18:18:10,236 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2431 [2024-11-25 18:18:10,488 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2953 [2024-11-25 18:18:10,711 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2626 [2024-11-25 18:18:10,952 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2185 [2024-11-25 18:18:11,206 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2327 [2024-11-25 18:18:11,477 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2370 [2024-11-25 18:18:11,713 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2696 [2024-11-25 18:18:11,972 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2144 [2024-11-25 18:18:12,235 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2633 [2024-11-25 18:18:12,460 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2288 [2024-11-25 18:18:12,697 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2002 [2024-11-25 18:18:12,923 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2193 [2024-11-25 18:18:13,146 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2165 [2024-11-25 18:18:13,418 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2708 [2024-11-25 18:18:13,680 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2657 [2024-11-25 18:18:13,948 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2301 [2024-11-25 18:18:14,214 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2403 [2024-11-25 18:18:14,475 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2610 [2024-11-25 18:18:14,735 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2841 [2024-11-25 18:18:15,002 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2354 [2024-11-25 18:18:15,257 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2556 [2024-11-25 18:18:15,523 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2567 [2024-11-25 18:18:15,789 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2810 [2024-11-25 18:18:16,044 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2751 [2024-11-25 18:18:16,274 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2041 [2024-11-25 18:18:16,533 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2688 [2024-11-25 18:18:16,769 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2511 [2024-11-25 18:18:16,996 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1964 [2024-11-25 18:18:17,211 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2357 [2024-11-25 18:18:17,427 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2376 [2024-11-25 18:18:18,197 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7661/0.8470/0.9961. [2024-11-25 18:18:18,197 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9980 [2024-11-25 18:18:18,197 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5362/0.6959 [2024-11-25 18:18:18,198 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:18:18,199 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7712 [2024-11-25 18:18:18,199 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:18:21,083 INFO misc.py line 119 2586773] Train: [25/50][1/376] Data 0.081 (0.081) Batch 0.593 (0.593) Remain 01:36:38 loss: 0.2252 Lr: 0.00226 [2024-11-25 18:18:21,591 INFO misc.py line 119 2586773] Train: [25/50][2/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 01:22:46 loss: 0.2109 Lr: 0.00226 [2024-11-25 18:18:22,062 INFO misc.py line 119 2586773] Train: [25/50][3/376] Data 0.002 (0.002) Batch 0.470 (0.470) Remain 01:16:35 loss: 0.2161 Lr: 0.00226 [2024-11-25 18:18:22,546 INFO misc.py line 119 2586773] Train: [25/50][4/376] Data 0.002 (0.002) Batch 0.484 (0.484) Remain 01:18:47 loss: 0.2075 Lr: 0.00226 [2024-11-25 18:18:23,007 INFO misc.py line 119 2586773] Train: [25/50][5/376] Data 0.002 (0.002) Batch 0.462 (0.473) Remain 01:16:59 loss: 0.2058 Lr: 0.00226 [2024-11-25 18:18:23,488 INFO misc.py line 119 2586773] Train: [25/50][6/376] Data 0.002 (0.002) Batch 0.481 (0.475) Remain 01:17:24 loss: 0.3569 Lr: 0.00226 [2024-11-25 18:18:24,018 INFO misc.py line 119 2586773] Train: [25/50][7/376] Data 0.002 (0.002) Batch 0.530 (0.489) Remain 01:19:38 loss: 0.2726 Lr: 0.00226 [2024-11-25 18:18:24,507 INFO misc.py line 119 2586773] Train: [25/50][8/376] Data 0.002 (0.002) Batch 0.489 (0.489) Remain 01:19:37 loss: 0.3117 Lr: 0.00226 [2024-11-25 18:18:25,026 INFO misc.py line 119 2586773] Train: [25/50][9/376] Data 0.002 (0.002) Batch 0.519 (0.494) Remain 01:20:25 loss: 0.2615 Lr: 0.00226 [2024-11-25 18:18:25,577 INFO misc.py line 119 2586773] Train: [25/50][10/376] Data 0.002 (0.002) Batch 0.551 (0.502) Remain 01:21:44 loss: 0.2732 Lr: 0.00226 [2024-11-25 18:18:26,071 INFO misc.py line 119 2586773] Train: [25/50][11/376] Data 0.002 (0.002) Batch 0.494 (0.501) Remain 01:21:33 loss: 0.2816 Lr: 0.00226 [2024-11-25 18:18:26,580 INFO misc.py line 119 2586773] Train: [25/50][12/376] Data 0.002 (0.002) Batch 0.510 (0.502) Remain 01:21:42 loss: 0.2010 Lr: 0.00226 [2024-11-25 18:18:27,065 INFO misc.py line 119 2586773] Train: [25/50][13/376] Data 0.002 (0.002) Batch 0.484 (0.500) Remain 01:21:24 loss: 0.1994 Lr: 0.00226 [2024-11-25 18:18:27,585 INFO misc.py line 119 2586773] Train: [25/50][14/376] Data 0.003 (0.002) Batch 0.520 (0.502) Remain 01:21:41 loss: 0.1974 Lr: 0.00226 [2024-11-25 18:18:28,069 INFO misc.py line 119 2586773] Train: [25/50][15/376] Data 0.002 (0.002) Batch 0.484 (0.501) Remain 01:21:26 loss: 0.1826 Lr: 0.00226 [2024-11-25 18:18:28,593 INFO misc.py line 119 2586773] Train: [25/50][16/376] Data 0.003 (0.002) Batch 0.524 (0.502) Remain 01:21:43 loss: 0.2557 Lr: 0.00226 [2024-11-25 18:18:29,104 INFO misc.py line 119 2586773] Train: [25/50][17/376] Data 0.002 (0.002) Batch 0.511 (0.503) Remain 01:21:48 loss: 0.1899 Lr: 0.00226 [2024-11-25 18:18:29,642 INFO misc.py line 119 2586773] Train: [25/50][18/376] Data 0.002 (0.002) Batch 0.538 (0.505) Remain 01:22:11 loss: 0.2287 Lr: 0.00226 [2024-11-25 18:18:30,150 INFO misc.py line 119 2586773] Train: [25/50][19/376] Data 0.003 (0.002) Batch 0.508 (0.505) Remain 01:22:12 loss: 0.2683 Lr: 0.00226 [2024-11-25 18:18:30,659 INFO misc.py line 119 2586773] Train: [25/50][20/376] Data 0.002 (0.002) Batch 0.510 (0.506) Remain 01:22:13 loss: 0.2278 Lr: 0.00226 [2024-11-25 18:18:31,183 INFO misc.py line 119 2586773] Train: [25/50][21/376] Data 0.003 (0.002) Batch 0.524 (0.507) Remain 01:22:23 loss: 0.2464 Lr: 0.00226 [2024-11-25 18:18:31,738 INFO misc.py line 119 2586773] Train: [25/50][22/376] Data 0.002 (0.002) Batch 0.555 (0.509) Remain 01:22:47 loss: 0.2049 Lr: 0.00226 [2024-11-25 18:18:32,247 INFO misc.py line 119 2586773] Train: [25/50][23/376] Data 0.003 (0.002) Batch 0.509 (0.509) Remain 01:22:46 loss: 0.2591 Lr: 0.00226 [2024-11-25 18:18:32,790 INFO misc.py line 119 2586773] Train: [25/50][24/376] Data 0.003 (0.002) Batch 0.543 (0.511) Remain 01:23:02 loss: 0.2480 Lr: 0.00226 [2024-11-25 18:18:33,324 INFO misc.py line 119 2586773] Train: [25/50][25/376] Data 0.002 (0.002) Batch 0.534 (0.512) Remain 01:23:11 loss: 0.2444 Lr: 0.00226 [2024-11-25 18:18:33,887 INFO misc.py line 119 2586773] Train: [25/50][26/376] Data 0.003 (0.002) Batch 0.563 (0.514) Remain 01:23:32 loss: 0.2354 Lr: 0.00226 [2024-11-25 18:18:34,394 INFO misc.py line 119 2586773] Train: [25/50][27/376] Data 0.003 (0.002) Batch 0.507 (0.514) Remain 01:23:29 loss: 0.1977 Lr: 0.00226 [2024-11-25 18:18:34,916 INFO misc.py line 119 2586773] Train: [25/50][28/376] Data 0.003 (0.002) Batch 0.522 (0.514) Remain 01:23:32 loss: 0.1988 Lr: 0.00226 [2024-11-25 18:18:35,398 INFO misc.py line 119 2586773] Train: [25/50][29/376] Data 0.003 (0.002) Batch 0.482 (0.513) Remain 01:23:19 loss: 0.2082 Lr: 0.00225 [2024-11-25 18:18:35,917 INFO misc.py line 119 2586773] Train: [25/50][30/376] Data 0.003 (0.002) Batch 0.518 (0.513) Remain 01:23:20 loss: 0.2389 Lr: 0.00225 [2024-11-25 18:18:36,425 INFO misc.py line 119 2586773] Train: [25/50][31/376] Data 0.003 (0.002) Batch 0.509 (0.513) Remain 01:23:19 loss: 0.1870 Lr: 0.00225 [2024-11-25 18:18:36,925 INFO misc.py line 119 2586773] Train: [25/50][32/376] Data 0.002 (0.002) Batch 0.499 (0.513) Remain 01:23:13 loss: 0.2237 Lr: 0.00225 [2024-11-25 18:18:37,418 INFO misc.py line 119 2586773] Train: [25/50][33/376] Data 0.003 (0.002) Batch 0.493 (0.512) Remain 01:23:07 loss: 0.2113 Lr: 0.00225 [2024-11-25 18:18:37,946 INFO misc.py line 119 2586773] Train: [25/50][34/376] Data 0.002 (0.002) Batch 0.528 (0.512) Remain 01:23:11 loss: 0.2726 Lr: 0.00225 [2024-11-25 18:18:38,455 INFO misc.py 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Batch 0.626 (0.522) Remain 01:23:07 loss: 0.2555 Lr: 0.00219 [2024-11-25 18:20:20,200 INFO misc.py line 119 2586773] Train: [25/50][229/376] Data 0.003 (0.002) Batch 0.612 (0.523) Remain 01:23:10 loss: 0.2060 Lr: 0.00219 [2024-11-25 18:20:20,777 INFO misc.py line 119 2586773] Train: [25/50][230/376] Data 0.002 (0.002) Batch 0.576 (0.523) Remain 01:23:12 loss: 0.1851 Lr: 0.00219 [2024-11-25 18:20:21,336 INFO misc.py line 119 2586773] Train: [25/50][231/376] Data 0.002 (0.002) Batch 0.559 (0.523) Remain 01:23:13 loss: 0.2661 Lr: 0.00219 [2024-11-25 18:20:21,912 INFO misc.py line 119 2586773] Train: [25/50][232/376] Data 0.002 (0.002) Batch 0.575 (0.523) Remain 01:23:14 loss: 0.2157 Lr: 0.00218 [2024-11-25 18:20:22,492 INFO misc.py line 119 2586773] Train: [25/50][233/376] Data 0.002 (0.002) Batch 0.581 (0.524) Remain 01:23:16 loss: 0.2415 Lr: 0.00218 [2024-11-25 18:20:23,079 INFO misc.py line 119 2586773] Train: [25/50][234/376] Data 0.002 (0.002) Batch 0.586 (0.524) Remain 01:23:18 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Batch 0.544 (0.530) Remain 01:23:46 loss: 0.2022 Lr: 0.00217 [2024-11-25 18:20:51,434 INFO misc.py line 119 2586773] Train: [25/50][285/376] Data 0.002 (0.002) Batch 0.567 (0.530) Remain 01:23:47 loss: 0.2145 Lr: 0.00217 [2024-11-25 18:20:51,920 INFO misc.py line 119 2586773] Train: [25/50][286/376] Data 0.002 (0.002) Batch 0.486 (0.530) Remain 01:23:45 loss: 0.2843 Lr: 0.00217 [2024-11-25 18:20:52,464 INFO misc.py line 119 2586773] Train: [25/50][287/376] Data 0.003 (0.002) Batch 0.544 (0.530) Remain 01:23:45 loss: 0.2193 Lr: 0.00217 [2024-11-25 18:20:52,980 INFO misc.py line 119 2586773] Train: [25/50][288/376] Data 0.003 (0.002) Batch 0.516 (0.530) Remain 01:23:44 loss: 0.1637 Lr: 0.00217 [2024-11-25 18:20:53,483 INFO misc.py line 119 2586773] Train: [25/50][289/376] Data 0.002 (0.002) Batch 0.503 (0.529) Remain 01:23:42 loss: 0.1923 Lr: 0.00216 [2024-11-25 18:20:54,015 INFO misc.py line 119 2586773] Train: [25/50][290/376] Data 0.002 (0.002) Batch 0.532 (0.529) Remain 01:23:42 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2586773] Train: [25/50][303/376] Data 0.002 (0.002) Batch 0.519 (0.529) Remain 01:23:35 loss: 0.2141 Lr: 0.00216 [2024-11-25 18:21:01,404 INFO misc.py line 119 2586773] Train: [25/50][304/376] Data 0.003 (0.002) Batch 0.498 (0.529) Remain 01:23:34 loss: 0.2191 Lr: 0.00216 [2024-11-25 18:21:01,892 INFO misc.py line 119 2586773] Train: [25/50][305/376] Data 0.002 (0.002) Batch 0.487 (0.529) Remain 01:23:32 loss: 0.1811 Lr: 0.00216 [2024-11-25 18:21:02,400 INFO misc.py line 119 2586773] Train: [25/50][306/376] Data 0.002 (0.002) Batch 0.508 (0.529) Remain 01:23:31 loss: 0.2396 Lr: 0.00216 [2024-11-25 18:21:02,939 INFO misc.py line 119 2586773] Train: [25/50][307/376] Data 0.003 (0.002) Batch 0.539 (0.529) Remain 01:23:30 loss: 0.2532 Lr: 0.00216 [2024-11-25 18:21:03,435 INFO misc.py line 119 2586773] Train: [25/50][308/376] Data 0.002 (0.002) Batch 0.496 (0.529) Remain 01:23:29 loss: 0.2018 Lr: 0.00216 [2024-11-25 18:21:03,925 INFO misc.py line 119 2586773] Train: [25/50][309/376] Data 0.002 (0.002) Batch 0.490 (0.529) Remain 01:23:27 loss: 0.2734 Lr: 0.00216 [2024-11-25 18:21:04,397 INFO misc.py line 119 2586773] Train: [25/50][310/376] Data 0.002 (0.002) Batch 0.471 (0.529) Remain 01:23:25 loss: 0.1935 Lr: 0.00216 [2024-11-25 18:21:04,930 INFO misc.py line 119 2586773] Train: [25/50][311/376] Data 0.002 (0.002) Batch 0.534 (0.529) Remain 01:23:25 loss: 0.2088 Lr: 0.00216 [2024-11-25 18:21:05,464 INFO misc.py line 119 2586773] Train: [25/50][312/376] Data 0.002 (0.002) Batch 0.533 (0.529) Remain 01:23:24 loss: 0.2557 Lr: 0.00216 [2024-11-25 18:21:05,964 INFO misc.py line 119 2586773] Train: [25/50][313/376] Data 0.003 (0.002) Batch 0.500 (0.529) Remain 01:23:23 loss: 0.2750 Lr: 0.00216 [2024-11-25 18:21:06,483 INFO misc.py line 119 2586773] Train: [25/50][314/376] Data 0.003 (0.002) Batch 0.519 (0.529) Remain 01:23:22 loss: 0.1681 Lr: 0.00216 [2024-11-25 18:21:07,025 INFO misc.py line 119 2586773] Train: [25/50][315/376] Data 0.003 (0.002) Batch 0.542 (0.529) Remain 01:23:22 loss: 0.2434 Lr: 0.00216 [2024-11-25 18:21:07,503 INFO misc.py line 119 2586773] Train: [25/50][316/376] Data 0.003 (0.002) Batch 0.478 (0.529) Remain 01:23:20 loss: 0.1773 Lr: 0.00216 [2024-11-25 18:21:08,059 INFO misc.py line 119 2586773] Train: [25/50][317/376] Data 0.003 (0.002) Batch 0.556 (0.529) Remain 01:23:20 loss: 0.1799 Lr: 0.00216 [2024-11-25 18:21:08,580 INFO misc.py line 119 2586773] Train: [25/50][318/376] Data 0.003 (0.002) Batch 0.521 (0.529) Remain 01:23:19 loss: 0.2292 Lr: 0.00215 [2024-11-25 18:21:09,111 INFO misc.py line 119 2586773] Train: [25/50][319/376] Data 0.003 (0.002) Batch 0.531 (0.529) Remain 01:23:19 loss: 0.1918 Lr: 0.00215 [2024-11-25 18:21:09,629 INFO misc.py line 119 2586773] Train: [25/50][320/376] Data 0.002 (0.002) Batch 0.518 (0.529) Remain 01:23:18 loss: 0.2100 Lr: 0.00215 [2024-11-25 18:21:10,117 INFO misc.py line 119 2586773] Train: [25/50][321/376] Data 0.003 (0.002) Batch 0.488 (0.528) Remain 01:23:16 loss: 0.2024 Lr: 0.00215 [2024-11-25 18:21:10,688 INFO misc.py line 119 2586773] Train: [25/50][322/376] Data 0.003 (0.002) Batch 0.571 (0.529) Remain 01:23:17 loss: 0.2658 Lr: 0.00215 [2024-11-25 18:21:11,231 INFO misc.py line 119 2586773] Train: [25/50][323/376] Data 0.003 (0.002) Batch 0.544 (0.529) Remain 01:23:17 loss: 0.2513 Lr: 0.00215 [2024-11-25 18:21:11,747 INFO misc.py line 119 2586773] Train: [25/50][324/376] Data 0.002 (0.002) Batch 0.516 (0.529) Remain 01:23:16 loss: 0.2197 Lr: 0.00215 [2024-11-25 18:21:12,250 INFO misc.py line 119 2586773] Train: [25/50][325/376] Data 0.002 (0.002) Batch 0.503 (0.529) Remain 01:23:15 loss: 0.2224 Lr: 0.00215 [2024-11-25 18:21:12,771 INFO misc.py line 119 2586773] Train: [25/50][326/376] Data 0.003 (0.002) Batch 0.522 (0.529) Remain 01:23:14 loss: 0.2006 Lr: 0.00215 [2024-11-25 18:21:13,282 INFO misc.py line 119 2586773] Train: [25/50][327/376] Data 0.003 (0.002) Batch 0.511 (0.528) Remain 01:23:13 loss: 0.2337 Lr: 0.00215 [2024-11-25 18:21:13,815 INFO misc.py line 119 2586773] Train: [25/50][328/376] Data 0.003 (0.002) Batch 0.533 (0.528) Remain 01:23:12 loss: 0.2102 Lr: 0.00215 [2024-11-25 18:21:14,303 INFO misc.py line 119 2586773] Train: [25/50][329/376] Data 0.002 (0.002) Batch 0.488 (0.528) Remain 01:23:11 loss: 0.2051 Lr: 0.00215 [2024-11-25 18:21:14,840 INFO misc.py line 119 2586773] Train: [25/50][330/376] Data 0.002 (0.002) Batch 0.537 (0.528) Remain 01:23:11 loss: 0.1851 Lr: 0.00215 [2024-11-25 18:21:15,349 INFO misc.py line 119 2586773] Train: [25/50][331/376] Data 0.002 (0.002) Batch 0.509 (0.528) Remain 01:23:09 loss: 0.2364 Lr: 0.00215 [2024-11-25 18:21:15,894 INFO misc.py line 119 2586773] Train: [25/50][332/376] Data 0.002 (0.002) Batch 0.545 (0.528) Remain 01:23:09 loss: 0.2366 Lr: 0.00215 [2024-11-25 18:21:16,415 INFO misc.py line 119 2586773] Train: [25/50][333/376] Data 0.002 (0.002) Batch 0.522 (0.528) Remain 01:23:09 loss: 0.2386 Lr: 0.00215 [2024-11-25 18:21:16,940 INFO misc.py line 119 2586773] Train: [25/50][334/376] Data 0.002 (0.002) Batch 0.525 (0.528) Remain 01:23:08 loss: 0.2165 Lr: 0.00215 [2024-11-25 18:21:17,451 INFO misc.py line 119 2586773] Train: [25/50][335/376] Data 0.002 (0.002) Batch 0.511 (0.528) Remain 01:23:07 loss: 0.2601 Lr: 0.00215 [2024-11-25 18:21:17,973 INFO misc.py line 119 2586773] Train: [25/50][336/376] Data 0.002 (0.002) Batch 0.522 (0.528) Remain 01:23:06 loss: 0.1893 Lr: 0.00215 [2024-11-25 18:21:18,484 INFO misc.py line 119 2586773] Train: [25/50][337/376] Data 0.002 (0.002) Batch 0.511 (0.528) Remain 01:23:05 loss: 0.2695 Lr: 0.00215 [2024-11-25 18:21:18,991 INFO misc.py line 119 2586773] Train: [25/50][338/376] Data 0.002 (0.002) Batch 0.507 (0.528) Remain 01:23:04 loss: 0.2121 Lr: 0.00215 [2024-11-25 18:21:19,508 INFO misc.py line 119 2586773] Train: [25/50][339/376] Data 0.002 (0.002) Batch 0.518 (0.528) Remain 01:23:03 loss: 0.2088 Lr: 0.00215 [2024-11-25 18:21:20,048 INFO misc.py line 119 2586773] Train: [25/50][340/376] Data 0.002 (0.002) Batch 0.540 (0.528) Remain 01:23:03 loss: 0.2256 Lr: 0.00215 [2024-11-25 18:21:20,565 INFO misc.py line 119 2586773] Train: [25/50][341/376] Data 0.002 (0.002) Batch 0.517 (0.528) Remain 01:23:02 loss: 0.2651 Lr: 0.00215 [2024-11-25 18:21:21,087 INFO misc.py line 119 2586773] Train: [25/50][342/376] Data 0.002 (0.002) Batch 0.523 (0.528) Remain 01:23:02 loss: 0.2527 Lr: 0.00215 [2024-11-25 18:21:21,597 INFO misc.py line 119 2586773] Train: [25/50][343/376] Data 0.002 (0.002) Batch 0.510 (0.528) Remain 01:23:01 loss: 0.2278 Lr: 0.00215 [2024-11-25 18:21:22,106 INFO misc.py line 119 2586773] Train: [25/50][344/376] Data 0.002 (0.002) Batch 0.509 (0.528) Remain 01:22:59 loss: 0.1919 Lr: 0.00215 [2024-11-25 18:21:22,615 INFO misc.py line 119 2586773] Train: [25/50][345/376] Data 0.003 (0.002) Batch 0.510 (0.528) Remain 01:22:58 loss: 0.2143 Lr: 0.00215 [2024-11-25 18:21:23,098 INFO misc.py line 119 2586773] Train: [25/50][346/376] Data 0.002 (0.002) Batch 0.482 (0.528) Remain 01:22:57 loss: 0.2928 Lr: 0.00215 [2024-11-25 18:21:23,569 INFO misc.py line 119 2586773] Train: [25/50][347/376] Data 0.003 (0.002) Batch 0.471 (0.528) Remain 01:22:55 loss: 0.2159 Lr: 0.00214 [2024-11-25 18:21:24,074 INFO misc.py line 119 2586773] Train: [25/50][348/376] Data 0.002 (0.002) Batch 0.505 (0.528) Remain 01:22:53 loss: 0.2047 Lr: 0.00214 [2024-11-25 18:21:24,589 INFO misc.py line 119 2586773] Train: [25/50][349/376] Data 0.002 (0.002) Batch 0.516 (0.528) Remain 01:22:53 loss: 0.2557 Lr: 0.00214 [2024-11-25 18:21:25,125 INFO misc.py line 119 2586773] Train: [25/50][350/376] Data 0.003 (0.002) Batch 0.536 (0.528) Remain 01:22:52 loss: 0.2511 Lr: 0.00214 [2024-11-25 18:21:25,635 INFO misc.py line 119 2586773] Train: [25/50][351/376] Data 0.002 (0.002) Batch 0.509 (0.528) Remain 01:22:51 loss: 0.1793 Lr: 0.00214 [2024-11-25 18:21:26,149 INFO misc.py line 119 2586773] Train: [25/50][352/376] Data 0.003 (0.002) Batch 0.514 (0.527) Remain 01:22:50 loss: 0.2143 Lr: 0.00214 [2024-11-25 18:21:26,638 INFO misc.py line 119 2586773] Train: [25/50][353/376] Data 0.003 (0.002) Batch 0.489 (0.527) Remain 01:22:49 loss: 0.2310 Lr: 0.00214 [2024-11-25 18:21:27,164 INFO misc.py line 119 2586773] Train: [25/50][354/376] Data 0.002 (0.002) Batch 0.526 (0.527) Remain 01:22:48 loss: 0.1932 Lr: 0.00214 [2024-11-25 18:21:27,718 INFO misc.py line 119 2586773] Train: [25/50][355/376] Data 0.002 (0.002) Batch 0.554 (0.527) Remain 01:22:48 loss: 0.2279 Lr: 0.00214 [2024-11-25 18:21:28,253 INFO misc.py line 119 2586773] Train: [25/50][356/376] Data 0.002 (0.002) Batch 0.535 (0.527) Remain 01:22:48 loss: 0.2385 Lr: 0.00214 [2024-11-25 18:21:28,730 INFO misc.py line 119 2586773] Train: [25/50][357/376] Data 0.002 (0.002) Batch 0.477 (0.527) Remain 01:22:46 loss: 0.2614 Lr: 0.00214 [2024-11-25 18:21:29,264 INFO misc.py line 119 2586773] Train: [25/50][358/376] Data 0.002 (0.002) Batch 0.533 (0.527) Remain 01:22:46 loss: 0.1763 Lr: 0.00214 [2024-11-25 18:21:29,732 INFO misc.py line 119 2586773] Train: [25/50][359/376] Data 0.002 (0.002) Batch 0.469 (0.527) Remain 01:22:44 loss: 0.2423 Lr: 0.00214 [2024-11-25 18:21:30,239 INFO misc.py line 119 2586773] Train: [25/50][360/376] Data 0.003 (0.002) Batch 0.507 (0.527) Remain 01:22:43 loss: 0.1942 Lr: 0.00214 [2024-11-25 18:21:30,718 INFO misc.py line 119 2586773] Train: [25/50][361/376] Data 0.002 (0.002) Batch 0.479 (0.527) Remain 01:22:41 loss: 0.2100 Lr: 0.00214 [2024-11-25 18:21:31,249 INFO misc.py line 119 2586773] Train: [25/50][362/376] Data 0.002 (0.002) Batch 0.531 (0.527) Remain 01:22:41 loss: 0.1926 Lr: 0.00214 [2024-11-25 18:21:31,762 INFO misc.py line 119 2586773] Train: [25/50][363/376] Data 0.002 (0.002) Batch 0.514 (0.527) Remain 01:22:40 loss: 0.2729 Lr: 0.00214 [2024-11-25 18:21:32,236 INFO misc.py line 119 2586773] Train: [25/50][364/376] Data 0.002 (0.002) Batch 0.474 (0.527) Remain 01:22:38 loss: 0.1952 Lr: 0.00214 [2024-11-25 18:21:32,718 INFO misc.py line 119 2586773] Train: [25/50][365/376] Data 0.002 (0.002) Batch 0.482 (0.527) Remain 01:22:36 loss: 0.2524 Lr: 0.00214 [2024-11-25 18:21:33,196 INFO misc.py line 119 2586773] Train: [25/50][366/376] Data 0.002 (0.002) Batch 0.478 (0.527) Remain 01:22:34 loss: 0.2349 Lr: 0.00214 [2024-11-25 18:21:33,716 INFO misc.py line 119 2586773] Train: [25/50][367/376] Data 0.002 (0.002) Batch 0.519 (0.527) Remain 01:22:34 loss: 0.2362 Lr: 0.00214 [2024-11-25 18:21:34,253 INFO misc.py line 119 2586773] Train: [25/50][368/376] Data 0.002 (0.002) Batch 0.537 (0.527) Remain 01:22:33 loss: 0.2231 Lr: 0.00214 [2024-11-25 18:21:34,736 INFO misc.py line 119 2586773] Train: [25/50][369/376] Data 0.002 (0.002) Batch 0.483 (0.526) Remain 01:22:32 loss: 0.2112 Lr: 0.00214 [2024-11-25 18:21:35,241 INFO misc.py line 119 2586773] Train: [25/50][370/376] Data 0.002 (0.002) Batch 0.506 (0.526) Remain 01:22:31 loss: 0.2131 Lr: 0.00214 [2024-11-25 18:21:35,744 INFO misc.py line 119 2586773] Train: [25/50][371/376] Data 0.002 (0.002) Batch 0.503 (0.526) Remain 01:22:29 loss: 0.2671 Lr: 0.00214 [2024-11-25 18:21:36,234 INFO misc.py line 119 2586773] Train: [25/50][372/376] Data 0.002 (0.002) Batch 0.490 (0.526) Remain 01:22:28 loss: 0.2247 Lr: 0.00214 [2024-11-25 18:21:36,739 INFO misc.py line 119 2586773] Train: [25/50][373/376] Data 0.002 (0.002) Batch 0.506 (0.526) Remain 01:22:27 loss: 0.2736 Lr: 0.00214 [2024-11-25 18:21:37,249 INFO misc.py line 119 2586773] Train: [25/50][374/376] Data 0.002 (0.002) Batch 0.509 (0.526) Remain 01:22:26 loss: 0.2629 Lr: 0.00214 [2024-11-25 18:21:37,712 INFO misc.py line 119 2586773] Train: [25/50][375/376] Data 0.002 (0.002) Batch 0.463 (0.526) Remain 01:22:24 loss: 0.2270 Lr: 0.00214 [2024-11-25 18:21:38,218 INFO misc.py line 119 2586773] Train: [25/50][376/376] Data 0.002 (0.002) Batch 0.506 (0.526) Remain 01:22:23 loss: 0.1880 Lr: 0.00213 [2024-11-25 18:21:38,218 INFO misc.py line 136 2586773] Train result: loss: 0.2244 [2024-11-25 18:21:38,219 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:21:48,950 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1817 [2024-11-25 18:21:49,218 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2162 [2024-11-25 18:21:49,482 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2752 [2024-11-25 18:21:49,713 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1880 [2024-11-25 18:21:49,976 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2915 [2024-11-25 18:21:50,243 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2039 [2024-11-25 18:21:50,466 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1995 [2024-11-25 18:21:50,736 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2570 [2024-11-25 18:21:50,962 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2620 [2024-11-25 18:21:51,223 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2388 [2024-11-25 18:21:51,454 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2086 [2024-11-25 18:21:51,726 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2353 [2024-11-25 18:21:51,991 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2533 [2024-11-25 18:21:52,253 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2485 [2024-11-25 18:21:52,484 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2385 [2024-11-25 18:21:52,728 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2950 [2024-11-25 18:21:52,995 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2831 [2024-11-25 18:21:53,242 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2033 [2024-11-25 18:21:53,472 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2355 [2024-11-25 18:21:53,732 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2583 [2024-11-25 18:21:53,966 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2463 [2024-11-25 18:21:54,239 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2730 [2024-11-25 18:21:54,477 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2062 [2024-11-25 18:21:54,744 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2461 [2024-11-25 18:21:55,006 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2316 [2024-11-25 18:21:55,243 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2599 [2024-11-25 18:21:55,496 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2567 [2024-11-25 18:21:55,741 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2376 [2024-11-25 18:21:56,008 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2782 [2024-11-25 18:21:56,261 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3189 [2024-11-25 18:21:56,495 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2616 [2024-11-25 18:21:56,758 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2190 [2024-11-25 18:21:56,982 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2707 [2024-11-25 18:21:57,222 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2329 [2024-11-25 18:21:57,483 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2142 [2024-11-25 18:21:57,728 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2633 [2024-11-25 18:21:57,957 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1977 [2024-11-25 18:21:58,226 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2426 [2024-11-25 18:21:58,457 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2614 [2024-11-25 18:21:58,691 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2561 [2024-11-25 18:21:58,959 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3024 [2024-11-25 18:21:59,210 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2826 [2024-11-25 18:21:59,447 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2466 [2024-11-25 18:21:59,679 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2403 [2024-11-25 18:21:59,915 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2410 [2024-11-25 18:22:00,167 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2414 [2024-11-25 18:22:00,427 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2496 [2024-11-25 18:22:00,677 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3009 [2024-11-25 18:22:00,901 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2124 [2024-11-25 18:22:01,136 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2108 [2024-11-25 18:22:01,359 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2621 [2024-11-25 18:22:01,613 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2570 [2024-11-25 18:22:01,880 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2449 [2024-11-25 18:22:02,150 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3050 [2024-11-25 18:22:02,382 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2579 [2024-11-25 18:22:02,632 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2314 [2024-11-25 18:22:02,888 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2559 [2024-11-25 18:22:03,164 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2804 [2024-11-25 18:22:03,418 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2463 [2024-11-25 18:22:03,677 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2634 [2024-11-25 18:22:03,930 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2188 [2024-11-25 18:22:04,201 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2498 [2024-11-25 18:22:04,430 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2471 [2024-11-25 18:22:04,693 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2595 [2024-11-25 18:22:04,965 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2526 [2024-11-25 18:22:05,228 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2042 [2024-11-25 18:22:05,473 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2201 [2024-11-25 18:22:05,731 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2621 [2024-11-25 18:22:06,000 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2393 [2024-11-25 18:22:06,262 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2891 [2024-11-25 18:22:06,518 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2213 [2024-11-25 18:22:06,763 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2765 [2024-11-25 18:22:07,018 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2560 [2024-11-25 18:22:07,270 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2656 [2024-11-25 18:22:07,490 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2575 [2024-11-25 18:22:07,721 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2051 [2024-11-25 18:22:07,999 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2432 [2024-11-25 18:22:08,241 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2408 [2024-11-25 18:22:08,499 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2281 [2024-11-25 18:22:08,752 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2997 [2024-11-25 18:22:08,994 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2271 [2024-11-25 18:22:09,260 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2694 [2024-11-25 18:22:09,509 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2052 [2024-11-25 18:22:09,765 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2522 [2024-11-25 18:22:10,033 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2480 [2024-11-25 18:22:10,271 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2522 [2024-11-25 18:22:10,534 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2548 [2024-11-25 18:22:10,794 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2354 [2024-11-25 18:22:11,050 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2795 [2024-11-25 18:22:11,307 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2631 [2024-11-25 18:22:11,548 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2597 [2024-11-25 18:22:11,801 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2633 [2024-11-25 18:22:12,067 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2548 [2024-11-25 18:22:12,341 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1938 [2024-11-25 18:22:12,608 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2361 [2024-11-25 18:22:12,861 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2129 [2024-11-25 18:22:13,131 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2526 [2024-11-25 18:22:13,350 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3077 [2024-11-25 18:22:13,622 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2468 [2024-11-25 18:22:13,861 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2661 [2024-11-25 18:22:14,130 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2104 [2024-11-25 18:22:14,391 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2637 [2024-11-25 18:22:14,652 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2685 [2024-11-25 18:22:14,903 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2883 [2024-11-25 18:22:15,126 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2452 [2024-11-25 18:22:15,361 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2361 [2024-11-25 18:22:15,619 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2346 [2024-11-25 18:22:15,887 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2797 [2024-11-25 18:22:16,120 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2655 [2024-11-25 18:22:16,381 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2250 [2024-11-25 18:22:16,644 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2408 [2024-11-25 18:22:16,873 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2355 [2024-11-25 18:22:17,108 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2084 [2024-11-25 18:22:17,330 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2206 [2024-11-25 18:22:17,555 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2237 [2024-11-25 18:22:17,828 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3237 [2024-11-25 18:22:18,089 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.3135 [2024-11-25 18:22:18,357 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2698 [2024-11-25 18:22:18,620 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2215 [2024-11-25 18:22:18,881 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3525 [2024-11-25 18:22:19,141 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2784 [2024-11-25 18:22:19,405 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2447 [2024-11-25 18:22:19,661 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2633 [2024-11-25 18:22:19,924 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2563 [2024-11-25 18:22:20,187 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2630 [2024-11-25 18:22:20,438 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2463 [2024-11-25 18:22:20,668 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1985 [2024-11-25 18:22:20,928 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2541 [2024-11-25 18:22:21,163 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2626 [2024-11-25 18:22:21,392 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1920 [2024-11-25 18:22:21,607 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2338 [2024-11-25 18:22:21,823 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2047 [2024-11-25 18:22:22,468 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7648/0.8315/0.9962. [2024-11-25 18:22:22,468 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9962/0.9984 [2024-11-25 18:22:22,468 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5334/0.6646 [2024-11-25 18:22:22,468 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:22:22,469 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7712 [2024-11-25 18:22:22,469 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:22:25,223 INFO misc.py line 119 2586773] Train: [26/50][1/376] Data 0.095 (0.095) Batch 0.586 (0.586) Remain 01:31:47 loss: 0.2161 Lr: 0.00213 [2024-11-25 18:22:25,753 INFO misc.py line 119 2586773] Train: [26/50][2/376] Data 0.002 (0.002) Batch 0.530 (0.530) Remain 01:23:02 loss: 0.2031 Lr: 0.00213 [2024-11-25 18:22:26,323 INFO misc.py line 119 2586773] Train: [26/50][3/376] Data 0.002 (0.002) Batch 0.570 (0.570) Remain 01:29:19 loss: 0.2152 Lr: 0.00213 [2024-11-25 18:22:26,860 INFO misc.py line 119 2586773] Train: [26/50][4/376] Data 0.002 (0.002) Batch 0.536 (0.536) Remain 01:23:59 loss: 0.1718 Lr: 0.00213 [2024-11-25 18:22:27,345 INFO misc.py line 119 2586773] Train: [26/50][5/376] Data 0.002 (0.002) Batch 0.486 (0.511) Remain 01:20:01 loss: 0.1699 Lr: 0.00213 [2024-11-25 18:22:27,851 INFO misc.py line 119 2586773] Train: [26/50][6/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 01:19:44 loss: 0.1926 Lr: 0.00213 [2024-11-25 18:22:28,341 INFO misc.py line 119 2586773] Train: [26/50][7/376] Data 0.002 (0.002) Batch 0.490 (0.504) Remain 01:18:58 loss: 0.2400 Lr: 0.00213 [2024-11-25 18:22:28,870 INFO misc.py line 119 2586773] Train: [26/50][8/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 01:19:44 loss: 0.2357 Lr: 0.00213 [2024-11-25 18:22:29,356 INFO misc.py line 119 2586773] Train: [26/50][9/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 01:19:07 loss: 0.1860 Lr: 0.00213 [2024-11-25 18:22:29,833 INFO misc.py line 119 2586773] Train: [26/50][10/376] Data 0.002 (0.002) Batch 0.476 (0.501) Remain 01:18:27 loss: 0.2428 Lr: 0.00213 [2024-11-25 18:22:30,358 INFO misc.py line 119 2586773] Train: [26/50][11/376] Data 0.003 (0.002) Batch 0.525 (0.504) Remain 01:18:55 loss: 0.3237 Lr: 0.00213 [2024-11-25 18:22:30,869 INFO misc.py line 119 2586773] Train: [26/50][12/376] Data 0.002 (0.002) Batch 0.511 (0.505) Remain 01:19:01 loss: 0.3216 Lr: 0.00213 [2024-11-25 18:22:31,441 INFO misc.py line 119 2586773] Train: [26/50][13/376] Data 0.002 (0.002) Batch 0.572 (0.512) Remain 01:20:04 loss: 0.1889 Lr: 0.00213 [2024-11-25 18:22:31,936 INFO misc.py line 119 2586773] Train: [26/50][14/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 01:19:48 loss: 0.2752 Lr: 0.00213 [2024-11-25 18:22:32,423 INFO misc.py line 119 2586773] Train: [26/50][15/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 01:19:30 loss: 0.2103 Lr: 0.00213 [2024-11-25 18:22:32,892 INFO misc.py line 119 2586773] Train: [26/50][16/376] Data 0.002 (0.002) Batch 0.469 (0.505) Remain 01:19:01 loss: 0.1945 Lr: 0.00213 [2024-11-25 18:22:33,401 INFO misc.py line 119 2586773] Train: [26/50][17/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 01:19:03 loss: 0.2039 Lr: 0.00213 [2024-11-25 18:22:33,889 INFO misc.py line 119 2586773] Train: [26/50][18/376] Data 0.002 (0.002) Batch 0.488 (0.504) Remain 01:18:52 loss: 0.2366 Lr: 0.00213 [2024-11-25 18:22:34,424 INFO misc.py line 119 2586773] Train: [26/50][19/376] Data 0.002 (0.002) Batch 0.535 (0.506) Remain 01:19:09 loss: 0.2400 Lr: 0.00213 [2024-11-25 18:22:34,928 INFO misc.py line 119 2586773] Train: [26/50][20/376] Data 0.002 (0.002) Batch 0.504 (0.506) Remain 01:19:07 loss: 0.1774 Lr: 0.00213 [2024-11-25 18:22:35,437 INFO misc.py line 119 2586773] Train: [26/50][21/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 01:19:08 loss: 0.2900 Lr: 0.00213 [2024-11-25 18:22:35,922 INFO misc.py line 119 2586773] Train: [26/50][22/376] Data 0.002 (0.002) Batch 0.486 (0.505) Remain 01:18:57 loss: 0.1891 Lr: 0.00213 [2024-11-25 18:22:36,457 INFO misc.py line 119 2586773] Train: [26/50][23/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 01:19:11 loss: 0.2360 Lr: 0.00213 [2024-11-25 18:22:36,926 INFO misc.py line 119 2586773] Train: [26/50][24/376] Data 0.002 (0.002) Batch 0.469 (0.505) Remain 01:18:53 loss: 0.2167 Lr: 0.00213 [2024-11-25 18:22:37,457 INFO misc.py line 119 2586773] Train: [26/50][25/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 01:19:04 loss: 0.1951 Lr: 0.00213 [2024-11-25 18:22:37,942 INFO misc.py line 119 2586773] Train: [26/50][26/376] Data 0.003 (0.002) Batch 0.485 (0.505) Remain 01:18:55 loss: 0.1805 Lr: 0.00213 [2024-11-25 18:22:38,486 INFO misc.py line 119 2586773] Train: [26/50][27/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 01:19:09 loss: 0.2346 Lr: 0.00213 [2024-11-25 18:22:38,995 INFO misc.py line 119 2586773] Train: [26/50][28/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:19:10 loss: 0.1943 Lr: 0.00213 [2024-11-25 18:22:39,528 INFO misc.py line 119 2586773] Train: [26/50][29/376] Data 0.002 (0.002) Batch 0.533 (0.508) Remain 01:19:19 loss: 0.2396 Lr: 0.00212 [2024-11-25 18:22:40,034 INFO misc.py line 119 2586773] Train: [26/50][30/376] Data 0.002 (0.002) Batch 0.506 (0.508) Remain 01:19:18 loss: 0.1882 Lr: 0.00212 [2024-11-25 18:22:40,532 INFO misc.py line 119 2586773] Train: [26/50][31/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:19:14 loss: 0.1917 Lr: 0.00212 [2024-11-25 18:22:41,021 INFO misc.py line 119 2586773] Train: [26/50][32/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:19:08 loss: 0.2006 Lr: 0.00212 [2024-11-25 18:22:41,534 INFO misc.py line 119 2586773] Train: [26/50][33/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 01:19:09 loss: 0.2074 Lr: 0.00212 [2024-11-25 18:22:42,049 INFO misc.py line 119 2586773] Train: [26/50][34/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:19:11 loss: 0.2075 Lr: 0.00212 [2024-11-25 18:22:42,607 INFO misc.py line 119 2586773] Train: [26/50][35/376] Data 0.002 (0.002) Batch 0.558 (0.509) Remain 01:19:25 loss: 0.2248 Lr: 0.00212 [2024-11-25 18:22:43,078 INFO misc.py line 119 2586773] Train: [26/50][36/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 01:19:14 loss: 0.2003 Lr: 0.00212 [2024-11-25 18:22:43,570 INFO misc.py line 119 2586773] Train: [26/50][37/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 01:19:09 loss: 0.1997 Lr: 0.00212 [2024-11-25 18:22:44,065 INFO misc.py line 119 2586773] Train: [26/50][38/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:19:05 loss: 0.2243 Lr: 0.00212 [2024-11-25 18:22:44,528 INFO misc.py line 119 2586773] Train: [26/50][39/376] Data 0.002 (0.002) Batch 0.463 (0.506) Remain 01:18:53 loss: 0.2163 Lr: 0.00212 [2024-11-25 18:22:45,022 INFO misc.py line 119 2586773] Train: [26/50][40/376] Data 0.002 (0.002) Batch 0.494 (0.505) Remain 01:18:50 loss: 0.1821 Lr: 0.00212 [2024-11-25 18:22:45,501 INFO misc.py line 119 2586773] Train: [26/50][41/376] Data 0.002 (0.002) Batch 0.479 (0.505) Remain 01:18:43 loss: 0.2037 Lr: 0.00212 [2024-11-25 18:22:46,012 INFO misc.py line 119 2586773] Train: [26/50][42/376] Data 0.002 (0.002) Batch 0.511 (0.505) Remain 01:18:44 loss: 0.2045 Lr: 0.00212 [2024-11-25 18:22:46,536 INFO misc.py line 119 2586773] Train: [26/50][43/376] Data 0.002 (0.002) Batch 0.525 (0.505) Remain 01:18:48 loss: 0.2054 Lr: 0.00212 [2024-11-25 18:22:47,071 INFO misc.py line 119 2586773] Train: [26/50][44/376] Data 0.002 (0.002) Batch 0.535 (0.506) Remain 01:18:54 loss: 0.1835 Lr: 0.00212 [2024-11-25 18:22:47,564 INFO misc.py line 119 2586773] Train: [26/50][45/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 01:18:51 loss: 0.2164 Lr: 0.00212 [2024-11-25 18:22:48,035 INFO misc.py line 119 2586773] Train: [26/50][46/376] Data 0.003 (0.002) Batch 0.471 (0.505) Remain 01:18:42 loss: 0.2321 Lr: 0.00212 [2024-11-25 18:22:48,538 INFO misc.py line 119 2586773] Train: [26/50][47/376] Data 0.002 (0.002) Batch 0.503 (0.505) Remain 01:18:42 loss: 0.2210 Lr: 0.00212 [2024-11-25 18:22:49,030 INFO misc.py line 119 2586773] Train: [26/50][48/376] Data 0.002 (0.002) Batch 0.492 (0.505) Remain 01:18:38 loss: 0.2154 Lr: 0.00212 [2024-11-25 18:22:49,517 INFO misc.py line 119 2586773] Train: [26/50][49/376] Data 0.002 (0.002) Batch 0.487 (0.504) Remain 01:18:34 loss: 0.2780 Lr: 0.00212 [2024-11-25 18:22:50,041 INFO misc.py line 119 2586773] Train: [26/50][50/376] Data 0.002 (0.002) Batch 0.524 (0.505) Remain 01:18:38 loss: 0.2295 Lr: 0.00212 [2024-11-25 18:22:50,516 INFO misc.py line 119 2586773] Train: [26/50][51/376] Data 0.002 (0.002) Batch 0.475 (0.504) Remain 01:18:32 loss: 0.2235 Lr: 0.00212 [2024-11-25 18:22:51,060 INFO misc.py line 119 2586773] Train: [26/50][52/376] Data 0.002 (0.002) Batch 0.543 (0.505) Remain 01:18:39 loss: 0.2196 Lr: 0.00212 [2024-11-25 18:22:51,544 INFO misc.py line 119 2586773] Train: [26/50][53/376] Data 0.002 (0.002) Batch 0.484 (0.504) Remain 01:18:34 loss: 0.2229 Lr: 0.00212 [2024-11-25 18:22:52,020 INFO misc.py line 119 2586773] Train: [26/50][54/376] Data 0.002 (0.002) Batch 0.476 (0.504) Remain 01:18:29 loss: 0.1912 Lr: 0.00212 [2024-11-25 18:22:52,577 INFO misc.py line 119 2586773] Train: [26/50][55/376] Data 0.002 (0.002) Batch 0.557 (0.505) Remain 01:18:38 loss: 0.2218 Lr: 0.00212 [2024-11-25 18:22:53,068 INFO misc.py line 119 2586773] Train: [26/50][56/376] Data 0.002 (0.002) Batch 0.491 (0.505) Remain 01:18:35 loss: 0.2640 Lr: 0.00212 [2024-11-25 18:22:53,555 INFO misc.py line 119 2586773] Train: [26/50][57/376] Data 0.002 (0.002) Batch 0.486 (0.504) Remain 01:18:31 loss: 0.1840 Lr: 0.00211 [2024-11-25 18:22:54,045 INFO misc.py line 119 2586773] Train: [26/50][58/376] Data 0.002 (0.002) Batch 0.490 (0.504) Remain 01:18:28 loss: 0.2006 Lr: 0.00211 [2024-11-25 18:22:54,577 INFO misc.py line 119 2586773] Train: [26/50][59/376] Data 0.002 (0.002) Batch 0.532 (0.505) Remain 01:18:32 loss: 0.2441 Lr: 0.00211 [2024-11-25 18:22:55,074 INFO misc.py line 119 2586773] 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Batch 0.545 (0.505) Remain 01:18:10 loss: 0.2112 Lr: 0.00209 [2024-11-25 18:23:23,905 INFO misc.py line 119 2586773] Train: [26/50][117/376] Data 0.002 (0.002) Batch 0.496 (0.505) Remain 01:18:08 loss: 0.2396 Lr: 0.00209 [2024-11-25 18:23:24,445 INFO misc.py line 119 2586773] Train: [26/50][118/376] Data 0.002 (0.002) Batch 0.540 (0.505) Remain 01:18:11 loss: 0.1776 Lr: 0.00209 [2024-11-25 18:23:24,927 INFO misc.py line 119 2586773] Train: [26/50][119/376] Data 0.002 (0.002) Batch 0.482 (0.505) Remain 01:18:08 loss: 0.2046 Lr: 0.00209 [2024-11-25 18:23:25,427 INFO misc.py line 119 2586773] Train: [26/50][120/376] Data 0.002 (0.002) Batch 0.499 (0.505) Remain 01:18:07 loss: 0.2101 Lr: 0.00209 [2024-11-25 18:23:25,918 INFO misc.py line 119 2586773] Train: [26/50][121/376] Data 0.004 (0.002) Batch 0.491 (0.505) Remain 01:18:06 loss: 0.2423 Lr: 0.00209 [2024-11-25 18:23:26,406 INFO misc.py line 119 2586773] Train: [26/50][122/376] Data 0.004 (0.002) Batch 0.488 (0.505) Remain 01:18:04 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Batch 0.542 (0.505) Remain 01:17:13 loss: 0.2015 Lr: 0.00206 [2024-11-25 18:24:20,533 INFO misc.py line 119 2586773] Train: [26/50][229/376] Data 0.003 (0.002) Batch 0.535 (0.505) Remain 01:17:14 loss: 0.2733 Lr: 0.00206 [2024-11-25 18:24:21,036 INFO misc.py line 119 2586773] Train: [26/50][230/376] Data 0.002 (0.002) Batch 0.503 (0.505) Remain 01:17:13 loss: 0.2816 Lr: 0.00205 [2024-11-25 18:24:21,535 INFO misc.py line 119 2586773] Train: [26/50][231/376] Data 0.002 (0.002) Batch 0.498 (0.505) Remain 01:17:13 loss: 0.2359 Lr: 0.00205 [2024-11-25 18:24:22,080 INFO misc.py line 119 2586773] Train: [26/50][232/376] Data 0.002 (0.002) Batch 0.546 (0.505) Remain 01:17:14 loss: 0.2520 Lr: 0.00205 [2024-11-25 18:24:22,582 INFO misc.py line 119 2586773] Train: [26/50][233/376] Data 0.003 (0.002) Batch 0.502 (0.505) Remain 01:17:13 loss: 0.2080 Lr: 0.00205 [2024-11-25 18:24:23,118 INFO misc.py line 119 2586773] Train: [26/50][234/376] Data 0.002 (0.002) Batch 0.536 (0.506) Remain 01:17:14 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line 119 2586773] Train: [26/50][328/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 01:16:34 loss: 0.2720 Lr: 0.00202 [2024-11-25 18:25:11,451 INFO misc.py line 119 2586773] Train: [26/50][329/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 01:16:34 loss: 0.2661 Lr: 0.00202 [2024-11-25 18:25:11,947 INFO misc.py line 119 2586773] Train: [26/50][330/376] Data 0.002 (0.002) Batch 0.496 (0.506) Remain 01:16:33 loss: 0.2269 Lr: 0.00202 [2024-11-25 18:25:12,479 INFO misc.py line 119 2586773] Train: [26/50][331/376] Data 0.003 (0.002) Batch 0.532 (0.507) Remain 01:16:34 loss: 0.3052 Lr: 0.00202 [2024-11-25 18:25:12,984 INFO misc.py line 119 2586773] Train: [26/50][332/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 01:16:33 loss: 0.1939 Lr: 0.00202 [2024-11-25 18:25:13,532 INFO misc.py line 119 2586773] Train: [26/50][333/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 01:16:34 loss: 0.2364 Lr: 0.00202 [2024-11-25 18:25:14,071 INFO misc.py line 119 2586773] Train: 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Batch 0.473 (0.507) Remain 01:16:29 loss: 0.2716 Lr: 0.00202 [2024-11-25 18:25:17,584 INFO misc.py line 119 2586773] Train: [26/50][341/376] Data 0.002 (0.002) Batch 0.544 (0.507) Remain 01:16:30 loss: 0.1940 Lr: 0.00202 [2024-11-25 18:25:18,098 INFO misc.py line 119 2586773] Train: [26/50][342/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 01:16:29 loss: 0.2238 Lr: 0.00202 [2024-11-25 18:25:18,617 INFO misc.py line 119 2586773] Train: [26/50][343/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 01:16:29 loss: 0.2375 Lr: 0.00202 [2024-11-25 18:25:19,098 INFO misc.py line 119 2586773] Train: [26/50][344/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 01:16:28 loss: 0.2010 Lr: 0.00202 [2024-11-25 18:25:19,622 INFO misc.py line 119 2586773] Train: [26/50][345/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 01:16:28 loss: 0.1973 Lr: 0.00201 [2024-11-25 18:25:20,114 INFO misc.py line 119 2586773] Train: [26/50][346/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 01:16:27 loss: 0.1947 Lr: 0.00201 [2024-11-25 18:25:20,612 INFO misc.py line 119 2586773] Train: [26/50][347/376] Data 0.003 (0.002) Batch 0.499 (0.507) Remain 01:16:26 loss: 0.2127 Lr: 0.00201 [2024-11-25 18:25:21,121 INFO misc.py line 119 2586773] Train: [26/50][348/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 01:16:26 loss: 0.2319 Lr: 0.00201 [2024-11-25 18:25:21,672 INFO misc.py line 119 2586773] Train: [26/50][349/376] Data 0.002 (0.002) Batch 0.551 (0.507) Remain 01:16:26 loss: 0.2217 Lr: 0.00201 [2024-11-25 18:25:22,161 INFO misc.py line 119 2586773] Train: [26/50][350/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 01:16:25 loss: 0.2294 Lr: 0.00201 [2024-11-25 18:25:22,677 INFO misc.py line 119 2586773] Train: [26/50][351/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 01:16:25 loss: 0.1796 Lr: 0.00201 [2024-11-25 18:25:23,151 INFO misc.py line 119 2586773] Train: [26/50][352/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 01:16:24 loss: 0.1940 Lr: 0.00201 [2024-11-25 18:25:23,690 INFO misc.py line 119 2586773] Train: [26/50][353/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 01:16:24 loss: 0.1855 Lr: 0.00201 [2024-11-25 18:25:24,210 INFO misc.py line 119 2586773] Train: [26/50][354/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 01:16:24 loss: 0.2200 Lr: 0.00201 [2024-11-25 18:25:24,745 INFO misc.py line 119 2586773] Train: [26/50][355/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 01:16:24 loss: 0.2208 Lr: 0.00201 [2024-11-25 18:25:25,264 INFO misc.py line 119 2586773] Train: [26/50][356/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:16:24 loss: 0.3277 Lr: 0.00201 [2024-11-25 18:25:25,748 INFO misc.py line 119 2586773] Train: [26/50][357/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 01:16:23 loss: 0.2039 Lr: 0.00201 [2024-11-25 18:25:26,244 INFO misc.py line 119 2586773] Train: [26/50][358/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:16:22 loss: 0.2125 Lr: 0.00201 [2024-11-25 18:25:26,783 INFO misc.py line 119 2586773] Train: [26/50][359/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 01:16:22 loss: 0.1875 Lr: 0.00201 [2024-11-25 18:25:27,261 INFO misc.py line 119 2586773] Train: [26/50][360/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 01:16:21 loss: 0.2016 Lr: 0.00201 [2024-11-25 18:25:27,768 INFO misc.py line 119 2586773] Train: [26/50][361/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:16:21 loss: 0.2504 Lr: 0.00201 [2024-11-25 18:25:28,212 INFO misc.py line 119 2586773] Train: [26/50][362/376] Data 0.002 (0.002) Batch 0.444 (0.507) Remain 01:16:19 loss: 0.2599 Lr: 0.00201 [2024-11-25 18:25:28,690 INFO misc.py line 119 2586773] Train: [26/50][363/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 01:16:17 loss: 0.1968 Lr: 0.00201 [2024-11-25 18:25:29,191 INFO misc.py line 119 2586773] Train: [26/50][364/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 01:16:17 loss: 0.1872 Lr: 0.00201 [2024-11-25 18:25:29,730 INFO misc.py line 119 2586773] Train: [26/50][365/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 01:16:17 loss: 0.2055 Lr: 0.00201 [2024-11-25 18:25:30,222 INFO misc.py line 119 2586773] Train: [26/50][366/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 01:16:16 loss: 0.2079 Lr: 0.00201 [2024-11-25 18:25:30,741 INFO misc.py line 119 2586773] Train: [26/50][367/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 01:16:16 loss: 0.2316 Lr: 0.00201 [2024-11-25 18:25:31,257 INFO misc.py line 119 2586773] Train: [26/50][368/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 01:16:16 loss: 0.2379 Lr: 0.00201 [2024-11-25 18:25:31,807 INFO misc.py line 119 2586773] Train: [26/50][369/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 01:16:16 loss: 0.2107 Lr: 0.00201 [2024-11-25 18:25:32,346 INFO misc.py line 119 2586773] Train: [26/50][370/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 01:16:17 loss: 0.2115 Lr: 0.00201 [2024-11-25 18:25:32,892 INFO misc.py line 119 2586773] Train: [26/50][371/376] Data 0.002 (0.002) Batch 0.547 (0.507) Remain 01:16:17 loss: 0.2188 Lr: 0.00201 [2024-11-25 18:25:33,391 INFO misc.py line 119 2586773] Train: [26/50][372/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 01:16:16 loss: 0.1952 Lr: 0.00201 [2024-11-25 18:25:33,918 INFO misc.py line 119 2586773] Train: [26/50][373/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 01:16:16 loss: 0.2265 Lr: 0.00201 [2024-11-25 18:25:34,418 INFO misc.py line 119 2586773] Train: [26/50][374/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 01:16:16 loss: 0.2228 Lr: 0.00200 [2024-11-25 18:25:34,919 INFO misc.py line 119 2586773] Train: [26/50][375/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 01:16:15 loss: 0.2832 Lr: 0.00200 [2024-11-25 18:25:35,407 INFO misc.py line 119 2586773] Train: [26/50][376/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:16:14 loss: 0.2143 Lr: 0.00200 [2024-11-25 18:25:35,407 INFO misc.py line 136 2586773] Train result: loss: 0.2207 [2024-11-25 18:25:35,408 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:25:46,470 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1899 [2024-11-25 18:25:46,724 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2122 [2024-11-25 18:25:46,985 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2427 [2024-11-25 18:25:47,210 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1902 [2024-11-25 18:25:47,471 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2660 [2024-11-25 18:25:47,742 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2017 [2024-11-25 18:25:47,965 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1889 [2024-11-25 18:25:48,236 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2002 [2024-11-25 18:25:48,461 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2257 [2024-11-25 18:25:48,723 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2392 [2024-11-25 18:25:48,957 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2038 [2024-11-25 18:25:49,229 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.1976 [2024-11-25 18:25:49,494 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2431 [2024-11-25 18:25:49,757 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2512 [2024-11-25 18:25:49,994 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2437 [2024-11-25 18:25:50,234 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2865 [2024-11-25 18:25:50,500 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2612 [2024-11-25 18:25:50,753 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1982 [2024-11-25 18:25:50,985 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2217 [2024-11-25 18:25:51,248 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2298 [2024-11-25 18:25:51,480 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2232 [2024-11-25 18:25:51,747 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2656 [2024-11-25 18:25:51,985 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2169 [2024-11-25 18:25:52,252 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2386 [2024-11-25 18:25:52,515 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2259 [2024-11-25 18:25:52,752 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2302 [2024-11-25 18:25:53,004 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2439 [2024-11-25 18:25:53,252 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2492 [2024-11-25 18:25:53,524 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2750 [2024-11-25 18:25:53,776 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2966 [2024-11-25 18:25:54,011 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2568 [2024-11-25 18:25:54,274 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2033 [2024-11-25 18:25:54,492 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2144 [2024-11-25 18:25:54,733 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2126 [2024-11-25 18:25:54,993 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2054 [2024-11-25 18:25:55,238 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2630 [2024-11-25 18:25:55,466 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1979 [2024-11-25 18:25:55,734 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2350 [2024-11-25 18:25:55,963 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2491 [2024-11-25 18:25:56,199 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2319 [2024-11-25 18:25:56,468 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2787 [2024-11-25 18:25:56,728 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2721 [2024-11-25 18:25:56,965 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2490 [2024-11-25 18:25:57,198 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2096 [2024-11-25 18:25:57,449 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2087 [2024-11-25 18:25:57,695 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2308 [2024-11-25 18:25:57,954 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2395 [2024-11-25 18:25:58,203 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3009 [2024-11-25 18:25:58,431 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2182 [2024-11-25 18:25:58,666 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2033 [2024-11-25 18:25:58,888 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2237 [2024-11-25 18:25:59,142 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2568 [2024-11-25 18:25:59,409 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2077 [2024-11-25 18:25:59,670 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2612 [2024-11-25 18:25:59,902 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2483 [2024-11-25 18:26:00,146 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2289 [2024-11-25 18:26:00,405 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2302 [2024-11-25 18:26:00,680 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2540 [2024-11-25 18:26:00,935 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2599 [2024-11-25 18:26:01,196 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2431 [2024-11-25 18:26:01,451 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2328 [2024-11-25 18:26:01,721 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2410 [2024-11-25 18:26:01,951 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2476 [2024-11-25 18:26:02,208 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2442 [2024-11-25 18:26:02,473 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2347 [2024-11-25 18:26:02,748 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1935 [2024-11-25 18:26:02,994 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1936 [2024-11-25 18:26:03,248 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2646 [2024-11-25 18:26:03,516 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2119 [2024-11-25 18:26:03,776 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2743 [2024-11-25 18:26:04,022 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2491 [2024-11-25 18:26:04,258 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2790 [2024-11-25 18:26:04,516 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2514 [2024-11-25 18:26:04,760 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2631 [2024-11-25 18:26:04,976 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2306 [2024-11-25 18:26:05,198 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2073 [2024-11-25 18:26:05,468 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2465 [2024-11-25 18:26:05,705 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2151 [2024-11-25 18:26:05,965 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2157 [2024-11-25 18:26:06,217 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2996 [2024-11-25 18:26:06,459 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2214 [2024-11-25 18:26:06,721 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2323 [2024-11-25 18:26:06,969 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1933 [2024-11-25 18:26:07,217 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2531 [2024-11-25 18:26:07,489 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2415 [2024-11-25 18:26:07,726 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2542 [2024-11-25 18:26:07,989 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2396 [2024-11-25 18:26:08,249 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2280 [2024-11-25 18:26:08,495 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2564 [2024-11-25 18:26:08,751 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2599 [2024-11-25 18:26:08,985 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2449 [2024-11-25 18:26:09,239 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2633 [2024-11-25 18:26:09,508 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2536 [2024-11-25 18:26:09,771 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2046 [2024-11-25 18:26:10,038 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2130 [2024-11-25 18:26:10,286 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2172 [2024-11-25 18:26:10,558 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2253 [2024-11-25 18:26:10,784 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2685 [2024-11-25 18:26:11,055 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2326 [2024-11-25 18:26:11,291 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2637 [2024-11-25 18:26:11,560 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2250 [2024-11-25 18:26:11,821 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2424 [2024-11-25 18:26:12,082 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2678 [2024-11-25 18:26:12,338 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2863 [2024-11-25 18:26:12,559 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2108 [2024-11-25 18:26:12,802 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2120 [2024-11-25 18:26:13,058 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2112 [2024-11-25 18:26:13,326 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2464 [2024-11-25 18:26:13,559 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2396 [2024-11-25 18:26:13,821 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2228 [2024-11-25 18:26:14,086 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2064 [2024-11-25 18:26:14,309 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2219 [2024-11-25 18:26:14,547 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2089 [2024-11-25 18:26:14,772 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2199 [2024-11-25 18:26:14,999 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2231 [2024-11-25 18:26:15,270 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2521 [2024-11-25 18:26:15,532 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2701 [2024-11-25 18:26:15,808 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2463 [2024-11-25 18:26:16,075 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2265 [2024-11-25 18:26:16,336 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2427 [2024-11-25 18:26:16,596 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2738 [2024-11-25 18:26:16,859 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1756 [2024-11-25 18:26:17,115 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2702 [2024-11-25 18:26:17,380 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2418 [2024-11-25 18:26:17,641 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2498 [2024-11-25 18:26:17,892 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2461 [2024-11-25 18:26:18,121 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2088 [2024-11-25 18:26:18,381 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2705 [2024-11-25 18:26:18,619 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2481 [2024-11-25 18:26:18,846 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2000 [2024-11-25 18:26:19,058 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2266 [2024-11-25 18:26:19,277 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2019 [2024-11-25 18:26:19,962 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7775/0.8550/0.9963. [2024-11-25 18:26:19,963 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 18:26:19,963 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5588/0.7118 [2024-11-25 18:26:19,963 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:26:19,964 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7775 [2024-11-25 18:26:19,964 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:26:19,964 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:26:24,535 INFO misc.py line 119 2586773] Train: [27/50][1/376] Data 0.106 (0.106) Batch 0.644 (0.644) Remain 01:36:51 loss: 0.2497 Lr: 0.00200 [2024-11-25 18:26:25,057 INFO misc.py line 119 2586773] Train: [27/50][2/376] Data 0.002 (0.002) Batch 0.522 (0.522) Remain 01:18:26 loss: 0.2585 Lr: 0.00200 [2024-11-25 18:26:25,594 INFO misc.py line 119 2586773] Train: [27/50][3/376] Data 0.002 (0.002) Batch 0.537 (0.537) Remain 01:20:44 loss: 0.2232 Lr: 0.00200 [2024-11-25 18:26:26,080 INFO misc.py line 119 2586773] Train: [27/50][4/376] Data 0.003 (0.003) Batch 0.486 (0.486) Remain 01:13:06 loss: 0.2309 Lr: 0.00200 [2024-11-25 18:26:26,633 INFO misc.py line 119 2586773] Train: [27/50][5/376] Data 0.003 (0.003) Batch 0.553 (0.519) Remain 01:18:05 loss: 0.2565 Lr: 0.00200 [2024-11-25 18:26:27,137 INFO misc.py line 119 2586773] Train: [27/50][6/376] Data 0.002 (0.002) Batch 0.504 (0.514) Remain 01:17:17 loss: 0.1915 Lr: 0.00200 [2024-11-25 18:26:27,648 INFO misc.py line 119 2586773] Train: [27/50][7/376] Data 0.002 (0.002) Batch 0.512 (0.514) Remain 01:17:11 loss: 0.2027 Lr: 0.00200 [2024-11-25 18:26:28,154 INFO misc.py line 119 2586773] Train: [27/50][8/376] Data 0.002 (0.002) Batch 0.506 (0.512) Remain 01:16:57 loss: 0.1801 Lr: 0.00200 [2024-11-25 18:26:28,704 INFO misc.py line 119 2586773] Train: [27/50][9/376] Data 0.003 (0.002) Batch 0.550 (0.518) Remain 01:17:53 loss: 0.1911 Lr: 0.00200 [2024-11-25 18:26:29,197 INFO misc.py line 119 2586773] Train: [27/50][10/376] Data 0.002 (0.002) Batch 0.493 (0.515) Remain 01:17:20 loss: 0.1943 Lr: 0.00200 [2024-11-25 18:26:29,735 INFO misc.py line 119 2586773] Train: [27/50][11/376] Data 0.002 (0.002) Batch 0.538 (0.518) Remain 01:17:45 loss: 0.2520 Lr: 0.00200 [2024-11-25 18:26:30,263 INFO misc.py line 119 2586773] Train: [27/50][12/376] Data 0.002 (0.002) Batch 0.528 (0.519) Remain 01:17:55 loss: 0.1998 Lr: 0.00200 [2024-11-25 18:26:30,792 INFO misc.py line 119 2586773] Train: [27/50][13/376] Data 0.002 (0.002) Batch 0.529 (0.520) Remain 01:18:04 loss: 0.2597 Lr: 0.00200 [2024-11-25 18:26:31,302 INFO misc.py line 119 2586773] Train: [27/50][14/376] Data 0.002 (0.002) Batch 0.509 (0.519) Remain 01:17:55 loss: 0.2513 Lr: 0.00200 [2024-11-25 18:26:31,769 INFO misc.py line 119 2586773] Train: [27/50][15/376] Data 0.003 (0.002) Batch 0.468 (0.515) Remain 01:17:16 loss: 0.2044 Lr: 0.00200 [2024-11-25 18:26:32,292 INFO misc.py line 119 2586773] Train: [27/50][16/376] Data 0.002 (0.002) Batch 0.522 (0.515) Remain 01:17:20 loss: 0.2090 Lr: 0.00200 [2024-11-25 18:26:32,830 INFO misc.py line 119 2586773] Train: [27/50][17/376] Data 0.002 (0.002) Batch 0.539 (0.517) Remain 01:17:35 loss: 0.2040 Lr: 0.00200 [2024-11-25 18:26:33,329 INFO misc.py line 119 2586773] Train: [27/50][18/376] Data 0.002 (0.002) Batch 0.498 (0.516) Remain 01:17:23 loss: 0.1784 Lr: 0.00200 [2024-11-25 18:26:33,851 INFO misc.py line 119 2586773] Train: [27/50][19/376] Data 0.003 (0.002) Batch 0.523 (0.516) Remain 01:17:27 loss: 0.1752 Lr: 0.00200 [2024-11-25 18:26:34,373 INFO misc.py line 119 2586773] Train: [27/50][20/376] Data 0.003 (0.002) Batch 0.522 (0.516) Remain 01:17:29 loss: 0.2971 Lr: 0.00200 [2024-11-25 18:26:34,886 INFO misc.py line 119 2586773] Train: [27/50][21/376] Data 0.002 (0.002) Batch 0.513 (0.516) Remain 01:17:27 loss: 0.2275 Lr: 0.00200 [2024-11-25 18:26:35,420 INFO misc.py line 119 2586773] Train: [27/50][22/376] Data 0.003 (0.002) Batch 0.535 (0.517) Remain 01:17:35 loss: 0.1965 Lr: 0.00200 [2024-11-25 18:26:35,891 INFO misc.py line 119 2586773] Train: [27/50][23/376] Data 0.002 (0.002) Batch 0.471 (0.515) Remain 01:17:14 loss: 0.2193 Lr: 0.00200 [2024-11-25 18:26:36,383 INFO misc.py line 119 2586773] Train: [27/50][24/376] Data 0.002 (0.002) Batch 0.492 (0.514) Remain 01:17:04 loss: 0.2038 Lr: 0.00200 [2024-11-25 18:26:36,886 INFO misc.py line 119 2586773] Train: [27/50][25/376] Data 0.002 (0.002) Batch 0.502 (0.513) Remain 01:16:58 loss: 0.1658 Lr: 0.00200 [2024-11-25 18:26:37,383 INFO misc.py line 119 2586773] Train: [27/50][26/376] Data 0.003 (0.002) Batch 0.497 (0.513) Remain 01:16:52 loss: 0.2127 Lr: 0.00200 [2024-11-25 18:26:37,921 INFO misc.py line 119 2586773] Train: [27/50][27/376] Data 0.003 (0.002) Batch 0.538 (0.514) Remain 01:17:01 loss: 0.2214 Lr: 0.00199 [2024-11-25 18:26:38,437 INFO misc.py line 119 2586773] Train: [27/50][28/376] Data 0.003 (0.002) Batch 0.516 (0.514) Remain 01:17:01 loss: 0.2279 Lr: 0.00199 [2024-11-25 18:26:38,939 INFO misc.py line 119 2586773] Train: [27/50][29/376] Data 0.002 (0.002) Batch 0.502 (0.513) Remain 01:16:56 loss: 0.1841 Lr: 0.00199 [2024-11-25 18:26:39,473 INFO misc.py line 119 2586773] Train: [27/50][30/376] Data 0.002 (0.002) Batch 0.533 (0.514) Remain 01:17:03 loss: 0.1895 Lr: 0.00199 [2024-11-25 18:26:39,968 INFO misc.py line 119 2586773] Train: [27/50][31/376] Data 0.002 (0.002) Batch 0.495 (0.513) Remain 01:16:56 loss: 0.1934 Lr: 0.00199 [2024-11-25 18:26:40,504 INFO misc.py line 119 2586773] Train: [27/50][32/376] Data 0.002 (0.002) Batch 0.536 (0.514) Remain 01:17:03 loss: 0.2130 Lr: 0.00199 [2024-11-25 18:26:41,017 INFO misc.py line 119 2586773] Train: [27/50][33/376] Data 0.003 (0.002) Batch 0.513 (0.514) Remain 01:17:02 loss: 0.1909 Lr: 0.00199 [2024-11-25 18:26:41,524 INFO misc.py line 119 2586773] Train: [27/50][34/376] Data 0.003 (0.002) Batch 0.507 (0.514) Remain 01:16:59 loss: 0.2070 Lr: 0.00199 [2024-11-25 18:26:42,023 INFO misc.py line 119 2586773] Train: [27/50][35/376] Data 0.003 (0.002) Batch 0.499 (0.513) Remain 01:16:55 loss: 0.1943 Lr: 0.00199 [2024-11-25 18:26:42,533 INFO misc.py line 119 2586773] Train: [27/50][36/376] Data 0.002 (0.002) Batch 0.510 (0.513) Remain 01:16:53 loss: 0.2314 Lr: 0.00199 [2024-11-25 18:26:43,040 INFO misc.py line 119 2586773] Train: [27/50][37/376] Data 0.002 (0.002) Batch 0.506 (0.513) Remain 01:16:51 loss: 0.2189 Lr: 0.00199 [2024-11-25 18:26:43,528 INFO misc.py line 119 2586773] Train: [27/50][38/376] Data 0.002 (0.002) Batch 0.488 (0.512) Remain 01:16:44 loss: 0.2821 Lr: 0.00199 [2024-11-25 18:26:44,016 INFO misc.py line 119 2586773] Train: [27/50][39/376] Data 0.002 (0.002) Batch 0.488 (0.512) Remain 01:16:37 loss: 0.2117 Lr: 0.00199 [2024-11-25 18:26:44,557 INFO misc.py line 119 2586773] Train: [27/50][40/376] Data 0.002 (0.002) Batch 0.542 (0.513) Remain 01:16:44 loss: 0.2000 Lr: 0.00199 [2024-11-25 18:26:45,087 INFO misc.py line 119 2586773] Train: [27/50][41/376] Data 0.002 (0.002) Batch 0.530 (0.513) Remain 01:16:48 loss: 0.2296 Lr: 0.00199 [2024-11-25 18:26:45,566 INFO misc.py line 119 2586773] Train: [27/50][42/376] Data 0.002 (0.002) Batch 0.478 (0.512) Remain 01:16:39 loss: 0.2191 Lr: 0.00199 [2024-11-25 18:26:46,093 INFO misc.py line 119 2586773] Train: [27/50][43/376] Data 0.003 (0.002) Batch 0.528 (0.512) Remain 01:16:42 loss: 0.2396 Lr: 0.00199 [2024-11-25 18:26:46,578 INFO misc.py line 119 2586773] Train: [27/50][44/376] Data 0.003 (0.002) Batch 0.484 (0.512) Remain 01:16:35 loss: 0.1829 Lr: 0.00199 [2024-11-25 18:26:47,074 INFO misc.py line 119 2586773] Train: [27/50][45/376] Data 0.002 (0.002) Batch 0.496 (0.511) Remain 01:16:32 loss: 0.1670 Lr: 0.00199 [2024-11-25 18:26:47,556 INFO misc.py line 119 2586773] Train: [27/50][46/376] Data 0.003 (0.002) Batch 0.483 (0.511) Remain 01:16:25 loss: 0.1967 Lr: 0.00199 [2024-11-25 18:26:48,088 INFO misc.py line 119 2586773] Train: [27/50][47/376] Data 0.002 (0.002) Batch 0.532 (0.511) Remain 01:16:29 loss: 0.2276 Lr: 0.00199 [2024-11-25 18:26:48,582 INFO misc.py line 119 2586773] Train: [27/50][48/376] Data 0.002 (0.002) Batch 0.493 (0.511) Remain 01:16:25 loss: 0.1928 Lr: 0.00199 [2024-11-25 18:26:49,093 INFO misc.py line 119 2586773] Train: [27/50][49/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 01:16:24 loss: 0.2040 Lr: 0.00199 [2024-11-25 18:26:49,595 INFO misc.py line 119 2586773] Train: [27/50][50/376] Data 0.003 (0.002) Batch 0.502 (0.511) Remain 01:16:22 loss: 0.2243 Lr: 0.00199 [2024-11-25 18:26:50,141 INFO misc.py line 119 2586773] Train: [27/50][51/376] Data 0.002 (0.002) Batch 0.547 (0.511) Remain 01:16:28 loss: 0.2249 Lr: 0.00199 [2024-11-25 18:26:50,652 INFO misc.py line 119 2586773] Train: [27/50][52/376] Data 0.003 (0.002) Batch 0.511 (0.511) Remain 01:16:28 loss: 0.1746 Lr: 0.00199 [2024-11-25 18:26:51,161 INFO misc.py line 119 2586773] Train: [27/50][53/376] Data 0.003 (0.002) Batch 0.509 (0.511) Remain 01:16:27 loss: 0.2139 Lr: 0.00199 [2024-11-25 18:26:51,655 INFO misc.py line 119 2586773] Train: [27/50][54/376] Data 0.003 (0.002) Batch 0.494 (0.511) Remain 01:16:23 loss: 0.2514 Lr: 0.00199 [2024-11-25 18:26:52,135 INFO misc.py line 119 2586773] Train: [27/50][55/376] Data 0.002 (0.002) Batch 0.479 (0.510) Remain 01:16:17 loss: 0.1814 Lr: 0.00198 [2024-11-25 18:26:52,636 INFO misc.py line 119 2586773] Train: [27/50][56/376] Data 0.003 (0.002) Batch 0.502 (0.510) Remain 01:16:15 loss: 0.1999 Lr: 0.00198 [2024-11-25 18:26:53,172 INFO misc.py line 119 2586773] Train: [27/50][57/376] Data 0.002 (0.002) Batch 0.535 (0.511) Remain 01:16:19 loss: 0.2262 Lr: 0.00198 [2024-11-25 18:26:53,689 INFO misc.py line 119 2586773] Train: [27/50][58/376] Data 0.003 (0.002) Batch 0.518 (0.511) Remain 01:16:20 loss: 0.1786 Lr: 0.00198 [2024-11-25 18:26:54,204 INFO misc.py line 119 2586773] Train: [27/50][59/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:16:20 loss: 0.2044 Lr: 0.00198 [2024-11-25 18:26:54,681 INFO misc.py line 119 2586773] Train: [27/50][60/376] Data 0.002 (0.002) Batch 0.476 (0.510) Remain 01:16:14 loss: 0.1848 Lr: 0.00198 [2024-11-25 18:26:55,162 INFO misc.py line 119 2586773] Train: [27/50][61/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 01:16:09 loss: 0.1985 Lr: 0.00198 [2024-11-25 18:26:55,654 INFO misc.py line 119 2586773] Train: [27/50][62/376] Data 0.002 (0.002) Batch 0.492 (0.509) Remain 01:16:06 loss: 0.2140 Lr: 0.00198 [2024-11-25 18:26:56,152 INFO misc.py line 119 2586773] Train: [27/50][63/376] Data 0.002 (0.002) Batch 0.497 (0.509) Remain 01:16:03 loss: 0.2347 Lr: 0.00198 [2024-11-25 18:26:56,686 INFO misc.py line 119 2586773] Train: [27/50][64/376] Data 0.003 (0.002) Batch 0.534 (0.510) Remain 01:16:06 loss: 0.2245 Lr: 0.00198 [2024-11-25 18:26:57,222 INFO misc.py line 119 2586773] Train: [27/50][65/376] Data 0.002 (0.002) Batch 0.537 (0.510) Remain 01:16:10 loss: 0.2680 Lr: 0.00198 [2024-11-25 18:26:57,745 INFO misc.py line 119 2586773] Train: [27/50][66/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 01:16:11 loss: 0.2232 Lr: 0.00198 [2024-11-25 18:26:58,279 INFO misc.py line 119 2586773] Train: [27/50][67/376] Data 0.002 (0.002) Batch 0.534 (0.511) Remain 01:16:14 loss: 0.2202 Lr: 0.00198 [2024-11-25 18:26:58,759 INFO misc.py line 119 2586773] Train: [27/50][68/376] Data 0.002 (0.002) Batch 0.480 (0.510) Remain 01:16:09 loss: 0.1875 Lr: 0.00198 [2024-11-25 18:26:59,283 INFO misc.py line 119 2586773] Train: [27/50][69/376] Data 0.002 (0.002) Batch 0.525 (0.510) Remain 01:16:10 loss: 0.1863 Lr: 0.00198 [2024-11-25 18:26:59,797 INFO misc.py line 119 2586773] Train: [27/50][70/376] Data 0.002 (0.002) Batch 0.514 (0.510) Remain 01:16:10 loss: 0.2009 Lr: 0.00198 [2024-11-25 18:27:00,311 INFO misc.py line 119 2586773] Train: [27/50][71/376] Data 0.002 (0.002) Batch 0.514 (0.511) Remain 01:16:10 loss: 0.2062 Lr: 0.00198 [2024-11-25 18:27:00,873 INFO misc.py line 119 2586773] Train: [27/50][72/376] Data 0.002 (0.002) Batch 0.562 (0.511) Remain 01:16:17 loss: 0.2565 Lr: 0.00198 [2024-11-25 18:27:01,403 INFO misc.py line 119 2586773] Train: [27/50][73/376] Data 0.002 (0.002) Batch 0.530 (0.512) Remain 01:16:18 loss: 0.2599 Lr: 0.00198 [2024-11-25 18:27:01,926 INFO misc.py line 119 2586773] Train: [27/50][74/376] Data 0.002 (0.002) Batch 0.523 (0.512) Remain 01:16:19 loss: 0.1655 Lr: 0.00198 [2024-11-25 18:27:02,425 INFO misc.py line 119 2586773] Train: [27/50][75/376] Data 0.002 (0.002) Batch 0.499 (0.512) Remain 01:16:17 loss: 0.2013 Lr: 0.00198 [2024-11-25 18:27:02,903 INFO misc.py line 119 2586773] Train: [27/50][76/376] Data 0.002 (0.002) Batch 0.478 (0.511) Remain 01:16:13 loss: 0.2448 Lr: 0.00198 [2024-11-25 18:27:03,408 INFO misc.py line 119 2586773] Train: [27/50][77/376] Data 0.002 (0.002) Batch 0.505 (0.511) Remain 01:16:11 loss: 0.1973 Lr: 0.00198 [2024-11-25 18:27:03,944 INFO misc.py line 119 2586773] Train: [27/50][78/376] Data 0.003 (0.002) Batch 0.536 (0.511) Remain 01:16:14 loss: 0.1902 Lr: 0.00198 [2024-11-25 18:27:04,486 INFO misc.py line 119 2586773] Train: [27/50][79/376] Data 0.002 (0.002) Batch 0.542 (0.512) Remain 01:16:17 loss: 0.2204 Lr: 0.00198 [2024-11-25 18:27:04,993 INFO misc.py line 119 2586773] Train: [27/50][80/376] Data 0.003 (0.002) Batch 0.507 (0.512) Remain 01:16:16 loss: 0.2334 Lr: 0.00198 [2024-11-25 18:27:05,474 INFO misc.py line 119 2586773] Train: [27/50][81/376] Data 0.002 (0.002) Batch 0.481 (0.511) Remain 01:16:12 loss: 0.2219 Lr: 0.00198 [2024-11-25 18:27:05,963 INFO misc.py line 119 2586773] Train: [27/50][82/376] Data 0.002 (0.002) Batch 0.488 (0.511) Remain 01:16:09 loss: 0.2185 Lr: 0.00198 [2024-11-25 18:27:06,461 INFO misc.py line 119 2586773] Train: [27/50][83/376] Data 0.003 (0.002) Batch 0.499 (0.511) Remain 01:16:07 loss: 0.2399 Lr: 0.00198 [2024-11-25 18:27:07,006 INFO misc.py line 119 2586773] Train: [27/50][84/376] Data 0.003 (0.002) Batch 0.545 (0.511) Remain 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Batch 0.511 (0.509) Remain 01:15:08 loss: 0.2241 Lr: 0.00195 [2024-11-25 18:27:45,455 INFO misc.py line 119 2586773] Train: [27/50][160/376] Data 0.002 (0.002) Batch 0.515 (0.509) Remain 01:15:08 loss: 0.1767 Lr: 0.00195 [2024-11-25 18:27:45,908 INFO misc.py line 119 2586773] Train: [27/50][161/376] Data 0.002 (0.002) Batch 0.452 (0.508) Remain 01:15:05 loss: 0.2263 Lr: 0.00195 [2024-11-25 18:27:46,432 INFO misc.py line 119 2586773] Train: [27/50][162/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 01:15:05 loss: 0.1652 Lr: 0.00195 [2024-11-25 18:27:46,974 INFO misc.py line 119 2586773] Train: [27/50][163/376] Data 0.002 (0.002) Batch 0.542 (0.509) Remain 01:15:06 loss: 0.2294 Lr: 0.00195 [2024-11-25 18:27:47,464 INFO misc.py line 119 2586773] Train: [27/50][164/376] Data 0.002 (0.002) Batch 0.490 (0.509) Remain 01:15:05 loss: 0.2024 Lr: 0.00195 [2024-11-25 18:27:47,990 INFO misc.py line 119 2586773] Train: [27/50][165/376] Data 0.002 (0.002) Batch 0.526 (0.509) Remain 01:15:05 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0.002 (0.002) Batch 0.519 (0.508) Remain 01:13:27 loss: 0.2203 Lr: 0.00188 [2024-11-25 18:29:23,454 INFO misc.py line 119 2586773] Train: [27/50][353/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 01:13:26 loss: 0.2090 Lr: 0.00188 [2024-11-25 18:29:23,946 INFO misc.py line 119 2586773] Train: [27/50][354/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 01:13:25 loss: 0.2470 Lr: 0.00188 [2024-11-25 18:29:24,432 INFO misc.py line 119 2586773] Train: [27/50][355/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 01:13:24 loss: 0.2337 Lr: 0.00188 [2024-11-25 18:29:24,977 INFO misc.py line 119 2586773] Train: [27/50][356/376] Data 0.002 (0.002) Batch 0.545 (0.508) Remain 01:13:24 loss: 0.2195 Lr: 0.00188 [2024-11-25 18:29:25,466 INFO misc.py line 119 2586773] Train: [27/50][357/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 01:13:23 loss: 0.2172 Lr: 0.00188 [2024-11-25 18:29:25,968 INFO misc.py line 119 2586773] Train: [27/50][358/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 01:13:23 loss: 0.1830 Lr: 0.00188 [2024-11-25 18:29:26,499 INFO misc.py line 119 2586773] Train: [27/50][359/376] Data 0.002 (0.002) Batch 0.531 (0.508) Remain 01:13:23 loss: 0.1929 Lr: 0.00188 [2024-11-25 18:29:27,023 INFO misc.py line 119 2586773] Train: [27/50][360/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 01:13:23 loss: 0.2146 Lr: 0.00188 [2024-11-25 18:29:27,526 INFO misc.py line 119 2586773] Train: [27/50][361/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 01:13:22 loss: 0.1956 Lr: 0.00188 [2024-11-25 18:29:28,024 INFO misc.py line 119 2586773] Train: [27/50][362/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 01:13:21 loss: 0.2089 Lr: 0.00188 [2024-11-25 18:29:28,571 INFO misc.py line 119 2586773] Train: [27/50][363/376] Data 0.002 (0.002) Batch 0.547 (0.508) Remain 01:13:22 loss: 0.2424 Lr: 0.00188 [2024-11-25 18:29:29,107 INFO misc.py line 119 2586773] Train: [27/50][364/376] Data 0.003 (0.002) Batch 0.536 (0.508) Remain 01:13:22 loss: 0.1802 Lr: 0.00188 [2024-11-25 18:29:29,664 INFO misc.py line 119 2586773] Train: [27/50][365/376] Data 0.002 (0.002) Batch 0.557 (0.508) Remain 01:13:22 loss: 0.2662 Lr: 0.00188 [2024-11-25 18:29:30,138 INFO misc.py line 119 2586773] Train: [27/50][366/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 01:13:21 loss: 0.2514 Lr: 0.00188 [2024-11-25 18:29:30,655 INFO misc.py line 119 2586773] Train: [27/50][367/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 01:13:21 loss: 0.2181 Lr: 0.00188 [2024-11-25 18:29:31,140 INFO misc.py line 119 2586773] Train: [27/50][368/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 01:13:20 loss: 0.1786 Lr: 0.00188 [2024-11-25 18:29:31,626 INFO misc.py line 119 2586773] Train: [27/50][369/376] Data 0.003 (0.002) Batch 0.486 (0.508) Remain 01:13:19 loss: 0.2459 Lr: 0.00188 [2024-11-25 18:29:32,116 INFO misc.py line 119 2586773] Train: [27/50][370/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 01:13:18 loss: 0.2017 Lr: 0.00188 [2024-11-25 18:29:32,645 INFO misc.py line 119 2586773] Train: [27/50][371/376] Data 0.003 (0.002) Batch 0.529 (0.508) Remain 01:13:18 loss: 0.1954 Lr: 0.00188 [2024-11-25 18:29:33,104 INFO misc.py line 119 2586773] Train: [27/50][372/376] Data 0.002 (0.002) Batch 0.459 (0.508) Remain 01:13:16 loss: 0.2046 Lr: 0.00187 [2024-11-25 18:29:33,597 INFO misc.py line 119 2586773] Train: [27/50][373/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 01:13:15 loss: 0.2703 Lr: 0.00187 [2024-11-25 18:29:34,129 INFO misc.py line 119 2586773] Train: [27/50][374/376] Data 0.002 (0.002) Batch 0.533 (0.508) Remain 01:13:15 loss: 0.2113 Lr: 0.00187 [2024-11-25 18:29:34,655 INFO misc.py line 119 2586773] Train: [27/50][375/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 01:13:15 loss: 0.2703 Lr: 0.00187 [2024-11-25 18:29:35,187 INFO misc.py line 119 2586773] Train: [27/50][376/376] Data 0.002 (0.002) Batch 0.532 (0.508) Remain 01:13:15 loss: 0.2169 Lr: 0.00187 [2024-11-25 18:29:35,188 INFO misc.py line 136 2586773] Train result: loss: 0.2178 [2024-11-25 18:29:35,188 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:29:46,115 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2067 [2024-11-25 18:29:46,378 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2281 [2024-11-25 18:29:46,641 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2966 [2024-11-25 18:29:46,863 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1982 [2024-11-25 18:29:47,126 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3014 [2024-11-25 18:29:47,397 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2037 [2024-11-25 18:29:47,620 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2091 [2024-11-25 18:29:47,889 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2412 [2024-11-25 18:29:48,113 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2535 [2024-11-25 18:29:48,375 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2501 [2024-11-25 18:29:48,606 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2070 [2024-11-25 18:29:48,881 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2411 [2024-11-25 18:29:49,146 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2362 [2024-11-25 18:29:49,407 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2724 [2024-11-25 18:29:49,640 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2341 [2024-11-25 18:29:49,880 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2827 [2024-11-25 18:29:50,148 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2840 [2024-11-25 18:29:50,395 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2420 [2024-11-25 18:29:50,627 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2075 [2024-11-25 18:29:50,888 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2455 [2024-11-25 18:29:51,122 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2448 [2024-11-25 18:29:51,390 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2812 [2024-11-25 18:29:51,625 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2293 [2024-11-25 18:29:51,893 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2598 [2024-11-25 18:29:52,154 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2391 [2024-11-25 18:29:52,393 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2758 [2024-11-25 18:29:52,648 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2559 [2024-11-25 18:29:52,896 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2644 [2024-11-25 18:29:53,161 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2920 [2024-11-25 18:29:53,415 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3146 [2024-11-25 18:29:53,649 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2554 [2024-11-25 18:29:53,915 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2173 [2024-11-25 18:29:54,133 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2597 [2024-11-25 18:29:54,374 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2136 [2024-11-25 18:29:54,635 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2208 [2024-11-25 18:29:54,879 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2299 [2024-11-25 18:29:55,108 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2036 [2024-11-25 18:29:55,377 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2586 [2024-11-25 18:29:55,607 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2750 [2024-11-25 18:29:55,841 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2562 [2024-11-25 18:29:56,110 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3221 [2024-11-25 18:29:56,360 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2824 [2024-11-25 18:29:56,598 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2652 [2024-11-25 18:29:56,831 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2313 [2024-11-25 18:29:57,066 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2192 [2024-11-25 18:29:57,320 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2541 [2024-11-25 18:29:57,581 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2435 [2024-11-25 18:29:57,831 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3161 [2024-11-25 18:29:58,054 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2210 [2024-11-25 18:29:58,288 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2207 [2024-11-25 18:29:58,509 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2673 [2024-11-25 18:29:58,765 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2557 [2024-11-25 18:29:59,032 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2422 [2024-11-25 18:29:59,291 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3295 [2024-11-25 18:29:59,525 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2277 [2024-11-25 18:29:59,764 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2195 [2024-11-25 18:30:00,021 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2608 [2024-11-25 18:30:00,287 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2717 [2024-11-25 18:30:00,544 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2660 [2024-11-25 18:30:00,805 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2588 [2024-11-25 18:30:01,060 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2323 [2024-11-25 18:30:01,332 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2454 [2024-11-25 18:30:01,561 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2129 [2024-11-25 18:30:01,819 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2576 [2024-11-25 18:30:02,084 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2826 [2024-11-25 18:30:02,354 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1983 [2024-11-25 18:30:02,597 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2167 [2024-11-25 18:30:02,854 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2614 [2024-11-25 18:30:03,124 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2300 [2024-11-25 18:30:03,386 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2767 [2024-11-25 18:30:03,629 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2139 [2024-11-25 18:30:03,861 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2710 [2024-11-25 18:30:04,117 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2463 [2024-11-25 18:30:04,364 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2735 [2024-11-25 18:30:04,579 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2710 [2024-11-25 18:30:04,800 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2198 [2024-11-25 18:30:05,068 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2754 [2024-11-25 18:30:05,304 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2300 [2024-11-25 18:30:05,563 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2580 [2024-11-25 18:30:05,813 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2918 [2024-11-25 18:30:06,055 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2520 [2024-11-25 18:30:06,320 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2574 [2024-11-25 18:30:06,569 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1902 [2024-11-25 18:30:06,818 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2505 [2024-11-25 18:30:07,085 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2189 [2024-11-25 18:30:07,322 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2505 [2024-11-25 18:30:07,583 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2503 [2024-11-25 18:30:07,842 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2410 [2024-11-25 18:30:08,089 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2722 [2024-11-25 18:30:08,337 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2233 [2024-11-25 18:30:08,571 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2547 [2024-11-25 18:30:08,825 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2739 [2024-11-25 18:30:09,096 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2732 [2024-11-25 18:30:09,362 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2211 [2024-11-25 18:30:09,625 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2296 [2024-11-25 18:30:09,873 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2444 [2024-11-25 18:30:10,143 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2312 [2024-11-25 18:30:10,362 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2965 [2024-11-25 18:30:10,635 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2682 [2024-11-25 18:30:10,874 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2527 [2024-11-25 18:30:11,142 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2168 [2024-11-25 18:30:11,402 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2625 [2024-11-25 18:30:11,661 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2469 [2024-11-25 18:30:11,916 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2981 [2024-11-25 18:30:12,136 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2512 [2024-11-25 18:30:12,377 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2297 [2024-11-25 18:30:12,634 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2246 [2024-11-25 18:30:12,903 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2384 [2024-11-25 18:30:13,139 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2526 [2024-11-25 18:30:13,400 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2510 [2024-11-25 18:30:13,664 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2386 [2024-11-25 18:30:13,887 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2474 [2024-11-25 18:30:14,123 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2081 [2024-11-25 18:30:14,341 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2056 [2024-11-25 18:30:14,565 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2104 [2024-11-25 18:30:14,836 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3075 [2024-11-25 18:30:15,097 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2746 [2024-11-25 18:30:15,363 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2465 [2024-11-25 18:30:15,629 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2430 [2024-11-25 18:30:15,895 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3267 [2024-11-25 18:30:16,155 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.3066 [2024-11-25 18:30:16,427 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2113 [2024-11-25 18:30:16,681 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2802 [2024-11-25 18:30:16,944 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2397 [2024-11-25 18:30:17,207 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2808 [2024-11-25 18:30:17,460 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2857 [2024-11-25 18:30:17,690 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2032 [2024-11-25 18:30:17,950 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2613 [2024-11-25 18:30:18,184 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2838 [2024-11-25 18:30:18,410 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2009 [2024-11-25 18:30:18,621 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2570 [2024-11-25 18:30:18,837 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1893 [2024-11-25 18:30:19,562 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7596/0.8328/0.9960. [2024-11-25 18:30:19,562 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9982 [2024-11-25 18:30:19,563 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5232/0.6674 [2024-11-25 18:30:19,563 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:30:19,564 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:30:19,564 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:30:22,329 INFO misc.py line 119 2586773] Train: [28/50][1/376] Data 0.099 (0.099) Batch 0.598 (0.598) Remain 01:26:08 loss: 0.2060 Lr: 0.00187 [2024-11-25 18:30:22,805 INFO misc.py line 119 2586773] Train: [28/50][2/376] Data 0.002 (0.002) Batch 0.476 (0.476) Remain 01:08:34 loss: 0.2091 Lr: 0.00187 [2024-11-25 18:30:23,319 INFO misc.py line 119 2586773] Train: [28/50][3/376] Data 0.003 (0.003) Batch 0.514 (0.514) Remain 01:14:03 loss: 0.1925 Lr: 0.00187 [2024-11-25 18:30:23,828 INFO misc.py line 119 2586773] Train: [28/50][4/376] Data 0.003 (0.003) Batch 0.509 (0.509) Remain 01:13:16 loss: 0.2528 Lr: 0.00187 [2024-11-25 18:30:24,337 INFO misc.py line 119 2586773] Train: [28/50][5/376] Data 0.003 (0.003) Batch 0.509 (0.509) Remain 01:13:18 loss: 0.2123 Lr: 0.00187 [2024-11-25 18:30:24,811 INFO misc.py line 119 2586773] Train: [28/50][6/376] Data 0.003 (0.003) Batch 0.474 (0.497) Remain 01:11:37 loss: 0.2391 Lr: 0.00187 [2024-11-25 18:30:25,331 INFO misc.py line 119 2586773] Train: [28/50][7/376] Data 0.002 (0.003) Batch 0.520 (0.503) Remain 01:12:26 loss: 0.1900 Lr: 0.00187 [2024-11-25 18:30:25,784 INFO misc.py line 119 2586773] Train: [28/50][8/376] Data 0.003 (0.003) Batch 0.453 (0.493) Remain 01:10:59 loss: 0.1828 Lr: 0.00187 [2024-11-25 18:30:26,277 INFO misc.py line 119 2586773] Train: [28/50][9/376] Data 0.003 (0.003) Batch 0.493 (0.493) Remain 01:10:58 loss: 0.2217 Lr: 0.00187 [2024-11-25 18:30:26,759 INFO misc.py line 119 2586773] Train: [28/50][10/376] Data 0.002 (0.003) Batch 0.482 (0.491) Remain 01:10:45 loss: 0.1905 Lr: 0.00187 [2024-11-25 18:30:27,238 INFO misc.py line 119 2586773] Train: [28/50][11/376] Data 0.003 (0.003) Batch 0.478 (0.490) Remain 01:10:30 loss: 0.1832 Lr: 0.00187 [2024-11-25 18:30:27,746 INFO misc.py line 119 2586773] Train: [28/50][12/376] Data 0.002 (0.003) Batch 0.508 (0.492) Remain 01:10:47 loss: 0.2386 Lr: 0.00187 [2024-11-25 18:30:28,269 INFO misc.py line 119 2586773] Train: [28/50][13/376] Data 0.003 (0.003) Batch 0.524 (0.495) Remain 01:11:14 loss: 0.2076 Lr: 0.00187 [2024-11-25 18:30:28,788 INFO misc.py line 119 2586773] Train: [28/50][14/376] Data 0.002 (0.003) Batch 0.519 (0.497) Remain 01:11:32 loss: 0.1918 Lr: 0.00187 [2024-11-25 18:30:29,291 INFO misc.py line 119 2586773] Train: [28/50][15/376] Data 0.002 (0.003) Batch 0.502 (0.498) Remain 01:11:36 loss: 0.1746 Lr: 0.00187 [2024-11-25 18:30:29,792 INFO misc.py line 119 2586773] Train: [28/50][16/376] Data 0.003 (0.003) Batch 0.502 (0.498) Remain 01:11:38 loss: 0.2137 Lr: 0.00187 [2024-11-25 18:30:30,335 INFO misc.py line 119 2586773] Train: [28/50][17/376] Data 0.002 (0.003) Batch 0.542 (0.501) Remain 01:12:05 loss: 0.1942 Lr: 0.00187 [2024-11-25 18:30:30,821 INFO misc.py line 119 2586773] Train: [28/50][18/376] Data 0.002 (0.003) Batch 0.486 (0.500) Remain 01:11:55 loss: 0.2275 Lr: 0.00187 [2024-11-25 18:30:31,331 INFO misc.py line 119 2586773] Train: [28/50][19/376] Data 0.003 (0.003) Batch 0.510 (0.501) Remain 01:12:00 loss: 0.1698 Lr: 0.00187 [2024-11-25 18:30:31,852 INFO misc.py line 119 2586773] Train: [28/50][20/376] Data 0.003 (0.003) Batch 0.521 (0.502) Remain 01:12:10 loss: 0.2346 Lr: 0.00187 [2024-11-25 18:30:32,347 INFO misc.py line 119 2586773] Train: [28/50][21/376] Data 0.003 (0.003) Batch 0.495 (0.502) Remain 01:12:06 loss: 0.1952 Lr: 0.00187 [2024-11-25 18:30:32,827 INFO misc.py line 119 2586773] Train: [28/50][22/376] Data 0.003 (0.003) Batch 0.481 (0.500) Remain 01:11:56 loss: 0.1929 Lr: 0.00187 [2024-11-25 18:30:33,327 INFO misc.py line 119 2586773] Train: [28/50][23/376] Data 0.002 (0.003) Batch 0.500 (0.500) Remain 01:11:56 loss: 0.2019 Lr: 0.00187 [2024-11-25 18:30:33,796 INFO misc.py line 119 2586773] Train: [28/50][24/376] Data 0.002 (0.003) Batch 0.469 (0.499) Remain 01:11:42 loss: 0.3866 Lr: 0.00187 [2024-11-25 18:30:34,352 INFO misc.py line 119 2586773] Train: [28/50][25/376] Data 0.002 (0.003) Batch 0.556 (0.501) Remain 01:12:04 loss: 0.2347 Lr: 0.00186 [2024-11-25 18:30:34,865 INFO misc.py line 119 2586773] Train: [28/50][26/376] Data 0.002 (0.003) Batch 0.514 (0.502) Remain 01:12:08 loss: 0.2206 Lr: 0.00186 [2024-11-25 18:30:35,357 INFO misc.py line 119 2586773] Train: [28/50][27/376] Data 0.002 (0.003) Batch 0.492 (0.502) Remain 01:12:04 loss: 0.2018 Lr: 0.00186 [2024-11-25 18:30:35,913 INFO misc.py line 119 2586773] Train: [28/50][28/376] Data 0.003 (0.003) Batch 0.556 (0.504) Remain 01:12:22 loss: 0.2630 Lr: 0.00186 [2024-11-25 18:30:36,449 INFO misc.py line 119 2586773] Train: [28/50][29/376] Data 0.002 (0.003) Batch 0.536 (0.505) Remain 01:12:32 loss: 0.1945 Lr: 0.00186 [2024-11-25 18:30:36,954 INFO misc.py line 119 2586773] Train: [28/50][30/376] Data 0.003 (0.003) Batch 0.505 (0.505) Remain 01:12:32 loss: 0.2008 Lr: 0.00186 [2024-11-25 18:30:37,458 INFO misc.py line 119 2586773] Train: [28/50][31/376] Data 0.003 (0.003) Batch 0.504 (0.505) Remain 01:12:31 loss: 0.1787 Lr: 0.00186 [2024-11-25 18:30:38,004 INFO misc.py line 119 2586773] Train: [28/50][32/376] Data 0.003 (0.003) Batch 0.547 (0.506) Remain 01:12:43 loss: 0.1964 Lr: 0.00186 [2024-11-25 18:30:38,495 INFO misc.py line 119 2586773] Train: [28/50][33/376] Data 0.002 (0.003) Batch 0.491 (0.506) Remain 01:12:38 loss: 0.2732 Lr: 0.00186 [2024-11-25 18:30:38,987 INFO misc.py line 119 2586773] Train: [28/50][34/376] Data 0.002 (0.003) Batch 0.491 (0.505) Remain 01:12:33 loss: 0.2240 Lr: 0.00186 [2024-11-25 18:30:39,557 INFO misc.py line 119 2586773] Train: [28/50][35/376] Data 0.002 (0.003) Batch 0.571 (0.507) Remain 01:12:50 loss: 0.2028 Lr: 0.00186 [2024-11-25 18:30:40,056 INFO misc.py line 119 2586773] Train: [28/50][36/376] Data 0.002 (0.003) Batch 0.499 (0.507) Remain 01:12:47 loss: 0.2410 Lr: 0.00186 [2024-11-25 18:30:40,588 INFO misc.py line 119 2586773] Train: [28/50][37/376] Data 0.002 (0.003) Batch 0.532 (0.508) Remain 01:12:53 loss: 0.1882 Lr: 0.00186 [2024-11-25 18:30:41,095 INFO misc.py line 119 2586773] Train: [28/50][38/376] Data 0.003 (0.003) Batch 0.507 (0.508) Remain 01:12:52 loss: 0.1922 Lr: 0.00186 [2024-11-25 18:30:41,661 INFO misc.py line 119 2586773] Train: [28/50][39/376] Data 0.003 (0.003) Batch 0.566 (0.509) Remain 01:13:06 loss: 0.2574 Lr: 0.00186 [2024-11-25 18:30:42,206 INFO misc.py line 119 2586773] Train: [28/50][40/376] Data 0.003 (0.003) Batch 0.545 (0.510) Remain 01:13:13 loss: 0.1904 Lr: 0.00186 [2024-11-25 18:30:42,734 INFO misc.py line 119 2586773] Train: [28/50][41/376] Data 0.003 (0.003) Batch 0.528 (0.511) Remain 01:13:17 loss: 0.2523 Lr: 0.00186 [2024-11-25 18:30:43,260 INFO misc.py line 119 2586773] Train: [28/50][42/376] Data 0.003 (0.003) Batch 0.526 (0.511) Remain 01:13:20 loss: 0.1909 Lr: 0.00186 [2024-11-25 18:30:43,793 INFO misc.py line 119 2586773] Train: [28/50][43/376] Data 0.002 (0.003) Batch 0.532 (0.512) Remain 01:13:24 loss: 0.2268 Lr: 0.00186 [2024-11-25 18:30:44,354 INFO misc.py line 119 2586773] Train: [28/50][44/376] Data 0.002 (0.003) Batch 0.561 (0.513) Remain 01:13:34 loss: 0.2000 Lr: 0.00186 [2024-11-25 18:30:44,850 INFO misc.py line 119 2586773] Train: [28/50][45/376] Data 0.003 (0.003) Batch 0.497 (0.513) Remain 01:13:30 loss: 0.2251 Lr: 0.00186 [2024-11-25 18:30:45,399 INFO misc.py line 119 2586773] Train: [28/50][46/376] Data 0.003 (0.003) Batch 0.549 (0.513) Remain 01:13:37 loss: 0.1840 Lr: 0.00186 [2024-11-25 18:30:45,923 INFO misc.py line 119 2586773] Train: [28/50][47/376] Data 0.003 (0.003) Batch 0.524 (0.514) Remain 01:13:38 loss: 0.2134 Lr: 0.00186 [2024-11-25 18:30:46,405 INFO misc.py line 119 2586773] Train: [28/50][48/376] Data 0.002 (0.003) Batch 0.482 (0.513) Remain 01:13:31 loss: 0.1965 Lr: 0.00186 [2024-11-25 18:30:46,942 INFO misc.py line 119 2586773] Train: [28/50][49/376] Data 0.002 (0.003) Batch 0.537 (0.514) Remain 01:13:35 loss: 0.2196 Lr: 0.00186 [2024-11-25 18:30:47,462 INFO misc.py line 119 2586773] Train: [28/50][50/376] Data 0.002 (0.003) Batch 0.520 (0.514) Remain 01:13:36 loss: 0.2058 Lr: 0.00186 [2024-11-25 18:30:47,968 INFO misc.py line 119 2586773] Train: [28/50][51/376] Data 0.003 (0.003) Batch 0.507 (0.514) Remain 01:13:34 loss: 0.2008 Lr: 0.00186 [2024-11-25 18:30:48,506 INFO misc.py line 119 2586773] Train: [28/50][52/376] Data 0.003 (0.003) Batch 0.538 (0.514) Remain 01:13:38 loss: 0.2910 Lr: 0.00186 [2024-11-25 18:30:48,994 INFO misc.py line 119 2586773] Train: [28/50][53/376] Data 0.003 (0.003) Batch 0.487 (0.513) Remain 01:13:33 loss: 0.1938 Lr: 0.00186 [2024-11-25 18:30:49,514 INFO misc.py line 119 2586773] Train: [28/50][54/376] Data 0.003 (0.003) Batch 0.520 (0.514) Remain 01:13:34 loss: 0.3638 Lr: 0.00185 [2024-11-25 18:30:50,020 INFO misc.py line 119 2586773] Train: [28/50][55/376] Data 0.003 (0.003) Batch 0.506 (0.513) Remain 01:13:32 loss: 0.2154 Lr: 0.00185 [2024-11-25 18:30:50,493 INFO misc.py line 119 2586773] Train: [28/50][56/376] Data 0.002 (0.003) Batch 0.473 (0.513) Remain 01:13:25 loss: 0.1960 Lr: 0.00185 [2024-11-25 18:30:51,050 INFO misc.py line 119 2586773] Train: [28/50][57/376] Data 0.003 (0.003) Batch 0.557 (0.514) Remain 01:13:31 loss: 0.2174 Lr: 0.00185 [2024-11-25 18:30:51,606 INFO misc.py line 119 2586773] Train: [28/50][58/376] Data 0.003 (0.003) Batch 0.556 (0.514) Remain 01:13:37 loss: 0.1659 Lr: 0.00185 [2024-11-25 18:30:52,086 INFO misc.py line 119 2586773] Train: [28/50][59/376] Data 0.003 (0.003) Batch 0.480 (0.514) Remain 01:13:32 loss: 0.2339 Lr: 0.00185 [2024-11-25 18:30:52,624 INFO misc.py line 119 2586773] Train: [28/50][60/376] Data 0.003 (0.003) Batch 0.538 (0.514) Remain 01:13:35 loss: 0.1889 Lr: 0.00185 [2024-11-25 18:30:53,139 INFO misc.py line 119 2586773] Train: [28/50][61/376] Data 0.002 (0.003) Batch 0.515 (0.514) Remain 01:13:34 loss: 0.2064 Lr: 0.00185 [2024-11-25 18:30:53,655 INFO misc.py line 119 2586773] Train: [28/50][62/376] Data 0.002 (0.003) Batch 0.516 (0.514) Remain 01:13:34 loss: 0.1693 Lr: 0.00185 [2024-11-25 18:30:54,172 INFO misc.py line 119 2586773] Train: [28/50][63/376] Data 0.003 (0.003) Batch 0.518 (0.514) Remain 01:13:34 loss: 0.2258 Lr: 0.00185 [2024-11-25 18:30:54,651 INFO misc.py line 119 2586773] Train: [28/50][64/376] Data 0.002 (0.003) Batch 0.479 (0.514) Remain 01:13:29 loss: 0.2592 Lr: 0.00185 [2024-11-25 18:30:55,195 INFO misc.py line 119 2586773] Train: [28/50][65/376] Data 0.002 (0.003) Batch 0.544 (0.514) Remain 01:13:32 loss: 0.2039 Lr: 0.00185 [2024-11-25 18:30:55,772 INFO misc.py line 119 2586773] Train: [28/50][66/376] Data 0.002 (0.003) Batch 0.577 (0.515) Remain 01:13:40 loss: 0.1786 Lr: 0.00185 [2024-11-25 18:30:56,313 INFO misc.py line 119 2586773] Train: [28/50][67/376] Data 0.002 (0.003) Batch 0.541 (0.516) Remain 01:13:43 loss: 0.2122 Lr: 0.00185 [2024-11-25 18:30:56,866 INFO misc.py line 119 2586773] Train: [28/50][68/376] Data 0.002 (0.003) Batch 0.552 (0.516) Remain 01:13:48 loss: 0.2126 Lr: 0.00185 [2024-11-25 18:30:57,378 INFO misc.py line 119 2586773] Train: [28/50][69/376] Data 0.003 (0.003) Batch 0.513 (0.516) Remain 01:13:47 loss: 0.1915 Lr: 0.00185 [2024-11-25 18:30:57,875 INFO misc.py line 119 2586773] Train: [28/50][70/376] Data 0.002 (0.003) Batch 0.497 (0.516) Remain 01:13:44 loss: 0.2128 Lr: 0.00185 [2024-11-25 18:30:58,434 INFO misc.py line 119 2586773] Train: [28/50][71/376] Data 0.002 (0.003) Batch 0.559 (0.516) Remain 01:13:49 loss: 0.2309 Lr: 0.00185 [2024-11-25 18:30:58,917 INFO misc.py line 119 2586773] Train: [28/50][72/376] Data 0.003 (0.003) Batch 0.483 (0.516) Remain 01:13:44 loss: 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INFO misc.py line 119 2586773] Train: [28/50][79/376] Data 0.003 (0.003) Batch 0.519 (0.517) Remain 01:13:54 loss: 0.2469 Lr: 0.00185 [2024-11-25 18:31:03,154 INFO misc.py line 119 2586773] Train: [28/50][80/376] Data 0.003 (0.003) Batch 0.505 (0.517) Remain 01:13:52 loss: 0.1787 Lr: 0.00185 [2024-11-25 18:31:03,635 INFO misc.py line 119 2586773] Train: [28/50][81/376] Data 0.002 (0.003) Batch 0.481 (0.517) Remain 01:13:48 loss: 0.2229 Lr: 0.00185 [2024-11-25 18:31:04,180 INFO misc.py line 119 2586773] Train: [28/50][82/376] Data 0.003 (0.003) Batch 0.544 (0.517) Remain 01:13:50 loss: 0.2409 Lr: 0.00184 [2024-11-25 18:31:04,670 INFO misc.py line 119 2586773] Train: [28/50][83/376] Data 0.002 (0.003) Batch 0.490 (0.517) Remain 01:13:47 loss: 0.1735 Lr: 0.00184 [2024-11-25 18:31:05,158 INFO misc.py line 119 2586773] Train: [28/50][84/376] Data 0.002 (0.003) Batch 0.488 (0.517) Remain 01:13:43 loss: 0.2512 Lr: 0.00184 [2024-11-25 18:31:05,664 INFO misc.py line 119 2586773] Train: 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Batch 0.507 (0.512) Remain 01:11:54 loss: 0.2359 Lr: 0.00179 [2024-11-25 18:32:19,123 INFO misc.py line 119 2586773] Train: [28/50][229/376] Data 0.003 (0.002) Batch 0.501 (0.512) Remain 01:11:53 loss: 0.1858 Lr: 0.00179 [2024-11-25 18:32:19,647 INFO misc.py line 119 2586773] Train: [28/50][230/376] Data 0.003 (0.002) Batch 0.524 (0.512) Remain 01:11:53 loss: 0.1993 Lr: 0.00179 [2024-11-25 18:32:20,163 INFO misc.py line 119 2586773] Train: [28/50][231/376] Data 0.002 (0.002) Batch 0.516 (0.512) Remain 01:11:53 loss: 0.2073 Lr: 0.00179 [2024-11-25 18:32:20,654 INFO misc.py line 119 2586773] Train: [28/50][232/376] Data 0.003 (0.002) Batch 0.492 (0.512) Remain 01:11:52 loss: 0.2492 Lr: 0.00179 [2024-11-25 18:32:21,133 INFO misc.py line 119 2586773] Train: [28/50][233/376] Data 0.002 (0.002) Batch 0.478 (0.512) Remain 01:11:50 loss: 0.1919 Lr: 0.00179 [2024-11-25 18:32:21,627 INFO misc.py line 119 2586773] Train: [28/50][234/376] Data 0.002 (0.002) Batch 0.494 (0.512) Remain 01:11:49 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[2024-11-25 18:32:38,023 INFO misc.py line 119 2586773] Train: [28/50][266/376] Data 0.002 (0.002) Batch 0.479 (0.512) Remain 01:11:33 loss: 0.2374 Lr: 0.00178 [2024-11-25 18:32:38,516 INFO misc.py line 119 2586773] Train: [28/50][267/376] Data 0.002 (0.002) Batch 0.492 (0.512) Remain 01:11:31 loss: 0.2405 Lr: 0.00178 [2024-11-25 18:32:39,000 INFO misc.py line 119 2586773] Train: [28/50][268/376] Data 0.002 (0.002) Batch 0.485 (0.512) Remain 01:11:30 loss: 0.1823 Lr: 0.00178 [2024-11-25 18:32:39,525 INFO misc.py line 119 2586773] Train: [28/50][269/376] Data 0.002 (0.002) Batch 0.525 (0.512) Remain 01:11:30 loss: 0.2243 Lr: 0.00178 [2024-11-25 18:32:40,058 INFO misc.py line 119 2586773] Train: [28/50][270/376] Data 0.002 (0.002) Batch 0.534 (0.512) Remain 01:11:30 loss: 0.2348 Lr: 0.00178 [2024-11-25 18:32:40,536 INFO misc.py line 119 2586773] Train: [28/50][271/376] Data 0.002 (0.002) Batch 0.477 (0.512) Remain 01:11:29 loss: 0.2068 Lr: 0.00178 [2024-11-25 18:32:41,021 INFO misc.py line 119 2586773] Train: [28/50][272/376] Data 0.002 (0.002) Batch 0.485 (0.512) Remain 01:11:27 loss: 0.1860 Lr: 0.00178 [2024-11-25 18:32:41,514 INFO misc.py line 119 2586773] Train: [28/50][273/376] Data 0.003 (0.002) Batch 0.493 (0.512) Remain 01:11:26 loss: 0.1655 Lr: 0.00178 [2024-11-25 18:32:42,015 INFO misc.py line 119 2586773] Train: [28/50][274/376] Data 0.002 (0.002) Batch 0.502 (0.512) Remain 01:11:25 loss: 0.2199 Lr: 0.00178 [2024-11-25 18:32:42,518 INFO misc.py line 119 2586773] Train: [28/50][275/376] Data 0.002 (0.002) Batch 0.503 (0.512) Remain 01:11:24 loss: 0.2929 Lr: 0.00178 [2024-11-25 18:32:43,038 INFO misc.py line 119 2586773] Train: [28/50][276/376] Data 0.003 (0.002) Batch 0.520 (0.512) Remain 01:11:24 loss: 0.1966 Lr: 0.00178 [2024-11-25 18:32:43,525 INFO misc.py line 119 2586773] Train: [28/50][277/376] Data 0.002 (0.002) Batch 0.486 (0.512) Remain 01:11:23 loss: 0.1787 Lr: 0.00178 [2024-11-25 18:32:44,045 INFO misc.py line 119 2586773] Train: 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Batch 0.499 (0.512) Remain 01:11:18 loss: 0.1965 Lr: 0.00178 [2024-11-25 18:32:47,643 INFO misc.py line 119 2586773] Train: [28/50][285/376] Data 0.002 (0.002) Batch 0.580 (0.512) Remain 01:11:20 loss: 0.2382 Lr: 0.00177 [2024-11-25 18:32:48,113 INFO misc.py line 119 2586773] Train: [28/50][286/376] Data 0.002 (0.002) Batch 0.470 (0.512) Remain 01:11:18 loss: 0.2368 Lr: 0.00177 [2024-11-25 18:32:48,628 INFO misc.py line 119 2586773] Train: [28/50][287/376] Data 0.003 (0.002) Batch 0.515 (0.512) Remain 01:11:17 loss: 0.1969 Lr: 0.00177 [2024-11-25 18:32:49,100 INFO misc.py line 119 2586773] Train: [28/50][288/376] Data 0.002 (0.002) Batch 0.472 (0.512) Remain 01:11:16 loss: 0.2237 Lr: 0.00177 [2024-11-25 18:32:49,609 INFO misc.py line 119 2586773] Train: [28/50][289/376] Data 0.002 (0.002) Batch 0.508 (0.512) Remain 01:11:15 loss: 0.2956 Lr: 0.00177 [2024-11-25 18:32:50,144 INFO misc.py line 119 2586773] Train: [28/50][290/376] Data 0.002 (0.002) Batch 0.535 (0.512) Remain 01:11:15 loss: 0.2145 Lr: 0.00177 [2024-11-25 18:32:50,649 INFO misc.py line 119 2586773] Train: [28/50][291/376] Data 0.002 (0.002) Batch 0.504 (0.512) Remain 01:11:15 loss: 0.2527 Lr: 0.00177 [2024-11-25 18:32:51,172 INFO misc.py line 119 2586773] Train: [28/50][292/376] Data 0.002 (0.002) Batch 0.524 (0.512) Remain 01:11:14 loss: 0.2190 Lr: 0.00177 [2024-11-25 18:32:51,648 INFO misc.py line 119 2586773] Train: [28/50][293/376] Data 0.002 (0.002) Batch 0.475 (0.511) Remain 01:11:13 loss: 0.1756 Lr: 0.00177 [2024-11-25 18:32:52,151 INFO misc.py line 119 2586773] Train: [28/50][294/376] Data 0.002 (0.002) Batch 0.503 (0.511) Remain 01:11:12 loss: 0.1639 Lr: 0.00177 [2024-11-25 18:32:52,618 INFO misc.py line 119 2586773] Train: [28/50][295/376] Data 0.002 (0.002) Batch 0.468 (0.511) Remain 01:11:10 loss: 0.1988 Lr: 0.00177 [2024-11-25 18:32:53,120 INFO misc.py line 119 2586773] Train: [28/50][296/376] Data 0.002 (0.002) Batch 0.502 (0.511) Remain 01:11:10 loss: 0.1821 Lr: 0.00177 [2024-11-25 18:32:53,636 INFO misc.py line 119 2586773] Train: [28/50][297/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:11:09 loss: 0.2717 Lr: 0.00177 [2024-11-25 18:32:54,107 INFO misc.py line 119 2586773] Train: [28/50][298/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 01:11:08 loss: 0.2046 Lr: 0.00177 [2024-11-25 18:32:54,659 INFO misc.py line 119 2586773] Train: [28/50][299/376] Data 0.002 (0.002) Batch 0.552 (0.511) Remain 01:11:08 loss: 0.2475 Lr: 0.00177 [2024-11-25 18:32:55,131 INFO misc.py line 119 2586773] Train: [28/50][300/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 01:11:07 loss: 0.2143 Lr: 0.00177 [2024-11-25 18:32:55,687 INFO misc.py line 119 2586773] Train: [28/50][301/376] Data 0.002 (0.002) Batch 0.557 (0.511) Remain 01:11:07 loss: 0.2172 Lr: 0.00177 [2024-11-25 18:32:56,197 INFO misc.py line 119 2586773] Train: [28/50][302/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 01:11:07 loss: 0.2599 Lr: 0.00177 [2024-11-25 18:32:56,688 INFO misc.py line 119 2586773] Train: [28/50][303/376] Data 0.002 (0.002) Batch 0.492 (0.511) Remain 01:11:06 loss: 0.2043 Lr: 0.00177 [2024-11-25 18:32:57,140 INFO misc.py line 119 2586773] Train: [28/50][304/376] Data 0.002 (0.002) Batch 0.451 (0.511) Remain 01:11:04 loss: 0.2210 Lr: 0.00177 [2024-11-25 18:32:57,670 INFO misc.py line 119 2586773] Train: [28/50][305/376] Data 0.003 (0.002) Batch 0.530 (0.511) Remain 01:11:04 loss: 0.1866 Lr: 0.00177 [2024-11-25 18:32:58,184 INFO misc.py line 119 2586773] Train: [28/50][306/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:11:03 loss: 0.1883 Lr: 0.00177 [2024-11-25 18:32:58,658 INFO misc.py line 119 2586773] Train: [28/50][307/376] Data 0.003 (0.002) Batch 0.473 (0.511) Remain 01:11:02 loss: 0.2203 Lr: 0.00177 [2024-11-25 18:32:59,156 INFO misc.py line 119 2586773] Train: [28/50][308/376] Data 0.003 (0.002) Batch 0.499 (0.511) Remain 01:11:01 loss: 0.1797 Lr: 0.00177 [2024-11-25 18:32:59,652 INFO misc.py line 119 2586773] Train: [28/50][309/376] Data 0.002 (0.002) Batch 0.496 (0.511) Remain 01:11:00 loss: 0.2436 Lr: 0.00177 [2024-11-25 18:33:00,171 INFO misc.py line 119 2586773] Train: [28/50][310/376] Data 0.002 (0.002) Batch 0.519 (0.511) Remain 01:11:00 loss: 0.1945 Lr: 0.00177 [2024-11-25 18:33:00,660 INFO misc.py line 119 2586773] Train: [28/50][311/376] Data 0.002 (0.002) Batch 0.489 (0.511) Remain 01:10:58 loss: 0.1933 Lr: 0.00177 [2024-11-25 18:33:01,176 INFO misc.py line 119 2586773] Train: [28/50][312/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:10:58 loss: 0.1763 Lr: 0.00177 [2024-11-25 18:33:01,671 INFO misc.py line 119 2586773] Train: [28/50][313/376] Data 0.003 (0.002) Batch 0.495 (0.511) Remain 01:10:57 loss: 0.2744 Lr: 0.00177 [2024-11-25 18:33:02,170 INFO misc.py line 119 2586773] Train: [28/50][314/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 01:10:56 loss: 0.2485 Lr: 0.00176 [2024-11-25 18:33:02,676 INFO misc.py line 119 2586773] Train: [28/50][315/376] Data 0.002 (0.002) Batch 0.506 (0.511) Remain 01:10:56 loss: 0.2441 Lr: 0.00176 [2024-11-25 18:33:03,187 INFO misc.py line 119 2586773] Train: [28/50][316/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 01:10:55 loss: 0.2019 Lr: 0.00176 [2024-11-25 18:33:03,727 INFO misc.py line 119 2586773] Train: [28/50][317/376] Data 0.002 (0.002) Batch 0.540 (0.511) Remain 01:10:55 loss: 0.2300 Lr: 0.00176 [2024-11-25 18:33:04,241 INFO misc.py line 119 2586773] Train: [28/50][318/376] Data 0.002 (0.002) Batch 0.514 (0.511) Remain 01:10:55 loss: 0.2009 Lr: 0.00176 [2024-11-25 18:33:04,738 INFO misc.py line 119 2586773] Train: [28/50][319/376] Data 0.003 (0.002) Batch 0.497 (0.511) Remain 01:10:54 loss: 0.2013 Lr: 0.00176 [2024-11-25 18:33:05,246 INFO misc.py line 119 2586773] Train: [28/50][320/376] Data 0.003 (0.002) Batch 0.508 (0.511) Remain 01:10:54 loss: 0.2051 Lr: 0.00176 [2024-11-25 18:33:05,700 INFO misc.py line 119 2586773] Train: [28/50][321/376] Data 0.002 (0.002) Batch 0.454 (0.511) Remain 01:10:52 loss: 0.2166 Lr: 0.00176 [2024-11-25 18:33:06,213 INFO misc.py line 119 2586773] Train: [28/50][322/376] Data 0.002 (0.002) Batch 0.513 (0.511) Remain 01:10:51 loss: 0.2451 Lr: 0.00176 [2024-11-25 18:33:06,718 INFO misc.py line 119 2586773] Train: [28/50][323/376] Data 0.002 (0.002) Batch 0.505 (0.511) Remain 01:10:50 loss: 0.2011 Lr: 0.00176 [2024-11-25 18:33:07,276 INFO misc.py line 119 2586773] Train: [28/50][324/376] Data 0.002 (0.002) Batch 0.558 (0.511) Remain 01:10:51 loss: 0.2268 Lr: 0.00176 [2024-11-25 18:33:07,808 INFO misc.py line 119 2586773] Train: [28/50][325/376] Data 0.002 (0.002) Batch 0.532 (0.511) Remain 01:10:51 loss: 0.3633 Lr: 0.00176 [2024-11-25 18:33:08,325 INFO misc.py line 119 2586773] Train: [28/50][326/376] Data 0.002 (0.002) Batch 0.517 (0.511) Remain 01:10:51 loss: 0.1525 Lr: 0.00176 [2024-11-25 18:33:08,802 INFO misc.py line 119 2586773] Train: [28/50][327/376] Data 0.002 (0.002) Batch 0.477 (0.511) Remain 01:10:49 loss: 0.1766 Lr: 0.00176 [2024-11-25 18:33:09,318 INFO misc.py line 119 2586773] Train: [28/50][328/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:10:49 loss: 0.2421 Lr: 0.00176 [2024-11-25 18:33:09,851 INFO misc.py line 119 2586773] Train: [28/50][329/376] Data 0.002 (0.002) Batch 0.533 (0.511) Remain 01:10:49 loss: 0.1665 Lr: 0.00176 [2024-11-25 18:33:10,365 INFO misc.py line 119 2586773] Train: [28/50][330/376] Data 0.002 (0.002) Batch 0.514 (0.511) Remain 01:10:49 loss: 0.2089 Lr: 0.00176 [2024-11-25 18:33:10,879 INFO misc.py line 119 2586773] Train: [28/50][331/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 01:10:48 loss: 0.2844 Lr: 0.00176 [2024-11-25 18:33:11,399 INFO misc.py line 119 2586773] Train: [28/50][332/376] Data 0.002 (0.002) Batch 0.519 (0.511) Remain 01:10:48 loss: 0.1879 Lr: 0.00176 [2024-11-25 18:33:11,872 INFO misc.py line 119 2586773] Train: [28/50][333/376] Data 0.002 (0.002) Batch 0.474 (0.511) Remain 01:10:47 loss: 0.2673 Lr: 0.00176 [2024-11-25 18:33:12,383 INFO misc.py line 119 2586773] Train: 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Batch 0.506 (0.510) Remain 01:10:40 loss: 0.3121 Lr: 0.00176 [2024-11-25 18:33:15,846 INFO misc.py line 119 2586773] Train: [28/50][341/376] Data 0.002 (0.002) Batch 0.527 (0.510) Remain 01:10:40 loss: 0.2382 Lr: 0.00176 [2024-11-25 18:33:16,363 INFO misc.py line 119 2586773] Train: [28/50][342/376] Data 0.002 (0.002) Batch 0.516 (0.510) Remain 01:10:39 loss: 0.1898 Lr: 0.00176 [2024-11-25 18:33:16,870 INFO misc.py line 119 2586773] Train: [28/50][343/376] Data 0.002 (0.002) Batch 0.507 (0.510) Remain 01:10:39 loss: 0.2679 Lr: 0.00175 [2024-11-25 18:33:17,362 INFO misc.py line 119 2586773] Train: [28/50][344/376] Data 0.003 (0.002) Batch 0.492 (0.510) Remain 01:10:38 loss: 0.2062 Lr: 0.00175 [2024-11-25 18:33:17,879 INFO misc.py line 119 2586773] Train: [28/50][345/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 01:10:37 loss: 0.2217 Lr: 0.00175 [2024-11-25 18:33:18,342 INFO misc.py line 119 2586773] Train: [28/50][346/376] Data 0.002 (0.002) Batch 0.464 (0.510) Remain 01:10:36 loss: 0.2810 Lr: 0.00175 [2024-11-25 18:33:18,862 INFO misc.py line 119 2586773] Train: [28/50][347/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 01:10:35 loss: 0.2254 Lr: 0.00175 [2024-11-25 18:33:19,363 INFO misc.py line 119 2586773] Train: [28/50][348/376] Data 0.002 (0.002) Batch 0.501 (0.510) Remain 01:10:35 loss: 0.1752 Lr: 0.00175 [2024-11-25 18:33:19,898 INFO misc.py line 119 2586773] Train: [28/50][349/376] Data 0.002 (0.002) Batch 0.535 (0.510) Remain 01:10:35 loss: 0.1975 Lr: 0.00175 [2024-11-25 18:33:20,379 INFO misc.py line 119 2586773] Train: [28/50][350/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 01:10:34 loss: 0.1979 Lr: 0.00175 [2024-11-25 18:33:20,909 INFO misc.py line 119 2586773] Train: [28/50][351/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 01:10:34 loss: 0.2540 Lr: 0.00175 [2024-11-25 18:33:21,359 INFO misc.py line 119 2586773] Train: [28/50][352/376] Data 0.002 (0.002) Batch 0.450 (0.510) Remain 01:10:32 loss: 0.2272 Lr: 0.00175 [2024-11-25 18:33:21,862 INFO misc.py line 119 2586773] Train: [28/50][353/376] Data 0.002 (0.002) Batch 0.503 (0.510) Remain 01:10:31 loss: 0.2232 Lr: 0.00175 [2024-11-25 18:33:22,330 INFO misc.py line 119 2586773] Train: [28/50][354/376] Data 0.003 (0.002) Batch 0.468 (0.510) Remain 01:10:29 loss: 0.2180 Lr: 0.00175 [2024-11-25 18:33:22,817 INFO misc.py line 119 2586773] Train: [28/50][355/376] Data 0.003 (0.002) Batch 0.488 (0.510) Remain 01:10:28 loss: 0.2145 Lr: 0.00175 [2024-11-25 18:33:23,326 INFO misc.py line 119 2586773] Train: [28/50][356/376] Data 0.003 (0.002) Batch 0.509 (0.510) Remain 01:10:28 loss: 0.2580 Lr: 0.00175 [2024-11-25 18:33:23,843 INFO misc.py line 119 2586773] Train: [28/50][357/376] Data 0.002 (0.002) Batch 0.517 (0.510) Remain 01:10:28 loss: 0.2326 Lr: 0.00175 [2024-11-25 18:33:24,336 INFO misc.py line 119 2586773] Train: [28/50][358/376] Data 0.002 (0.002) Batch 0.492 (0.510) Remain 01:10:27 loss: 0.1911 Lr: 0.00175 [2024-11-25 18:33:24,860 INFO misc.py line 119 2586773] Train: [28/50][359/376] Data 0.002 (0.002) Batch 0.524 (0.510) Remain 01:10:26 loss: 0.2254 Lr: 0.00175 [2024-11-25 18:33:25,316 INFO misc.py line 119 2586773] Train: [28/50][360/376] Data 0.002 (0.002) Batch 0.456 (0.510) Remain 01:10:25 loss: 0.2420 Lr: 0.00175 [2024-11-25 18:33:25,832 INFO misc.py line 119 2586773] Train: [28/50][361/376] Data 0.002 (0.002) Batch 0.516 (0.510) Remain 01:10:24 loss: 0.2037 Lr: 0.00175 [2024-11-25 18:33:26,377 INFO misc.py line 119 2586773] Train: [28/50][362/376] Data 0.003 (0.002) Batch 0.545 (0.510) Remain 01:10:25 loss: 0.2088 Lr: 0.00175 [2024-11-25 18:33:26,938 INFO misc.py line 119 2586773] Train: [28/50][363/376] Data 0.002 (0.002) Batch 0.561 (0.510) Remain 01:10:25 loss: 0.2342 Lr: 0.00175 [2024-11-25 18:33:27,406 INFO misc.py line 119 2586773] Train: [28/50][364/376] Data 0.002 (0.002) Batch 0.468 (0.510) Remain 01:10:24 loss: 0.2693 Lr: 0.00175 [2024-11-25 18:33:27,910 INFO misc.py line 119 2586773] Train: [28/50][365/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 01:10:23 loss: 0.1895 Lr: 0.00175 [2024-11-25 18:33:28,416 INFO misc.py line 119 2586773] Train: [28/50][366/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 01:10:23 loss: 0.2003 Lr: 0.00175 [2024-11-25 18:33:28,984 INFO misc.py line 119 2586773] Train: [28/50][367/376] Data 0.002 (0.002) Batch 0.568 (0.510) Remain 01:10:23 loss: 0.1913 Lr: 0.00175 [2024-11-25 18:33:29,517 INFO misc.py line 119 2586773] Train: [28/50][368/376] Data 0.002 (0.002) Batch 0.533 (0.510) Remain 01:10:23 loss: 0.1918 Lr: 0.00175 [2024-11-25 18:33:30,003 INFO misc.py line 119 2586773] Train: [28/50][369/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 01:10:22 loss: 0.2223 Lr: 0.00175 [2024-11-25 18:33:30,508 INFO misc.py line 119 2586773] Train: [28/50][370/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 01:10:22 loss: 0.1902 Lr: 0.00175 [2024-11-25 18:33:31,029 INFO misc.py line 119 2586773] Train: [28/50][371/376] Data 0.002 (0.002) Batch 0.521 (0.510) Remain 01:10:21 loss: 0.1706 Lr: 0.00175 [2024-11-25 18:33:31,551 INFO misc.py line 119 2586773] Train: [28/50][372/376] Data 0.003 (0.002) Batch 0.521 (0.510) Remain 01:10:21 loss: 0.2102 Lr: 0.00174 [2024-11-25 18:33:32,062 INFO misc.py line 119 2586773] Train: [28/50][373/376] Data 0.002 (0.002) Batch 0.511 (0.510) Remain 01:10:21 loss: 0.2232 Lr: 0.00174 [2024-11-25 18:33:32,553 INFO misc.py line 119 2586773] Train: [28/50][374/376] Data 0.003 (0.002) Batch 0.491 (0.510) Remain 01:10:20 loss: 0.2172 Lr: 0.00174 [2024-11-25 18:33:33,094 INFO misc.py line 119 2586773] Train: [28/50][375/376] Data 0.002 (0.002) Batch 0.541 (0.510) Remain 01:10:20 loss: 0.2193 Lr: 0.00174 [2024-11-25 18:33:33,599 INFO misc.py line 119 2586773] Train: [28/50][376/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 01:10:19 loss: 0.1698 Lr: 0.00174 [2024-11-25 18:33:33,600 INFO misc.py line 136 2586773] Train result: loss: 0.2160 [2024-11-25 18:33:33,600 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:33:44,524 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2010 [2024-11-25 18:33:44,777 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2100 [2024-11-25 18:33:45,041 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2540 [2024-11-25 18:33:45,264 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1892 [2024-11-25 18:33:45,529 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3000 [2024-11-25 18:33:45,797 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2117 [2024-11-25 18:33:46,023 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2023 [2024-11-25 18:33:46,293 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2190 [2024-11-25 18:33:46,517 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2655 [2024-11-25 18:33:46,781 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2506 [2024-11-25 18:33:47,011 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2236 [2024-11-25 18:33:47,284 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2378 [2024-11-25 18:33:47,549 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2510 [2024-11-25 18:33:47,812 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2605 [2024-11-25 18:33:48,045 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2412 [2024-11-25 18:33:48,285 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3013 [2024-11-25 18:33:48,549 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3037 [2024-11-25 18:33:48,799 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1922 [2024-11-25 18:33:49,030 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2475 [2024-11-25 18:33:49,289 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2648 [2024-11-25 18:33:49,522 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2532 [2024-11-25 18:33:49,789 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2775 [2024-11-25 18:33:50,029 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2246 [2024-11-25 18:33:50,295 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2459 [2024-11-25 18:33:50,560 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2364 [2024-11-25 18:33:50,795 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2684 [2024-11-25 18:33:51,046 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2714 [2024-11-25 18:33:51,292 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2356 [2024-11-25 18:33:51,561 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2893 [2024-11-25 18:33:51,816 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3046 [2024-11-25 18:33:52,049 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2639 [2024-11-25 18:33:52,312 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2085 [2024-11-25 18:33:52,533 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2652 [2024-11-25 18:33:52,775 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2305 [2024-11-25 18:33:53,039 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2257 [2024-11-25 18:33:53,283 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2629 [2024-11-25 18:33:53,511 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1976 [2024-11-25 18:33:53,779 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2421 [2024-11-25 18:33:54,010 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2782 [2024-11-25 18:33:54,245 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2680 [2024-11-25 18:33:54,516 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2933 [2024-11-25 18:33:54,766 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2928 [2024-11-25 18:33:55,004 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2502 [2024-11-25 18:33:55,235 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2348 [2024-11-25 18:33:55,475 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2170 [2024-11-25 18:33:55,724 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2381 [2024-11-25 18:33:55,982 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2417 [2024-11-25 18:33:56,234 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3057 [2024-11-25 18:33:56,457 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2203 [2024-11-25 18:33:56,691 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2197 [2024-11-25 18:33:56,911 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2578 [2024-11-25 18:33:57,162 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2447 [2024-11-25 18:33:57,430 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2477 [2024-11-25 18:33:57,691 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3074 [2024-11-25 18:33:57,923 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2499 [2024-11-25 18:33:58,167 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2291 [2024-11-25 18:33:58,424 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2732 [2024-11-25 18:33:58,691 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2630 [2024-11-25 18:33:58,947 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2621 [2024-11-25 18:33:59,209 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2501 [2024-11-25 18:33:59,465 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2211 [2024-11-25 18:33:59,734 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2458 [2024-11-25 18:33:59,963 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2571 [2024-11-25 18:34:00,221 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2666 [2024-11-25 18:34:00,487 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2581 [2024-11-25 18:34:00,751 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2160 [2024-11-25 18:34:00,996 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2046 [2024-11-25 18:34:01,250 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2828 [2024-11-25 18:34:01,518 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2263 [2024-11-25 18:34:01,778 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2916 [2024-11-25 18:34:02,022 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2323 [2024-11-25 18:34:02,257 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2799 [2024-11-25 18:34:02,514 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2598 [2024-11-25 18:34:02,758 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2832 [2024-11-25 18:34:02,973 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2583 [2024-11-25 18:34:03,194 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2155 [2024-11-25 18:34:03,462 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2413 [2024-11-25 18:34:03,697 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2264 [2024-11-25 18:34:03,955 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2218 [2024-11-25 18:34:04,205 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3010 [2024-11-25 18:34:04,450 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2131 [2024-11-25 18:34:04,709 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2676 [2024-11-25 18:34:04,962 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2040 [2024-11-25 18:34:05,210 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2591 [2024-11-25 18:34:05,484 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2501 [2024-11-25 18:34:05,720 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2496 [2024-11-25 18:34:05,985 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2490 [2024-11-25 18:34:06,246 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2516 [2024-11-25 18:34:06,492 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2700 [2024-11-25 18:34:06,740 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2738 [2024-11-25 18:34:06,973 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2397 [2024-11-25 18:34:07,225 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2746 [2024-11-25 18:34:07,491 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2534 [2024-11-25 18:34:07,760 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2021 [2024-11-25 18:34:08,028 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2242 [2024-11-25 18:34:08,279 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2185 [2024-11-25 18:34:08,548 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2552 [2024-11-25 18:34:08,769 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2813 [2024-11-25 18:34:09,037 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2517 [2024-11-25 18:34:09,275 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2787 [2024-11-25 18:34:09,544 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2165 [2024-11-25 18:34:09,807 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2769 [2024-11-25 18:34:10,066 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2627 [2024-11-25 18:34:10,321 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2867 [2024-11-25 18:34:10,545 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2251 [2024-11-25 18:34:10,781 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2224 [2024-11-25 18:34:11,039 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2317 [2024-11-25 18:34:11,308 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2716 [2024-11-25 18:34:11,542 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2562 [2024-11-25 18:34:11,803 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2479 [2024-11-25 18:34:12,067 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2304 [2024-11-25 18:34:12,289 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2394 [2024-11-25 18:34:12,527 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2228 [2024-11-25 18:34:12,745 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2317 [2024-11-25 18:34:12,971 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2239 [2024-11-25 18:34:13,242 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2757 [2024-11-25 18:34:13,500 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2694 [2024-11-25 18:34:13,768 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2617 [2024-11-25 18:34:14,034 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2258 [2024-11-25 18:34:14,294 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2848 [2024-11-25 18:34:14,556 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2907 [2024-11-25 18:34:14,821 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2225 [2024-11-25 18:34:15,078 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2716 [2024-11-25 18:34:15,343 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2594 [2024-11-25 18:34:15,609 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2546 [2024-11-25 18:34:15,864 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2513 [2024-11-25 18:34:16,093 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2104 [2024-11-25 18:34:16,352 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2666 [2024-11-25 18:34:16,587 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2686 [2024-11-25 18:34:16,814 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1921 [2024-11-25 18:34:17,024 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2457 [2024-11-25 18:34:17,240 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2165 [2024-11-25 18:34:17,932 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7667/0.8262/0.9963. [2024-11-25 18:34:17,932 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9986 [2024-11-25 18:34:17,932 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5372/0.6538 [2024-11-25 18:34:17,933 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:34:17,934 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:34:17,934 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:34:20,619 INFO misc.py line 119 2586773] Train: [29/50][1/376] Data 0.091 (0.091) Batch 0.567 (0.567) Remain 01:18:09 loss: 0.1845 Lr: 0.00174 [2024-11-25 18:34:21,069 INFO misc.py line 119 2586773] Train: [29/50][2/376] Data 0.002 (0.002) Batch 0.451 (0.451) Remain 01:02:08 loss: 0.2101 Lr: 0.00174 [2024-11-25 18:34:21,580 INFO misc.py line 119 2586773] Train: [29/50][3/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 01:10:23 loss: 0.1855 Lr: 0.00174 [2024-11-25 18:34:22,116 INFO misc.py line 119 2586773] Train: [29/50][4/376] Data 0.002 (0.002) Batch 0.536 (0.536) Remain 01:13:54 loss: 0.2195 Lr: 0.00174 [2024-11-25 18:34:22,669 INFO misc.py line 119 2586773] Train: [29/50][5/376] Data 0.002 (0.002) Batch 0.552 (0.544) Remain 01:14:59 loss: 0.2248 Lr: 0.00174 [2024-11-25 18:34:23,214 INFO misc.py line 119 2586773] Train: [29/50][6/376] Data 0.002 (0.002) Batch 0.545 (0.545) Remain 01:15:02 loss: 0.2356 Lr: 0.00174 [2024-11-25 18:34:23,740 INFO misc.py line 119 2586773] Train: [29/50][7/376] Data 0.002 (0.002) Batch 0.525 (0.540) Remain 01:14:21 loss: 0.2549 Lr: 0.00174 [2024-11-25 18:34:24,226 INFO misc.py line 119 2586773] Train: [29/50][8/376] Data 0.002 (0.002) Batch 0.487 (0.529) Remain 01:12:53 loss: 0.1879 Lr: 0.00174 [2024-11-25 18:34:24,792 INFO misc.py line 119 2586773] Train: [29/50][9/376] Data 0.002 (0.002) Batch 0.566 (0.535) Remain 01:13:43 loss: 0.2310 Lr: 0.00174 [2024-11-25 18:34:25,294 INFO misc.py line 119 2586773] Train: [29/50][10/376] Data 0.002 (0.002) Batch 0.502 (0.531) Remain 01:13:03 loss: 0.3012 Lr: 0.00174 [2024-11-25 18:34:25,813 INFO misc.py line 119 2586773] Train: [29/50][11/376] Data 0.003 (0.002) Batch 0.519 (0.529) Remain 01:12:50 loss: 0.2241 Lr: 0.00174 [2024-11-25 18:34:26,314 INFO misc.py line 119 2586773] Train: [29/50][12/376] Data 0.002 (0.002) Batch 0.501 (0.526) Remain 01:12:24 loss: 0.2047 Lr: 0.00174 [2024-11-25 18:34:26,815 INFO misc.py line 119 2586773] Train: [29/50][13/376] Data 0.002 (0.002) Batch 0.501 (0.523) Remain 01:12:03 loss: 0.2155 Lr: 0.00174 [2024-11-25 18:34:27,343 INFO misc.py line 119 2586773] Train: [29/50][14/376] Data 0.003 (0.002) Batch 0.528 (0.524) Remain 01:12:06 loss: 0.1920 Lr: 0.00174 [2024-11-25 18:34:27,818 INFO misc.py line 119 2586773] Train: [29/50][15/376] Data 0.002 (0.002) Batch 0.476 (0.520) Remain 01:11:32 loss: 0.2320 Lr: 0.00174 [2024-11-25 18:34:28,329 INFO misc.py line 119 2586773] Train: [29/50][16/376] Data 0.002 (0.002) Batch 0.511 (0.519) Remain 01:11:26 loss: 0.2340 Lr: 0.00174 [2024-11-25 18:34:28,832 INFO misc.py line 119 2586773] Train: [29/50][17/376] Data 0.002 (0.002) Batch 0.503 (0.518) Remain 01:11:15 loss: 0.3140 Lr: 0.00174 [2024-11-25 18:34:29,341 INFO misc.py line 119 2586773] Train: [29/50][18/376] Data 0.002 (0.002) Batch 0.509 (0.517) Remain 01:11:10 loss: 0.2197 Lr: 0.00174 [2024-11-25 18:34:29,846 INFO misc.py line 119 2586773] Train: [29/50][19/376] Data 0.003 (0.002) Batch 0.506 (0.517) Remain 01:11:03 loss: 0.2503 Lr: 0.00174 [2024-11-25 18:34:30,340 INFO misc.py line 119 2586773] Train: [29/50][20/376] Data 0.002 (0.002) Batch 0.493 (0.515) Remain 01:10:52 loss: 0.2067 Lr: 0.00174 [2024-11-25 18:34:30,887 INFO misc.py line 119 2586773] Train: [29/50][21/376] Data 0.002 (0.002) Batch 0.547 (0.517) Remain 01:11:06 loss: 0.2171 Lr: 0.00174 [2024-11-25 18:34:31,408 INFO misc.py line 119 2586773] Train: [29/50][22/376] Data 0.002 (0.002) Batch 0.521 (0.517) Remain 01:11:07 loss: 0.2275 Lr: 0.00174 [2024-11-25 18:34:31,908 INFO misc.py line 119 2586773] Train: [29/50][23/376] Data 0.002 (0.002) Batch 0.500 (0.516) Remain 01:10:59 loss: 0.1776 Lr: 0.00174 [2024-11-25 18:34:32,386 INFO misc.py line 119 2586773] Train: [29/50][24/376] Data 0.003 (0.002) Batch 0.478 (0.515) Remain 01:10:44 loss: 0.1769 Lr: 0.00174 [2024-11-25 18:34:32,872 INFO misc.py line 119 2586773] Train: [29/50][25/376] Data 0.002 (0.002) Batch 0.485 (0.513) Remain 01:10:32 loss: 0.2175 Lr: 0.00173 [2024-11-25 18:34:33,360 INFO misc.py line 119 2586773] Train: [29/50][26/376] Data 0.003 (0.002) Batch 0.488 (0.512) Remain 01:10:23 loss: 0.2185 Lr: 0.00173 [2024-11-25 18:34:33,867 INFO misc.py line 119 2586773] Train: [29/50][27/376] Data 0.003 (0.002) Batch 0.507 (0.512) Remain 01:10:21 loss: 0.2006 Lr: 0.00173 [2024-11-25 18:34:34,362 INFO misc.py line 119 2586773] Train: [29/50][28/376] Data 0.003 (0.002) Batch 0.495 (0.511) Remain 01:10:14 loss: 0.1712 Lr: 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18:36:50,543 INFO misc.py line 119 2586773] Train: [29/50][297/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 01:07:20 loss: 0.2293 Lr: 0.00164 [2024-11-25 18:36:51,036 INFO misc.py line 119 2586773] Train: [29/50][298/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 01:07:19 loss: 0.2019 Lr: 0.00164 [2024-11-25 18:36:51,549 INFO misc.py line 119 2586773] Train: [29/50][299/376] Data 0.003 (0.002) Batch 0.512 (0.507) Remain 01:07:19 loss: 0.2037 Lr: 0.00164 [2024-11-25 18:36:52,040 INFO misc.py line 119 2586773] Train: [29/50][300/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 01:07:18 loss: 0.2137 Lr: 0.00164 [2024-11-25 18:36:52,548 INFO misc.py line 119 2586773] Train: [29/50][301/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 01:07:18 loss: 0.1831 Lr: 0.00164 [2024-11-25 18:36:53,044 INFO misc.py line 119 2586773] Train: [29/50][302/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:07:17 loss: 0.2558 Lr: 0.00164 [2024-11-25 18:36:53,553 INFO misc.py line 119 2586773] Train: [29/50][303/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 01:07:16 loss: 0.1971 Lr: 0.00164 [2024-11-25 18:36:54,039 INFO misc.py line 119 2586773] Train: [29/50][304/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 01:07:15 loss: 0.2507 Lr: 0.00164 [2024-11-25 18:36:54,517 INFO misc.py line 119 2586773] Train: [29/50][305/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 01:07:14 loss: 0.2021 Lr: 0.00164 [2024-11-25 18:36:54,987 INFO misc.py line 119 2586773] Train: [29/50][306/376] Data 0.002 (0.002) Batch 0.470 (0.506) Remain 01:07:13 loss: 0.2089 Lr: 0.00164 [2024-11-25 18:36:55,448 INFO misc.py line 119 2586773] Train: [29/50][307/376] Data 0.003 (0.002) Batch 0.462 (0.506) Remain 01:07:11 loss: 0.2266 Lr: 0.00164 [2024-11-25 18:36:55,949 INFO misc.py line 119 2586773] Train: [29/50][308/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 01:07:10 loss: 0.2272 Lr: 0.00164 [2024-11-25 18:36:56,484 INFO misc.py line 119 2586773] Train: [29/50][309/376] Data 0.002 (0.002) Batch 0.535 (0.506) Remain 01:07:11 loss: 0.1850 Lr: 0.00164 [2024-11-25 18:36:56,961 INFO misc.py line 119 2586773] Train: [29/50][310/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 01:07:09 loss: 0.2176 Lr: 0.00164 [2024-11-25 18:36:57,486 INFO misc.py line 119 2586773] Train: [29/50][311/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 01:07:09 loss: 0.1628 Lr: 0.00164 [2024-11-25 18:36:57,993 INFO misc.py line 119 2586773] Train: [29/50][312/376] Data 0.002 (0.002) Batch 0.507 (0.506) Remain 01:07:09 loss: 0.1992 Lr: 0.00164 [2024-11-25 18:36:58,477 INFO misc.py line 119 2586773] Train: [29/50][313/376] Data 0.002 (0.002) Batch 0.485 (0.506) Remain 01:07:08 loss: 0.1982 Lr: 0.00164 [2024-11-25 18:36:58,990 INFO misc.py line 119 2586773] Train: [29/50][314/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 01:07:07 loss: 0.2032 Lr: 0.00164 [2024-11-25 18:36:59,473 INFO misc.py line 119 2586773] Train: [29/50][315/376] Data 0.002 (0.002) Batch 0.484 (0.506) Remain 01:07:06 loss: 0.2377 Lr: 0.00164 [2024-11-25 18:37:00,005 INFO misc.py line 119 2586773] Train: [29/50][316/376] Data 0.002 (0.002) Batch 0.532 (0.506) Remain 01:07:06 loss: 0.2162 Lr: 0.00163 [2024-11-25 18:37:00,485 INFO misc.py line 119 2586773] Train: [29/50][317/376] Data 0.003 (0.002) Batch 0.480 (0.506) Remain 01:07:05 loss: 0.1970 Lr: 0.00163 [2024-11-25 18:37:01,047 INFO misc.py line 119 2586773] Train: [29/50][318/376] Data 0.002 (0.002) Batch 0.562 (0.506) Remain 01:07:06 loss: 0.2433 Lr: 0.00163 [2024-11-25 18:37:01,543 INFO misc.py line 119 2586773] Train: [29/50][319/376] Data 0.002 (0.002) Batch 0.496 (0.506) Remain 01:07:05 loss: 0.2825 Lr: 0.00163 [2024-11-25 18:37:02,046 INFO misc.py line 119 2586773] Train: [29/50][320/376] Data 0.003 (0.002) Batch 0.503 (0.506) Remain 01:07:05 loss: 0.1980 Lr: 0.00163 [2024-11-25 18:37:02,512 INFO misc.py line 119 2586773] Train: [29/50][321/376] Data 0.003 (0.002) Batch 0.465 (0.506) Remain 01:07:03 loss: 0.2013 Lr: 0.00163 [2024-11-25 18:37:03,013 INFO misc.py line 119 2586773] Train: [29/50][322/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 01:07:03 loss: 0.2445 Lr: 0.00163 [2024-11-25 18:37:03,540 INFO misc.py line 119 2586773] Train: [29/50][323/376] Data 0.002 (0.002) Batch 0.527 (0.506) Remain 01:07:03 loss: 0.2331 Lr: 0.00163 [2024-11-25 18:37:04,073 INFO misc.py line 119 2586773] Train: [29/50][324/376] Data 0.002 (0.002) Batch 0.533 (0.506) Remain 01:07:03 loss: 0.2588 Lr: 0.00163 [2024-11-25 18:37:04,607 INFO misc.py line 119 2586773] Train: [29/50][325/376] Data 0.003 (0.002) Batch 0.534 (0.506) Remain 01:07:03 loss: 0.2129 Lr: 0.00163 [2024-11-25 18:37:05,110 INFO misc.py line 119 2586773] Train: [29/50][326/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 01:07:02 loss: 0.3025 Lr: 0.00163 [2024-11-25 18:37:05,672 INFO misc.py line 119 2586773] Train: [29/50][327/376] Data 0.003 (0.002) Batch 0.562 (0.506) Remain 01:07:03 loss: 0.1986 Lr: 0.00163 [2024-11-25 18:37:06,201 INFO misc.py line 119 2586773] Train: [29/50][328/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 01:07:03 loss: 0.1676 Lr: 0.00163 [2024-11-25 18:37:06,672 INFO misc.py line 119 2586773] Train: [29/50][329/376] Data 0.002 (0.002) Batch 0.471 (0.506) Remain 01:07:02 loss: 0.1970 Lr: 0.00163 [2024-11-25 18:37:07,190 INFO misc.py line 119 2586773] Train: [29/50][330/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 01:07:02 loss: 0.1947 Lr: 0.00163 [2024-11-25 18:37:07,733 INFO misc.py line 119 2586773] Train: [29/50][331/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 01:07:02 loss: 0.1765 Lr: 0.00163 [2024-11-25 18:37:08,222 INFO misc.py line 119 2586773] Train: [29/50][332/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 01:07:01 loss: 0.2521 Lr: 0.00163 [2024-11-25 18:37:08,711 INFO misc.py line 119 2586773] Train: [29/50][333/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 01:07:00 loss: 0.2552 Lr: 0.00163 [2024-11-25 18:37:09,226 INFO misc.py line 119 2586773] Train: [29/50][334/376] Data 0.003 (0.002) Batch 0.515 (0.506) Remain 01:07:00 loss: 0.1518 Lr: 0.00163 [2024-11-25 18:37:09,711 INFO misc.py line 119 2586773] Train: [29/50][335/376] Data 0.003 (0.002) Batch 0.485 (0.506) Remain 01:06:59 loss: 0.1996 Lr: 0.00163 [2024-11-25 18:37:10,187 INFO misc.py line 119 2586773] Train: [29/50][336/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 01:06:58 loss: 0.3194 Lr: 0.00163 [2024-11-25 18:37:10,660 INFO misc.py line 119 2586773] Train: [29/50][337/376] Data 0.002 (0.002) Batch 0.473 (0.506) Remain 01:06:56 loss: 0.1779 Lr: 0.00163 [2024-11-25 18:37:11,168 INFO misc.py line 119 2586773] Train: [29/50][338/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 01:06:56 loss: 0.2112 Lr: 0.00163 [2024-11-25 18:37:11,716 INFO misc.py line 119 2586773] Train: [29/50][339/376] Data 0.002 (0.002) Batch 0.548 (0.506) Remain 01:06:56 loss: 0.2601 Lr: 0.00163 [2024-11-25 18:37:12,188 INFO misc.py line 119 2586773] Train: [29/50][340/376] Data 0.002 (0.002) Batch 0.472 (0.506) Remain 01:06:55 loss: 0.2298 Lr: 0.00163 [2024-11-25 18:37:12,674 INFO misc.py line 119 2586773] Train: [29/50][341/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 01:06:54 loss: 0.2203 Lr: 0.00163 [2024-11-25 18:37:13,161 INFO misc.py line 119 2586773] Train: [29/50][342/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 01:06:53 loss: 0.2682 Lr: 0.00163 [2024-11-25 18:37:13,685 INFO misc.py line 119 2586773] Train: [29/50][343/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 01:06:53 loss: 0.2541 Lr: 0.00163 [2024-11-25 18:37:14,191 INFO misc.py line 119 2586773] Train: [29/50][344/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 01:06:53 loss: 0.2428 Lr: 0.00163 [2024-11-25 18:37:14,720 INFO misc.py line 119 2586773] Train: [29/50][345/376] Data 0.003 (0.002) Batch 0.529 (0.506) Remain 01:06:53 loss: 0.2063 Lr: 0.00162 [2024-11-25 18:37:15,231 INFO misc.py line 119 2586773] Train: [29/50][346/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 01:06:52 loss: 0.1793 Lr: 0.00162 [2024-11-25 18:37:15,705 INFO misc.py line 119 2586773] Train: [29/50][347/376] Data 0.003 (0.002) Batch 0.475 (0.506) Remain 01:06:51 loss: 0.2234 Lr: 0.00162 [2024-11-25 18:37:16,220 INFO misc.py line 119 2586773] Train: [29/50][348/376] Data 0.002 (0.002) Batch 0.514 (0.506) Remain 01:06:51 loss: 0.2356 Lr: 0.00162 [2024-11-25 18:37:16,736 INFO misc.py line 119 2586773] Train: [29/50][349/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 01:06:50 loss: 0.1676 Lr: 0.00162 [2024-11-25 18:37:17,275 INFO misc.py line 119 2586773] Train: [29/50][350/376] Data 0.002 (0.002) Batch 0.539 (0.506) Remain 01:06:51 loss: 0.2234 Lr: 0.00162 [2024-11-25 18:37:17,774 INFO misc.py line 119 2586773] Train: [29/50][351/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 01:06:50 loss: 0.1757 Lr: 0.00162 [2024-11-25 18:37:18,220 INFO misc.py line 119 2586773] Train: [29/50][352/376] Data 0.002 (0.002) Batch 0.446 (0.506) Remain 01:06:48 loss: 0.1910 Lr: 0.00162 [2024-11-25 18:37:18,750 INFO misc.py line 119 2586773] Train: [29/50][353/376] Data 0.002 (0.002) Batch 0.529 (0.506) Remain 01:06:48 loss: 0.3632 Lr: 0.00162 [2024-11-25 18:37:19,229 INFO misc.py line 119 2586773] Train: [29/50][354/376] Data 0.002 (0.002) Batch 0.479 (0.506) Remain 01:06:47 loss: 0.2095 Lr: 0.00162 [2024-11-25 18:37:19,696 INFO misc.py line 119 2586773] Train: [29/50][355/376] Data 0.003 (0.002) Batch 0.467 (0.506) Remain 01:06:46 loss: 0.2403 Lr: 0.00162 [2024-11-25 18:37:20,223 INFO misc.py line 119 2586773] Train: [29/50][356/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 01:06:46 loss: 0.2024 Lr: 0.00162 [2024-11-25 18:37:20,743 INFO misc.py line 119 2586773] Train: [29/50][357/376] Data 0.003 (0.002) Batch 0.520 (0.506) Remain 01:06:45 loss: 0.1599 Lr: 0.00162 [2024-11-25 18:37:21,254 INFO misc.py line 119 2586773] Train: [29/50][358/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 01:06:45 loss: 0.2218 Lr: 0.00162 [2024-11-25 18:37:21,779 INFO misc.py line 119 2586773] Train: [29/50][359/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 01:06:45 loss: 0.2031 Lr: 0.00162 [2024-11-25 18:37:22,257 INFO misc.py line 119 2586773] Train: [29/50][360/376] Data 0.002 (0.002) Batch 0.478 (0.506) Remain 01:06:44 loss: 0.2215 Lr: 0.00162 [2024-11-25 18:37:22,748 INFO misc.py line 119 2586773] Train: [29/50][361/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 01:06:43 loss: 0.1887 Lr: 0.00162 [2024-11-25 18:37:23,273 INFO misc.py line 119 2586773] Train: [29/50][362/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 01:06:43 loss: 0.2386 Lr: 0.00162 [2024-11-25 18:37:23,814 INFO misc.py line 119 2586773] Train: [29/50][363/376] Data 0.002 (0.002) Batch 0.541 (0.506) Remain 01:06:43 loss: 0.1881 Lr: 0.00162 [2024-11-25 18:37:24,320 INFO misc.py line 119 2586773] Train: [29/50][364/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 01:06:43 loss: 0.2778 Lr: 0.00162 [2024-11-25 18:37:24,840 INFO misc.py line 119 2586773] Train: [29/50][365/376] Data 0.002 (0.002) Batch 0.520 (0.506) Remain 01:06:42 loss: 0.2660 Lr: 0.00162 [2024-11-25 18:37:25,316 INFO misc.py line 119 2586773] Train: [29/50][366/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 01:06:41 loss: 0.1922 Lr: 0.00162 [2024-11-25 18:37:25,828 INFO misc.py line 119 2586773] Train: [29/50][367/376] Data 0.003 (0.002) Batch 0.513 (0.506) Remain 01:06:41 loss: 0.1994 Lr: 0.00162 [2024-11-25 18:37:26,361 INFO misc.py line 119 2586773] Train: [29/50][368/376] Data 0.002 (0.002) Batch 0.533 (0.506) Remain 01:06:41 loss: 0.1686 Lr: 0.00162 [2024-11-25 18:37:26,830 INFO misc.py line 119 2586773] Train: [29/50][369/376] Data 0.002 (0.002) Batch 0.469 (0.506) Remain 01:06:40 loss: 0.1949 Lr: 0.00162 [2024-11-25 18:37:27,342 INFO misc.py line 119 2586773] Train: [29/50][370/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 01:06:39 loss: 0.2398 Lr: 0.00162 [2024-11-25 18:37:27,882 INFO misc.py line 119 2586773] Train: [29/50][371/376] Data 0.002 (0.002) Batch 0.540 (0.506) Remain 01:06:39 loss: 0.2035 Lr: 0.00162 [2024-11-25 18:37:28,357 INFO misc.py line 119 2586773] Train: [29/50][372/376] Data 0.002 (0.002) Batch 0.475 (0.506) Remain 01:06:38 loss: 0.2075 Lr: 0.00162 [2024-11-25 18:37:28,816 INFO misc.py line 119 2586773] Train: [29/50][373/376] Data 0.002 (0.002) Batch 0.459 (0.506) Remain 01:06:37 loss: 0.1911 Lr: 0.00162 [2024-11-25 18:37:29,285 INFO misc.py line 119 2586773] Train: [29/50][374/376] Data 0.002 (0.002) Batch 0.469 (0.506) Remain 01:06:35 loss: 0.1695 Lr: 0.00161 [2024-11-25 18:37:29,751 INFO misc.py line 119 2586773] Train: [29/50][375/376] Data 0.002 (0.002) Batch 0.465 (0.506) Remain 01:06:34 loss: 0.1811 Lr: 0.00161 [2024-11-25 18:37:30,261 INFO misc.py line 119 2586773] Train: [29/50][376/376] Data 0.002 (0.002) Batch 0.510 (0.506) Remain 01:06:34 loss: 0.2094 Lr: 0.00161 [2024-11-25 18:37:30,261 INFO misc.py line 136 2586773] Train result: loss: 0.2151 [2024-11-25 18:37:30,262 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:37:41,111 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.2081 [2024-11-25 18:37:41,369 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2377 [2024-11-25 18:37:41,632 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.3372 [2024-11-25 18:37:41,864 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1993 [2024-11-25 18:37:42,125 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3078 [2024-11-25 18:37:42,391 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2146 [2024-11-25 18:37:42,622 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2262 [2024-11-25 18:37:42,890 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2524 [2024-11-25 18:37:43,123 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2630 [2024-11-25 18:37:43,383 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2744 [2024-11-25 18:37:43,624 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2180 [2024-11-25 18:37:43,895 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2453 [2024-11-25 18:37:44,160 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2792 [2024-11-25 18:37:44,428 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2679 [2024-11-25 18:37:44,660 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2398 [2024-11-25 18:37:44,902 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2933 [2024-11-25 18:37:45,168 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2987 [2024-11-25 18:37:45,422 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2450 [2024-11-25 18:37:45,652 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2485 [2024-11-25 18:37:45,915 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2508 [2024-11-25 18:37:46,152 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2446 [2024-11-25 18:37:46,418 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2655 [2024-11-25 18:37:46,661 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2299 [2024-11-25 18:37:46,930 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2615 [2024-11-25 18:37:47,194 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2391 [2024-11-25 18:37:47,429 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2702 [2024-11-25 18:37:47,689 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2815 [2024-11-25 18:37:47,938 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2558 [2024-11-25 18:37:48,204 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2892 [2024-11-25 18:37:48,459 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3037 [2024-11-25 18:37:48,703 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2690 [2024-11-25 18:37:48,966 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2174 [2024-11-25 18:37:49,193 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2818 [2024-11-25 18:37:49,430 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2494 [2024-11-25 18:37:49,690 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2321 [2024-11-25 18:37:49,934 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2656 [2024-11-25 18:37:50,162 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2103 [2024-11-25 18:37:50,431 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2519 [2024-11-25 18:37:50,661 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2832 [2024-11-25 18:37:50,894 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2578 [2024-11-25 18:37:51,169 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3133 [2024-11-25 18:37:51,429 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2907 [2024-11-25 18:37:51,668 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2651 [2024-11-25 18:37:51,901 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2354 [2024-11-25 18:37:52,137 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2356 [2024-11-25 18:37:52,383 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2283 [2024-11-25 18:37:52,647 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2609 [2024-11-25 18:37:52,899 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3123 [2024-11-25 18:37:53,122 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2148 [2024-11-25 18:37:53,355 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2268 [2024-11-25 18:37:53,581 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2756 [2024-11-25 18:37:53,834 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2550 [2024-11-25 18:37:54,099 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2534 [2024-11-25 18:37:54,359 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3148 [2024-11-25 18:37:54,591 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2651 [2024-11-25 18:37:54,830 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2296 [2024-11-25 18:37:55,087 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2790 [2024-11-25 18:37:55,354 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2775 [2024-11-25 18:37:55,612 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2671 [2024-11-25 18:37:55,874 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2499 [2024-11-25 18:37:56,133 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2452 [2024-11-25 18:37:56,403 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2618 [2024-11-25 18:37:56,632 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2572 [2024-11-25 18:37:56,890 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2639 [2024-11-25 18:37:57,158 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2889 [2024-11-25 18:37:57,423 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2206 [2024-11-25 18:37:57,682 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2097 [2024-11-25 18:37:57,939 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2667 [2024-11-25 18:37:58,206 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2642 [2024-11-25 18:37:58,467 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2877 [2024-11-25 18:37:58,710 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2340 [2024-11-25 18:37:58,943 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2823 [2024-11-25 18:37:59,203 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2579 [2024-11-25 18:37:59,448 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2776 [2024-11-25 18:37:59,671 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2601 [2024-11-25 18:37:59,892 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2278 [2024-11-25 18:38:00,159 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2670 [2024-11-25 18:38:00,395 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2390 [2024-11-25 18:38:00,656 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2544 [2024-11-25 18:38:00,905 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2998 [2024-11-25 18:38:01,150 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2611 [2024-11-25 18:38:01,412 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2616 [2024-11-25 18:38:01,663 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2060 [2024-11-25 18:38:01,911 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2584 [2024-11-25 18:38:02,183 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2476 [2024-11-25 18:38:02,419 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2676 [2024-11-25 18:38:02,680 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2641 [2024-11-25 18:38:02,938 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2571 [2024-11-25 18:38:03,188 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2813 [2024-11-25 18:38:03,434 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2772 [2024-11-25 18:38:03,667 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2623 [2024-11-25 18:38:03,918 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2783 [2024-11-25 18:38:04,184 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2568 [2024-11-25 18:38:04,449 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2258 [2024-11-25 18:38:04,717 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2417 [2024-11-25 18:38:04,964 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2210 [2024-11-25 18:38:05,231 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2458 [2024-11-25 18:38:05,451 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3188 [2024-11-25 18:38:05,722 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2707 [2024-11-25 18:38:05,962 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2575 [2024-11-25 18:38:06,233 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2124 [2024-11-25 18:38:06,494 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2846 [2024-11-25 18:38:06,754 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2573 [2024-11-25 18:38:07,005 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2884 [2024-11-25 18:38:07,230 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2611 [2024-11-25 18:38:07,465 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2413 [2024-11-25 18:38:07,719 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2300 [2024-11-25 18:38:07,987 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2547 [2024-11-25 18:38:08,221 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2706 [2024-11-25 18:38:08,487 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2397 [2024-11-25 18:38:08,750 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2550 [2024-11-25 18:38:08,975 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2594 [2024-11-25 18:38:09,211 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2111 [2024-11-25 18:38:09,441 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2135 [2024-11-25 18:38:09,666 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2177 [2024-11-25 18:38:09,935 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3095 [2024-11-25 18:38:10,193 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2858 [2024-11-25 18:38:10,461 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2342 [2024-11-25 18:38:10,731 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2285 [2024-11-25 18:38:10,993 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3503 [2024-11-25 18:38:11,252 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2928 [2024-11-25 18:38:11,517 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2389 [2024-11-25 18:38:11,773 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2622 [2024-11-25 18:38:12,040 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2736 [2024-11-25 18:38:12,303 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2807 [2024-11-25 18:38:12,556 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2851 [2024-11-25 18:38:12,784 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2253 [2024-11-25 18:38:13,044 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2633 [2024-11-25 18:38:13,280 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2771 [2024-11-25 18:38:13,506 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2012 [2024-11-25 18:38:13,719 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2464 [2024-11-25 18:38:13,935 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2052 [2024-11-25 18:38:14,574 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7579/0.8263/0.9960. [2024-11-25 18:38:14,575 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9960/0.9983 [2024-11-25 18:38:14,575 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5198/0.6544 [2024-11-25 18:38:14,575 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:38:14,576 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:38:14,576 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:38:17,332 INFO misc.py line 119 2586773] Train: [30/50][1/376] Data 0.110 (0.110) Batch 0.607 (0.607) Remain 01:19:55 loss: 0.2204 Lr: 0.00161 [2024-11-25 18:38:17,861 INFO misc.py line 119 2586773] Train: [30/50][2/376] Data 0.002 (0.002) Batch 0.530 (0.530) Remain 01:09:40 loss: 0.2416 Lr: 0.00161 [2024-11-25 18:38:18,343 INFO misc.py line 119 2586773] Train: [30/50][3/376] Data 0.003 (0.003) Batch 0.482 (0.482) Remain 01:03:24 loss: 0.2195 Lr: 0.00161 [2024-11-25 18:38:18,837 INFO misc.py line 119 2586773] Train: [30/50][4/376] Data 0.002 (0.002) Batch 0.494 (0.494) Remain 01:04:54 loss: 0.1748 Lr: 0.00161 [2024-11-25 18:38:19,356 INFO misc.py line 119 2586773] Train: [30/50][5/376] Data 0.003 (0.002) Batch 0.519 (0.506) Remain 01:06:36 loss: 0.2102 Lr: 0.00161 [2024-11-25 18:38:19,828 INFO misc.py line 119 2586773] Train: [30/50][6/376] Data 0.002 (0.002) Batch 0.472 (0.495) Remain 01:05:05 loss: 0.2403 Lr: 0.00161 [2024-11-25 18:38:20,329 INFO misc.py line 119 2586773] Train: [30/50][7/376] Data 0.002 (0.002) Batch 0.501 (0.497) Remain 01:05:17 loss: 0.3544 Lr: 0.00161 [2024-11-25 18:38:20,855 INFO misc.py line 119 2586773] Train: [30/50][8/376] Data 0.002 (0.002) Batch 0.526 (0.502) Remain 01:06:02 loss: 0.1766 Lr: 0.00161 [2024-11-25 18:38:21,408 INFO misc.py line 119 2586773] Train: [30/50][9/376] Data 0.003 (0.002) Batch 0.553 (0.511) Remain 01:07:08 loss: 0.1558 Lr: 0.00161 [2024-11-25 18:38:21,927 INFO misc.py line 119 2586773] Train: [30/50][10/376] Data 0.003 (0.002) Batch 0.519 (0.512) Remain 01:07:17 loss: 0.2124 Lr: 0.00161 [2024-11-25 18:38:22,413 INFO misc.py line 119 2586773] Train: [30/50][11/376] Data 0.002 (0.002) Batch 0.486 (0.509) Remain 01:06:50 loss: 0.1690 Lr: 0.00161 [2024-11-25 18:38:22,946 INFO misc.py line 119 2586773] Train: [30/50][12/376] Data 0.003 (0.003) Batch 0.533 (0.511) Remain 01:07:12 loss: 0.1844 Lr: 0.00161 [2024-11-25 18:38:23,458 INFO misc.py line 119 2586773] Train: [30/50][13/376] Data 0.003 (0.003) Batch 0.512 (0.512) Remain 01:07:12 loss: 0.2580 Lr: 0.00161 [2024-11-25 18:38:23,916 INFO misc.py line 119 2586773] Train: [30/50][14/376] Data 0.002 (0.003) Batch 0.458 (0.507) Remain 01:06:33 loss: 0.1721 Lr: 0.00161 [2024-11-25 18:38:24,417 INFO misc.py line 119 2586773] Train: [30/50][15/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 01:06:28 loss: 0.1916 Lr: 0.00161 [2024-11-25 18:38:24,900 INFO misc.py line 119 2586773] Train: [30/50][16/376] Data 0.002 (0.002) Batch 0.484 (0.504) Remain 01:06:14 loss: 0.2136 Lr: 0.00161 [2024-11-25 18:38:25,400 INFO misc.py line 119 2586773] Train: [30/50][17/376] Data 0.003 (0.002) Batch 0.499 (0.504) Remain 01:06:11 loss: 0.1747 Lr: 0.00161 [2024-11-25 18:38:25,896 INFO misc.py line 119 2586773] Train: [30/50][18/376] Data 0.003 (0.002) Batch 0.496 (0.504) Remain 01:06:06 loss: 0.2224 Lr: 0.00161 [2024-11-25 18:38:26,399 INFO misc.py line 119 2586773] Train: [30/50][19/376] Data 0.003 (0.002) Batch 0.503 (0.503) Remain 01:06:05 loss: 0.2137 Lr: 0.00161 [2024-11-25 18:38:26,952 INFO misc.py line 119 2586773] Train: [30/50][20/376] Data 0.002 (0.002) Batch 0.553 (0.506) Remain 01:06:28 loss: 0.1920 Lr: 0.00161 [2024-11-25 18:38:27,485 INFO misc.py line 119 2586773] Train: [30/50][21/376] Data 0.003 (0.002) Batch 0.534 (0.508) Remain 01:06:39 loss: 0.1901 Lr: 0.00161 [2024-11-25 18:38:28,018 INFO misc.py line 119 2586773] Train: [30/50][22/376] Data 0.003 (0.003) Batch 0.533 (0.509) Remain 01:06:49 loss: 0.2464 Lr: 0.00161 [2024-11-25 18:38:28,509 INFO misc.py line 119 2586773] Train: [30/50][23/376] Data 0.003 (0.003) Batch 0.491 (0.508) Remain 01:06:41 loss: 0.3303 Lr: 0.00161 [2024-11-25 18:38:29,009 INFO misc.py line 119 2586773] Train: [30/50][24/376] Data 0.003 (0.003) Batch 0.499 (0.508) Remain 01:06:38 loss: 0.2101 Lr: 0.00161 [2024-11-25 18:38:29,565 INFO misc.py line 119 2586773] Train: [30/50][25/376] Data 0.003 (0.003) Batch 0.556 (0.510) Remain 01:06:54 loss: 0.2460 Lr: 0.00161 [2024-11-25 18:38:30,079 INFO misc.py line 119 2586773] Train: [30/50][26/376] Data 0.003 (0.003) Batch 0.514 (0.510) Remain 01:06:55 loss: 0.1904 Lr: 0.00161 [2024-11-25 18:38:30,619 INFO misc.py line 119 2586773] Train: [30/50][27/376] Data 0.003 (0.003) Batch 0.540 (0.512) Remain 01:07:05 loss: 0.2319 Lr: 0.00161 [2024-11-25 18:38:31,138 INFO misc.py line 119 2586773] Train: [30/50][28/376] Data 0.003 (0.003) Batch 0.518 (0.512) Remain 01:07:06 loss: 0.2762 Lr: 0.00160 [2024-11-25 18:38:31,641 INFO misc.py line 119 2586773] Train: [30/50][29/376] Data 0.003 (0.003) Batch 0.503 (0.511) Remain 01:07:03 loss: 0.1961 Lr: 0.00160 [2024-11-25 18:38:32,175 INFO misc.py line 119 2586773] Train: [30/50][30/376] Data 0.003 (0.003) Batch 0.534 (0.512) Remain 01:07:09 loss: 0.1985 Lr: 0.00160 [2024-11-25 18:38:32,701 INFO misc.py line 119 2586773] Train: [30/50][31/376] Data 0.003 (0.003) Batch 0.526 (0.513) Remain 01:07:13 loss: 0.3035 Lr: 0.00160 [2024-11-25 18:38:33,237 INFO misc.py line 119 2586773] Train: [30/50][32/376] Data 0.003 (0.003) Batch 0.536 (0.514) Remain 01:07:18 loss: 0.1805 Lr: 0.00160 [2024-11-25 18:38:33,780 INFO misc.py line 119 2586773] Train: [30/50][33/376] Data 0.002 (0.003) Batch 0.543 (0.515) Remain 01:07:25 loss: 0.1828 Lr: 0.00160 [2024-11-25 18:38:34,285 INFO misc.py line 119 2586773] Train: [30/50][34/376] Data 0.003 (0.003) Batch 0.505 (0.514) Remain 01:07:23 loss: 0.1986 Lr: 0.00160 [2024-11-25 18:38:34,785 INFO misc.py line 119 2586773] Train: [30/50][35/376] Data 0.002 (0.003) Batch 0.499 (0.514) Remain 01:07:18 loss: 0.1860 Lr: 0.00160 [2024-11-25 18:38:35,280 INFO misc.py line 119 2586773] Train: [30/50][36/376] Data 0.003 (0.003) Batch 0.496 (0.513) Remain 01:07:14 loss: 0.1968 Lr: 0.00160 [2024-11-25 18:38:35,849 INFO misc.py line 119 2586773] Train: [30/50][37/376] Data 0.003 (0.003) Batch 0.568 (0.515) Remain 01:07:26 loss: 0.2015 Lr: 0.00160 [2024-11-25 18:38:36,372 INFO misc.py line 119 2586773] Train: [30/50][38/376] Data 0.003 (0.003) Batch 0.523 (0.515) Remain 01:07:27 loss: 0.1769 Lr: 0.00160 [2024-11-25 18:38:36,872 INFO misc.py line 119 2586773] Train: [30/50][39/376] Data 0.002 (0.003) Batch 0.501 (0.515) Remain 01:07:23 loss: 0.1726 Lr: 0.00160 [2024-11-25 18:38:37,428 INFO misc.py line 119 2586773] Train: [30/50][40/376] Data 0.003 (0.003) Batch 0.556 (0.516) Remain 01:07:32 loss: 0.2291 Lr: 0.00160 [2024-11-25 18:38:37,909 INFO misc.py line 119 2586773] Train: [30/50][41/376] Data 0.003 (0.003) Batch 0.481 (0.515) Remain 01:07:24 loss: 0.2092 Lr: 0.00160 [2024-11-25 18:38:38,488 INFO misc.py line 119 2586773] Train: [30/50][42/376] Data 0.002 (0.003) Batch 0.579 (0.517) Remain 01:07:36 loss: 0.2221 Lr: 0.00160 [2024-11-25 18:38:38,983 INFO misc.py line 119 2586773] Train: [30/50][43/376] Data 0.003 (0.003) Batch 0.495 (0.516) Remain 01:07:32 loss: 0.1852 Lr: 0.00160 [2024-11-25 18:38:39,482 INFO misc.py line 119 2586773] Train: [30/50][44/376] Data 0.003 (0.003) Batch 0.499 (0.516) Remain 01:07:28 loss: 0.1873 Lr: 0.00160 [2024-11-25 18:38:40,017 INFO misc.py line 119 2586773] Train: [30/50][45/376] Data 0.002 (0.003) Batch 0.535 (0.516) Remain 01:07:31 loss: 0.1704 Lr: 0.00160 [2024-11-25 18:38:40,535 INFO misc.py line 119 2586773] Train: [30/50][46/376] Data 0.002 (0.003) Batch 0.518 (0.516) Remain 01:07:31 loss: 0.1830 Lr: 0.00160 [2024-11-25 18:38:41,064 INFO misc.py line 119 2586773] Train: [30/50][47/376] Data 0.002 (0.003) Batch 0.529 (0.516) Remain 01:07:33 loss: 0.2080 Lr: 0.00160 [2024-11-25 18:38:41,619 INFO misc.py line 119 2586773] Train: [30/50][48/376] Data 0.003 (0.003) Batch 0.555 (0.517) Remain 01:07:39 loss: 0.1798 Lr: 0.00160 [2024-11-25 18:38:42,106 INFO misc.py line 119 2586773] Train: [30/50][49/376] Data 0.003 (0.003) Batch 0.487 (0.517) Remain 01:07:33 loss: 0.2135 Lr: 0.00160 [2024-11-25 18:38:42,626 INFO misc.py line 119 2586773] Train: [30/50][50/376] Data 0.003 (0.003) Batch 0.520 (0.517) Remain 01:07:33 loss: 0.1720 Lr: 0.00160 [2024-11-25 18:38:43,118 INFO misc.py line 119 2586773] Train: [30/50][51/376] Data 0.002 (0.003) Batch 0.492 (0.516) Remain 01:07:29 loss: 0.2909 Lr: 0.00160 [2024-11-25 18:38:43,653 INFO misc.py line 119 2586773] Train: [30/50][52/376] Data 0.003 (0.003) Batch 0.535 (0.517) Remain 01:07:31 loss: 0.2836 Lr: 0.00160 [2024-11-25 18:38:44,158 INFO misc.py line 119 2586773] Train: [30/50][53/376] Data 0.003 (0.003) Batch 0.505 (0.516) Remain 01:07:29 loss: 0.1557 Lr: 0.00160 [2024-11-25 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[2024-11-25 18:41:03,846 INFO misc.py line 119 2586773] Train: [30/50][322/376] Data 0.002 (0.002) Batch 0.524 (0.519) Remain 01:05:29 loss: 0.1827 Lr: 0.00151 [2024-11-25 18:41:04,347 INFO misc.py line 119 2586773] Train: [30/50][323/376] Data 0.003 (0.002) Batch 0.502 (0.519) Remain 01:05:28 loss: 0.2015 Lr: 0.00151 [2024-11-25 18:41:04,907 INFO misc.py line 119 2586773] Train: [30/50][324/376] Data 0.002 (0.002) Batch 0.559 (0.519) Remain 01:05:29 loss: 0.1799 Lr: 0.00150 [2024-11-25 18:41:05,404 INFO misc.py line 119 2586773] Train: [30/50][325/376] Data 0.003 (0.002) Batch 0.497 (0.519) Remain 01:05:27 loss: 0.1603 Lr: 0.00150 [2024-11-25 18:41:05,940 INFO misc.py line 119 2586773] Train: [30/50][326/376] Data 0.007 (0.002) Batch 0.535 (0.519) Remain 01:05:27 loss: 0.2033 Lr: 0.00150 [2024-11-25 18:41:06,434 INFO misc.py line 119 2586773] Train: [30/50][327/376] Data 0.003 (0.002) Batch 0.494 (0.519) Remain 01:05:26 loss: 0.1752 Lr: 0.00150 [2024-11-25 18:41:06,913 INFO misc.py line 119 2586773] Train: [30/50][328/376] Data 0.003 (0.002) Batch 0.479 (0.519) Remain 01:05:25 loss: 0.1673 Lr: 0.00150 [2024-11-25 18:41:07,435 INFO misc.py line 119 2586773] Train: [30/50][329/376] Data 0.003 (0.002) Batch 0.522 (0.519) Remain 01:05:24 loss: 0.2784 Lr: 0.00150 [2024-11-25 18:41:07,969 INFO misc.py line 119 2586773] Train: [30/50][330/376] Data 0.002 (0.002) Batch 0.534 (0.519) Remain 01:05:24 loss: 0.2569 Lr: 0.00150 [2024-11-25 18:41:08,494 INFO misc.py line 119 2586773] Train: [30/50][331/376] Data 0.002 (0.002) Batch 0.524 (0.519) Remain 01:05:24 loss: 0.2577 Lr: 0.00150 [2024-11-25 18:41:09,009 INFO misc.py line 119 2586773] Train: [30/50][332/376] Data 0.002 (0.002) Batch 0.515 (0.519) Remain 01:05:23 loss: 0.1794 Lr: 0.00150 [2024-11-25 18:41:09,592 INFO misc.py line 119 2586773] Train: [30/50][333/376] Data 0.002 (0.002) Batch 0.583 (0.519) Remain 01:05:24 loss: 0.2311 Lr: 0.00150 [2024-11-25 18:41:10,084 INFO misc.py line 119 2586773] Train: [30/50][334/376] Data 0.002 (0.002) Batch 0.492 (0.519) Remain 01:05:23 loss: 0.2467 Lr: 0.00150 [2024-11-25 18:41:10,580 INFO misc.py line 119 2586773] Train: [30/50][335/376] Data 0.002 (0.002) Batch 0.496 (0.519) Remain 01:05:22 loss: 0.1701 Lr: 0.00150 [2024-11-25 18:41:11,105 INFO misc.py line 119 2586773] Train: [30/50][336/376] Data 0.002 (0.002) Batch 0.525 (0.519) Remain 01:05:22 loss: 0.2011 Lr: 0.00150 [2024-11-25 18:41:11,608 INFO misc.py line 119 2586773] Train: [30/50][337/376] Data 0.002 (0.002) Batch 0.503 (0.519) Remain 01:05:21 loss: 0.2937 Lr: 0.00150 [2024-11-25 18:41:12,125 INFO misc.py line 119 2586773] Train: [30/50][338/376] Data 0.002 (0.002) Batch 0.517 (0.519) Remain 01:05:20 loss: 0.2212 Lr: 0.00150 [2024-11-25 18:41:12,627 INFO misc.py line 119 2586773] Train: [30/50][339/376] Data 0.003 (0.002) Batch 0.502 (0.519) Remain 01:05:19 loss: 0.1918 Lr: 0.00150 [2024-11-25 18:41:13,135 INFO misc.py line 119 2586773] Train: [30/50][340/376] Data 0.002 (0.002) Batch 0.508 (0.519) Remain 01:05:19 loss: 0.2292 Lr: 0.00150 [2024-11-25 18:41:13,652 INFO misc.py line 119 2586773] Train: [30/50][341/376] Data 0.002 (0.002) Batch 0.517 (0.519) Remain 01:05:18 loss: 0.1923 Lr: 0.00150 [2024-11-25 18:41:14,164 INFO misc.py line 119 2586773] Train: [30/50][342/376] Data 0.003 (0.002) Batch 0.511 (0.519) Remain 01:05:17 loss: 0.1997 Lr: 0.00150 [2024-11-25 18:41:14,665 INFO misc.py line 119 2586773] Train: [30/50][343/376] Data 0.002 (0.002) Batch 0.501 (0.519) Remain 01:05:16 loss: 0.1963 Lr: 0.00150 [2024-11-25 18:41:15,188 INFO misc.py line 119 2586773] Train: [30/50][344/376] Data 0.002 (0.002) Batch 0.523 (0.519) Remain 01:05:16 loss: 0.2425 Lr: 0.00150 [2024-11-25 18:41:15,707 INFO misc.py line 119 2586773] Train: [30/50][345/376] Data 0.002 (0.002) Batch 0.519 (0.519) Remain 01:05:15 loss: 0.2368 Lr: 0.00150 [2024-11-25 18:41:16,256 INFO misc.py line 119 2586773] Train: [30/50][346/376] Data 0.002 (0.002) Batch 0.549 (0.519) Remain 01:05:16 loss: 0.1957 Lr: 0.00150 [2024-11-25 18:41:16,725 INFO misc.py line 119 2586773] Train: [30/50][347/376] Data 0.002 (0.002) Batch 0.469 (0.519) Remain 01:05:14 loss: 0.1883 Lr: 0.00150 [2024-11-25 18:41:17,231 INFO misc.py line 119 2586773] Train: [30/50][348/376] Data 0.002 (0.002) Batch 0.507 (0.519) Remain 01:05:13 loss: 0.2302 Lr: 0.00150 [2024-11-25 18:41:17,759 INFO misc.py line 119 2586773] Train: [30/50][349/376] Data 0.002 (0.002) Batch 0.528 (0.519) Remain 01:05:13 loss: 0.1751 Lr: 0.00150 [2024-11-25 18:41:18,266 INFO misc.py line 119 2586773] Train: [30/50][350/376] Data 0.003 (0.002) Batch 0.508 (0.519) Remain 01:05:12 loss: 0.2266 Lr: 0.00150 [2024-11-25 18:41:18,782 INFO misc.py line 119 2586773] Train: [30/50][351/376] Data 0.003 (0.002) Batch 0.515 (0.519) Remain 01:05:12 loss: 0.1896 Lr: 0.00150 [2024-11-25 18:41:19,272 INFO misc.py line 119 2586773] Train: [30/50][352/376] Data 0.002 (0.002) Batch 0.490 (0.518) Remain 01:05:10 loss: 0.2233 Lr: 0.00150 [2024-11-25 18:41:19,767 INFO misc.py line 119 2586773] Train: [30/50][353/376] Data 0.002 (0.002) Batch 0.495 (0.518) Remain 01:05:09 loss: 0.2107 Lr: 0.00149 [2024-11-25 18:41:20,277 INFO misc.py line 119 2586773] Train: [30/50][354/376] Data 0.002 (0.002) Batch 0.509 (0.518) Remain 01:05:09 loss: 0.2386 Lr: 0.00149 [2024-11-25 18:41:20,853 INFO misc.py line 119 2586773] Train: [30/50][355/376] Data 0.002 (0.002) Batch 0.576 (0.518) Remain 01:05:09 loss: 0.2589 Lr: 0.00149 [2024-11-25 18:41:21,348 INFO misc.py line 119 2586773] Train: [30/50][356/376] Data 0.002 (0.002) Batch 0.495 (0.518) Remain 01:05:08 loss: 0.1700 Lr: 0.00149 [2024-11-25 18:41:21,867 INFO misc.py line 119 2586773] Train: [30/50][357/376] Data 0.002 (0.002) Batch 0.519 (0.518) Remain 01:05:08 loss: 0.2000 Lr: 0.00149 [2024-11-25 18:41:22,399 INFO misc.py line 119 2586773] Train: [30/50][358/376] Data 0.003 (0.002) Batch 0.531 (0.518) Remain 01:05:08 loss: 0.2775 Lr: 0.00149 [2024-11-25 18:41:22,899 INFO misc.py line 119 2586773] Train: [30/50][359/376] Data 0.003 (0.002) Batch 0.500 (0.518) Remain 01:05:07 loss: 0.1810 Lr: 0.00149 [2024-11-25 18:41:23,373 INFO misc.py line 119 2586773] Train: [30/50][360/376] Data 0.002 (0.002) Batch 0.474 (0.518) Remain 01:05:05 loss: 0.2188 Lr: 0.00149 [2024-11-25 18:41:23,871 INFO misc.py line 119 2586773] Train: [30/50][361/376] Data 0.002 (0.002) Batch 0.498 (0.518) Remain 01:05:04 loss: 0.1931 Lr: 0.00149 [2024-11-25 18:41:24,410 INFO misc.py line 119 2586773] Train: [30/50][362/376] Data 0.002 (0.002) Batch 0.539 (0.518) Remain 01:05:04 loss: 0.2770 Lr: 0.00149 [2024-11-25 18:41:24,910 INFO misc.py line 119 2586773] Train: [30/50][363/376] Data 0.002 (0.002) Batch 0.500 (0.518) Remain 01:05:03 loss: 0.1933 Lr: 0.00149 [2024-11-25 18:41:25,418 INFO misc.py line 119 2586773] Train: [30/50][364/376] Data 0.002 (0.002) Batch 0.508 (0.518) Remain 01:05:03 loss: 0.2425 Lr: 0.00149 [2024-11-25 18:41:25,890 INFO misc.py line 119 2586773] Train: [30/50][365/376] Data 0.002 (0.002) Batch 0.472 (0.518) Remain 01:05:01 loss: 0.3151 Lr: 0.00149 [2024-11-25 18:41:26,377 INFO misc.py line 119 2586773] Train: [30/50][366/376] Data 0.002 (0.002) Batch 0.486 (0.518) Remain 01:05:00 loss: 0.2975 Lr: 0.00149 [2024-11-25 18:41:26,886 INFO misc.py line 119 2586773] Train: [30/50][367/376] Data 0.002 (0.002) Batch 0.509 (0.518) Remain 01:04:59 loss: 0.1860 Lr: 0.00149 [2024-11-25 18:41:27,369 INFO misc.py line 119 2586773] Train: [30/50][368/376] Data 0.002 (0.002) Batch 0.483 (0.518) Remain 01:04:58 loss: 0.1828 Lr: 0.00149 [2024-11-25 18:41:27,851 INFO misc.py line 119 2586773] Train: [30/50][369/376] Data 0.002 (0.002) Batch 0.482 (0.518) Remain 01:04:57 loss: 0.2766 Lr: 0.00149 [2024-11-25 18:41:28,338 INFO misc.py line 119 2586773] Train: [30/50][370/376] Data 0.002 (0.002) Batch 0.487 (0.518) Remain 01:04:56 loss: 0.1904 Lr: 0.00149 [2024-11-25 18:41:28,809 INFO misc.py line 119 2586773] Train: [30/50][371/376] Data 0.002 (0.002) Batch 0.470 (0.518) Remain 01:04:54 loss: 0.2281 Lr: 0.00149 [2024-11-25 18:41:29,331 INFO misc.py line 119 2586773] Train: [30/50][372/376] Data 0.002 (0.002) Batch 0.522 (0.518) Remain 01:04:54 loss: 0.2972 Lr: 0.00149 [2024-11-25 18:41:29,830 INFO misc.py line 119 2586773] Train: [30/50][373/376] Data 0.002 (0.002) Batch 0.499 (0.518) Remain 01:04:53 loss: 0.2114 Lr: 0.00149 [2024-11-25 18:41:30,359 INFO misc.py line 119 2586773] Train: [30/50][374/376] Data 0.002 (0.002) Batch 0.528 (0.518) Remain 01:04:53 loss: 0.2071 Lr: 0.00149 [2024-11-25 18:41:30,858 INFO misc.py line 119 2586773] Train: [30/50][375/376] Data 0.002 (0.002) Batch 0.500 (0.518) Remain 01:04:52 loss: 0.2322 Lr: 0.00149 [2024-11-25 18:41:31,361 INFO misc.py line 119 2586773] Train: [30/50][376/376] Data 0.002 (0.002) Batch 0.503 (0.517) Remain 01:04:51 loss: 0.2393 Lr: 0.00149 [2024-11-25 18:41:31,362 INFO misc.py line 136 2586773] Train result: loss: 0.2127 [2024-11-25 18:41:31,362 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:41:42,423 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1822 [2024-11-25 18:41:42,676 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2109 [2024-11-25 18:41:42,940 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2546 [2024-11-25 18:41:43,161 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1861 [2024-11-25 18:41:43,425 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2869 [2024-11-25 18:41:43,694 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1974 [2024-11-25 18:41:43,917 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2225 [2024-11-25 18:41:44,187 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2201 [2024-11-25 18:41:44,410 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2631 [2024-11-25 18:41:44,670 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2369 [2024-11-25 18:41:44,901 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2053 [2024-11-25 18:41:45,173 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2423 [2024-11-25 18:41:45,438 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2655 [2024-11-25 18:41:45,701 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2422 [2024-11-25 18:41:45,938 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2387 [2024-11-25 18:41:46,178 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3023 [2024-11-25 18:41:46,445 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2692 [2024-11-25 18:41:46,693 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2071 [2024-11-25 18:41:46,925 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2437 [2024-11-25 18:41:47,185 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2562 [2024-11-25 18:41:47,419 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2470 [2024-11-25 18:41:47,685 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2692 [2024-11-25 18:41:47,925 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2216 [2024-11-25 18:41:48,192 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2367 [2024-11-25 18:41:48,454 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2302 [2024-11-25 18:41:48,690 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2546 [2024-11-25 18:41:48,941 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2445 [2024-11-25 18:41:49,186 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2324 [2024-11-25 18:41:49,452 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2686 [2024-11-25 18:41:49,706 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2877 [2024-11-25 18:41:49,940 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2645 [2024-11-25 18:41:50,212 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1971 [2024-11-25 18:41:50,431 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2516 [2024-11-25 18:41:50,671 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2371 [2024-11-25 18:41:50,933 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2137 [2024-11-25 18:41:51,179 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2532 [2024-11-25 18:41:51,404 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1927 [2024-11-25 18:41:51,671 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2348 [2024-11-25 18:41:51,901 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2538 [2024-11-25 18:41:52,135 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2523 [2024-11-25 18:41:52,406 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3005 [2024-11-25 18:41:52,656 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2931 [2024-11-25 18:41:52,891 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2477 [2024-11-25 18:41:53,125 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2438 [2024-11-25 18:41:53,362 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2446 [2024-11-25 18:41:53,616 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2190 [2024-11-25 18:41:53,874 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2497 [2024-11-25 18:41:54,124 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3014 [2024-11-25 18:41:54,349 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2136 [2024-11-25 18:41:54,583 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2095 [2024-11-25 18:41:54,803 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2576 [2024-11-25 18:41:55,058 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2292 [2024-11-25 18:41:55,325 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2194 [2024-11-25 18:41:55,586 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3030 [2024-11-25 18:41:55,819 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2478 [2024-11-25 18:41:56,063 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2334 [2024-11-25 18:41:56,321 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2423 [2024-11-25 18:41:56,589 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2711 [2024-11-25 18:41:56,846 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2531 [2024-11-25 18:41:57,105 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2629 [2024-11-25 18:41:57,356 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2245 [2024-11-25 18:41:57,628 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2415 [2024-11-25 18:41:57,856 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2435 [2024-11-25 18:41:58,115 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2577 [2024-11-25 18:41:58,379 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2491 [2024-11-25 18:41:58,648 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1956 [2024-11-25 18:41:58,895 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2040 [2024-11-25 18:41:59,150 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2718 [2024-11-25 18:41:59,420 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2632 [2024-11-25 18:41:59,683 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2962 [2024-11-25 18:41:59,926 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2033 [2024-11-25 18:42:00,159 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2718 [2024-11-25 18:42:00,414 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2564 [2024-11-25 18:42:00,658 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2609 [2024-11-25 18:42:00,874 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2537 [2024-11-25 18:42:01,099 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2015 [2024-11-25 18:42:01,368 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2423 [2024-11-25 18:42:01,603 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2103 [2024-11-25 18:42:01,863 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2210 [2024-11-25 18:42:02,116 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2978 [2024-11-25 18:42:02,357 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2423 [2024-11-25 18:42:02,618 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2601 [2024-11-25 18:42:02,866 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1981 [2024-11-25 18:42:03,114 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2475 [2024-11-25 18:42:03,385 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2437 [2024-11-25 18:42:03,623 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2711 [2024-11-25 18:42:03,888 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2458 [2024-11-25 18:42:04,147 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2424 [2024-11-25 18:42:04,395 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2641 [2024-11-25 18:42:04,644 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2731 [2024-11-25 18:42:04,878 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2473 [2024-11-25 18:42:05,130 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2688 [2024-11-25 18:42:05,395 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2489 [2024-11-25 18:42:05,661 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2117 [2024-11-25 18:42:05,928 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2239 [2024-11-25 18:42:06,175 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2089 [2024-11-25 18:42:06,441 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2383 [2024-11-25 18:42:06,662 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2956 [2024-11-25 18:42:06,932 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2462 [2024-11-25 18:42:07,170 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2484 [2024-11-25 18:42:07,443 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1982 [2024-11-25 18:42:07,704 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2613 [2024-11-25 18:42:07,961 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2397 [2024-11-25 18:42:08,217 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2900 [2024-11-25 18:42:08,439 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2464 [2024-11-25 18:42:08,679 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2442 [2024-11-25 18:42:08,933 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2258 [2024-11-25 18:42:09,202 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2361 [2024-11-25 18:42:09,435 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2490 [2024-11-25 18:42:09,697 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2057 [2024-11-25 18:42:09,960 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2500 [2024-11-25 18:42:10,184 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2231 [2024-11-25 18:42:10,423 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2018 [2024-11-25 18:42:10,640 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2182 [2024-11-25 18:42:10,867 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2093 [2024-11-25 18:42:11,140 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2712 [2024-11-25 18:42:11,401 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2724 [2024-11-25 18:42:11,668 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2570 [2024-11-25 18:42:11,932 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2183 [2024-11-25 18:42:12,193 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2960 [2024-11-25 18:42:12,450 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2820 [2024-11-25 18:42:12,715 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2313 [2024-11-25 18:42:12,973 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2659 [2024-11-25 18:42:13,237 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2358 [2024-11-25 18:42:13,500 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2418 [2024-11-25 18:42:13,750 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2511 [2024-11-25 18:42:13,981 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2049 [2024-11-25 18:42:14,241 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2542 [2024-11-25 18:42:14,477 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2536 [2024-11-25 18:42:14,705 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1939 [2024-11-25 18:42:14,916 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2198 [2024-11-25 18:42:15,134 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1939 [2024-11-25 18:42:15,888 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7730/0.8503/0.9962. [2024-11-25 18:42:15,888 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9962/0.9982 [2024-11-25 18:42:15,888 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5497/0.7024 [2024-11-25 18:42:15,888 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:42:15,889 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:42:15,889 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:42:18,493 INFO misc.py line 119 2586773] Train: [31/50][1/376] Data 0.121 (0.121) Batch 0.588 (0.588) Remain 01:13:39 loss: 0.1930 Lr: 0.00149 [2024-11-25 18:42:18,972 INFO misc.py line 119 2586773] Train: [31/50][2/376] Data 0.002 (0.002) Batch 0.479 (0.479) Remain 01:00:02 loss: 0.2190 Lr: 0.00149 [2024-11-25 18:42:19,488 INFO misc.py line 119 2586773] Train: [31/50][3/376] Data 0.003 (0.003) Batch 0.516 (0.516) Remain 01:04:37 loss: 0.2531 Lr: 0.00149 [2024-11-25 18:42:19,969 INFO misc.py line 119 2586773] Train: [31/50][4/376] Data 0.003 (0.003) Batch 0.481 (0.481) Remain 01:00:17 loss: 0.1855 Lr: 0.00149 [2024-11-25 18:42:20,462 INFO misc.py line 119 2586773] Train: [31/50][5/376] Data 0.003 (0.003) Batch 0.493 (0.487) Remain 01:01:01 loss: 0.2130 Lr: 0.00149 [2024-11-25 18:42:20,996 INFO misc.py line 119 2586773] Train: [31/50][6/376] Data 0.002 (0.002) Batch 0.533 (0.503) Remain 01:02:55 loss: 0.2276 Lr: 0.00149 [2024-11-25 18:42:21,480 INFO misc.py line 119 2586773] Train: [31/50][7/376] Data 0.003 (0.003) Batch 0.484 (0.498) Remain 01:02:20 loss: 0.2690 Lr: 0.00148 [2024-11-25 18:42:22,006 INFO misc.py line 119 2586773] Train: [31/50][8/376] Data 0.002 (0.002) Batch 0.526 (0.504) Remain 01:03:02 loss: 0.1822 Lr: 0.00148 [2024-11-25 18:42:22,497 INFO misc.py line 119 2586773] Train: [31/50][9/376] Data 0.002 (0.002) Batch 0.491 (0.501) Remain 01:02:46 loss: 0.2517 Lr: 0.00148 [2024-11-25 18:42:23,035 INFO misc.py line 119 2586773] Train: [31/50][10/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 01:03:25 loss: 0.1981 Lr: 0.00148 [2024-11-25 18:42:23,575 INFO misc.py line 119 2586773] Train: [31/50][11/376] Data 0.003 (0.002) Batch 0.541 (0.511) Remain 01:03:56 loss: 0.2292 Lr: 0.00148 [2024-11-25 18:42:24,099 INFO misc.py line 119 2586773] Train: [31/50][12/376] Data 0.003 (0.002) Batch 0.523 (0.512) Remain 01:04:06 loss: 0.2104 Lr: 0.00148 [2024-11-25 18:42:24,611 INFO misc.py line 119 2586773] Train: [31/50][13/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 01:04:05 loss: 0.2009 Lr: 0.00148 [2024-11-25 18:42:25,105 INFO misc.py line 119 2586773] Train: [31/50][14/376] Data 0.003 (0.002) Batch 0.495 (0.511) Remain 01:03:52 loss: 0.2158 Lr: 0.00148 [2024-11-25 18:42:25,627 INFO misc.py line 119 2586773] Train: [31/50][15/376] Data 0.002 (0.002) Batch 0.522 (0.512) Remain 01:03:59 loss: 0.1940 Lr: 0.00148 [2024-11-25 18:42:26,165 INFO misc.py line 119 2586773] Train: [31/50][16/376] Data 0.002 (0.002) Batch 0.538 (0.514) Remain 01:04:14 loss: 0.2274 Lr: 0.00148 [2024-11-25 18:42:26,648 INFO misc.py line 119 2586773] Train: [31/50][17/376] Data 0.002 (0.002) Batch 0.483 (0.511) Remain 01:03:57 loss: 0.1696 Lr: 0.00148 [2024-11-25 18:42:27,148 INFO misc.py line 119 2586773] Train: [31/50][18/376] Data 0.003 (0.002) Batch 0.500 (0.511) Remain 01:03:51 loss: 0.1673 Lr: 0.00148 [2024-11-25 18:42:27,655 INFO misc.py line 119 2586773] Train: [31/50][19/376] Data 0.003 (0.002) Batch 0.507 (0.510) Remain 01:03:48 loss: 0.2489 Lr: 0.00148 [2024-11-25 18:42:28,170 INFO misc.py line 119 2586773] Train: [31/50][20/376] Data 0.003 (0.002) Batch 0.514 (0.511) Remain 01:03:50 loss: 0.1880 Lr: 0.00148 [2024-11-25 18:42:28,685 INFO misc.py line 119 2586773] Train: [31/50][21/376] Data 0.003 (0.002) Batch 0.515 (0.511) Remain 01:03:51 loss: 0.3483 Lr: 0.00148 [2024-11-25 18:42:29,169 INFO misc.py line 119 2586773] Train: [31/50][22/376] Data 0.003 (0.003) Batch 0.485 (0.510) Remain 01:03:40 loss: 0.2005 Lr: 0.00148 [2024-11-25 18:42:29,687 INFO misc.py line 119 2586773] Train: [31/50][23/376] Data 0.003 (0.003) Batch 0.517 (0.510) Remain 01:03:42 loss: 0.2100 Lr: 0.00148 [2024-11-25 18:42:30,176 INFO misc.py line 119 2586773] Train: [31/50][24/376] Data 0.003 (0.003) Batch 0.489 (0.509) Remain 01:03:35 loss: 0.1763 Lr: 0.00148 [2024-11-25 18:42:30,667 INFO misc.py line 119 2586773] Train: [31/50][25/376] Data 0.002 (0.003) Batch 0.490 (0.508) Remain 01:03:28 loss: 0.1981 Lr: 0.00148 [2024-11-25 18:42:31,158 INFO misc.py line 119 2586773] Train: [31/50][26/376] Data 0.002 (0.003) Batch 0.491 (0.507) Remain 01:03:22 loss: 0.1979 Lr: 0.00148 [2024-11-25 18:42:31,686 INFO misc.py line 119 2586773] Train: [31/50][27/376] Data 0.003 (0.003) Batch 0.528 (0.508) Remain 01:03:28 loss: 0.2879 Lr: 0.00148 [2024-11-25 18:42:32,178 INFO misc.py line 119 2586773] Train: [31/50][28/376] Data 0.003 (0.003) Batch 0.492 (0.508) Remain 01:03:22 loss: 0.2045 Lr: 0.00148 [2024-11-25 18:42:32,668 INFO misc.py line 119 2586773] Train: [31/50][29/376] Data 0.003 (0.003) Batch 0.490 (0.507) Remain 01:03:17 loss: 0.2858 Lr: 0.00148 [2024-11-25 18:42:33,180 INFO misc.py line 119 2586773] Train: [31/50][30/376] Data 0.003 (0.003) Batch 0.512 (0.507) Remain 01:03:18 loss: 0.1682 Lr: 0.00148 [2024-11-25 18:42:33,705 INFO misc.py line 119 2586773] Train: [31/50][31/376] Data 0.003 (0.003) Batch 0.524 (0.508) Remain 01:03:22 loss: 0.2330 Lr: 0.00148 [2024-11-25 18:42:34,210 INFO misc.py line 119 2586773] Train: [31/50][32/376] Data 0.003 (0.003) Batch 0.506 (0.508) Remain 01:03:21 loss: 0.3249 Lr: 0.00148 [2024-11-25 18:42:34,703 INFO misc.py line 119 2586773] Train: [31/50][33/376] Data 0.002 (0.003) Batch 0.493 (0.507) Remain 01:03:17 loss: 0.1989 Lr: 0.00148 [2024-11-25 18:42:35,199 INFO misc.py line 119 2586773] Train: [31/50][34/376] Data 0.003 (0.003) Batch 0.496 (0.507) Remain 01:03:13 loss: 0.2323 Lr: 0.00148 [2024-11-25 18:42:35,732 INFO misc.py line 119 2586773] Train: [31/50][35/376] Data 0.002 (0.003) Batch 0.533 (0.508) Remain 01:03:19 loss: 0.2043 Lr: 0.00148 [2024-11-25 18:42:36,244 INFO misc.py line 119 2586773] Train: [31/50][36/376] Data 0.002 (0.003) Batch 0.512 (0.508) Remain 01:03:20 loss: 0.2665 Lr: 0.00148 [2024-11-25 18:42:36,806 INFO misc.py line 119 2586773] Train: [31/50][37/376] Data 0.002 (0.003) Batch 0.562 (0.509) Remain 01:03:31 loss: 0.1989 Lr: 0.00147 [2024-11-25 18:42:37,351 INFO misc.py line 119 2586773] Train: [31/50][38/376] Data 0.002 (0.002) Batch 0.545 (0.510) Remain 01:03:38 loss: 0.2274 Lr: 0.00147 [2024-11-25 18:42:37,839 INFO misc.py line 119 2586773] Train: [31/50][39/376] Data 0.002 (0.002) Batch 0.488 (0.510) Remain 01:03:33 loss: 0.2227 Lr: 0.00147 [2024-11-25 18:42:38,334 INFO misc.py line 119 2586773] Train: [31/50][40/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 01:03:29 loss: 0.1692 Lr: 0.00147 [2024-11-25 18:42:38,832 INFO misc.py line 119 2586773] Train: [31/50][41/376] Data 0.003 (0.002) Batch 0.499 (0.509) Remain 01:03:27 loss: 0.2219 Lr: 0.00147 [2024-11-25 18:42:39,335 INFO misc.py line 119 2586773] Train: [31/50][42/376] Data 0.002 (0.002) Batch 0.503 (0.509) Remain 01:03:25 loss: 0.2616 Lr: 0.00147 [2024-11-25 18:42:39,822 INFO misc.py line 119 2586773] Train: [31/50][43/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 01:03:20 loss: 0.2068 Lr: 0.00147 [2024-11-25 18:42:40,284 INFO misc.py line 119 2586773] Train: [31/50][44/376] Data 0.002 (0.002) Batch 0.461 (0.507) Remain 01:03:11 loss: 0.1985 Lr: 0.00147 [2024-11-25 18:42:40,791 INFO misc.py line 119 2586773] Train: [31/50][45/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 01:03:11 loss: 0.1822 Lr: 0.00147 [2024-11-25 18:42:41,272 INFO misc.py line 119 2586773] Train: [31/50][46/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 01:03:06 loss: 0.1707 Lr: 0.00147 [2024-11-25 18:42:41,756 INFO misc.py line 119 2586773] Train: [31/50][47/376] Data 0.003 (0.002) Batch 0.484 (0.506) Remain 01:03:01 loss: 0.1890 Lr: 0.00147 [2024-11-25 18:42:42,276 INFO misc.py line 119 2586773] Train: [31/50][48/376] Data 0.003 (0.002) Batch 0.521 (0.506) Remain 01:03:03 loss: 0.2836 Lr: 0.00147 [2024-11-25 18:42:42,820 INFO misc.py line 119 2586773] Train: [31/50][49/376] Data 0.003 (0.002) Batch 0.544 (0.507) Remain 01:03:09 loss: 0.1849 Lr: 0.00147 [2024-11-25 18:42:43,287 INFO misc.py line 119 2586773] Train: [31/50][50/376] Data 0.002 (0.002) Batch 0.467 (0.506) Remain 01:03:02 loss: 0.2199 Lr: 0.00147 [2024-11-25 18:42:43,773 INFO misc.py line 119 2586773] Train: [31/50][51/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 01:02:58 loss: 0.2164 Lr: 0.00147 [2024-11-25 18:42:44,306 INFO misc.py line 119 2586773] Train: [31/50][52/376] Data 0.002 (0.002) Batch 0.533 (0.506) Remain 01:03:02 loss: 0.2153 Lr: 0.00147 [2024-11-25 18:42:44,823 INFO misc.py line 119 2586773] Train: [31/50][53/376] Data 0.003 (0.002) Batch 0.517 (0.507) Remain 01:03:03 loss: 0.2093 Lr: 0.00147 [2024-11-25 18:42:45,338 INFO misc.py line 119 2586773] Train: [31/50][54/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:03:04 loss: 0.2168 Lr: 0.00147 [2024-11-25 18:42:45,853 INFO misc.py line 119 2586773] Train: [31/50][55/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 01:03:04 loss: 0.2523 Lr: 0.00147 [2024-11-25 18:42:46,346 INFO misc.py line 119 2586773] Train: [31/50][56/376] Data 0.003 (0.002) Batch 0.493 (0.507) Remain 01:03:02 loss: 0.1828 Lr: 0.00147 [2024-11-25 18:42:46,866 INFO misc.py line 119 2586773] Train: [31/50][57/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 01:03:03 loss: 0.2398 Lr: 0.00147 [2024-11-25 18:42:47,390 INFO misc.py line 119 2586773] Train: [31/50][58/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 01:03:05 loss: 0.2165 Lr: 0.00147 [2024-11-25 18:42:47,946 INFO misc.py line 119 2586773] Train: [31/50][59/376] Data 0.002 (0.002) Batch 0.556 (0.508) Remain 01:03:11 loss: 0.2140 Lr: 0.00147 [2024-11-25 18:42:48,447 INFO misc.py line 119 2586773] Train: [31/50][60/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 01:03:10 loss: 0.1972 Lr: 0.00147 [2024-11-25 18:42:48,934 INFO misc.py line 119 2586773] Train: [31/50][61/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 01:03:06 loss: 0.2253 Lr: 0.00147 [2024-11-25 18:42:49,478 INFO misc.py line 119 2586773] Train: [31/50][62/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 01:03:10 loss: 0.2256 Lr: 0.00147 [2024-11-25 18:42:49,957 INFO misc.py line 119 2586773] Train: [31/50][63/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 01:03:06 loss: 0.2006 Lr: 0.00147 [2024-11-25 18:42:50,460 INFO misc.py line 119 2586773] Train: [31/50][64/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 01:03:05 loss: 0.2053 Lr: 0.00147 [2024-11-25 18:42:50,978 INFO misc.py line 119 2586773] Train: [31/50][65/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 01:03:06 loss: 0.1846 Lr: 0.00147 [2024-11-25 18:42:51,493 INFO misc.py line 119 2586773] Train: [31/50][66/376] Data 0.002 (0.002) Batch 0.515 (0.508) Remain 01:03:06 loss: 0.1931 Lr: 0.00147 [2024-11-25 18:42:52,015 INFO misc.py line 119 2586773] Train: [31/50][67/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 01:03:07 loss: 0.2057 Lr: 0.00146 [2024-11-25 18:42:52,503 INFO misc.py line 119 2586773] Train: [31/50][68/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 01:03:05 loss: 0.2258 Lr: 0.00146 [2024-11-25 18:42:52,995 INFO misc.py line 119 2586773] Train: [31/50][69/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 01:03:02 loss: 0.2718 Lr: 0.00146 [2024-11-25 18:42:53,444 INFO misc.py line 119 2586773] Train: [31/50][70/376] Data 0.002 (0.002) Batch 0.449 (0.507) Remain 01:02:55 loss: 0.3050 Lr: 0.00146 [2024-11-25 18:42:53,927 INFO misc.py line 119 2586773] Train: [31/50][71/376] Data 0.002 (0.002) Batch 0.484 (0.506) Remain 01:02:52 loss: 0.1836 Lr: 0.00146 [2024-11-25 18:42:54,445 INFO misc.py line 119 2586773] Train: [31/50][72/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:02:53 loss: 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loss: 0.2385 Lr: 0.00137 [2024-11-25 18:45:13,962 INFO misc.py line 119 2586773] Train: [31/50][347/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 01:00:38 loss: 0.2028 Lr: 0.00137 [2024-11-25 18:45:14,494 INFO misc.py line 119 2586773] Train: [31/50][348/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 01:00:38 loss: 0.2276 Lr: 0.00137 [2024-11-25 18:45:14,985 INFO misc.py line 119 2586773] Train: [31/50][349/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 01:00:37 loss: 0.1754 Lr: 0.00137 [2024-11-25 18:45:15,447 INFO misc.py line 119 2586773] Train: [31/50][350/376] Data 0.003 (0.002) Batch 0.463 (0.507) Remain 01:00:35 loss: 0.2293 Lr: 0.00137 [2024-11-25 18:45:15,951 INFO misc.py line 119 2586773] Train: [31/50][351/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 01:00:35 loss: 0.1599 Lr: 0.00137 [2024-11-25 18:45:16,445 INFO misc.py line 119 2586773] Train: [31/50][352/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 01:00:34 loss: 0.2388 Lr: 0.00137 [2024-11-25 18:45:16,951 INFO misc.py line 119 2586773] Train: [31/50][353/376] Data 0.003 (0.002) Batch 0.506 (0.507) Remain 01:00:33 loss: 0.2250 Lr: 0.00137 [2024-11-25 18:45:17,437 INFO misc.py line 119 2586773] Train: [31/50][354/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 01:00:32 loss: 0.1886 Lr: 0.00137 [2024-11-25 18:45:17,944 INFO misc.py line 119 2586773] Train: [31/50][355/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:00:32 loss: 0.1760 Lr: 0.00137 [2024-11-25 18:45:18,451 INFO misc.py line 119 2586773] Train: [31/50][356/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 01:00:31 loss: 0.2109 Lr: 0.00137 [2024-11-25 18:45:18,947 INFO misc.py line 119 2586773] Train: [31/50][357/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 01:00:31 loss: 0.2134 Lr: 0.00137 [2024-11-25 18:45:19,457 INFO misc.py line 119 2586773] Train: [31/50][358/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 01:00:30 loss: 0.2076 Lr: 0.00137 [2024-11-25 18:45:19,954 INFO misc.py line 119 2586773] Train: [31/50][359/376] Data 0.003 (0.002) Batch 0.497 (0.507) Remain 01:00:30 loss: 0.1768 Lr: 0.00137 [2024-11-25 18:45:20,459 INFO misc.py line 119 2586773] Train: [31/50][360/376] Data 0.003 (0.002) Batch 0.505 (0.507) Remain 01:00:29 loss: 0.1919 Lr: 0.00137 [2024-11-25 18:45:21,012 INFO misc.py line 119 2586773] Train: [31/50][361/376] Data 0.002 (0.002) Batch 0.553 (0.507) Remain 01:00:29 loss: 0.1916 Lr: 0.00137 [2024-11-25 18:45:21,543 INFO misc.py line 119 2586773] Train: [31/50][362/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 01:00:29 loss: 0.2104 Lr: 0.00137 [2024-11-25 18:45:22,035 INFO misc.py line 119 2586773] Train: [31/50][363/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 01:00:29 loss: 0.2008 Lr: 0.00137 [2024-11-25 18:45:22,533 INFO misc.py line 119 2586773] Train: [31/50][364/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:00:28 loss: 0.1976 Lr: 0.00137 [2024-11-25 18:45:23,035 INFO misc.py line 119 2586773] Train: [31/50][365/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 01:00:27 loss: 0.1670 Lr: 0.00137 [2024-11-25 18:45:23,547 INFO misc.py line 119 2586773] Train: [31/50][366/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 01:00:27 loss: 0.3318 Lr: 0.00137 [2024-11-25 18:45:24,070 INFO misc.py line 119 2586773] Train: [31/50][367/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 01:00:27 loss: 0.1888 Lr: 0.00137 [2024-11-25 18:45:24,568 INFO misc.py line 119 2586773] Train: [31/50][368/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 01:00:26 loss: 0.2207 Lr: 0.00136 [2024-11-25 18:45:25,036 INFO misc.py line 119 2586773] Train: [31/50][369/376] Data 0.003 (0.002) Batch 0.468 (0.507) Remain 01:00:25 loss: 0.1965 Lr: 0.00136 [2024-11-25 18:45:25,500 INFO misc.py line 119 2586773] Train: [31/50][370/376] Data 0.002 (0.002) Batch 0.464 (0.507) Remain 01:00:23 loss: 0.3133 Lr: 0.00136 [2024-11-25 18:45:26,058 INFO misc.py line 119 2586773] Train: [31/50][371/376] Data 0.002 (0.002) Batch 0.558 (0.507) Remain 01:00:24 loss: 0.2501 Lr: 0.00136 [2024-11-25 18:45:26,578 INFO misc.py line 119 2586773] Train: [31/50][372/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 01:00:24 loss: 0.2149 Lr: 0.00136 [2024-11-25 18:45:27,102 INFO misc.py line 119 2586773] Train: [31/50][373/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 01:00:23 loss: 0.1739 Lr: 0.00136 [2024-11-25 18:45:27,574 INFO misc.py line 119 2586773] Train: [31/50][374/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 01:00:22 loss: 0.1897 Lr: 0.00136 [2024-11-25 18:45:28,122 INFO misc.py line 119 2586773] Train: [31/50][375/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 01:00:23 loss: 0.2150 Lr: 0.00136 [2024-11-25 18:45:28,605 INFO misc.py line 119 2586773] Train: [31/50][376/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 01:00:22 loss: 0.2251 Lr: 0.00136 [2024-11-25 18:45:28,606 INFO misc.py line 136 2586773] Train result: loss: 0.2143 [2024-11-25 18:45:28,606 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:45:39,421 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1966 [2024-11-25 18:45:39,682 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2315 [2024-11-25 18:45:39,944 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2992 [2024-11-25 18:45:40,167 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2085 [2024-11-25 18:45:40,434 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3023 [2024-11-25 18:45:40,702 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2080 [2024-11-25 18:45:40,924 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2210 [2024-11-25 18:45:41,192 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2638 [2024-11-25 18:45:41,415 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2884 [2024-11-25 18:45:41,677 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2667 [2024-11-25 18:45:41,907 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2194 [2024-11-25 18:45:42,188 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2593 [2024-11-25 18:45:42,455 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2743 [2024-11-25 18:45:42,716 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2789 [2024-11-25 18:45:42,951 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2374 [2024-11-25 18:45:43,190 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3138 [2024-11-25 18:45:43,457 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.3255 [2024-11-25 18:45:43,704 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2287 [2024-11-25 18:45:43,936 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2742 [2024-11-25 18:45:44,199 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2632 [2024-11-25 18:45:44,443 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2600 [2024-11-25 18:45:44,710 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2631 [2024-11-25 18:45:44,945 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2206 [2024-11-25 18:45:45,212 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2490 [2024-11-25 18:45:45,476 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2435 [2024-11-25 18:45:45,711 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2638 [2024-11-25 18:45:45,964 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2642 [2024-11-25 18:45:46,209 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2323 [2024-11-25 18:45:46,474 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2952 [2024-11-25 18:45:46,729 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2998 [2024-11-25 18:45:46,966 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2935 [2024-11-25 18:45:47,229 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2222 [2024-11-25 18:45:47,447 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2746 [2024-11-25 18:45:47,689 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2607 [2024-11-25 18:45:47,949 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2218 [2024-11-25 18:45:48,196 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2699 [2024-11-25 18:45:48,422 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2158 [2024-11-25 18:45:48,695 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2478 [2024-11-25 18:45:48,927 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2790 [2024-11-25 18:45:49,163 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2652 [2024-11-25 18:45:49,433 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3094 [2024-11-25 18:45:49,686 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2857 [2024-11-25 18:45:49,923 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2591 [2024-11-25 18:45:50,156 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2336 [2024-11-25 18:45:50,394 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2507 [2024-11-25 18:45:50,643 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2389 [2024-11-25 18:45:50,901 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2671 [2024-11-25 18:45:51,153 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2980 [2024-11-25 18:45:51,377 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2181 [2024-11-25 18:45:51,612 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2181 [2024-11-25 18:45:51,831 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2638 [2024-11-25 18:45:52,084 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2414 [2024-11-25 18:45:52,350 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2481 [2024-11-25 18:45:52,610 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3207 [2024-11-25 18:45:52,844 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2532 [2024-11-25 18:45:53,083 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2407 [2024-11-25 18:45:53,339 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2667 [2024-11-25 18:45:53,607 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2717 [2024-11-25 18:45:53,863 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2633 [2024-11-25 18:45:54,124 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2494 [2024-11-25 18:45:54,379 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2400 [2024-11-25 18:45:54,646 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2616 [2024-11-25 18:45:54,875 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2582 [2024-11-25 18:45:55,135 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2618 [2024-11-25 18:45:55,404 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2837 [2024-11-25 18:45:55,668 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2131 [2024-11-25 18:45:55,915 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2005 [2024-11-25 18:45:56,174 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2711 [2024-11-25 18:45:56,443 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2688 [2024-11-25 18:45:56,706 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2879 [2024-11-25 18:45:56,949 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2159 [2024-11-25 18:45:57,186 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2808 [2024-11-25 18:45:57,443 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2843 [2024-11-25 18:45:57,689 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2770 [2024-11-25 18:45:57,925 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2768 [2024-11-25 18:45:58,151 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2211 [2024-11-25 18:45:58,419 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2661 [2024-11-25 18:45:58,661 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2294 [2024-11-25 18:45:58,927 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2393 [2024-11-25 18:45:59,176 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3097 [2024-11-25 18:45:59,425 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2475 [2024-11-25 18:45:59,689 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2722 [2024-11-25 18:45:59,937 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2285 [2024-11-25 18:46:00,187 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2641 [2024-11-25 18:46:00,489 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2529 [2024-11-25 18:46:00,724 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2737 [2024-11-25 18:46:00,986 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2609 [2024-11-25 18:46:01,248 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2428 [2024-11-25 18:46:01,503 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2815 [2024-11-25 18:46:01,757 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2992 [2024-11-25 18:46:02,008 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2603 [2024-11-25 18:46:02,262 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2721 [2024-11-25 18:46:02,531 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2521 [2024-11-25 18:46:02,805 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2178 [2024-11-25 18:46:03,071 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2572 [2024-11-25 18:46:03,329 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2372 [2024-11-25 18:46:03,598 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2564 [2024-11-25 18:46:03,824 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3070 [2024-11-25 18:46:04,095 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2481 [2024-11-25 18:46:04,342 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2695 [2024-11-25 18:46:04,626 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2153 [2024-11-25 18:46:04,892 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2904 [2024-11-25 18:46:05,151 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2497 [2024-11-25 18:46:05,405 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2813 [2024-11-25 18:46:05,634 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2530 [2024-11-25 18:46:05,876 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2406 [2024-11-25 18:46:06,139 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2254 [2024-11-25 18:46:06,416 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2540 [2024-11-25 18:46:06,655 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.3050 [2024-11-25 18:46:06,916 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2574 [2024-11-25 18:46:07,187 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2576 [2024-11-25 18:46:07,418 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2350 [2024-11-25 18:46:07,661 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2313 [2024-11-25 18:46:07,885 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2361 [2024-11-25 18:46:08,115 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2234 [2024-11-25 18:46:08,387 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3220 [2024-11-25 18:46:08,645 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2862 [2024-11-25 18:46:08,916 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2502 [2024-11-25 18:46:09,189 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2402 [2024-11-25 18:46:09,449 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3484 [2024-11-25 18:46:09,709 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2946 [2024-11-25 18:46:09,973 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2445 [2024-11-25 18:46:10,230 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2629 [2024-11-25 18:46:10,507 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.3001 [2024-11-25 18:46:10,769 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2721 [2024-11-25 18:46:11,020 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2794 [2024-11-25 18:46:11,251 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2081 [2024-11-25 18:46:11,516 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2736 [2024-11-25 18:46:11,751 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2663 [2024-11-25 18:46:11,987 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2014 [2024-11-25 18:46:12,202 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2456 [2024-11-25 18:46:12,423 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2084 [2024-11-25 18:46:12,988 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7603/0.8288/0.9961. [2024-11-25 18:46:12,988 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9961/0.9983 [2024-11-25 18:46:12,988 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5245/0.6593 [2024-11-25 18:46:12,989 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:46:12,990 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:46:12,990 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:46:15,723 INFO misc.py line 119 2586773] Train: [32/50][1/376] Data 0.099 (0.099) Batch 0.603 (0.603) Remain 01:11:48 loss: 0.1905 Lr: 0.00136 [2024-11-25 18:46:16,222 INFO misc.py line 119 2586773] Train: [32/50][2/376] Data 0.003 (0.003) Batch 0.498 (0.498) Remain 00:59:20 loss: 0.1699 Lr: 0.00136 [2024-11-25 18:46:16,725 INFO misc.py line 119 2586773] Train: [32/50][3/376] Data 0.002 (0.002) Batch 0.503 (0.503) Remain 00:59:51 loss: 0.1935 Lr: 0.00136 [2024-11-25 18:46:17,223 INFO misc.py line 119 2586773] Train: [32/50][4/376] Data 0.002 (0.002) Batch 0.498 (0.498) Remain 00:59:14 loss: 0.1963 Lr: 0.00136 [2024-11-25 18:46:17,721 INFO misc.py line 119 2586773] Train: [32/50][5/376] Data 0.002 (0.002) Batch 0.498 (0.498) Remain 00:59:16 loss: 0.2284 Lr: 0.00136 [2024-11-25 18:46:18,234 INFO misc.py line 119 2586773] Train: [32/50][6/376] Data 0.002 (0.002) Batch 0.513 (0.503) Remain 00:59:51 loss: 0.2128 Lr: 0.00136 [2024-11-25 18:46:18,748 INFO misc.py line 119 2586773] Train: [32/50][7/376] Data 0.002 (0.002) Batch 0.514 (0.506) Remain 01:00:10 loss: 0.1746 Lr: 0.00136 [2024-11-25 18:46:19,255 INFO misc.py line 119 2586773] Train: [32/50][8/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 01:00:10 loss: 0.2221 Lr: 0.00136 [2024-11-25 18:46:19,746 INFO misc.py line 119 2586773] Train: [32/50][9/376] Data 0.002 (0.002) Batch 0.492 (0.504) Remain 00:59:53 loss: 0.1442 Lr: 0.00136 [2024-11-25 18:46:20,224 INFO misc.py line 119 2586773] Train: [32/50][10/376] Data 0.002 (0.002) Batch 0.478 (0.500) Remain 00:59:26 loss: 0.2391 Lr: 0.00136 [2024-11-25 18:46:20,787 INFO misc.py line 119 2586773] Train: [32/50][11/376] Data 0.003 (0.002) Batch 0.563 (0.508) Remain 01:00:22 loss: 0.2344 Lr: 0.00136 [2024-11-25 18:46:21,262 INFO misc.py line 119 2586773] Train: [32/50][12/376] Data 0.002 (0.002) Batch 0.474 (0.504) Remain 00:59:55 loss: 0.2094 Lr: 0.00136 [2024-11-25 18:46:21,733 INFO misc.py line 119 2586773] Train: [32/50][13/376] Data 0.002 (0.002) Batch 0.471 (0.501) Remain 00:59:31 loss: 0.2502 Lr: 0.00136 [2024-11-25 18:46:22,223 INFO misc.py line 119 2586773] Train: [32/50][14/376] Data 0.002 (0.002) Batch 0.490 (0.500) Remain 00:59:23 loss: 0.2310 Lr: 0.00136 [2024-11-25 18:46:22,722 INFO misc.py line 119 2586773] Train: [32/50][15/376] Data 0.002 (0.002) Batch 0.499 (0.500) Remain 00:59:22 loss: 0.2184 Lr: 0.00136 [2024-11-25 18:46:23,208 INFO misc.py line 119 2586773] Train: [32/50][16/376] Data 0.002 (0.002) Batch 0.486 (0.499) Remain 00:59:14 loss: 0.2229 Lr: 0.00136 [2024-11-25 18:46:23,684 INFO misc.py line 119 2586773] Train: [32/50][17/376] Data 0.002 (0.002) Batch 0.476 (0.497) Remain 00:59:02 loss: 0.2614 Lr: 0.00136 [2024-11-25 18:46:24,187 INFO misc.py line 119 2586773] Train: [32/50][18/376] Data 0.002 (0.002) Batch 0.502 (0.497) Remain 00:59:04 loss: 0.2228 Lr: 0.00136 [2024-11-25 18:46:24,712 INFO misc.py line 119 2586773] Train: [32/50][19/376] Data 0.002 (0.002) Batch 0.525 (0.499) Remain 00:59:16 loss: 0.2682 Lr: 0.00136 [2024-11-25 18:46:25,243 INFO misc.py line 119 2586773] Train: [32/50][20/376] Data 0.002 (0.002) Batch 0.531 (0.501) Remain 00:59:29 loss: 0.3152 Lr: 0.00136 [2024-11-25 18:46:25,702 INFO misc.py line 119 2586773] Train: [32/50][21/376] Data 0.002 (0.002) Batch 0.459 (0.499) Remain 00:59:12 loss: 0.1805 Lr: 0.00136 [2024-11-25 18:46:26,204 INFO misc.py line 119 2586773] Train: [32/50][22/376] Data 0.002 (0.002) Batch 0.502 (0.499) Remain 00:59:13 loss: 0.2065 Lr: 0.00135 [2024-11-25 18:46:26,677 INFO misc.py line 119 2586773] Train: [32/50][23/376] Data 0.002 (0.002) Batch 0.474 (0.498) Remain 00:59:03 loss: 0.2603 Lr: 0.00135 [2024-11-25 18:46:27,186 INFO misc.py line 119 2586773] Train: [32/50][24/376] Data 0.002 (0.002) Batch 0.509 (0.498) Remain 00:59:06 loss: 0.1801 Lr: 0.00135 [2024-11-25 18:46:27,676 INFO misc.py line 119 2586773] Train: [32/50][25/376] Data 0.002 (0.002) Batch 0.490 (0.498) Remain 00:59:03 loss: 0.1907 Lr: 0.00135 [2024-11-25 18:46:28,163 INFO misc.py line 119 2586773] Train: [32/50][26/376] Data 0.002 (0.002) Batch 0.486 (0.497) Remain 00:58:59 loss: 0.2202 Lr: 0.00135 [2024-11-25 18:46:28,689 INFO misc.py line 119 2586773] Train: [32/50][27/376] Data 0.003 (0.002) Batch 0.527 (0.499) Remain 00:59:07 loss: 0.2561 Lr: 0.00135 [2024-11-25 18:46:29,200 INFO misc.py line 119 2586773] Train: [32/50][28/376] Data 0.003 (0.002) Batch 0.511 (0.499) Remain 00:59:10 loss: 0.2192 Lr: 0.00135 [2024-11-25 18:46:29,694 INFO misc.py line 119 2586773] Train: [32/50][29/376] Data 0.003 (0.002) Batch 0.494 (0.499) Remain 00:59:09 loss: 0.2122 Lr: 0.00135 [2024-11-25 18:46:30,253 INFO misc.py line 119 2586773] Train: [32/50][30/376] Data 0.002 (0.002) Batch 0.559 (0.501) Remain 00:59:24 loss: 0.2593 Lr: 0.00135 [2024-11-25 18:46:30,766 INFO misc.py line 119 2586773] Train: [32/50][31/376] Data 0.002 (0.002) Batch 0.512 (0.501) Remain 00:59:26 loss: 0.1956 Lr: 0.00135 [2024-11-25 18:46:31,267 INFO misc.py line 119 2586773] Train: [32/50][32/376] Data 0.003 (0.002) Batch 0.502 (0.501) Remain 00:59:26 loss: 0.1731 Lr: 0.00135 [2024-11-25 18:46:31,752 INFO misc.py line 119 2586773] Train: [32/50][33/376] Data 0.002 (0.002) Batch 0.484 (0.501) Remain 00:59:21 loss: 0.1778 Lr: 0.00135 [2024-11-25 18:46:32,250 INFO misc.py line 119 2586773] Train: [32/50][34/376] Data 0.002 (0.002) Batch 0.498 (0.501) Remain 00:59:20 loss: 0.1591 Lr: 0.00135 [2024-11-25 18:46:32,764 INFO misc.py line 119 2586773] Train: [32/50][35/376] Data 0.002 (0.002) Batch 0.514 (0.501) Remain 00:59:23 loss: 0.2212 Lr: 0.00135 [2024-11-25 18:46:33,211 INFO misc.py line 119 2586773] Train: [32/50][36/376] Data 0.002 (0.002) Batch 0.447 (0.500) Remain 00:59:10 loss: 0.1745 Lr: 0.00135 [2024-11-25 18:46:33,689 INFO misc.py line 119 2586773] Train: [32/50][37/376] Data 0.002 (0.002) Batch 0.478 (0.499) Remain 00:59:05 loss: 0.2076 Lr: 0.00135 [2024-11-25 18:46:34,185 INFO misc.py line 119 2586773] Train: [32/50][38/376] Data 0.002 (0.002) Batch 0.497 (0.499) Remain 00:59:04 loss: 0.1744 Lr: 0.00135 [2024-11-25 18:46:34,658 INFO misc.py line 119 2586773] Train: [32/50][39/376] Data 0.002 (0.002) Batch 0.472 (0.498) Remain 00:58:59 loss: 0.1755 Lr: 0.00135 [2024-11-25 18:46:35,205 INFO misc.py line 119 2586773] Train: [32/50][40/376] Data 0.002 (0.002) Batch 0.547 (0.499) Remain 00:59:08 loss: 0.1841 Lr: 0.00135 [2024-11-25 18:46:35,713 INFO misc.py line 119 2586773] Train: [32/50][41/376] Data 0.003 (0.002) Batch 0.508 (0.500) Remain 00:59:09 loss: 0.2160 Lr: 0.00135 [2024-11-25 18:46:36,234 INFO misc.py line 119 2586773] Train: [32/50][42/376] Data 0.002 (0.002) Batch 0.522 (0.500) Remain 00:59:12 loss: 0.2511 Lr: 0.00135 [2024-11-25 18:46:36,748 INFO misc.py line 119 2586773] Train: [32/50][43/376] Data 0.002 (0.002) Batch 0.514 (0.501) Remain 00:59:14 loss: 0.2229 Lr: 0.00135 [2024-11-25 18:46:37,284 INFO misc.py line 119 2586773] Train: [32/50][44/376] Data 0.003 (0.002) Batch 0.536 (0.501) Remain 00:59:20 loss: 0.1840 Lr: 0.00135 [2024-11-25 18:46:37,787 INFO misc.py line 119 2586773] Train: [32/50][45/376] Data 0.002 (0.002) Batch 0.503 (0.501) Remain 00:59:20 loss: 0.1759 Lr: 0.00135 [2024-11-25 18:46:38,285 INFO misc.py line 119 2586773] Train: [32/50][46/376] Data 0.003 (0.002) Batch 0.498 (0.501) Remain 00:59:18 loss: 0.1952 Lr: 0.00135 [2024-11-25 18:46:38,771 INFO misc.py line 119 2586773] Train: [32/50][47/376] Data 0.002 (0.002) Batch 0.487 (0.501) Remain 00:59:16 loss: 0.1842 Lr: 0.00135 [2024-11-25 18:46:39,322 INFO misc.py line 119 2586773] Train: [32/50][48/376] Data 0.003 (0.002) Batch 0.550 (0.502) Remain 00:59:23 loss: 0.2501 Lr: 0.00135 [2024-11-25 18:46:39,789 INFO misc.py line 119 2586773] Train: [32/50][49/376] Data 0.003 (0.002) Batch 0.467 (0.501) Remain 00:59:17 loss: 0.1896 Lr: 0.00135 [2024-11-25 18:46:40,267 INFO misc.py line 119 2586773] Train: [32/50][50/376] Data 0.002 (0.002) Batch 0.478 (0.501) Remain 00:59:13 loss: 0.2044 Lr: 0.00135 [2024-11-25 18:46:40,736 INFO misc.py line 119 2586773] Train: [32/50][51/376] Data 0.002 (0.002) Batch 0.468 (0.500) Remain 00:59:08 loss: 0.1563 Lr: 0.00135 [2024-11-25 18:46:41,289 INFO misc.py line 119 2586773] Train: [32/50][52/376] Data 0.002 (0.002) Batch 0.554 (0.501) Remain 00:59:15 loss: 0.2250 Lr: 0.00134 [2024-11-25 18:46:41,820 INFO misc.py line 119 2586773] Train: [32/50][53/376] Data 0.002 (0.002) Batch 0.531 (0.502) Remain 00:59:19 loss: 0.1942 Lr: 0.00134 [2024-11-25 18:46:42,320 INFO misc.py line 119 2586773] Train: [32/50][54/376] Data 0.002 (0.002) Batch 0.499 (0.502) Remain 00:59:18 loss: 0.1715 Lr: 0.00134 [2024-11-25 18:46:42,840 INFO misc.py line 119 2586773] Train: [32/50][55/376] Data 0.002 (0.002) Batch 0.520 (0.502) Remain 00:59:20 loss: 0.1816 Lr: 0.00134 [2024-11-25 18:46:43,370 INFO misc.py line 119 2586773] Train: [32/50][56/376] Data 0.002 (0.002) Batch 0.530 (0.503) Remain 00:59:23 loss: 0.2068 Lr: 0.00134 [2024-11-25 18:46:43,920 INFO misc.py line 119 2586773] Train: [32/50][57/376] Data 0.003 (0.002) Batch 0.551 (0.504) Remain 00:59:29 loss: 0.2727 Lr: 0.00134 [2024-11-25 18:46:44,453 INFO misc.py line 119 2586773] Train: [32/50][58/376] Data 0.002 (0.002) Batch 0.533 (0.504) Remain 00:59:32 loss: 0.2239 Lr: 0.00134 [2024-11-25 18:46:44,965 INFO misc.py line 119 2586773] Train: [32/50][59/376] Data 0.002 (0.002) Batch 0.511 (0.504) Remain 00:59:32 loss: 0.1973 Lr: 0.00134 [2024-11-25 18:46:45,462 INFO misc.py line 119 2586773] Train: [32/50][60/376] Data 0.002 (0.002) Batch 0.497 (0.504) Remain 00:59:31 loss: 0.2234 Lr: 0.00134 [2024-11-25 18:46:46,005 INFO misc.py line 119 2586773] Train: [32/50][61/376] Data 0.002 (0.002) Batch 0.544 (0.505) Remain 00:59:35 loss: 0.2355 Lr: 0.00134 [2024-11-25 18:46:46,478 INFO misc.py line 119 2586773] Train: [32/50][62/376] Data 0.003 (0.002) Batch 0.473 (0.504) Remain 00:59:31 loss: 0.2578 Lr: 0.00134 [2024-11-25 18:46:47,012 INFO misc.py line 119 2586773] Train: [32/50][63/376] Data 0.002 (0.002) Batch 0.534 (0.505) Remain 00:59:34 loss: 0.2266 Lr: 0.00134 [2024-11-25 18:46:47,540 INFO misc.py line 119 2586773] Train: [32/50][64/376] Data 0.002 (0.002) Batch 0.528 (0.505) Remain 00:59:36 loss: 0.2249 Lr: 0.00134 [2024-11-25 18:46:48,026 INFO misc.py line 119 2586773] Train: [32/50][65/376] Data 0.003 (0.002) Batch 0.486 (0.505) Remain 00:59:33 loss: 0.2352 Lr: 0.00134 [2024-11-25 18:46:48,494 INFO misc.py line 119 2586773] Train: [32/50][66/376] Data 0.002 (0.002) Batch 0.468 (0.504) Remain 00:59:29 loss: 0.1921 Lr: 0.00134 [2024-11-25 18:46:49,000 INFO misc.py line 119 2586773] Train: [32/50][67/376] Data 0.002 (0.002) Batch 0.506 (0.504) Remain 00:59:28 loss: 0.1740 Lr: 0.00134 [2024-11-25 18:46:49,493 INFO misc.py line 119 2586773] Train: [32/50][68/376] Data 0.002 (0.002) Batch 0.493 (0.504) Remain 00:59:27 loss: 0.1897 Lr: 0.00134 [2024-11-25 18:46:49,960 INFO misc.py line 119 2586773] Train: [32/50][69/376] Data 0.002 (0.002) Batch 0.467 (0.504) Remain 00:59:22 loss: 0.2460 Lr: 0.00134 [2024-11-25 18:46:50,489 INFO misc.py line 119 2586773] Train: [32/50][70/376] Data 0.002 (0.002) Batch 0.529 (0.504) Remain 00:59:24 loss: 0.1921 Lr: 0.00134 [2024-11-25 18:46:51,001 INFO misc.py line 119 2586773] Train: [32/50][71/376] Data 0.002 (0.002) Batch 0.512 (0.504) Remain 00:59:25 loss: 0.2110 Lr: 0.00134 [2024-11-25 18:46:51,530 INFO misc.py line 119 2586773] Train: [32/50][72/376] Data 0.002 (0.002) Batch 0.529 (0.504) Remain 00:59:27 loss: 0.2529 Lr: 0.00134 [2024-11-25 18:46:52,049 INFO misc.py line 119 2586773] Train: [32/50][73/376] Data 0.002 (0.002) Batch 0.519 (0.505) Remain 00:59:28 loss: 0.1843 Lr: 0.00134 [2024-11-25 18:46:52,541 INFO misc.py line 119 2586773] Train: [32/50][74/376] Data 0.003 (0.002) Batch 0.492 (0.504) Remain 00:59:26 loss: 0.3772 Lr: 0.00134 [2024-11-25 18:46:53,060 INFO misc.py line 119 2586773] Train: [32/50][75/376] Data 0.002 (0.002) Batch 0.519 (0.505) Remain 00:59:27 loss: 0.2363 Lr: 0.00134 [2024-11-25 18:46:53,558 INFO misc.py line 119 2586773] Train: [32/50][76/376] Data 0.002 (0.002) Batch 0.499 (0.505) Remain 00:59:26 loss: 0.2118 Lr: 0.00134 [2024-11-25 18:46:54,049 INFO misc.py line 119 2586773] Train: [32/50][77/376] Data 0.003 (0.002) Batch 0.490 (0.504) Remain 00:59:24 loss: 0.2165 Lr: 0.00134 [2024-11-25 18:46:54,572 INFO misc.py line 119 2586773] Train: [32/50][78/376] Data 0.003 (0.002) Batch 0.523 (0.505) Remain 00:59:25 loss: 0.1726 Lr: 0.00134 [2024-11-25 18:46:55,124 INFO misc.py line 119 2586773] Train: [32/50][79/376] Data 0.002 (0.002) Batch 0.552 (0.505) Remain 00:59:29 loss: 0.1738 Lr: 0.00134 [2024-11-25 18:46:55,626 INFO misc.py line 119 2586773] Train: [32/50][80/376] Data 0.003 (0.002) Batch 0.502 (0.505) Remain 00:59:28 loss: 0.2216 Lr: 0.00134 [2024-11-25 18:46:56,112 INFO misc.py line 119 2586773] Train: [32/50][81/376] Data 0.003 (0.002) Batch 0.486 (0.505) Remain 00:59:26 loss: 0.1866 Lr: 0.00134 [2024-11-25 18:46:56,658 INFO misc.py line 119 2586773] Train: [32/50][82/376] Data 0.003 (0.002) Batch 0.546 (0.505) Remain 00:59:29 loss: 0.1737 Lr: 0.00134 [2024-11-25 18:46:57,205 INFO misc.py line 119 2586773] Train: [32/50][83/376] Data 0.002 (0.002) Batch 0.547 (0.506) Remain 00:59:32 loss: 0.2083 Lr: 0.00133 [2024-11-25 18:46:57,691 INFO misc.py line 119 2586773] Train: [32/50][84/376] Data 0.003 (0.002) Batch 0.486 (0.506) Remain 00:59:30 loss: 0.2347 Lr: 0.00133 [2024-11-25 18:46:58,190 INFO misc.py line 119 2586773] Train: 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(0.506) Remain 00:59:25 loss: 0.1603 Lr: 0.00133 [2024-11-25 18:47:01,722 INFO misc.py line 119 2586773] Train: [32/50][92/376] Data 0.002 (0.002) Batch 0.504 (0.506) Remain 00:59:25 loss: 0.2727 Lr: 0.00133 [2024-11-25 18:47:02,209 INFO misc.py line 119 2586773] Train: [32/50][93/376] Data 0.002 (0.002) Batch 0.488 (0.505) Remain 00:59:23 loss: 0.2337 Lr: 0.00133 [2024-11-25 18:47:02,720 INFO misc.py line 119 2586773] Train: [32/50][94/376] Data 0.002 (0.002) Batch 0.511 (0.505) Remain 00:59:23 loss: 0.2523 Lr: 0.00133 [2024-11-25 18:47:03,242 INFO misc.py line 119 2586773] Train: [32/50][95/376] Data 0.002 (0.002) Batch 0.521 (0.506) Remain 00:59:24 loss: 0.2615 Lr: 0.00133 [2024-11-25 18:47:03,751 INFO misc.py line 119 2586773] Train: [32/50][96/376] Data 0.002 (0.002) Batch 0.510 (0.506) Remain 00:59:23 loss: 0.1937 Lr: 0.00133 [2024-11-25 18:47:04,232 INFO misc.py line 119 2586773] Train: [32/50][97/376] Data 0.002 (0.002) Batch 0.481 (0.505) Remain 00:59:21 loss: 0.1980 Lr: 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Batch 0.523 (0.508) Remain 00:58:34 loss: 0.1989 Lr: 0.00129 [2024-11-25 18:48:11,576 INFO misc.py line 119 2586773] Train: [32/50][229/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 00:58:34 loss: 0.2279 Lr: 0.00129 [2024-11-25 18:48:12,048 INFO misc.py line 119 2586773] Train: [32/50][230/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 00:58:32 loss: 0.1801 Lr: 0.00129 [2024-11-25 18:48:12,563 INFO misc.py line 119 2586773] Train: [32/50][231/376] Data 0.002 (0.002) Batch 0.515 (0.508) Remain 00:58:32 loss: 0.2104 Lr: 0.00129 [2024-11-25 18:48:13,087 INFO misc.py line 119 2586773] Train: [32/50][232/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 00:58:32 loss: 0.1840 Lr: 0.00129 [2024-11-25 18:48:13,601 INFO misc.py line 119 2586773] Train: [32/50][233/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:58:31 loss: 0.2676 Lr: 0.00129 [2024-11-25 18:48:14,079 INFO misc.py line 119 2586773] Train: [32/50][234/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 00:58:30 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line 119 2586773] Train: [32/50][272/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 00:58:08 loss: 0.2061 Lr: 0.00127 [2024-11-25 18:48:33,774 INFO misc.py line 119 2586773] Train: [32/50][273/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:58:07 loss: 0.2074 Lr: 0.00127 [2024-11-25 18:48:34,323 INFO misc.py line 119 2586773] Train: [32/50][274/376] Data 0.002 (0.002) Batch 0.549 (0.508) Remain 00:58:08 loss: 0.2333 Lr: 0.00127 [2024-11-25 18:48:34,819 INFO misc.py line 119 2586773] Train: [32/50][275/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 00:58:07 loss: 0.1723 Lr: 0.00127 [2024-11-25 18:48:35,324 INFO misc.py line 119 2586773] Train: [32/50][276/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 00:58:06 loss: 0.2523 Lr: 0.00127 [2024-11-25 18:48:35,833 INFO misc.py line 119 2586773] Train: [32/50][277/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:58:06 loss: 0.2134 Lr: 0.00127 [2024-11-25 18:48:36,381 INFO misc.py line 119 2586773] Train: 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Batch 0.566 (0.508) Remain 00:58:04 loss: 0.1870 Lr: 0.00127 [2024-11-25 18:48:39,975 INFO misc.py line 119 2586773] Train: [32/50][285/376] Data 0.002 (0.002) Batch 0.518 (0.508) Remain 00:58:04 loss: 0.1754 Lr: 0.00127 [2024-11-25 18:48:40,453 INFO misc.py line 119 2586773] Train: [32/50][286/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 00:58:02 loss: 0.2327 Lr: 0.00127 [2024-11-25 18:48:40,940 INFO misc.py line 119 2586773] Train: [32/50][287/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 00:58:01 loss: 0.1880 Lr: 0.00127 [2024-11-25 18:48:41,471 INFO misc.py line 119 2586773] Train: [32/50][288/376] Data 0.002 (0.002) Batch 0.530 (0.508) Remain 00:58:02 loss: 0.3498 Lr: 0.00127 [2024-11-25 18:48:42,000 INFO misc.py line 119 2586773] Train: [32/50][289/376] Data 0.002 (0.002) Batch 0.530 (0.508) Remain 00:58:02 loss: 0.2111 Lr: 0.00127 [2024-11-25 18:48:42,491 INFO misc.py line 119 2586773] Train: [32/50][290/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 00:58:01 loss: 0.1958 Lr: 0.00127 [2024-11-25 18:48:43,020 INFO misc.py line 119 2586773] Train: [32/50][291/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 00:58:01 loss: 0.1673 Lr: 0.00127 [2024-11-25 18:48:43,541 INFO misc.py line 119 2586773] Train: [32/50][292/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 00:58:00 loss: 0.2161 Lr: 0.00127 [2024-11-25 18:48:44,013 INFO misc.py line 119 2586773] Train: [32/50][293/376] Data 0.002 (0.002) Batch 0.473 (0.508) Remain 00:57:59 loss: 0.2151 Lr: 0.00127 [2024-11-25 18:48:44,531 INFO misc.py line 119 2586773] Train: [32/50][294/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 00:57:59 loss: 0.1697 Lr: 0.00127 [2024-11-25 18:48:45,011 INFO misc.py line 119 2586773] Train: [32/50][295/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 00:57:58 loss: 0.1901 Lr: 0.00127 [2024-11-25 18:48:45,500 INFO misc.py line 119 2586773] Train: [32/50][296/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:57:57 loss: 0.1970 Lr: 0.00127 [2024-11-25 18:48:45,990 INFO misc.py line 119 2586773] Train: [32/50][297/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 00:57:56 loss: 0.2011 Lr: 0.00127 [2024-11-25 18:48:46,445 INFO misc.py line 119 2586773] Train: [32/50][298/376] Data 0.002 (0.002) Batch 0.455 (0.508) Remain 00:57:54 loss: 0.2137 Lr: 0.00126 [2024-11-25 18:48:46,935 INFO misc.py line 119 2586773] Train: [32/50][299/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:57:53 loss: 0.1754 Lr: 0.00126 [2024-11-25 18:48:47,434 INFO misc.py line 119 2586773] Train: [32/50][300/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:57:52 loss: 0.2397 Lr: 0.00126 [2024-11-25 18:48:47,913 INFO misc.py line 119 2586773] Train: [32/50][301/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:57:51 loss: 0.2086 Lr: 0.00126 [2024-11-25 18:48:48,407 INFO misc.py line 119 2586773] Train: [32/50][302/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:57:50 loss: 0.2629 Lr: 0.00126 [2024-11-25 18:48:48,894 INFO misc.py line 119 2586773] Train: [32/50][303/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:57:49 loss: 0.1866 Lr: 0.00126 [2024-11-25 18:48:49,414 INFO misc.py line 119 2586773] Train: [32/50][304/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:57:49 loss: 0.2120 Lr: 0.00126 [2024-11-25 18:48:49,896 INFO misc.py line 119 2586773] Train: [32/50][305/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 00:57:48 loss: 0.2033 Lr: 0.00126 [2024-11-25 18:48:50,421 INFO misc.py line 119 2586773] Train: [32/50][306/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:57:48 loss: 0.1919 Lr: 0.00126 [2024-11-25 18:48:50,921 INFO misc.py line 119 2586773] Train: [32/50][307/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:57:47 loss: 0.2151 Lr: 0.00126 [2024-11-25 18:48:51,480 INFO misc.py line 119 2586773] Train: [32/50][308/376] Data 0.002 (0.002) Batch 0.559 (0.507) Remain 00:57:48 loss: 0.1773 Lr: 0.00126 [2024-11-25 18:48:52,004 INFO misc.py line 119 2586773] Train: [32/50][309/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:57:48 loss: 0.1873 Lr: 0.00126 [2024-11-25 18:48:52,565 INFO misc.py line 119 2586773] Train: [32/50][310/376] Data 0.002 (0.002) Batch 0.561 (0.508) Remain 00:57:49 loss: 0.2275 Lr: 0.00126 [2024-11-25 18:48:53,057 INFO misc.py line 119 2586773] Train: [32/50][311/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 00:57:48 loss: 0.1935 Lr: 0.00126 [2024-11-25 18:48:53,596 INFO misc.py line 119 2586773] Train: [32/50][312/376] Data 0.002 (0.002) Batch 0.539 (0.508) Remain 00:57:48 loss: 0.2971 Lr: 0.00126 [2024-11-25 18:48:54,106 INFO misc.py line 119 2586773] Train: [32/50][313/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 00:57:47 loss: 0.2114 Lr: 0.00126 [2024-11-25 18:48:54,588 INFO misc.py line 119 2586773] Train: [32/50][314/376] Data 0.003 (0.002) Batch 0.482 (0.508) Remain 00:57:46 loss: 0.1858 Lr: 0.00126 [2024-11-25 18:48:55,098 INFO misc.py line 119 2586773] Train: [32/50][315/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 00:57:46 loss: 0.2115 Lr: 0.00126 [2024-11-25 18:48:55,629 INFO misc.py line 119 2586773] Train: [32/50][316/376] Data 0.002 (0.002) Batch 0.531 (0.508) Remain 00:57:46 loss: 0.2260 Lr: 0.00126 [2024-11-25 18:48:56,118 INFO misc.py line 119 2586773] Train: [32/50][317/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:57:45 loss: 0.1919 Lr: 0.00126 [2024-11-25 18:48:56,588 INFO misc.py line 119 2586773] Train: [32/50][318/376] Data 0.002 (0.002) Batch 0.470 (0.508) Remain 00:57:44 loss: 0.2088 Lr: 0.00126 [2024-11-25 18:48:57,065 INFO misc.py line 119 2586773] Train: [32/50][319/376] Data 0.002 (0.002) Batch 0.477 (0.507) Remain 00:57:43 loss: 0.2330 Lr: 0.00126 [2024-11-25 18:48:57,577 INFO misc.py line 119 2586773] Train: [32/50][320/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:57:42 loss: 0.1925 Lr: 0.00126 [2024-11-25 18:48:58,098 INFO misc.py line 119 2586773] Train: [32/50][321/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 00:57:42 loss: 0.1829 Lr: 0.00126 [2024-11-25 18:48:58,638 INFO misc.py line 119 2586773] Train: [32/50][322/376] Data 0.002 (0.002) Batch 0.540 (0.508) Remain 00:57:42 loss: 0.2068 Lr: 0.00126 [2024-11-25 18:48:59,137 INFO misc.py line 119 2586773] Train: [32/50][323/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 00:57:41 loss: 0.2006 Lr: 0.00126 [2024-11-25 18:48:59,609 INFO misc.py line 119 2586773] Train: [32/50][324/376] Data 0.002 (0.002) Batch 0.472 (0.507) Remain 00:57:40 loss: 0.2189 Lr: 0.00126 [2024-11-25 18:49:00,133 INFO misc.py line 119 2586773] Train: [32/50][325/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:57:40 loss: 0.1939 Lr: 0.00126 [2024-11-25 18:49:00,676 INFO misc.py line 119 2586773] Train: [32/50][326/376] Data 0.003 (0.002) Batch 0.543 (0.508) Remain 00:57:40 loss: 0.1985 Lr: 0.00126 [2024-11-25 18:49:01,126 INFO misc.py line 119 2586773] Train: [32/50][327/376] Data 0.002 (0.002) Batch 0.450 (0.507) Remain 00:57:39 loss: 0.1833 Lr: 0.00126 [2024-11-25 18:49:01,620 INFO misc.py line 119 2586773] Train: [32/50][328/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:57:38 loss: 0.1789 Lr: 0.00126 [2024-11-25 18:49:02,150 INFO misc.py line 119 2586773] Train: [32/50][329/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 00:57:38 loss: 0.2013 Lr: 0.00125 [2024-11-25 18:49:02,623 INFO misc.py line 119 2586773] Train: [32/50][330/376] Data 0.003 (0.002) Batch 0.473 (0.507) Remain 00:57:36 loss: 0.1863 Lr: 0.00125 [2024-11-25 18:49:03,138 INFO misc.py line 119 2586773] Train: [32/50][331/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:57:36 loss: 0.1906 Lr: 0.00125 [2024-11-25 18:49:03,658 INFO misc.py line 119 2586773] Train: [32/50][332/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:57:36 loss: 0.2317 Lr: 0.00125 [2024-11-25 18:49:04,150 INFO misc.py line 119 2586773] Train: [32/50][333/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:57:35 loss: 0.2209 Lr: 0.00125 [2024-11-25 18:49:04,692 INFO misc.py line 119 2586773] Train: [32/50][334/376] Data 0.002 (0.002) Batch 0.542 (0.507) Remain 00:57:35 loss: 0.1860 Lr: 0.00125 [2024-11-25 18:49:05,192 INFO misc.py line 119 2586773] Train: [32/50][335/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:57:35 loss: 0.2026 Lr: 0.00125 [2024-11-25 18:49:05,677 INFO misc.py line 119 2586773] Train: [32/50][336/376] Data 0.003 (0.002) Batch 0.485 (0.507) Remain 00:57:34 loss: 0.2379 Lr: 0.00125 [2024-11-25 18:49:06,164 INFO misc.py line 119 2586773] Train: [32/50][337/376] Data 0.003 (0.002) Batch 0.487 (0.507) Remain 00:57:33 loss: 0.1992 Lr: 0.00125 [2024-11-25 18:49:06,710 INFO misc.py line 119 2586773] Train: [32/50][338/376] Data 0.002 (0.002) Batch 0.546 (0.507) Remain 00:57:33 loss: 0.2602 Lr: 0.00125 [2024-11-25 18:49:07,232 INFO misc.py line 119 2586773] Train: [32/50][339/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:57:33 loss: 0.2140 Lr: 0.00125 [2024-11-25 18:49:07,718 INFO misc.py line 119 2586773] Train: [32/50][340/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:57:32 loss: 0.1942 Lr: 0.00125 [2024-11-25 18:49:08,244 INFO misc.py line 119 2586773] Train: [32/50][341/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 00:57:32 loss: 0.1981 Lr: 0.00125 [2024-11-25 18:49:08,696 INFO misc.py line 119 2586773] Train: [32/50][342/376] Data 0.002 (0.002) Batch 0.453 (0.507) Remain 00:57:30 loss: 0.2230 Lr: 0.00125 [2024-11-25 18:49:09,215 INFO misc.py line 119 2586773] Train: [32/50][343/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:57:30 loss: 0.2661 Lr: 0.00125 [2024-11-25 18:49:09,741 INFO misc.py line 119 2586773] Train: [32/50][344/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 00:57:30 loss: 0.2318 Lr: 0.00125 [2024-11-25 18:49:10,250 INFO misc.py line 119 2586773] Train: [32/50][345/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:57:29 loss: 0.1822 Lr: 0.00125 [2024-11-25 18:49:10,742 INFO misc.py line 119 2586773] Train: [32/50][346/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:57:28 loss: 0.1801 Lr: 0.00125 [2024-11-25 18:49:11,277 INFO misc.py line 119 2586773] Train: [32/50][347/376] Data 0.002 (0.002) Batch 0.534 (0.507) Remain 00:57:28 loss: 0.1918 Lr: 0.00125 [2024-11-25 18:49:11,734 INFO misc.py line 119 2586773] Train: [32/50][348/376] Data 0.002 (0.002) Batch 0.457 (0.507) Remain 00:57:27 loss: 0.1945 Lr: 0.00125 [2024-11-25 18:49:12,231 INFO misc.py line 119 2586773] Train: [32/50][349/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 00:57:26 loss: 0.1786 Lr: 0.00125 [2024-11-25 18:49:12,730 INFO misc.py line 119 2586773] Train: [32/50][350/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:57:26 loss: 0.1924 Lr: 0.00125 [2024-11-25 18:49:13,217 INFO misc.py line 119 2586773] Train: [32/50][351/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:57:25 loss: 0.2334 Lr: 0.00125 [2024-11-25 18:49:13,720 INFO misc.py line 119 2586773] Train: [32/50][352/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:57:24 loss: 0.2045 Lr: 0.00125 [2024-11-25 18:49:14,278 INFO misc.py line 119 2586773] Train: [32/50][353/376] Data 0.002 (0.002) Batch 0.558 (0.507) Remain 00:57:25 loss: 0.1941 Lr: 0.00125 [2024-11-25 18:49:14,764 INFO misc.py line 119 2586773] Train: [32/50][354/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:57:24 loss: 0.1768 Lr: 0.00125 [2024-11-25 18:49:15,263 INFO misc.py line 119 2586773] Train: [32/50][355/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:57:23 loss: 0.3833 Lr: 0.00125 [2024-11-25 18:49:15,816 INFO misc.py line 119 2586773] Train: [32/50][356/376] Data 0.002 (0.002) Batch 0.553 (0.507) Remain 00:57:23 loss: 0.2193 Lr: 0.00125 [2024-11-25 18:49:16,320 INFO misc.py line 119 2586773] Train: [32/50][357/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:57:23 loss: 0.2077 Lr: 0.00125 [2024-11-25 18:49:16,890 INFO misc.py line 119 2586773] Train: [32/50][358/376] Data 0.002 (0.002) Batch 0.570 (0.508) Remain 00:57:23 loss: 0.1622 Lr: 0.00125 [2024-11-25 18:49:17,370 INFO misc.py line 119 2586773] Train: [32/50][359/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:57:22 loss: 0.2227 Lr: 0.00125 [2024-11-25 18:49:17,913 INFO misc.py line 119 2586773] Train: [32/50][360/376] Data 0.002 (0.002) Batch 0.543 (0.508) Remain 00:57:23 loss: 0.2407 Lr: 0.00124 [2024-11-25 18:49:18,391 INFO misc.py line 119 2586773] Train: [32/50][361/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 00:57:21 loss: 0.1769 Lr: 0.00124 [2024-11-25 18:49:18,892 INFO misc.py line 119 2586773] Train: [32/50][362/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:57:21 loss: 0.3164 Lr: 0.00124 [2024-11-25 18:49:19,377 INFO misc.py line 119 2586773] Train: [32/50][363/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:57:20 loss: 0.2352 Lr: 0.00124 [2024-11-25 18:49:19,885 INFO misc.py line 119 2586773] Train: [32/50][364/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 00:57:19 loss: 0.2050 Lr: 0.00124 [2024-11-25 18:49:20,386 INFO misc.py line 119 2586773] Train: [32/50][365/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:57:19 loss: 0.1861 Lr: 0.00124 [2024-11-25 18:49:20,933 INFO misc.py line 119 2586773] Train: [32/50][366/376] Data 0.002 (0.002) Batch 0.547 (0.507) Remain 00:57:19 loss: 0.1716 Lr: 0.00124 [2024-11-25 18:49:21,406 INFO misc.py line 119 2586773] Train: [32/50][367/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 00:57:18 loss: 0.2139 Lr: 0.00124 [2024-11-25 18:49:21,929 INFO misc.py line 119 2586773] Train: [32/50][368/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:57:18 loss: 0.2028 Lr: 0.00124 [2024-11-25 18:49:22,417 INFO misc.py line 119 2586773] Train: [32/50][369/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 00:57:17 loss: 0.2238 Lr: 0.00124 [2024-11-25 18:49:22,925 INFO misc.py line 119 2586773] Train: [32/50][370/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 00:57:16 loss: 0.2238 Lr: 0.00124 [2024-11-25 18:49:23,404 INFO misc.py line 119 2586773] Train: [32/50][371/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:57:15 loss: 0.1901 Lr: 0.00124 [2024-11-25 18:49:23,911 INFO misc.py line 119 2586773] Train: [32/50][372/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 00:57:15 loss: 0.2123 Lr: 0.00124 [2024-11-25 18:49:24,390 INFO misc.py line 119 2586773] Train: [32/50][373/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:57:14 loss: 0.3229 Lr: 0.00124 [2024-11-25 18:49:24,863 INFO misc.py line 119 2586773] Train: [32/50][374/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 00:57:13 loss: 0.3074 Lr: 0.00124 [2024-11-25 18:49:25,385 INFO misc.py line 119 2586773] Train: [32/50][375/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:57:12 loss: 0.2184 Lr: 0.00124 [2024-11-25 18:49:25,886 INFO misc.py line 119 2586773] Train: [32/50][376/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:57:12 loss: 0.1831 Lr: 0.00124 [2024-11-25 18:49:25,887 INFO misc.py line 136 2586773] Train result: loss: 0.2110 [2024-11-25 18:49:25,887 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:49:36,770 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1947 [2024-11-25 18:49:37,029 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2204 [2024-11-25 18:49:37,293 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2658 [2024-11-25 18:49:37,523 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2104 [2024-11-25 18:49:37,786 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2939 [2024-11-25 18:49:38,052 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2102 [2024-11-25 18:49:38,282 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2242 [2024-11-25 18:49:38,553 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2200 [2024-11-25 18:49:38,778 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2735 [2024-11-25 18:49:39,041 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2619 [2024-11-25 18:49:39,290 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2250 [2024-11-25 18:49:39,561 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2490 [2024-11-25 18:49:39,829 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2670 [2024-11-25 18:49:40,093 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2426 [2024-11-25 18:49:40,326 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2514 [2024-11-25 18:49:40,564 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3184 [2024-11-25 18:49:40,831 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2589 [2024-11-25 18:49:41,077 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2121 [2024-11-25 18:49:41,311 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2487 [2024-11-25 18:49:41,572 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2428 [2024-11-25 18:49:41,809 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2695 [2024-11-25 18:49:42,074 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2439 [2024-11-25 18:49:42,317 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2277 [2024-11-25 18:49:42,588 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2537 [2024-11-25 18:49:42,856 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2392 [2024-11-25 18:49:43,090 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2689 [2024-11-25 18:49:43,349 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2571 [2024-11-25 18:49:43,595 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2347 [2024-11-25 18:49:43,879 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2851 [2024-11-25 18:49:44,131 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2850 [2024-11-25 18:49:44,376 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2710 [2024-11-25 18:49:44,650 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2199 [2024-11-25 18:49:44,873 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2625 [2024-11-25 18:49:45,111 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2410 [2024-11-25 18:49:45,372 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2187 [2024-11-25 18:49:45,616 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2755 [2024-11-25 18:49:45,844 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2073 [2024-11-25 18:49:46,113 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2381 [2024-11-25 18:49:46,344 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2726 [2024-11-25 18:49:46,576 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2466 [2024-11-25 18:49:46,848 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2842 [2024-11-25 18:49:47,104 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2729 [2024-11-25 18:49:47,341 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2571 [2024-11-25 18:49:47,572 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2233 [2024-11-25 18:49:47,808 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2346 [2024-11-25 18:49:48,056 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2398 [2024-11-25 18:49:48,322 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2568 [2024-11-25 18:49:48,571 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2895 [2024-11-25 18:49:48,801 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2196 [2024-11-25 18:49:49,035 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2139 [2024-11-25 18:49:49,259 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2248 [2024-11-25 18:49:49,520 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2471 [2024-11-25 18:49:49,784 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2186 [2024-11-25 18:49:50,043 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2886 [2024-11-25 18:49:50,273 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2566 [2024-11-25 18:49:50,514 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2396 [2024-11-25 18:49:50,772 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2621 [2024-11-25 18:49:51,041 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2662 [2024-11-25 18:49:51,298 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2488 [2024-11-25 18:49:51,559 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2504 [2024-11-25 18:49:51,811 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2270 [2024-11-25 18:49:52,079 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2463 [2024-11-25 18:49:52,307 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2698 [2024-11-25 18:49:52,565 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2443 [2024-11-25 18:49:52,831 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2717 [2024-11-25 18:49:53,098 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2158 [2024-11-25 18:49:53,341 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2085 [2024-11-25 18:49:53,597 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2644 [2024-11-25 18:49:53,872 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2470 [2024-11-25 18:49:54,134 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2650 [2024-11-25 18:49:54,378 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2233 [2024-11-25 18:49:54,612 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2793 [2024-11-25 18:49:54,869 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2832 [2024-11-25 18:49:55,113 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2733 [2024-11-25 18:49:55,329 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2696 [2024-11-25 18:49:55,555 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2138 [2024-11-25 18:49:55,834 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2617 [2024-11-25 18:49:56,070 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2253 [2024-11-25 18:49:56,335 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2341 [2024-11-25 18:49:56,586 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2936 [2024-11-25 18:49:56,831 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2427 [2024-11-25 18:49:57,092 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2683 [2024-11-25 18:49:57,341 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2071 [2024-11-25 18:49:57,591 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2616 [2024-11-25 18:49:57,864 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2630 [2024-11-25 18:49:58,098 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2630 [2024-11-25 18:49:58,360 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2498 [2024-11-25 18:49:58,620 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2355 [2024-11-25 18:49:58,866 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2663 [2024-11-25 18:49:59,116 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2884 [2024-11-25 18:49:59,348 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2398 [2024-11-25 18:49:59,601 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2795 [2024-11-25 18:49:59,870 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2460 [2024-11-25 18:50:00,136 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2148 [2024-11-25 18:50:00,401 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2415 [2024-11-25 18:50:00,650 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2118 [2024-11-25 18:50:00,920 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2391 [2024-11-25 18:50:01,140 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2778 [2024-11-25 18:50:01,409 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2387 [2024-11-25 18:50:01,654 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2655 [2024-11-25 18:50:01,937 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2175 [2024-11-25 18:50:02,200 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2679 [2024-11-25 18:50:02,461 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2478 [2024-11-25 18:50:02,712 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2830 [2024-11-25 18:50:02,938 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2348 [2024-11-25 18:50:03,172 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2226 [2024-11-25 18:50:03,429 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2240 [2024-11-25 18:50:03,703 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2620 [2024-11-25 18:50:03,937 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2652 [2024-11-25 18:50:04,198 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2236 [2024-11-25 18:50:04,466 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2340 [2024-11-25 18:50:04,689 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2334 [2024-11-25 18:50:04,927 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2177 [2024-11-25 18:50:05,147 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2208 [2024-11-25 18:50:05,371 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2268 [2024-11-25 18:50:05,645 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2774 [2024-11-25 18:50:05,905 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2638 [2024-11-25 18:50:06,173 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2484 [2024-11-25 18:50:06,438 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2350 [2024-11-25 18:50:06,697 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3070 [2024-11-25 18:50:06,956 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2756 [2024-11-25 18:50:07,221 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2247 [2024-11-25 18:50:07,474 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2506 [2024-11-25 18:50:07,738 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2524 [2024-11-25 18:50:08,003 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2564 [2024-11-25 18:50:08,254 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2628 [2024-11-25 18:50:08,489 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2191 [2024-11-25 18:50:08,746 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2757 [2024-11-25 18:50:08,981 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2508 [2024-11-25 18:50:09,207 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2041 [2024-11-25 18:50:09,420 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2301 [2024-11-25 18:50:09,636 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2067 [2024-11-25 18:50:10,281 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7678/0.8349/0.9962. [2024-11-25 18:50:10,281 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9962/0.9984 [2024-11-25 18:50:10,281 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5395/0.6715 [2024-11-25 18:50:10,281 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:50:10,282 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:50:10,282 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:50:12,999 INFO misc.py line 119 2586773] Train: [33/50][1/376] Data 0.091 (0.091) Batch 0.595 (0.595) Remain 01:07:04 loss: 0.2387 Lr: 0.00124 [2024-11-25 18:50:13,489 INFO misc.py line 119 2586773] Train: [33/50][2/376] Data 0.002 (0.002) Batch 0.490 (0.490) Remain 00:55:12 loss: 0.2051 Lr: 0.00124 [2024-11-25 18:50:13,974 INFO misc.py line 119 2586773] Train: [33/50][3/376] Data 0.003 (0.003) Batch 0.485 (0.485) Remain 00:54:42 loss: 0.2636 Lr: 0.00124 [2024-11-25 18:50:14,450 INFO misc.py line 119 2586773] Train: [33/50][4/376] Data 0.003 (0.003) Batch 0.476 (0.476) Remain 00:53:41 loss: 0.1996 Lr: 0.00124 [2024-11-25 18:50:14,950 INFO misc.py line 119 2586773] Train: [33/50][5/376] Data 0.003 (0.003) Batch 0.500 (0.488) Remain 00:55:01 loss: 0.2415 Lr: 0.00124 [2024-11-25 18:50:15,477 INFO misc.py line 119 2586773] Train: [33/50][6/376] Data 0.002 (0.003) Batch 0.527 (0.501) Remain 00:56:28 loss: 0.2030 Lr: 0.00124 [2024-11-25 18:50:15,985 INFO misc.py line 119 2586773] Train: [33/50][7/376] Data 0.002 (0.003) Batch 0.508 (0.503) Remain 00:56:38 loss: 0.2084 Lr: 0.00124 [2024-11-25 18:50:16,516 INFO misc.py line 119 2586773] Train: [33/50][8/376] Data 0.003 (0.003) Batch 0.531 (0.508) Remain 00:57:16 loss: 0.2041 Lr: 0.00124 [2024-11-25 18:50:17,001 INFO misc.py line 119 2586773] Train: [33/50][9/376] Data 0.002 (0.003) Batch 0.486 (0.505) Remain 00:56:50 loss: 0.1782 Lr: 0.00124 [2024-11-25 18:50:17,500 INFO misc.py line 119 2586773] Train: [33/50][10/376] Data 0.002 (0.002) Batch 0.499 (0.504) Remain 00:56:44 loss: 0.2260 Lr: 0.00124 [2024-11-25 18:50:18,017 INFO misc.py line 119 2586773] Train: [33/50][11/376] Data 0.003 (0.003) Batch 0.517 (0.505) Remain 00:56:55 loss: 0.1920 Lr: 0.00124 [2024-11-25 18:50:18,528 INFO misc.py line 119 2586773] Train: [33/50][12/376] Data 0.002 (0.003) Batch 0.510 (0.506) Remain 00:56:58 loss: 0.1979 Lr: 0.00124 [2024-11-25 18:50:19,016 INFO misc.py line 119 2586773] Train: [33/50][13/376] Data 0.002 (0.002) Batch 0.488 (0.504) Remain 00:56:45 loss: 0.1760 Lr: 0.00124 [2024-11-25 18:50:19,538 INFO misc.py line 119 2586773] Train: [33/50][14/376] Data 0.003 (0.003) Batch 0.522 (0.506) Remain 00:56:56 loss: 0.1901 Lr: 0.00124 [2024-11-25 18:50:20,072 INFO misc.py line 119 2586773] Train: [33/50][15/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 00:57:11 loss: 0.1722 Lr: 0.00123 [2024-11-25 18:50:20,618 INFO misc.py line 119 2586773] Train: [33/50][16/376] Data 0.002 (0.002) Batch 0.546 (0.511) Remain 00:57:30 loss: 0.2259 Lr: 0.00123 [2024-11-25 18:50:21,072 INFO misc.py line 119 2586773] Train: [33/50][17/376] Data 0.003 (0.002) Batch 0.453 (0.507) Remain 00:57:02 loss: 0.2125 Lr: 0.00123 [2024-11-25 18:50:21,579 INFO misc.py line 119 2586773] Train: [33/50][18/376] Data 0.003 (0.003) Batch 0.507 (0.507) Remain 00:57:02 loss: 0.1827 Lr: 0.00123 [2024-11-25 18:50:22,104 INFO misc.py line 119 2586773] Train: [33/50][19/376] Data 0.003 (0.003) Batch 0.525 (0.508) Remain 00:57:09 loss: 0.1990 Lr: 0.00123 [2024-11-25 18:50:22,637 INFO misc.py line 119 2586773] Train: [33/50][20/376] Data 0.003 (0.003) Batch 0.534 (0.510) Remain 00:57:18 loss: 0.1998 Lr: 0.00123 [2024-11-25 18:50:23,153 INFO misc.py line 119 2586773] Train: [33/50][21/376] Data 0.003 (0.003) Batch 0.516 (0.510) Remain 00:57:20 loss: 0.2281 Lr: 0.00123 [2024-11-25 18:50:23,642 INFO misc.py line 119 2586773] Train: [33/50][22/376] Data 0.003 (0.003) Batch 0.488 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loss: 0.2053 Lr: 0.00115 [2024-11-25 18:52:40,156 INFO misc.py line 119 2586773] Train: [33/50][291/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 00:54:47 loss: 0.2380 Lr: 0.00115 [2024-11-25 18:52:40,614 INFO misc.py line 119 2586773] Train: [33/50][292/376] Data 0.003 (0.002) Batch 0.458 (0.507) Remain 00:54:45 loss: 0.2634 Lr: 0.00115 [2024-11-25 18:52:41,120 INFO misc.py line 119 2586773] Train: [33/50][293/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:54:45 loss: 0.2040 Lr: 0.00115 [2024-11-25 18:52:41,612 INFO misc.py line 119 2586773] Train: [33/50][294/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:54:44 loss: 0.2105 Lr: 0.00115 [2024-11-25 18:52:42,141 INFO misc.py line 119 2586773] Train: [33/50][295/376] Data 0.003 (0.002) Batch 0.530 (0.507) Remain 00:54:44 loss: 0.2486 Lr: 0.00115 [2024-11-25 18:52:42,692 INFO misc.py line 119 2586773] Train: [33/50][296/376] Data 0.002 (0.002) Batch 0.550 (0.508) Remain 00:54:44 loss: 0.2112 Lr: 0.00115 [2024-11-25 18:52:43,221 INFO misc.py line 119 2586773] Train: [33/50][297/376] Data 0.003 (0.002) Batch 0.530 (0.508) Remain 00:54:44 loss: 0.2071 Lr: 0.00115 [2024-11-25 18:52:43,760 INFO misc.py line 119 2586773] Train: [33/50][298/376] Data 0.002 (0.002) Batch 0.539 (0.508) Remain 00:54:45 loss: 0.2305 Lr: 0.00114 [2024-11-25 18:52:44,285 INFO misc.py line 119 2586773] Train: [33/50][299/376] Data 0.002 (0.002) Batch 0.525 (0.508) Remain 00:54:44 loss: 0.2060 Lr: 0.00114 [2024-11-25 18:52:44,815 INFO misc.py line 119 2586773] Train: [33/50][300/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 00:54:44 loss: 0.1752 Lr: 0.00114 [2024-11-25 18:52:45,373 INFO misc.py line 119 2586773] Train: [33/50][301/376] Data 0.003 (0.002) Batch 0.558 (0.508) Remain 00:54:45 loss: 0.1854 Lr: 0.00114 [2024-11-25 18:52:45,861 INFO misc.py line 119 2586773] Train: [33/50][302/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 00:54:44 loss: 0.2110 Lr: 0.00114 [2024-11-25 18:52:46,380 INFO misc.py line 119 2586773] Train: [33/50][303/376] Data 0.002 (0.002) Batch 0.519 (0.508) Remain 00:54:44 loss: 0.2058 Lr: 0.00114 [2024-11-25 18:52:46,889 INFO misc.py line 119 2586773] Train: [33/50][304/376] Data 0.003 (0.002) Batch 0.508 (0.508) Remain 00:54:43 loss: 0.1960 Lr: 0.00114 [2024-11-25 18:52:47,364 INFO misc.py line 119 2586773] Train: [33/50][305/376] Data 0.002 (0.002) Batch 0.475 (0.508) Remain 00:54:42 loss: 0.2267 Lr: 0.00114 [2024-11-25 18:52:47,867 INFO misc.py line 119 2586773] Train: [33/50][306/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:54:42 loss: 0.1960 Lr: 0.00114 [2024-11-25 18:52:48,351 INFO misc.py line 119 2586773] Train: [33/50][307/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:54:40 loss: 0.2157 Lr: 0.00114 [2024-11-25 18:52:48,837 INFO misc.py line 119 2586773] Train: [33/50][308/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 00:54:40 loss: 0.1993 Lr: 0.00114 [2024-11-25 18:52:49,326 INFO misc.py line 119 2586773] Train: [33/50][309/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 00:54:39 loss: 0.1853 Lr: 0.00114 [2024-11-25 18:52:49,834 INFO misc.py line 119 2586773] Train: [33/50][310/376] Data 0.002 (0.002) Batch 0.507 (0.508) Remain 00:54:38 loss: 0.2132 Lr: 0.00114 [2024-11-25 18:52:50,291 INFO misc.py line 119 2586773] Train: [33/50][311/376] Data 0.002 (0.002) Batch 0.458 (0.508) Remain 00:54:37 loss: 0.2505 Lr: 0.00114 [2024-11-25 18:52:50,794 INFO misc.py line 119 2586773] Train: [33/50][312/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:54:36 loss: 0.2073 Lr: 0.00114 [2024-11-25 18:52:51,287 INFO misc.py line 119 2586773] Train: [33/50][313/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:54:35 loss: 0.1845 Lr: 0.00114 [2024-11-25 18:52:51,803 INFO misc.py line 119 2586773] Train: [33/50][314/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:54:35 loss: 0.2544 Lr: 0.00114 [2024-11-25 18:52:52,296 INFO misc.py line 119 2586773] Train: [33/50][315/376] Data 0.003 (0.002) Batch 0.493 (0.507) Remain 00:54:34 loss: 0.2392 Lr: 0.00114 [2024-11-25 18:52:52,802 INFO misc.py line 119 2586773] Train: [33/50][316/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:54:33 loss: 0.2439 Lr: 0.00114 [2024-11-25 18:52:53,265 INFO misc.py line 119 2586773] Train: [33/50][317/376] Data 0.002 (0.002) Batch 0.463 (0.507) Remain 00:54:32 loss: 0.1735 Lr: 0.00114 [2024-11-25 18:52:53,746 INFO misc.py line 119 2586773] Train: [33/50][318/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 00:54:31 loss: 0.1808 Lr: 0.00114 [2024-11-25 18:52:54,250 INFO misc.py line 119 2586773] Train: [33/50][319/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:54:30 loss: 0.2300 Lr: 0.00114 [2024-11-25 18:52:54,805 INFO misc.py line 119 2586773] Train: [33/50][320/376] Data 0.002 (0.002) Batch 0.554 (0.507) Remain 00:54:31 loss: 0.2543 Lr: 0.00114 [2024-11-25 18:52:55,284 INFO misc.py line 119 2586773] Train: [33/50][321/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:54:30 loss: 0.2297 Lr: 0.00114 [2024-11-25 18:52:55,788 INFO misc.py line 119 2586773] Train: [33/50][322/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:54:29 loss: 0.2117 Lr: 0.00114 [2024-11-25 18:52:56,304 INFO misc.py line 119 2586773] Train: [33/50][323/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:54:29 loss: 0.2310 Lr: 0.00114 [2024-11-25 18:52:56,774 INFO misc.py line 119 2586773] Train: [33/50][324/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 00:54:28 loss: 0.2921 Lr: 0.00114 [2024-11-25 18:52:57,287 INFO misc.py line 119 2586773] Train: [33/50][325/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 00:54:27 loss: 0.2008 Lr: 0.00114 [2024-11-25 18:52:57,750 INFO misc.py line 119 2586773] Train: [33/50][326/376] Data 0.002 (0.002) Batch 0.463 (0.507) Remain 00:54:26 loss: 0.2203 Lr: 0.00114 [2024-11-25 18:52:58,218 INFO misc.py line 119 2586773] Train: [33/50][327/376] Data 0.002 (0.002) Batch 0.467 (0.507) Remain 00:54:25 loss: 0.2153 Lr: 0.00114 [2024-11-25 18:52:58,719 INFO misc.py line 119 2586773] Train: [33/50][328/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:54:24 loss: 0.1643 Lr: 0.00114 [2024-11-25 18:52:59,247 INFO misc.py line 119 2586773] Train: [33/50][329/376] Data 0.003 (0.002) Batch 0.528 (0.507) Remain 00:54:24 loss: 0.2175 Lr: 0.00114 [2024-11-25 18:52:59,711 INFO misc.py line 119 2586773] Train: [33/50][330/376] Data 0.002 (0.002) Batch 0.464 (0.507) Remain 00:54:23 loss: 0.2040 Lr: 0.00113 [2024-11-25 18:53:00,187 INFO misc.py line 119 2586773] Train: [33/50][331/376] Data 0.002 (0.002) Batch 0.475 (0.507) Remain 00:54:21 loss: 0.2386 Lr: 0.00113 [2024-11-25 18:53:00,716 INFO misc.py line 119 2586773] Train: [33/50][332/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 00:54:21 loss: 0.2928 Lr: 0.00113 [2024-11-25 18:53:01,212 INFO misc.py line 119 2586773] Train: [33/50][333/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:54:21 loss: 0.2385 Lr: 0.00113 [2024-11-25 18:53:01,727 INFO misc.py line 119 2586773] Train: [33/50][334/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:54:20 loss: 0.1919 Lr: 0.00113 [2024-11-25 18:53:02,216 INFO misc.py line 119 2586773] Train: [33/50][335/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:54:19 loss: 0.2430 Lr: 0.00113 [2024-11-25 18:53:02,764 INFO misc.py line 119 2586773] Train: [33/50][336/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 00:54:20 loss: 0.1808 Lr: 0.00113 [2024-11-25 18:53:03,229 INFO misc.py line 119 2586773] Train: [33/50][337/376] Data 0.002 (0.002) Batch 0.465 (0.507) Remain 00:54:18 loss: 0.2020 Lr: 0.00113 [2024-11-25 18:53:03,748 INFO misc.py line 119 2586773] Train: [33/50][338/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:54:18 loss: 0.1964 Lr: 0.00113 [2024-11-25 18:53:04,275 INFO misc.py line 119 2586773] Train: [33/50][339/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:54:18 loss: 0.3246 Lr: 0.00113 [2024-11-25 18:53:04,773 INFO misc.py line 119 2586773] Train: [33/50][340/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 00:54:17 loss: 0.3027 Lr: 0.00113 [2024-11-25 18:53:05,260 INFO misc.py line 119 2586773] Train: [33/50][341/376] Data 0.002 (0.002) Batch 0.488 (0.507) Remain 00:54:16 loss: 0.1911 Lr: 0.00113 [2024-11-25 18:53:05,752 INFO misc.py line 119 2586773] Train: [33/50][342/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 00:54:16 loss: 0.2007 Lr: 0.00113 [2024-11-25 18:53:06,244 INFO misc.py line 119 2586773] Train: [33/50][343/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:54:15 loss: 0.1842 Lr: 0.00113 [2024-11-25 18:53:06,776 INFO misc.py line 119 2586773] Train: [33/50][344/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 00:54:15 loss: 0.2074 Lr: 0.00113 [2024-11-25 18:53:07,300 INFO misc.py line 119 2586773] Train: [33/50][345/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:54:15 loss: 0.2149 Lr: 0.00113 [2024-11-25 18:53:07,809 INFO misc.py line 119 2586773] Train: [33/50][346/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 00:54:14 loss: 0.1870 Lr: 0.00113 [2024-11-25 18:53:08,306 INFO misc.py line 119 2586773] Train: [33/50][347/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:54:14 loss: 0.1767 Lr: 0.00113 [2024-11-25 18:53:08,826 INFO misc.py line 119 2586773] Train: [33/50][348/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:54:13 loss: 0.2100 Lr: 0.00113 [2024-11-25 18:53:09,357 INFO misc.py line 119 2586773] Train: [33/50][349/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 00:54:13 loss: 0.2607 Lr: 0.00113 [2024-11-25 18:53:09,877 INFO misc.py line 119 2586773] Train: [33/50][350/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 00:54:13 loss: 0.3344 Lr: 0.00113 [2024-11-25 18:53:10,411 INFO misc.py line 119 2586773] Train: [33/50][351/376] Data 0.002 (0.002) Batch 0.534 (0.507) Remain 00:54:13 loss: 0.1873 Lr: 0.00113 [2024-11-25 18:53:10,914 INFO misc.py line 119 2586773] Train: [33/50][352/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:54:12 loss: 0.2095 Lr: 0.00113 [2024-11-25 18:53:11,397 INFO misc.py line 119 2586773] Train: [33/50][353/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 00:54:11 loss: 0.1802 Lr: 0.00113 [2024-11-25 18:53:11,891 INFO misc.py line 119 2586773] Train: [33/50][354/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:54:11 loss: 0.2065 Lr: 0.00113 [2024-11-25 18:53:12,428 INFO misc.py line 119 2586773] Train: [33/50][355/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 00:54:11 loss: 0.2364 Lr: 0.00113 [2024-11-25 18:53:12,896 INFO misc.py line 119 2586773] Train: [33/50][356/376] Data 0.003 (0.002) Batch 0.468 (0.507) Remain 00:54:09 loss: 0.1898 Lr: 0.00113 [2024-11-25 18:53:13,399 INFO misc.py line 119 2586773] Train: [33/50][357/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:54:09 loss: 0.1870 Lr: 0.00113 [2024-11-25 18:53:13,900 INFO misc.py line 119 2586773] Train: [33/50][358/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:54:08 loss: 0.2017 Lr: 0.00113 [2024-11-25 18:53:14,413 INFO misc.py line 119 2586773] Train: [33/50][359/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:54:08 loss: 0.1635 Lr: 0.00113 [2024-11-25 18:53:14,916 INFO misc.py line 119 2586773] Train: [33/50][360/376] Data 0.003 (0.002) Batch 0.503 (0.507) Remain 00:54:07 loss: 0.1686 Lr: 0.00113 [2024-11-25 18:53:15,444 INFO misc.py line 119 2586773] Train: [33/50][361/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:54:07 loss: 0.1939 Lr: 0.00113 [2024-11-25 18:53:15,963 INFO misc.py line 119 2586773] Train: [33/50][362/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:54:07 loss: 0.2305 Lr: 0.00112 [2024-11-25 18:53:16,481 INFO misc.py line 119 2586773] Train: [33/50][363/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:54:07 loss: 0.3360 Lr: 0.00112 [2024-11-25 18:53:16,970 INFO misc.py line 119 2586773] Train: [33/50][364/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:54:06 loss: 0.2483 Lr: 0.00112 [2024-11-25 18:53:17,473 INFO misc.py line 119 2586773] Train: [33/50][365/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:54:05 loss: 0.1928 Lr: 0.00112 [2024-11-25 18:53:17,981 INFO misc.py line 119 2586773] Train: [33/50][366/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:54:05 loss: 0.1930 Lr: 0.00112 [2024-11-25 18:53:18,485 INFO misc.py line 119 2586773] Train: [33/50][367/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:54:04 loss: 0.1971 Lr: 0.00112 [2024-11-25 18:53:18,969 INFO misc.py line 119 2586773] Train: [33/50][368/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:54:03 loss: 0.2006 Lr: 0.00112 [2024-11-25 18:53:19,427 INFO misc.py line 119 2586773] Train: [33/50][369/376] Data 0.002 (0.002) Batch 0.458 (0.507) Remain 00:54:02 loss: 0.2105 Lr: 0.00112 [2024-11-25 18:53:19,903 INFO misc.py line 119 2586773] Train: [33/50][370/376] Data 0.002 (0.002) Batch 0.476 (0.507) Remain 00:54:01 loss: 0.1868 Lr: 0.00112 [2024-11-25 18:53:20,443 INFO misc.py line 119 2586773] Train: [33/50][371/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 00:54:01 loss: 0.1928 Lr: 0.00112 [2024-11-25 18:53:20,981 INFO misc.py line 119 2586773] Train: [33/50][372/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 00:54:01 loss: 0.2241 Lr: 0.00112 [2024-11-25 18:53:21,466 INFO misc.py line 119 2586773] Train: [33/50][373/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:54:00 loss: 0.1980 Lr: 0.00112 [2024-11-25 18:53:22,032 INFO misc.py line 119 2586773] Train: [33/50][374/376] Data 0.002 (0.002) Batch 0.565 (0.507) Remain 00:54:01 loss: 0.1762 Lr: 0.00112 [2024-11-25 18:53:22,587 INFO misc.py line 119 2586773] Train: [33/50][375/376] Data 0.002 (0.002) Batch 0.555 (0.507) Remain 00:54:01 loss: 0.0390 Lr: 0.00112 [2024-11-25 18:53:23,124 INFO misc.py line 119 2586773] Train: [33/50][376/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 00:54:01 loss: 0.2802 Lr: 0.00112 [2024-11-25 18:53:23,124 INFO misc.py line 136 2586773] Train result: loss: 0.2122 [2024-11-25 18:53:23,125 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:53:33,914 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1753 [2024-11-25 18:53:34,317 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2060 [2024-11-25 18:53:34,580 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2558 [2024-11-25 18:53:34,802 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1996 [2024-11-25 18:53:35,066 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2865 [2024-11-25 18:53:35,335 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1912 [2024-11-25 18:53:35,559 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1883 [2024-11-25 18:53:35,829 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2078 [2024-11-25 18:53:36,054 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2589 [2024-11-25 18:53:36,314 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2418 [2024-11-25 18:53:36,545 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2068 [2024-11-25 18:53:36,817 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2113 [2024-11-25 18:53:37,084 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2326 [2024-11-25 18:53:37,347 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2318 [2024-11-25 18:53:37,580 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2139 [2024-11-25 18:53:37,825 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2957 [2024-11-25 18:53:38,092 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2562 [2024-11-25 18:53:38,340 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2086 [2024-11-25 18:53:38,573 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2292 [2024-11-25 18:53:38,833 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2262 [2024-11-25 18:53:39,068 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2349 [2024-11-25 18:53:39,333 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2659 [2024-11-25 18:53:39,569 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.1990 [2024-11-25 18:53:39,836 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2344 [2024-11-25 18:53:40,112 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2308 [2024-11-25 18:53:40,350 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2486 [2024-11-25 18:53:40,619 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2461 [2024-11-25 18:53:40,867 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2162 [2024-11-25 18:53:41,135 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2656 [2024-11-25 18:53:41,389 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2734 [2024-11-25 18:53:41,636 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2542 [2024-11-25 18:53:41,907 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1930 [2024-11-25 18:53:42,132 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2435 [2024-11-25 18:53:42,373 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.1944 [2024-11-25 18:53:42,635 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2179 [2024-11-25 18:53:42,881 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2524 [2024-11-25 18:53:43,109 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1862 [2024-11-25 18:53:43,387 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2263 [2024-11-25 18:53:43,618 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2571 [2024-11-25 18:53:43,863 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2304 [2024-11-25 18:53:44,143 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3163 [2024-11-25 18:53:44,392 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2793 [2024-11-25 18:53:44,630 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2511 [2024-11-25 18:53:44,867 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2275 [2024-11-25 18:53:45,102 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.1949 [2024-11-25 18:53:45,349 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2317 [2024-11-25 18:53:45,608 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2210 [2024-11-25 18:53:45,860 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2946 [2024-11-25 18:53:46,082 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2186 [2024-11-25 18:53:46,315 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2126 [2024-11-25 18:53:46,550 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2431 [2024-11-25 18:53:46,812 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2223 [2024-11-25 18:53:47,080 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2336 [2024-11-25 18:53:47,339 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2867 [2024-11-25 18:53:47,570 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2447 [2024-11-25 18:53:47,812 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2037 [2024-11-25 18:53:48,070 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2605 [2024-11-25 18:53:48,338 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2778 [2024-11-25 18:53:48,596 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2445 [2024-11-25 18:53:48,856 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2291 [2024-11-25 18:53:49,107 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2109 [2024-11-25 18:53:49,378 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2224 [2024-11-25 18:53:49,610 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2267 [2024-11-25 18:53:49,868 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2368 [2024-11-25 18:53:50,134 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2534 [2024-11-25 18:53:50,396 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1988 [2024-11-25 18:53:50,640 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1987 [2024-11-25 18:53:50,898 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2708 [2024-11-25 18:53:51,167 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2166 [2024-11-25 18:53:51,429 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2853 [2024-11-25 18:53:51,674 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.1945 [2024-11-25 18:53:51,908 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2689 [2024-11-25 18:53:52,164 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2512 [2024-11-25 18:53:52,408 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2683 [2024-11-25 18:53:52,623 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2439 [2024-11-25 18:53:52,844 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2260 [2024-11-25 18:53:53,111 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2438 [2024-11-25 18:53:53,347 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2185 [2024-11-25 18:53:53,607 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2126 [2024-11-25 18:53:53,858 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2796 [2024-11-25 18:53:54,100 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2227 [2024-11-25 18:53:54,362 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2517 [2024-11-25 18:53:54,610 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1837 [2024-11-25 18:53:54,858 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2342 [2024-11-25 18:53:55,131 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2222 [2024-11-25 18:53:55,365 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2375 [2024-11-25 18:53:55,627 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2373 [2024-11-25 18:53:55,888 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2192 [2024-11-25 18:53:56,135 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2553 [2024-11-25 18:53:56,382 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2538 [2024-11-25 18:53:56,616 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2045 [2024-11-25 18:53:56,870 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2688 [2024-11-25 18:53:57,138 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2465 [2024-11-25 18:53:57,405 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1900 [2024-11-25 18:53:57,672 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2261 [2024-11-25 18:53:57,921 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2187 [2024-11-25 18:53:58,188 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2341 [2024-11-25 18:53:58,412 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2966 [2024-11-25 18:53:58,683 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2365 [2024-11-25 18:53:58,924 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2567 [2024-11-25 18:53:59,193 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2047 [2024-11-25 18:53:59,453 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2601 [2024-11-25 18:53:59,713 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2254 [2024-11-25 18:53:59,965 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2815 [2024-11-25 18:54:00,188 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2388 [2024-11-25 18:54:00,422 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2252 [2024-11-25 18:54:00,680 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2072 [2024-11-25 18:54:00,950 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2344 [2024-11-25 18:54:01,184 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2406 [2024-11-25 18:54:01,446 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2224 [2024-11-25 18:54:01,709 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2231 [2024-11-25 18:54:01,930 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2296 [2024-11-25 18:54:02,165 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2024 [2024-11-25 18:54:02,387 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2194 [2024-11-25 18:54:02,611 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2173 [2024-11-25 18:54:02,889 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2841 [2024-11-25 18:54:03,151 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2647 [2024-11-25 18:54:03,419 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2205 [2024-11-25 18:54:03,683 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2151 [2024-11-25 18:54:03,946 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2660 [2024-11-25 18:54:04,205 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2999 [2024-11-25 18:54:04,468 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1803 [2024-11-25 18:54:04,724 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2562 [2024-11-25 18:54:04,989 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2361 [2024-11-25 18:54:05,251 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2294 [2024-11-25 18:54:05,502 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2447 [2024-11-25 18:54:05,733 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1974 [2024-11-25 18:54:05,992 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2519 [2024-11-25 18:54:06,228 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2716 [2024-11-25 18:54:06,454 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1895 [2024-11-25 18:54:06,665 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2322 [2024-11-25 18:54:06,882 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1858 [2024-11-25 18:54:07,567 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7742/0.8348/0.9964. [2024-11-25 18:54:07,568 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9964/0.9986 [2024-11-25 18:54:07,568 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5521/0.6711 [2024-11-25 18:54:07,568 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:54:07,569 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:54:07,569 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:54:10,317 INFO misc.py line 119 2586773] Train: [34/50][1/376] Data 0.132 (0.132) Batch 0.603 (0.603) Remain 01:04:12 loss: 0.1704 Lr: 0.00112 [2024-11-25 18:54:10,849 INFO misc.py line 119 2586773] Train: [34/50][2/376] Data 0.002 (0.002) Batch 0.532 (0.532) Remain 00:56:40 loss: 0.2136 Lr: 0.00112 [2024-11-25 18:54:11,382 INFO misc.py line 119 2586773] Train: [34/50][3/376] Data 0.002 (0.002) Batch 0.533 (0.533) Remain 00:56:42 loss: 0.2535 Lr: 0.00112 [2024-11-25 18:54:11,889 INFO misc.py line 119 2586773] Train: [34/50][4/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 00:54:02 loss: 0.1693 Lr: 0.00112 [2024-11-25 18:54:12,390 INFO misc.py line 119 2586773] Train: [34/50][5/376] Data 0.002 (0.002) Batch 0.500 (0.504) Remain 00:53:39 loss: 0.1769 Lr: 0.00112 [2024-11-25 18:54:12,919 INFO misc.py line 119 2586773] Train: [34/50][6/376] Data 0.002 (0.002) Batch 0.529 (0.512) Remain 00:54:32 loss: 0.1757 Lr: 0.00112 [2024-11-25 18:54:13,448 INFO misc.py line 119 2586773] Train: [34/50][7/376] Data 0.002 (0.002) Batch 0.529 (0.517) Remain 00:54:57 loss: 0.1912 Lr: 0.00112 [2024-11-25 18:54:13,981 INFO misc.py line 119 2586773] Train: [34/50][8/376] Data 0.002 (0.002) Batch 0.533 (0.520) Remain 00:55:18 loss: 0.2064 Lr: 0.00112 [2024-11-25 18:54:14,465 INFO misc.py line 119 2586773] Train: [34/50][9/376] Data 0.002 (0.002) Batch 0.484 (0.514) Remain 00:54:39 loss: 0.2378 Lr: 0.00112 [2024-11-25 18:54:14,955 INFO misc.py line 119 2586773] Train: [34/50][10/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 00:54:17 loss: 0.1903 Lr: 0.00112 [2024-11-25 18:54:15,461 INFO misc.py line 119 2586773] Train: [34/50][11/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 00:54:13 loss: 0.1868 Lr: 0.00112 [2024-11-25 18:54:15,954 INFO misc.py line 119 2586773] Train: [34/50][12/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 00:54:01 loss: 0.1905 Lr: 0.00112 [2024-11-25 18:54:16,501 INFO misc.py line 119 2586773] Train: [34/50][13/376] Data 0.002 (0.002) Batch 0.547 (0.512) Remain 00:54:25 loss: 0.2085 Lr: 0.00112 [2024-11-25 18:54:17,031 INFO misc.py line 119 2586773] Train: [34/50][14/376] Data 0.002 (0.002) Batch 0.529 (0.514) Remain 00:54:35 loss: 0.2527 Lr: 0.00112 [2024-11-25 18:54:17,558 INFO misc.py line 119 2586773] Train: [34/50][15/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 00:54:41 loss: 0.2312 Lr: 0.00112 [2024-11-25 18:54:18,043 INFO misc.py line 119 2586773] Train: [34/50][16/376] Data 0.002 (0.002) Batch 0.485 (0.512) Remain 00:54:26 loss: 0.1669 Lr: 0.00112 [2024-11-25 18:54:18,552 INFO misc.py line 119 2586773] Train: [34/50][17/376] Data 0.003 (0.002) Batch 0.509 (0.512) Remain 00:54:25 loss: 0.2028 Lr: 0.00112 [2024-11-25 18:54:19,104 INFO misc.py line 119 2586773] Train: [34/50][18/376] Data 0.002 (0.002) Batch 0.552 (0.515) Remain 00:54:41 loss: 0.2056 Lr: 0.00111 [2024-11-25 18:54:19,618 INFO misc.py line 119 2586773] Train: [34/50][19/376] Data 0.003 (0.002) Batch 0.514 (0.515) Remain 00:54:40 loss: 0.1706 Lr: 0.00111 [2024-11-25 18:54:20,117 INFO misc.py line 119 2586773] Train: [34/50][20/376] Data 0.002 (0.002) Batch 0.499 (0.514) Remain 00:54:34 loss: 0.1823 Lr: 0.00111 [2024-11-25 18:54:20,622 INFO misc.py line 119 2586773] Train: [34/50][21/376] Data 0.002 (0.002) Batch 0.505 (0.513) Remain 00:54:30 loss: 0.1790 Lr: 0.00111 [2024-11-25 18:54:21,116 INFO misc.py line 119 2586773] Train: [34/50][22/376] Data 0.002 (0.002) Batch 0.494 (0.512) Remain 00:54:23 loss: 0.2265 Lr: 0.00111 [2024-11-25 18:54:21,624 INFO misc.py line 119 2586773] Train: [34/50][23/376] Data 0.002 (0.002) Batch 0.508 (0.512) Remain 00:54:21 loss: 0.1812 Lr: 0.00111 [2024-11-25 18:54:22,113 INFO misc.py line 119 2586773] Train: [34/50][24/376] Data 0.002 (0.002) Batch 0.490 (0.511) Remain 00:54:14 loss: 0.2266 Lr: 0.00111 [2024-11-25 18:54:22,615 INFO misc.py line 119 2586773] Train: [34/50][25/376] Data 0.002 (0.002) Batch 0.502 (0.511) Remain 00:54:11 loss: 0.1961 Lr: 0.00111 [2024-11-25 18:54:23,110 INFO misc.py line 119 2586773] Train: [34/50][26/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 00:54:06 loss: 0.1846 Lr: 0.00111 [2024-11-25 18:54:23,608 INFO misc.py line 119 2586773] Train: [34/50][27/376] Data 0.003 (0.002) Batch 0.499 (0.509) Remain 00:54:02 loss: 0.2055 Lr: 0.00111 [2024-11-25 18:54:24,127 INFO misc.py line 119 2586773] Train: [34/50][28/376] Data 0.003 (0.002) Batch 0.518 (0.510) Remain 00:54:04 loss: 0.2188 Lr: 0.00111 [2024-11-25 18:54:24,631 INFO misc.py line 119 2586773] Train: [34/50][29/376] Data 0.003 (0.002) Batch 0.505 (0.510) Remain 00:54:02 loss: 0.2023 Lr: 0.00111 [2024-11-25 18:54:25,132 INFO misc.py line 119 2586773] Train: [34/50][30/376] Data 0.003 (0.002) Batch 0.501 (0.509) Remain 00:53:59 loss: 0.1896 Lr: 0.00111 [2024-11-25 18:54:25,676 INFO misc.py line 119 2586773] Train: [34/50][31/376] Data 0.003 (0.002) Batch 0.544 (0.510) Remain 00:54:07 loss: 0.2128 Lr: 0.00111 [2024-11-25 18:54:26,179 INFO misc.py line 119 2586773] Train: [34/50][32/376] Data 0.003 (0.002) Batch 0.503 (0.510) Remain 00:54:05 loss: 0.2279 Lr: 0.00111 [2024-11-25 18:54:26,657 INFO misc.py line 119 2586773] Train: [34/50][33/376] Data 0.003 (0.002) Batch 0.478 (0.509) Remain 00:53:57 loss: 0.2036 Lr: 0.00111 [2024-11-25 18:54:27,176 INFO misc.py line 119 2586773] Train: [34/50][34/376] Data 0.003 (0.002) Batch 0.519 (0.509) Remain 00:53:59 loss: 0.3538 Lr: 0.00111 [2024-11-25 18:54:27,678 INFO misc.py line 119 2586773] Train: [34/50][35/376] Data 0.002 (0.002) Batch 0.502 (0.509) Remain 00:53:57 loss: 0.2476 Lr: 0.00111 [2024-11-25 18:54:28,164 INFO misc.py line 119 2586773] Train: [34/50][36/376] Data 0.002 (0.002) Batch 0.486 (0.509) Remain 00:53:52 loss: 0.1705 Lr: 0.00111 [2024-11-25 18:54:28,668 INFO misc.py line 119 2586773] Train: [34/50][37/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 00:53:50 loss: 0.2095 Lr: 0.00111 [2024-11-25 18:54:29,185 INFO misc.py line 119 2586773] Train: [34/50][38/376] Data 0.003 (0.002) Batch 0.517 (0.509) Remain 00:53:52 loss: 0.2343 Lr: 0.00111 [2024-11-25 18:54:29,705 INFO misc.py line 119 2586773] Train: [34/50][39/376] Data 0.002 (0.002) Batch 0.520 (0.509) Remain 00:53:53 loss: 0.1961 Lr: 0.00111 [2024-11-25 18:54:30,250 INFO misc.py line 119 2586773] Train: [34/50][40/376] Data 0.003 (0.002) Batch 0.545 (0.510) Remain 00:53:59 loss: 0.1646 Lr: 0.00111 [2024-11-25 18:54:30,754 INFO misc.py line 119 2586773] Train: [34/50][41/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 00:53:57 loss: 0.1683 Lr: 0.00111 [2024-11-25 18:54:31,241 INFO misc.py line 119 2586773] Train: [34/50][42/376] Data 0.002 (0.002) Batch 0.487 (0.509) Remain 00:53:53 loss: 0.1701 Lr: 0.00111 [2024-11-25 18:54:31,752 INFO misc.py line 119 2586773] Train: [34/50][43/376] Data 0.003 (0.002) Batch 0.511 (0.509) Remain 00:53:53 loss: 0.1878 Lr: 0.00111 [2024-11-25 18:54:32,280 INFO misc.py line 119 2586773] Train: [34/50][44/376] Data 0.002 (0.002) Batch 0.528 (0.510) Remain 00:53:55 loss: 0.2545 Lr: 0.00111 [2024-11-25 18:54:32,850 INFO misc.py line 119 2586773] Train: [34/50][45/376] Data 0.003 (0.002) Batch 0.570 (0.511) Remain 00:54:04 loss: 0.2160 Lr: 0.00111 [2024-11-25 18:54:33,383 INFO misc.py line 119 2586773] Train: [34/50][46/376] Data 0.002 (0.002) Batch 0.532 (0.512) Remain 00:54:06 loss: 0.2484 Lr: 0.00111 [2024-11-25 18:54:33,927 INFO misc.py line 119 2586773] Train: [34/50][47/376] Data 0.002 (0.002) Batch 0.544 (0.512) Remain 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Train: [34/50][60/376] Data 0.002 (0.002) Batch 0.565 (0.513) Remain 00:54:06 loss: 0.2055 Lr: 0.00110 [2024-11-25 18:54:41,071 INFO misc.py line 119 2586773] Train: [34/50][61/376] Data 0.002 (0.002) Batch 0.465 (0.512) Remain 00:54:00 loss: 0.1833 Lr: 0.00110 [2024-11-25 18:54:41,584 INFO misc.py line 119 2586773] Train: [34/50][62/376] Data 0.002 (0.002) Batch 0.513 (0.512) Remain 00:54:00 loss: 0.1816 Lr: 0.00110 [2024-11-25 18:54:42,052 INFO misc.py line 119 2586773] Train: [34/50][63/376] Data 0.003 (0.002) Batch 0.468 (0.511) Remain 00:53:55 loss: 0.2433 Lr: 0.00110 [2024-11-25 18:54:42,562 INFO misc.py line 119 2586773] Train: [34/50][64/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 00:53:54 loss: 0.1951 Lr: 0.00110 [2024-11-25 18:54:43,088 INFO misc.py line 119 2586773] Train: [34/50][65/376] Data 0.002 (0.002) Batch 0.526 (0.511) Remain 00:53:55 loss: 0.1787 Lr: 0.00110 [2024-11-25 18:54:43,624 INFO misc.py line 119 2586773] Train: [34/50][66/376] Data 0.003 (0.002) 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Batch 0.511 (0.506) Remain 00:52:28 loss: 0.1616 Lr: 0.00107 [2024-11-25 18:55:37,400 INFO misc.py line 119 2586773] Train: [34/50][173/376] Data 0.002 (0.002) Batch 0.465 (0.506) Remain 00:52:26 loss: 0.2253 Lr: 0.00107 [2024-11-25 18:55:37,898 INFO misc.py line 119 2586773] Train: [34/50][174/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 00:52:25 loss: 0.2236 Lr: 0.00107 [2024-11-25 18:55:38,398 INFO misc.py line 119 2586773] Train: [34/50][175/376] Data 0.003 (0.002) Batch 0.500 (0.506) Remain 00:52:25 loss: 0.2459 Lr: 0.00107 [2024-11-25 18:55:38,903 INFO misc.py line 119 2586773] Train: [34/50][176/376] Data 0.002 (0.002) Batch 0.505 (0.506) Remain 00:52:24 loss: 0.1680 Lr: 0.00107 [2024-11-25 18:55:39,356 INFO misc.py line 119 2586773] Train: [34/50][177/376] Data 0.002 (0.002) Batch 0.453 (0.506) Remain 00:52:22 loss: 0.2758 Lr: 0.00107 [2024-11-25 18:55:39,895 INFO misc.py line 119 2586773] Train: [34/50][178/376] Data 0.002 (0.002) Batch 0.539 (0.506) Remain 00:52:22 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[2024-11-25 18:56:52,780 INFO misc.py line 119 2586773] Train: [34/50][322/376] Data 0.002 (0.002) Batch 0.509 (0.506) Remain 00:51:11 loss: 0.2239 Lr: 0.00102 [2024-11-25 18:56:53,272 INFO misc.py line 119 2586773] Train: [34/50][323/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 00:51:10 loss: 0.1973 Lr: 0.00102 [2024-11-25 18:56:53,750 INFO misc.py line 119 2586773] Train: [34/50][324/376] Data 0.002 (0.002) Batch 0.478 (0.506) Remain 00:51:09 loss: 0.1632 Lr: 0.00102 [2024-11-25 18:56:54,293 INFO misc.py line 119 2586773] Train: [34/50][325/376] Data 0.002 (0.002) Batch 0.542 (0.506) Remain 00:51:09 loss: 0.2087 Lr: 0.00102 [2024-11-25 18:56:54,800 INFO misc.py line 119 2586773] Train: [34/50][326/376] Data 0.002 (0.002) Batch 0.507 (0.506) Remain 00:51:08 loss: 0.2378 Lr: 0.00102 [2024-11-25 18:56:55,260 INFO misc.py line 119 2586773] Train: [34/50][327/376] Data 0.003 (0.002) Batch 0.461 (0.506) Remain 00:51:07 loss: 0.2141 Lr: 0.00102 [2024-11-25 18:56:55,797 INFO misc.py line 119 2586773] Train: [34/50][328/376] Data 0.002 (0.002) Batch 0.537 (0.506) Remain 00:51:07 loss: 0.1924 Lr: 0.00102 [2024-11-25 18:56:56,285 INFO misc.py line 119 2586773] Train: [34/50][329/376] Data 0.002 (0.002) Batch 0.488 (0.506) Remain 00:51:06 loss: 0.1572 Lr: 0.00102 [2024-11-25 18:56:56,792 INFO misc.py line 119 2586773] Train: [34/50][330/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 00:51:06 loss: 0.1709 Lr: 0.00102 [2024-11-25 18:56:57,269 INFO misc.py line 119 2586773] Train: [34/50][331/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 00:51:05 loss: 0.1838 Lr: 0.00102 [2024-11-25 18:56:57,801 INFO misc.py line 119 2586773] Train: [34/50][332/376] Data 0.002 (0.002) Batch 0.532 (0.506) Remain 00:51:05 loss: 0.1480 Lr: 0.00102 [2024-11-25 18:56:58,325 INFO misc.py line 119 2586773] Train: [34/50][333/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 00:51:05 loss: 0.1984 Lr: 0.00102 [2024-11-25 18:56:58,837 INFO misc.py line 119 2586773] Train: [34/50][334/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 00:51:04 loss: 0.1902 Lr: 0.00102 [2024-11-25 18:56:59,326 INFO misc.py line 119 2586773] Train: [34/50][335/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 00:51:03 loss: 0.1737 Lr: 0.00102 [2024-11-25 18:56:59,805 INFO misc.py line 119 2586773] Train: [34/50][336/376] Data 0.002 (0.002) Batch 0.479 (0.506) Remain 00:51:02 loss: 0.2002 Lr: 0.00102 [2024-11-25 18:57:00,322 INFO misc.py line 119 2586773] Train: [34/50][337/376] Data 0.002 (0.002) Batch 0.517 (0.506) Remain 00:51:02 loss: 0.1724 Lr: 0.00102 [2024-11-25 18:57:00,825 INFO misc.py line 119 2586773] Train: [34/50][338/376] Data 0.002 (0.002) Batch 0.502 (0.506) Remain 00:51:02 loss: 0.2338 Lr: 0.00102 [2024-11-25 18:57:01,331 INFO misc.py line 119 2586773] Train: [34/50][339/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 00:51:01 loss: 0.2458 Lr: 0.00102 [2024-11-25 18:57:01,862 INFO misc.py line 119 2586773] Train: [34/50][340/376] Data 0.002 (0.002) Batch 0.532 (0.506) Remain 00:51:01 loss: 0.2307 Lr: 0.00102 [2024-11-25 18:57:02,349 INFO misc.py line 119 2586773] Train: [34/50][341/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 00:51:00 loss: 0.2563 Lr: 0.00102 [2024-11-25 18:57:02,899 INFO misc.py line 119 2586773] Train: [34/50][342/376] Data 0.002 (0.002) Batch 0.550 (0.506) Remain 00:51:00 loss: 0.2191 Lr: 0.00102 [2024-11-25 18:57:03,387 INFO misc.py line 119 2586773] Train: [34/50][343/376] Data 0.002 (0.002) Batch 0.488 (0.506) Remain 00:51:00 loss: 0.1810 Lr: 0.00102 [2024-11-25 18:57:03,928 INFO misc.py line 119 2586773] Train: [34/50][344/376] Data 0.002 (0.002) Batch 0.542 (0.506) Remain 00:51:00 loss: 0.2111 Lr: 0.00101 [2024-11-25 18:57:04,439 INFO misc.py line 119 2586773] Train: [34/50][345/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 00:50:59 loss: 0.2451 Lr: 0.00101 [2024-11-25 18:57:04,941 INFO misc.py line 119 2586773] Train: [34/50][346/376] Data 0.002 (0.002) Batch 0.502 (0.506) Remain 00:50:59 loss: 0.2272 Lr: 0.00101 [2024-11-25 18:57:05,467 INFO misc.py line 119 2586773] Train: [34/50][347/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 00:50:59 loss: 0.2049 Lr: 0.00101 [2024-11-25 18:57:05,995 INFO misc.py line 119 2586773] Train: [34/50][348/376] Data 0.002 (0.002) Batch 0.528 (0.506) Remain 00:50:59 loss: 0.2182 Lr: 0.00101 [2024-11-25 18:57:06,565 INFO misc.py line 119 2586773] Train: [34/50][349/376] Data 0.002 (0.002) Batch 0.570 (0.506) Remain 00:50:59 loss: 0.2279 Lr: 0.00101 [2024-11-25 18:57:07,096 INFO misc.py line 119 2586773] Train: [34/50][350/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 00:50:59 loss: 0.3146 Lr: 0.00101 [2024-11-25 18:57:07,589 INFO misc.py line 119 2586773] Train: [34/50][351/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 00:50:58 loss: 0.1921 Lr: 0.00101 [2024-11-25 18:57:08,089 INFO misc.py line 119 2586773] Train: [34/50][352/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 00:50:58 loss: 0.1769 Lr: 0.00101 [2024-11-25 18:57:08,570 INFO misc.py line 119 2586773] Train: [34/50][353/376] Data 0.002 (0.002) Batch 0.482 (0.506) Remain 00:50:57 loss: 0.1680 Lr: 0.00101 [2024-11-25 18:57:09,096 INFO misc.py line 119 2586773] Train: [34/50][354/376] Data 0.003 (0.002) Batch 0.525 (0.506) Remain 00:50:57 loss: 0.1637 Lr: 0.00101 [2024-11-25 18:57:09,589 INFO misc.py line 119 2586773] Train: [34/50][355/376] Data 0.002 (0.002) Batch 0.494 (0.506) Remain 00:50:56 loss: 0.2056 Lr: 0.00101 [2024-11-25 18:57:10,146 INFO misc.py line 119 2586773] Train: [34/50][356/376] Data 0.002 (0.002) Batch 0.557 (0.506) Remain 00:50:56 loss: 0.2042 Lr: 0.00101 [2024-11-25 18:57:10,672 INFO misc.py line 119 2586773] Train: [34/50][357/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 00:50:56 loss: 0.2140 Lr: 0.00101 [2024-11-25 18:57:11,171 INFO misc.py line 119 2586773] Train: [34/50][358/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 00:50:55 loss: 0.2039 Lr: 0.00101 [2024-11-25 18:57:11,649 INFO misc.py line 119 2586773] Train: [34/50][359/376] Data 0.002 (0.002) Batch 0.479 (0.506) Remain 00:50:54 loss: 0.2090 Lr: 0.00101 [2024-11-25 18:57:12,167 INFO misc.py line 119 2586773] Train: [34/50][360/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 00:50:54 loss: 0.2016 Lr: 0.00101 [2024-11-25 18:57:12,673 INFO misc.py line 119 2586773] Train: [34/50][361/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 00:50:54 loss: 0.1919 Lr: 0.00101 [2024-11-25 18:57:13,197 INFO misc.py line 119 2586773] Train: [34/50][362/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 00:50:53 loss: 0.2208 Lr: 0.00101 [2024-11-25 18:57:13,669 INFO misc.py line 119 2586773] Train: [34/50][363/376] Data 0.002 (0.002) Batch 0.472 (0.506) Remain 00:50:52 loss: 0.2891 Lr: 0.00101 [2024-11-25 18:57:14,180 INFO misc.py line 119 2586773] Train: [34/50][364/376] Data 0.002 (0.002) Batch 0.511 (0.506) Remain 00:50:52 loss: 0.2428 Lr: 0.00101 [2024-11-25 18:57:14,723 INFO misc.py line 119 2586773] Train: [34/50][365/376] Data 0.002 (0.002) Batch 0.543 (0.506) Remain 00:50:52 loss: 0.2431 Lr: 0.00101 [2024-11-25 18:57:15,239 INFO misc.py line 119 2586773] Train: [34/50][366/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 00:50:52 loss: 0.1989 Lr: 0.00101 [2024-11-25 18:57:15,740 INFO misc.py line 119 2586773] Train: [34/50][367/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 00:50:51 loss: 0.1822 Lr: 0.00101 [2024-11-25 18:57:16,242 INFO misc.py line 119 2586773] Train: [34/50][368/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 00:50:50 loss: 0.2285 Lr: 0.00101 [2024-11-25 18:57:16,715 INFO misc.py line 119 2586773] Train: [34/50][369/376] Data 0.002 (0.002) Batch 0.474 (0.506) Remain 00:50:49 loss: 0.2123 Lr: 0.00101 [2024-11-25 18:57:17,211 INFO misc.py line 119 2586773] Train: [34/50][370/376] Data 0.002 (0.002) Batch 0.496 (0.506) Remain 00:50:49 loss: 0.2012 Lr: 0.00101 [2024-11-25 18:57:17,743 INFO misc.py line 119 2586773] Train: [34/50][371/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 00:50:49 loss: 0.2147 Lr: 0.00101 [2024-11-25 18:57:18,225 INFO misc.py line 119 2586773] Train: [34/50][372/376] Data 0.002 (0.002) Batch 0.482 (0.506) Remain 00:50:48 loss: 0.1849 Lr: 0.00101 [2024-11-25 18:57:18,723 INFO misc.py line 119 2586773] Train: [34/50][373/376] Data 0.002 (0.002) Batch 0.498 (0.506) Remain 00:50:47 loss: 0.1476 Lr: 0.00101 [2024-11-25 18:57:19,254 INFO misc.py line 119 2586773] Train: [34/50][374/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 00:50:47 loss: 0.1780 Lr: 0.00101 [2024-11-25 18:57:19,780 INFO misc.py line 119 2586773] Train: [34/50][375/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 00:50:47 loss: 0.1662 Lr: 0.00101 [2024-11-25 18:57:20,269 INFO misc.py line 119 2586773] Train: [34/50][376/376] Data 0.003 (0.002) Batch 0.490 (0.506) Remain 00:50:46 loss: 0.1758 Lr: 0.00101 [2024-11-25 18:57:20,269 INFO misc.py line 136 2586773] Train result: loss: 0.2076 [2024-11-25 18:57:20,270 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 18:57:31,141 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1861 [2024-11-25 18:57:31,517 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2423 [2024-11-25 18:57:31,786 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2376 [2024-11-25 18:57:32,009 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2028 [2024-11-25 18:57:32,272 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.3023 [2024-11-25 18:57:32,540 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2090 [2024-11-25 18:57:32,762 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2341 [2024-11-25 18:57:33,031 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2507 [2024-11-25 18:57:33,255 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2667 [2024-11-25 18:57:33,517 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2579 [2024-11-25 18:57:33,748 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2104 [2024-11-25 18:57:34,021 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2592 [2024-11-25 18:57:34,285 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2794 [2024-11-25 18:57:34,546 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2330 [2024-11-25 18:57:34,778 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2363 [2024-11-25 18:57:35,016 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3155 [2024-11-25 18:57:35,281 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2684 [2024-11-25 18:57:35,526 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2273 [2024-11-25 18:57:35,758 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2644 [2024-11-25 18:57:36,019 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2337 [2024-11-25 18:57:36,255 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2624 [2024-11-25 18:57:36,523 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2682 [2024-11-25 18:57:36,760 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2220 [2024-11-25 18:57:37,028 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2520 [2024-11-25 18:57:37,292 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2347 [2024-11-25 18:57:37,530 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2572 [2024-11-25 18:57:37,780 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2680 [2024-11-25 18:57:38,029 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2346 [2024-11-25 18:57:38,297 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2745 [2024-11-25 18:57:38,552 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2854 [2024-11-25 18:57:38,785 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2675 [2024-11-25 18:57:39,054 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2079 [2024-11-25 18:57:39,272 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2807 [2024-11-25 18:57:39,511 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2528 [2024-11-25 18:57:39,773 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2214 [2024-11-25 18:57:40,018 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2735 [2024-11-25 18:57:40,245 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.2091 [2024-11-25 18:57:40,516 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2386 [2024-11-25 18:57:40,747 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2764 [2024-11-25 18:57:40,983 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2429 [2024-11-25 18:57:41,257 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3130 [2024-11-25 18:57:41,506 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2834 [2024-11-25 18:57:41,744 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2563 [2024-11-25 18:57:41,979 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2363 [2024-11-25 18:57:42,215 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2527 [2024-11-25 18:57:42,464 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2280 [2024-11-25 18:57:42,722 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2449 [2024-11-25 18:57:42,971 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2958 [2024-11-25 18:57:43,195 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2239 [2024-11-25 18:57:43,431 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2163 [2024-11-25 18:57:43,651 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2691 [2024-11-25 18:57:43,906 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2478 [2024-11-25 18:57:44,173 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2355 [2024-11-25 18:57:44,433 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3226 [2024-11-25 18:57:44,665 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2530 [2024-11-25 18:57:44,902 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2382 [2024-11-25 18:57:45,159 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2523 [2024-11-25 18:57:45,425 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2845 [2024-11-25 18:57:45,680 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2560 [2024-11-25 18:57:45,941 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2463 [2024-11-25 18:57:46,197 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2280 [2024-11-25 18:57:46,468 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2471 [2024-11-25 18:57:46,696 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2555 [2024-11-25 18:57:46,953 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2444 [2024-11-25 18:57:47,218 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2744 [2024-11-25 18:57:47,487 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2151 [2024-11-25 18:57:47,730 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.2211 [2024-11-25 18:57:47,986 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2709 [2024-11-25 18:57:48,255 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2600 [2024-11-25 18:57:48,517 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2801 [2024-11-25 18:57:48,760 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2100 [2024-11-25 18:57:48,993 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.3029 [2024-11-25 18:57:49,253 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2684 [2024-11-25 18:57:49,499 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2563 [2024-11-25 18:57:49,716 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2622 [2024-11-25 18:57:49,936 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2189 [2024-11-25 18:57:50,208 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2605 [2024-11-25 18:57:50,443 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2242 [2024-11-25 18:57:50,702 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2473 [2024-11-25 18:57:50,952 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.3007 [2024-11-25 18:57:51,194 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2402 [2024-11-25 18:57:51,454 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2679 [2024-11-25 18:57:51,706 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2227 [2024-11-25 18:57:51,957 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2700 [2024-11-25 18:57:52,225 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2656 [2024-11-25 18:57:52,465 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2809 [2024-11-25 18:57:52,728 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2522 [2024-11-25 18:57:52,990 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2277 [2024-11-25 18:57:53,237 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2957 [2024-11-25 18:57:53,483 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2790 [2024-11-25 18:57:53,717 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2564 [2024-11-25 18:57:53,971 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2760 [2024-11-25 18:57:54,239 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2468 [2024-11-25 18:57:54,507 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2181 [2024-11-25 18:57:54,774 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2451 [2024-11-25 18:57:55,024 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2229 [2024-11-25 18:57:55,291 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2338 [2024-11-25 18:57:55,511 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3025 [2024-11-25 18:57:55,782 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2461 [2024-11-25 18:57:56,019 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2578 [2024-11-25 18:57:56,291 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2201 [2024-11-25 18:57:56,552 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2689 [2024-11-25 18:57:56,812 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2446 [2024-11-25 18:57:57,064 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2766 [2024-11-25 18:57:57,286 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2491 [2024-11-25 18:57:57,545 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2326 [2024-11-25 18:57:57,801 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2290 [2024-11-25 18:57:58,069 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2333 [2024-11-25 18:57:58,303 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2706 [2024-11-25 18:57:58,564 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2236 [2024-11-25 18:57:58,829 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2475 [2024-11-25 18:57:59,052 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2331 [2024-11-25 18:57:59,293 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2151 [2024-11-25 18:57:59,515 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2241 [2024-11-25 18:57:59,739 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2227 [2024-11-25 18:58:00,011 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2621 [2024-11-25 18:58:00,269 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2767 [2024-11-25 18:58:00,537 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2257 [2024-11-25 18:58:00,804 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2362 [2024-11-25 18:58:01,064 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2573 [2024-11-25 18:58:01,327 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2914 [2024-11-25 18:58:01,592 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2277 [2024-11-25 18:58:01,847 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2574 [2024-11-25 18:58:02,111 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2473 [2024-11-25 18:58:02,378 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2517 [2024-11-25 18:58:02,628 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2683 [2024-11-25 18:58:02,859 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2360 [2024-11-25 18:58:03,119 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2649 [2024-11-25 18:58:03,354 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2625 [2024-11-25 18:58:03,582 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2025 [2024-11-25 18:58:03,797 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2250 [2024-11-25 18:58:04,014 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.2092 [2024-11-25 18:58:04,713 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7663/0.8345/0.9962. [2024-11-25 18:58:04,713 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9962/0.9983 [2024-11-25 18:58:04,713 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5364/0.6707 [2024-11-25 18:58:04,713 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 18:58:04,714 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 18:58:04,714 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 18:58:07,429 INFO misc.py line 119 2586773] Train: [35/50][1/376] Data 0.085 (0.085) Batch 0.626 (0.626) Remain 01:02:43 loss: 0.2301 Lr: 0.00100 [2024-11-25 18:58:07,925 INFO misc.py line 119 2586773] Train: [35/50][2/376] Data 0.002 (0.002) Batch 0.497 (0.497) Remain 00:49:48 loss: 0.1937 Lr: 0.00100 [2024-11-25 18:58:08,446 INFO misc.py line 119 2586773] Train: [35/50][3/376] Data 0.002 (0.002) Batch 0.520 (0.520) Remain 00:52:07 loss: 0.2097 Lr: 0.00100 [2024-11-25 18:58:08,987 INFO misc.py line 119 2586773] Train: [35/50][4/376] Data 0.002 (0.002) Batch 0.542 (0.542) Remain 00:54:16 loss: 0.2573 Lr: 0.00100 [2024-11-25 18:58:09,500 INFO misc.py line 119 2586773] Train: [35/50][5/376] Data 0.002 (0.002) Batch 0.513 (0.527) Remain 00:52:48 loss: 0.1986 Lr: 0.00100 [2024-11-25 18:58:10,011 INFO misc.py line 119 2586773] Train: [35/50][6/376] Data 0.002 (0.002) Batch 0.512 (0.522) Remain 00:52:16 loss: 0.1592 Lr: 0.00100 [2024-11-25 18:58:10,503 INFO misc.py line 119 2586773] Train: [35/50][7/376] Data 0.003 (0.002) Batch 0.492 (0.514) Remain 00:51:31 loss: 0.1930 Lr: 0.00100 [2024-11-25 18:58:11,061 INFO misc.py line 119 2586773] Train: [35/50][8/376] Data 0.002 (0.002) Batch 0.558 (0.523) Remain 00:52:22 loss: 0.1743 Lr: 0.00100 [2024-11-25 18:58:11,625 INFO misc.py line 119 2586773] Train: [35/50][9/376] Data 0.002 (0.002) Batch 0.564 (0.530) Remain 00:53:03 loss: 0.2147 Lr: 0.00100 [2024-11-25 18:58:12,144 INFO misc.py line 119 2586773] Train: [35/50][10/376] Data 0.002 (0.002) Batch 0.518 (0.528) Remain 00:52:53 loss: 0.1879 Lr: 0.00100 [2024-11-25 18:58:12,621 INFO misc.py line 119 2586773] Train: [35/50][11/376] Data 0.003 (0.002) Batch 0.477 (0.522) Remain 00:52:14 loss: 0.1549 Lr: 0.00100 [2024-11-25 18:58:13,126 INFO misc.py line 119 2586773] Train: [35/50][12/376] Data 0.002 (0.002) Batch 0.504 (0.520) Remain 00:52:02 loss: 0.2117 Lr: 0.00100 [2024-11-25 18:58:13,672 INFO misc.py line 119 2586773] Train: [35/50][13/376] Data 0.002 (0.002) Batch 0.547 (0.523) Remain 00:52:17 loss: 0.1622 Lr: 0.00100 [2024-11-25 18:58:14,196 INFO misc.py line 119 2586773] Train: [35/50][14/376] Data 0.002 (0.002) Batch 0.523 (0.523) Remain 00:52:17 loss: 0.1833 Lr: 0.00100 [2024-11-25 18:58:14,700 INFO misc.py line 119 2586773] Train: [35/50][15/376] Data 0.002 (0.002) Batch 0.504 (0.521) Remain 00:52:07 loss: 0.1725 Lr: 0.00100 [2024-11-25 18:58:15,195 INFO misc.py line 119 2586773] Train: [35/50][16/376] Data 0.002 (0.002) Batch 0.495 (0.519) Remain 00:51:54 loss: 0.1913 Lr: 0.00100 [2024-11-25 18:58:15,731 INFO misc.py line 119 2586773] Train: [35/50][17/376] Data 0.002 (0.002) Batch 0.536 (0.520) Remain 00:52:01 loss: 0.1666 Lr: 0.00100 [2024-11-25 18:58:16,258 INFO misc.py line 119 2586773] Train: [35/50][18/376] Data 0.003 (0.002) Batch 0.527 (0.521) Remain 00:52:03 loss: 0.2009 Lr: 0.00100 [2024-11-25 18:58:16,776 INFO misc.py line 119 2586773] Train: [35/50][19/376] Data 0.002 (0.002) Batch 0.518 (0.521) Remain 00:52:02 loss: 0.1817 Lr: 0.00100 [2024-11-25 18:58:17,269 INFO misc.py line 119 2586773] Train: [35/50][20/376] Data 0.002 (0.002) Batch 0.493 (0.519) Remain 00:51:52 loss: 0.2549 Lr: 0.00100 [2024-11-25 18:58:17,728 INFO misc.py line 119 2586773] Train: [35/50][21/376] Data 0.002 (0.002) Batch 0.459 (0.516) Remain 00:51:31 loss: 0.2162 Lr: 0.00100 [2024-11-25 18:58:18,226 INFO misc.py line 119 2586773] Train: [35/50][22/376] Data 0.002 (0.002) Batch 0.498 (0.515) Remain 00:51:25 loss: 0.2527 Lr: 0.00100 [2024-11-25 18:58:18,729 INFO misc.py line 119 2586773] Train: [35/50][23/376] Data 0.002 (0.002) Batch 0.503 (0.514) Remain 00:51:21 loss: 0.2122 Lr: 0.00100 [2024-11-25 18:58:19,231 INFO misc.py line 119 2586773] Train: [35/50][24/376] Data 0.002 (0.002) Batch 0.502 (0.514) Remain 00:51:17 loss: 0.1935 Lr: 0.00100 [2024-11-25 18:58:19,740 INFO misc.py line 119 2586773] Train: [35/50][25/376] Data 0.002 (0.002) Batch 0.508 (0.513) Remain 00:51:15 loss: 0.2011 Lr: 0.00100 [2024-11-25 18:58:20,271 INFO misc.py line 119 2586773] Train: [35/50][26/376] Data 0.003 (0.002) Batch 0.532 (0.514) Remain 00:51:19 loss: 0.2760 Lr: 0.00100 [2024-11-25 18:58:20,760 INFO misc.py line 119 2586773] Train: [35/50][27/376] Data 0.003 (0.002) Batch 0.488 (0.513) Remain 00:51:12 loss: 0.1756 Lr: 0.00100 [2024-11-25 18:58:21,260 INFO misc.py line 119 2586773] Train: [35/50][28/376] Data 0.002 (0.002) Batch 0.500 (0.513) Remain 00:51:09 loss: 0.1850 Lr: 0.00100 [2024-11-25 18:58:21,778 INFO misc.py line 119 2586773] Train: [35/50][29/376] Data 0.002 (0.002) Batch 0.518 (0.513) Remain 00:51:09 loss: 0.2222 Lr: 0.00100 [2024-11-25 18:58:22,309 INFO misc.py line 119 2586773] Train: [35/50][30/376] Data 0.003 (0.002) Batch 0.532 (0.513) Remain 00:51:13 loss: 0.1909 Lr: 0.00100 [2024-11-25 18:58:22,798 INFO misc.py line 119 2586773] Train: [35/50][31/376] Data 0.003 (0.002) Batch 0.489 (0.513) Remain 00:51:07 loss: 0.1994 Lr: 0.00100 [2024-11-25 18:58:23,320 INFO misc.py line 119 2586773] Train: [35/50][32/376] Data 0.002 (0.002) Batch 0.521 (0.513) Remain 00:51:09 loss: 0.2178 Lr: 0.00100 [2024-11-25 18:58:23,802 INFO misc.py line 119 2586773] Train: [35/50][33/376] Data 0.002 (0.002) Batch 0.482 (0.512) Remain 00:51:02 loss: 0.1645 Lr: 0.00100 [2024-11-25 18:58:24,354 INFO misc.py line 119 2586773] Train: [35/50][34/376] Data 0.002 (0.002) Batch 0.552 (0.513) Remain 00:51:09 loss: 0.2673 Lr: 0.00099 [2024-11-25 18:58:24,912 INFO misc.py line 119 2586773] Train: [35/50][35/376] Data 0.002 (0.002) Batch 0.558 (0.515) Remain 00:51:17 loss: 0.1521 Lr: 0.00099 [2024-11-25 18:58:25,439 INFO misc.py line 119 2586773] Train: [35/50][36/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 00:51:19 loss: 0.2074 Lr: 0.00099 [2024-11-25 18:58:25,919 INFO misc.py line 119 2586773] Train: [35/50][37/376] Data 0.002 (0.002) Batch 0.480 (0.514) Remain 00:51:12 loss: 0.1799 Lr: 0.00099 [2024-11-25 18:58:26,394 INFO misc.py line 119 2586773] Train: [35/50][38/376] Data 0.002 (0.002) Batch 0.475 (0.513) Remain 00:51:05 loss: 0.1862 Lr: 0.00099 [2024-11-25 18:58:26,912 INFO misc.py line 119 2586773] Train: [35/50][39/376] Data 0.002 (0.002) Batch 0.519 (0.513) Remain 00:51:05 loss: 0.1835 Lr: 0.00099 [2024-11-25 18:58:27,439 INFO misc.py line 119 2586773] Train: [35/50][40/376] Data 0.002 (0.002) Batch 0.527 (0.513) Remain 00:51:07 loss: 0.3195 Lr: 0.00099 [2024-11-25 18:58:27,981 INFO misc.py line 119 2586773] Train: [35/50][41/376] Data 0.002 (0.002) Batch 0.542 (0.514) Remain 00:51:11 loss: 0.1560 Lr: 0.00099 [2024-11-25 18:58:28,488 INFO misc.py line 119 2586773] Train: [35/50][42/376] Data 0.002 (0.002) Batch 0.507 (0.514) Remain 00:51:10 loss: 0.2163 Lr: 0.00099 [2024-11-25 18:58:28,950 INFO misc.py line 119 2586773] Train: [35/50][43/376] Data 0.002 (0.002) Batch 0.462 (0.513) Remain 00:51:01 loss: 0.2042 Lr: 0.00099 [2024-11-25 18:58:29,442 INFO misc.py line 119 2586773] Train: [35/50][44/376] Data 0.002 (0.002) Batch 0.492 (0.512) Remain 00:50:58 loss: 0.2499 Lr: 0.00099 [2024-11-25 18:58:29,974 INFO misc.py line 119 2586773] Train: [35/50][45/376] Data 0.002 (0.002) Batch 0.532 (0.513) Remain 00:51:00 loss: 0.1958 Lr: 0.00099 [2024-11-25 18:58:30,451 INFO misc.py line 119 2586773] Train: [35/50][46/376] Data 0.002 (0.002) Batch 0.478 (0.512) Remain 00:50:55 loss: 0.2133 Lr: 0.00099 [2024-11-25 18:58:30,959 INFO misc.py line 119 2586773] Train: [35/50][47/376] Data 0.002 (0.002) Batch 0.508 (0.512) Remain 00:50:54 loss: 0.2795 Lr: 0.00099 [2024-11-25 18:58:31,489 INFO misc.py line 119 2586773] Train: [35/50][48/376] Data 0.002 (0.002) Batch 0.530 (0.512) Remain 00:50:56 loss: 0.2070 Lr: 0.00099 [2024-11-25 18:58:31,965 INFO misc.py line 119 2586773] Train: [35/50][49/376] Data 0.002 (0.002) Batch 0.475 (0.511) Remain 00:50:50 loss: 0.2282 Lr: 0.00099 [2024-11-25 18:58:32,439 INFO misc.py line 119 2586773] Train: [35/50][50/376] Data 0.003 (0.002) Batch 0.475 (0.511) Remain 00:50:45 loss: 0.2525 Lr: 0.00099 [2024-11-25 18:58:32,950 INFO misc.py line 119 2586773] Train: [35/50][51/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 00:50:45 loss: 0.2466 Lr: 0.00099 [2024-11-25 18:58:33,505 INFO misc.py line 119 2586773] Train: [35/50][52/376] Data 0.002 (0.002) Batch 0.554 (0.511) Remain 00:50:50 loss: 0.2359 Lr: 0.00099 [2024-11-25 18:58:33,987 INFO misc.py line 119 2586773] Train: [35/50][53/376] Data 0.002 (0.002) Batch 0.482 (0.511) Remain 00:50:46 loss: 0.1849 Lr: 0.00099 [2024-11-25 18:58:34,524 INFO misc.py line 119 2586773] Train: [35/50][54/376] Data 0.002 (0.002) Batch 0.538 (0.511) Remain 00:50:48 loss: 0.2210 Lr: 0.00099 [2024-11-25 18:58:35,016 INFO misc.py line 119 2586773] Train: [35/50][55/376] Data 0.002 (0.002) Batch 0.492 (0.511) Remain 00:50:45 loss: 0.1855 Lr: 0.00099 [2024-11-25 18:58:35,510 INFO misc.py line 119 2586773] Train: [35/50][56/376] Data 0.003 (0.002) Batch 0.494 (0.511) Remain 00:50:43 loss: 0.2918 Lr: 0.00099 [2024-11-25 18:58:36,025 INFO misc.py line 119 2586773] Train: [35/50][57/376] Data 0.002 (0.002) Batch 0.515 (0.511) Remain 00:50:43 loss: 0.1826 Lr: 0.00099 [2024-11-25 18:58:36,544 INFO misc.py line 119 2586773] Train: [35/50][58/376] Data 0.003 (0.002) Batch 0.519 (0.511) Remain 00:50:43 loss: 0.1781 Lr: 0.00099 [2024-11-25 18:58:37,022 INFO misc.py line 119 2586773] Train: [35/50][59/376] Data 0.002 (0.002) Batch 0.479 (0.510) Remain 00:50:39 loss: 0.1939 Lr: 0.00099 [2024-11-25 18:58:37,545 INFO misc.py line 119 2586773] Train: [35/50][60/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 00:50:40 loss: 0.2630 Lr: 0.00099 [2024-11-25 18:58:38,038 INFO misc.py line 119 2586773] Train: [35/50][61/376] Data 0.003 (0.002) Batch 0.494 (0.510) Remain 00:50:38 loss: 0.2768 Lr: 0.00099 [2024-11-25 18:58:38,515 INFO misc.py line 119 2586773] Train: [35/50][62/376] Data 0.002 (0.002) Batch 0.477 (0.510) Remain 00:50:34 loss: 0.1829 Lr: 0.00099 [2024-11-25 18:58:39,026 INFO misc.py line 119 2586773] Train: [35/50][63/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:50:34 loss: 0.1850 Lr: 0.00099 [2024-11-25 18:58:39,508 INFO misc.py line 119 2586773] Train: [35/50][64/376] Data 0.002 (0.002) Batch 0.483 (0.509) Remain 00:50:30 loss: 0.1875 Lr: 0.00099 [2024-11-25 18:58:39,978 INFO misc.py line 119 2586773] Train: [35/50][65/376] Data 0.003 (0.002) Batch 0.470 (0.509) Remain 00:50:26 loss: 0.2076 Lr: 0.00099 [2024-11-25 18:58:40,462 INFO misc.py line 119 2586773] Train: [35/50][66/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:50:23 loss: 0.2770 Lr: 0.00099 [2024-11-25 18:58:40,987 INFO misc.py line 119 2586773] Train: [35/50][67/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 00:50:24 loss: 0.1913 Lr: 0.00098 [2024-11-25 18:58:41,490 INFO misc.py line 119 2586773] Train: [35/50][68/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:50:23 loss: 0.2577 Lr: 0.00098 [2024-11-25 18:58:41,957 INFO misc.py line 119 2586773] Train: [35/50][69/376] Data 0.002 (0.002) Batch 0.467 (0.508) Remain 00:50:19 loss: 0.2048 Lr: 0.00098 [2024-11-25 18:58:42,466 INFO misc.py line 119 2586773] Train: [35/50][70/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:50:19 loss: 0.1556 Lr: 0.00098 [2024-11-25 18:58:42,943 INFO misc.py line 119 2586773] Train: [35/50][71/376] Data 0.002 (0.002) Batch 0.477 (0.507) Remain 00:50:15 loss: 0.2549 Lr: 0.00098 [2024-11-25 18:58:43,477 INFO misc.py line 119 2586773] Train: [35/50][72/376] Data 0.002 (0.002) Batch 0.535 (0.508) Remain 00:50:17 loss: 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Batch 0.484 (0.508) Remain 00:48:01 loss: 0.1792 Lr: 0.00090 [2024-11-25 19:01:00,047 INFO misc.py line 119 2586773] Train: [35/50][341/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 00:48:01 loss: 0.2550 Lr: 0.00090 [2024-11-25 19:01:00,509 INFO misc.py line 119 2586773] Train: [35/50][342/376] Data 0.002 (0.002) Batch 0.462 (0.508) Remain 00:47:59 loss: 0.2531 Lr: 0.00090 [2024-11-25 19:01:00,997 INFO misc.py line 119 2586773] Train: [35/50][343/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 00:47:59 loss: 0.1921 Lr: 0.00090 [2024-11-25 19:01:01,507 INFO misc.py line 119 2586773] Train: [35/50][344/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:47:58 loss: 0.1687 Lr: 0.00090 [2024-11-25 19:01:02,028 INFO misc.py line 119 2586773] Train: [35/50][345/376] Data 0.002 (0.002) Batch 0.521 (0.508) Remain 00:47:58 loss: 0.2306 Lr: 0.00090 [2024-11-25 19:01:02,499 INFO misc.py line 119 2586773] Train: [35/50][346/376] Data 0.003 (0.002) Batch 0.471 (0.507) Remain 00:47:57 loss: 0.2683 Lr: 0.00090 [2024-11-25 19:01:02,996 INFO misc.py line 119 2586773] Train: [35/50][347/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:47:56 loss: 0.1952 Lr: 0.00090 [2024-11-25 19:01:03,505 INFO misc.py line 119 2586773] Train: [35/50][348/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:47:56 loss: 0.1745 Lr: 0.00090 [2024-11-25 19:01:04,028 INFO misc.py line 119 2586773] Train: [35/50][349/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:47:55 loss: 0.1873 Lr: 0.00090 [2024-11-25 19:01:04,498 INFO misc.py line 119 2586773] Train: [35/50][350/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 00:47:54 loss: 0.1869 Lr: 0.00090 [2024-11-25 19:01:04,991 INFO misc.py line 119 2586773] Train: [35/50][351/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:47:53 loss: 0.2136 Lr: 0.00090 [2024-11-25 19:01:05,494 INFO misc.py line 119 2586773] Train: [35/50][352/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:47:53 loss: 0.2176 Lr: 0.00090 [2024-11-25 19:01:05,974 INFO misc.py line 119 2586773] Train: [35/50][353/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:47:52 loss: 0.1763 Lr: 0.00090 [2024-11-25 19:01:06,503 INFO misc.py line 119 2586773] Train: [35/50][354/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 00:47:52 loss: 0.1788 Lr: 0.00090 [2024-11-25 19:01:07,034 INFO misc.py line 119 2586773] Train: [35/50][355/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 00:47:52 loss: 0.2005 Lr: 0.00090 [2024-11-25 19:01:07,532 INFO misc.py line 119 2586773] Train: [35/50][356/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 00:47:51 loss: 0.2231 Lr: 0.00090 [2024-11-25 19:01:08,025 INFO misc.py line 119 2586773] Train: [35/50][357/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:47:50 loss: 0.2231 Lr: 0.00090 [2024-11-25 19:01:08,586 INFO misc.py line 119 2586773] Train: [35/50][358/376] Data 0.002 (0.002) Batch 0.561 (0.507) Remain 00:47:51 loss: 0.2290 Lr: 0.00090 [2024-11-25 19:01:09,058 INFO misc.py line 119 2586773] Train: [35/50][359/376] Data 0.002 (0.002) Batch 0.472 (0.507) Remain 00:47:49 loss: 0.2152 Lr: 0.00090 [2024-11-25 19:01:09,578 INFO misc.py line 119 2586773] Train: [35/50][360/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:47:49 loss: 0.2459 Lr: 0.00090 [2024-11-25 19:01:10,063 INFO misc.py line 119 2586773] Train: [35/50][361/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:47:48 loss: 0.2062 Lr: 0.00090 [2024-11-25 19:01:10,541 INFO misc.py line 119 2586773] Train: [35/50][362/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 00:47:47 loss: 0.1786 Lr: 0.00090 [2024-11-25 19:01:11,062 INFO misc.py line 119 2586773] Train: [35/50][363/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 00:47:47 loss: 0.2478 Lr: 0.00090 [2024-11-25 19:01:11,561 INFO misc.py line 119 2586773] Train: [35/50][364/376] Data 0.003 (0.002) Batch 0.500 (0.507) Remain 00:47:46 loss: 0.1754 Lr: 0.00090 [2024-11-25 19:01:12,076 INFO misc.py line 119 2586773] Train: [35/50][365/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 00:47:46 loss: 0.1975 Lr: 0.00090 [2024-11-25 19:01:12,577 INFO misc.py line 119 2586773] Train: [35/50][366/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:47:45 loss: 0.1603 Lr: 0.00090 [2024-11-25 19:01:13,085 INFO misc.py line 119 2586773] Train: [35/50][367/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 00:47:45 loss: 0.1907 Lr: 0.00090 [2024-11-25 19:01:13,584 INFO misc.py line 119 2586773] Train: [35/50][368/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:47:44 loss: 0.1990 Lr: 0.00090 [2024-11-25 19:01:14,132 INFO misc.py line 119 2586773] Train: [35/50][369/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 00:47:44 loss: 0.2338 Lr: 0.00090 [2024-11-25 19:01:14,679 INFO misc.py line 119 2586773] Train: [35/50][370/376] Data 0.002 (0.002) Batch 0.546 (0.507) Remain 00:47:45 loss: 0.2504 Lr: 0.00090 [2024-11-25 19:01:15,183 INFO misc.py line 119 2586773] Train: [35/50][371/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:47:44 loss: 0.1902 Lr: 0.00090 [2024-11-25 19:01:15,652 INFO misc.py line 119 2586773] Train: [35/50][372/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:47:43 loss: 0.1819 Lr: 0.00090 [2024-11-25 19:01:16,150 INFO misc.py line 119 2586773] Train: [35/50][373/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 00:47:42 loss: 0.2045 Lr: 0.00090 [2024-11-25 19:01:16,668 INFO misc.py line 119 2586773] Train: [35/50][374/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:47:42 loss: 0.2119 Lr: 0.00089 [2024-11-25 19:01:17,177 INFO misc.py line 119 2586773] Train: [35/50][375/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:47:41 loss: 0.2049 Lr: 0.00089 [2024-11-25 19:01:17,686 INFO misc.py line 119 2586773] Train: [35/50][376/376] Data 0.003 (0.002) Batch 0.509 (0.507) Remain 00:47:41 loss: 0.2532 Lr: 0.00089 [2024-11-25 19:01:17,686 INFO misc.py line 136 2586773] Train result: loss: 0.2062 [2024-11-25 19:01:17,687 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:01:28,246 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1760 [2024-11-25 19:01:28,633 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2230 [2024-11-25 19:01:28,895 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2616 [2024-11-25 19:01:29,120 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1931 [2024-11-25 19:01:29,381 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2884 [2024-11-25 19:01:29,656 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1933 [2024-11-25 19:01:29,878 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2301 [2024-11-25 19:01:30,146 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2397 [2024-11-25 19:01:30,372 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2754 [2024-11-25 19:01:30,638 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2491 [2024-11-25 19:01:30,868 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2065 [2024-11-25 19:01:31,141 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2418 [2024-11-25 19:01:31,407 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2683 [2024-11-25 19:01:31,668 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2396 [2024-11-25 19:01:31,902 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2273 [2024-11-25 19:01:32,141 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3048 [2024-11-25 19:01:32,409 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2852 [2024-11-25 19:01:32,658 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2107 [2024-11-25 19:01:32,888 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2499 [2024-11-25 19:01:33,150 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2443 [2024-11-25 19:01:33,384 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2539 [2024-11-25 19:01:33,652 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2522 [2024-11-25 19:01:33,888 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2117 [2024-11-25 19:01:34,159 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2380 [2024-11-25 19:01:34,419 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2217 [2024-11-25 19:01:34,659 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2477 [2024-11-25 19:01:34,912 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2614 [2024-11-25 19:01:35,158 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2176 [2024-11-25 19:01:35,427 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2565 [2024-11-25 19:01:35,681 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3025 [2024-11-25 19:01:35,916 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2541 [2024-11-25 19:01:36,180 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1974 [2024-11-25 19:01:36,399 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2659 [2024-11-25 19:01:36,639 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2265 [2024-11-25 19:01:36,901 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1990 [2024-11-25 19:01:37,146 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2505 [2024-11-25 19:01:37,371 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1815 [2024-11-25 19:01:37,640 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2427 [2024-11-25 19:01:37,870 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2681 [2024-11-25 19:01:38,104 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2279 [2024-11-25 19:01:38,373 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3111 [2024-11-25 19:01:38,624 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2755 [2024-11-25 19:01:38,862 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2496 [2024-11-25 19:01:39,101 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2284 [2024-11-25 19:01:39,336 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2401 [2024-11-25 19:01:39,585 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2314 [2024-11-25 19:01:39,849 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2287 [2024-11-25 19:01:40,100 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2993 [2024-11-25 19:01:40,331 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2151 [2024-11-25 19:01:40,564 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2102 [2024-11-25 19:01:40,790 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2576 [2024-11-25 19:01:41,053 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2278 [2024-11-25 19:01:41,320 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2176 [2024-11-25 19:01:41,581 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3113 [2024-11-25 19:01:41,818 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2409 [2024-11-25 19:01:42,057 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2376 [2024-11-25 19:01:42,322 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2509 [2024-11-25 19:01:42,597 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2609 [2024-11-25 19:01:42,852 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2473 [2024-11-25 19:01:43,113 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2462 [2024-11-25 19:01:43,373 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2213 [2024-11-25 19:01:43,653 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2380 [2024-11-25 19:01:43,884 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2393 [2024-11-25 19:01:44,164 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2454 [2024-11-25 19:01:44,430 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2688 [2024-11-25 19:01:44,712 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1898 [2024-11-25 19:01:44,968 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1997 [2024-11-25 19:01:45,225 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2721 [2024-11-25 19:01:45,494 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2513 [2024-11-25 19:01:45,756 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2759 [2024-11-25 19:01:46,004 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2061 [2024-11-25 19:01:46,250 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2792 [2024-11-25 19:01:46,506 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2565 [2024-11-25 19:01:46,754 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2509 [2024-11-25 19:01:46,979 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2559 [2024-11-25 19:01:47,206 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2147 [2024-11-25 19:01:47,475 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2540 [2024-11-25 19:01:47,719 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2203 [2024-11-25 19:01:47,986 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2365 [2024-11-25 19:01:48,238 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2801 [2024-11-25 19:01:48,488 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2427 [2024-11-25 19:01:48,750 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2579 [2024-11-25 19:01:48,999 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1929 [2024-11-25 19:01:49,246 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2337 [2024-11-25 19:01:49,519 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2428 [2024-11-25 19:01:49,757 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2689 [2024-11-25 19:01:50,018 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2753 [2024-11-25 19:01:50,276 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2416 [2024-11-25 19:01:50,523 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2775 [2024-11-25 19:01:50,773 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2652 [2024-11-25 19:01:51,005 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2435 [2024-11-25 19:01:51,257 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2695 [2024-11-25 19:01:51,523 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2481 [2024-11-25 19:01:51,791 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2131 [2024-11-25 19:01:52,056 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2196 [2024-11-25 19:01:52,310 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2037 [2024-11-25 19:01:52,576 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2422 [2024-11-25 19:01:52,796 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2944 [2024-11-25 19:01:53,067 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2450 [2024-11-25 19:01:53,303 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2559 [2024-11-25 19:01:53,576 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2088 [2024-11-25 19:01:53,838 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2602 [2024-11-25 19:01:54,095 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2365 [2024-11-25 19:01:54,349 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2709 [2024-11-25 19:01:54,575 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2380 [2024-11-25 19:01:54,811 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2375 [2024-11-25 19:01:55,069 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2278 [2024-11-25 19:01:55,336 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2567 [2024-11-25 19:01:55,572 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2454 [2024-11-25 19:01:55,833 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2162 [2024-11-25 19:01:56,096 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2365 [2024-11-25 19:01:56,319 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2259 [2024-11-25 19:01:56,554 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2027 [2024-11-25 19:01:56,774 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2102 [2024-11-25 19:01:56,999 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2184 [2024-11-25 19:01:57,271 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2911 [2024-11-25 19:01:57,530 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2632 [2024-11-25 19:01:57,797 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2583 [2024-11-25 19:01:58,062 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2200 [2024-11-25 19:01:58,323 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2662 [2024-11-25 19:01:58,585 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2877 [2024-11-25 19:01:58,850 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2277 [2024-11-25 19:01:59,105 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2515 [2024-11-25 19:01:59,368 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2421 [2024-11-25 19:01:59,636 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2471 [2024-11-25 19:01:59,888 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2493 [2024-11-25 19:02:00,119 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1961 [2024-11-25 19:02:00,378 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2458 [2024-11-25 19:02:00,615 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2620 [2024-11-25 19:02:00,842 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1859 [2024-11-25 19:02:01,053 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2230 [2024-11-25 19:02:01,270 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1889 [2024-11-25 19:02:01,948 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7708/0.8392/0.9963. [2024-11-25 19:02:01,948 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9984 [2024-11-25 19:02:01,948 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5453/0.6800 [2024-11-25 19:02:01,948 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:02:01,949 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7775 [2024-11-25 19:02:01,949 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:02:04,733 INFO misc.py line 119 2586773] Train: [36/50][1/376] Data 0.114 (0.114) Batch 0.585 (0.585) Remain 00:54:56 loss: 0.2488 Lr: 0.00089 [2024-11-25 19:02:05,268 INFO misc.py line 119 2586773] Train: [36/50][2/376] Data 0.002 (0.002) Batch 0.535 (0.535) Remain 00:50:14 loss: 0.1976 Lr: 0.00089 [2024-11-25 19:02:05,783 INFO misc.py line 119 2586773] Train: [36/50][3/376] Data 0.002 (0.002) Batch 0.515 (0.515) Remain 00:48:20 loss: 0.1780 Lr: 0.00089 [2024-11-25 19:02:06,326 INFO misc.py line 119 2586773] Train: [36/50][4/376] Data 0.002 (0.002) Batch 0.543 (0.543) Remain 00:51:01 loss: 0.2090 Lr: 0.00089 [2024-11-25 19:02:06,822 INFO misc.py line 119 2586773] Train: [36/50][5/376] Data 0.002 (0.002) Batch 0.496 (0.520) Remain 00:48:49 loss: 0.2476 Lr: 0.00089 [2024-11-25 19:02:07,353 INFO misc.py line 119 2586773] Train: [36/50][6/376] Data 0.003 (0.002) Batch 0.531 (0.524) Remain 00:49:09 loss: 0.2150 Lr: 0.00089 [2024-11-25 19:02:07,874 INFO misc.py line 119 2586773] Train: [36/50][7/376] Data 0.002 (0.002) Batch 0.520 (0.523) Remain 00:49:04 loss: 0.1729 Lr: 0.00089 [2024-11-25 19:02:08,396 INFO misc.py line 119 2586773] Train: [36/50][8/376] Data 0.002 (0.002) Batch 0.522 (0.523) Remain 00:49:03 loss: 0.2177 Lr: 0.00089 [2024-11-25 19:02:08,874 INFO misc.py line 119 2586773] Train: [36/50][9/376] Data 0.002 (0.002) Batch 0.478 (0.515) Remain 00:48:20 loss: 0.1747 Lr: 0.00089 [2024-11-25 19:02:09,366 INFO misc.py line 119 2586773] Train: [36/50][10/376] Data 0.002 (0.002) Batch 0.492 (0.512) Remain 00:48:02 loss: 0.2753 Lr: 0.00089 [2024-11-25 19:02:09,863 INFO misc.py line 119 2586773] Train: [36/50][11/376] Data 0.002 (0.002) Batch 0.497 (0.510) Remain 00:47:51 loss: 0.1685 Lr: 0.00089 [2024-11-25 19:02:10,416 INFO misc.py line 119 2586773] Train: [36/50][12/376] Data 0.002 (0.002) Batch 0.553 (0.515) Remain 00:48:17 loss: 0.2095 Lr: 0.00089 [2024-11-25 19:02:10,901 INFO misc.py line 119 2586773] Train: [36/50][13/376] Data 0.002 (0.002) Batch 0.485 (0.512) Remain 00:48:00 loss: 0.2365 Lr: 0.00089 [2024-11-25 19:02:11,435 INFO misc.py line 119 2586773] Train: [36/50][14/376] Data 0.003 (0.002) Batch 0.534 (0.514) Remain 00:48:10 loss: 0.1959 Lr: 0.00089 [2024-11-25 19:02:11,917 INFO misc.py line 119 2586773] Train: [36/50][15/376] Data 0.002 (0.002) Batch 0.482 (0.511) Remain 00:47:55 loss: 0.2152 Lr: 0.00089 [2024-11-25 19:02:12,425 INFO misc.py line 119 2586773] Train: [36/50][16/376] Data 0.003 (0.002) Batch 0.508 (0.511) Remain 00:47:53 loss: 0.1729 Lr: 0.00089 [2024-11-25 19:02:12,924 INFO misc.py line 119 2586773] Train: [36/50][17/376] Data 0.002 (0.002) Batch 0.499 (0.510) Remain 00:47:48 loss: 0.3467 Lr: 0.00089 [2024-11-25 19:02:13,482 INFO misc.py line 119 2586773] Train: [36/50][18/376] Data 0.003 (0.002) Batch 0.558 (0.513) Remain 00:48:05 loss: 0.1797 Lr: 0.00089 [2024-11-25 19:02:13,961 INFO misc.py line 119 2586773] Train: [36/50][19/376] Data 0.003 (0.002) Batch 0.479 (0.511) Remain 00:47:53 loss: 0.2398 Lr: 0.00089 [2024-11-25 19:02:14,466 INFO misc.py line 119 2586773] Train: [36/50][20/376] Data 0.002 (0.002) Batch 0.504 (0.511) Remain 00:47:50 loss: 0.1770 Lr: 0.00089 [2024-11-25 19:02:14,940 INFO misc.py line 119 2586773] Train: [36/50][21/376] Data 0.002 (0.002) Batch 0.474 (0.509) Remain 00:47:38 loss: 0.2390 Lr: 0.00089 [2024-11-25 19:02:15,459 INFO misc.py line 119 2586773] Train: [36/50][22/376] Data 0.003 (0.002) Batch 0.519 (0.509) Remain 00:47:41 loss: 0.1900 Lr: 0.00089 [2024-11-25 19:02:15,956 INFO misc.py line 119 2586773] Train: [36/50][23/376] Data 0.003 (0.002) Batch 0.498 (0.509) Remain 00:47:37 loss: 0.1625 Lr: 0.00089 [2024-11-25 19:02:16,452 INFO misc.py line 119 2586773] Train: [36/50][24/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:47:33 loss: 0.1837 Lr: 0.00089 [2024-11-25 19:02:17,003 INFO misc.py line 119 2586773] Train: [36/50][25/376] Data 0.003 (0.002) Batch 0.551 (0.510) Remain 00:47:43 loss: 0.2114 Lr: 0.00089 [2024-11-25 19:02:17,531 INFO misc.py line 119 2586773] Train: [36/50][26/376] Data 0.002 (0.002) Batch 0.528 (0.511) Remain 00:47:47 loss: 0.2174 Lr: 0.00089 [2024-11-25 19:02:18,064 INFO misc.py line 119 2586773] Train: [36/50][27/376] Data 0.002 (0.002) Batch 0.533 (0.512) Remain 00:47:52 loss: 0.1839 Lr: 0.00089 [2024-11-25 19:02:18,552 INFO misc.py line 119 2586773] Train: [36/50][28/376] Data 0.002 (0.002) Batch 0.488 (0.511) Remain 00:47:46 loss: 0.1982 Lr: 0.00089 [2024-11-25 19:02:19,042 INFO misc.py line 119 2586773] Train: [36/50][29/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 00:47:41 loss: 0.2360 Lr: 0.00089 [2024-11-25 19:02:19,597 INFO misc.py line 119 2586773] Train: [36/50][30/376] Data 0.002 (0.002) Batch 0.555 (0.512) Remain 00:47:50 loss: 0.1956 Lr: 0.00089 [2024-11-25 19:02:20,085 INFO misc.py line 119 2586773] Train: [36/50][31/376] Data 0.003 (0.002) Batch 0.488 (0.511) Remain 00:47:45 loss: 0.1907 Lr: 0.00089 [2024-11-25 19:02:20,588 INFO misc.py line 119 2586773] Train: [36/50][32/376] Data 0.002 (0.002) Batch 0.503 (0.511) Remain 00:47:43 loss: 0.1989 Lr: 0.00089 [2024-11-25 19:02:21,078 INFO misc.py line 119 2586773] Train: [36/50][33/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 00:47:38 loss: 0.2369 Lr: 0.00088 [2024-11-25 19:02:21,506 INFO misc.py line 119 2586773] Train: [36/50][34/376] Data 0.002 (0.002) Batch 0.428 (0.507) Remain 00:47:23 loss: 0.2062 Lr: 0.00088 [2024-11-25 19:02:21,981 INFO misc.py line 119 2586773] Train: [36/50][35/376] Data 0.002 (0.002) Batch 0.475 (0.506) Remain 00:47:17 loss: 0.2315 Lr: 0.00088 [2024-11-25 19:02:22,486 INFO misc.py line 119 2586773] Train: [36/50][36/376] Data 0.002 (0.002) Batch 0.505 (0.506) Remain 00:47:16 loss: 0.1912 Lr: 0.00088 [2024-11-25 19:02:22,998 INFO misc.py line 119 2586773] Train: [36/50][37/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 00:47:17 loss: 0.2361 Lr: 0.00088 [2024-11-25 19:02:23,530 INFO misc.py line 119 2586773] Train: [36/50][38/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 00:47:20 loss: 0.2201 Lr: 0.00088 [2024-11-25 19:02:24,024 INFO misc.py line 119 2586773] Train: [36/50][39/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:47:17 loss: 0.1943 Lr: 0.00088 [2024-11-25 19:02:24,538 INFO misc.py line 119 2586773] Train: [36/50][40/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 00:47:18 loss: 0.1878 Lr: 0.00088 [2024-11-25 19:02:24,998 INFO misc.py line 119 2586773] Train: [36/50][41/376] Data 0.002 (0.002) Batch 0.460 (0.506) Remain 00:47:11 loss: 0.1968 Lr: 0.00088 [2024-11-25 19:02:25,478 INFO misc.py line 119 2586773] Train: [36/50][42/376] Data 0.003 (0.002) Batch 0.480 (0.505) Remain 00:47:07 loss: 0.1657 Lr: 0.00088 [2024-11-25 19:02:25,980 INFO misc.py line 119 2586773] Train: [36/50][43/376] Data 0.002 (0.002) Batch 0.502 (0.505) Remain 00:47:06 loss: 0.2123 Lr: 0.00088 [2024-11-25 19:02:26,469 INFO misc.py line 119 2586773] Train: [36/50][44/376] Data 0.003 (0.002) Batch 0.489 (0.505) Remain 00:47:03 loss: 0.2002 Lr: 0.00088 [2024-11-25 19:02:26,987 INFO misc.py line 119 2586773] Train: [36/50][45/376] Data 0.003 (0.002) Batch 0.518 (0.505) Remain 00:47:04 loss: 0.1851 Lr: 0.00088 [2024-11-25 19:02:27,486 INFO misc.py line 119 2586773] Train: [36/50][46/376] Data 0.002 (0.002) Batch 0.499 (0.505) Remain 00:47:03 loss: 0.1706 Lr: 0.00088 [2024-11-25 19:02:27,982 INFO misc.py line 119 2586773] Train: [36/50][47/376] Data 0.003 (0.002) Batch 0.496 (0.505) Remain 00:47:01 loss: 0.1969 Lr: 0.00088 [2024-11-25 19:02:28,522 INFO misc.py line 119 2586773] Train: [36/50][48/376] Data 0.003 (0.002) Batch 0.540 (0.505) Remain 00:47:05 loss: 0.1790 Lr: 0.00088 [2024-11-25 19:02:29,015 INFO misc.py line 119 2586773] Train: [36/50][49/376] Data 0.002 (0.002) Batch 0.493 (0.505) Remain 00:47:03 loss: 0.2543 Lr: 0.00088 [2024-11-25 19:02:29,536 INFO misc.py line 119 2586773] Train: [36/50][50/376] Data 0.003 (0.002) Batch 0.522 (0.505) Remain 00:47:05 loss: 0.2173 Lr: 0.00088 [2024-11-25 19:02:30,022 INFO misc.py line 119 2586773] Train: [36/50][51/376] Data 0.003 (0.002) Batch 0.486 (0.505) Remain 00:47:02 loss: 0.2077 Lr: 0.00088 [2024-11-25 19:02:30,525 INFO misc.py line 119 2586773] Train: [36/50][52/376] Data 0.003 (0.002) Batch 0.503 (0.505) Remain 00:47:01 loss: 0.1519 Lr: 0.00088 [2024-11-25 19:02:31,056 INFO misc.py line 119 2586773] Train: [36/50][53/376] Data 0.003 (0.002) Batch 0.531 (0.505) Remain 00:47:04 loss: 0.2162 Lr: 0.00088 [2024-11-25 19:02:31,596 INFO misc.py line 119 2586773] Train: [36/50][54/376] Data 0.002 (0.002) Batch 0.540 (0.506) Remain 00:47:07 loss: 0.2151 Lr: 0.00088 [2024-11-25 19:02:32,077 INFO misc.py line 119 2586773] Train: [36/50][55/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 00:47:04 loss: 0.1780 Lr: 0.00088 [2024-11-25 19:02:32,573 INFO misc.py line 119 2586773] Train: [36/50][56/376] Data 0.003 (0.002) Batch 0.497 (0.505) Remain 00:47:02 loss: 0.2581 Lr: 0.00088 [2024-11-25 19:02:33,096 INFO misc.py line 119 2586773] Train: [36/50][57/376] Data 0.003 (0.002) Batch 0.523 (0.506) Remain 00:47:03 loss: 0.1570 Lr: 0.00088 [2024-11-25 19:02:33,577 INFO misc.py line 119 2586773] Train: [36/50][58/376] Data 0.003 (0.002) Batch 0.481 (0.505) Remain 00:47:00 loss: 0.1686 Lr: 0.00088 [2024-11-25 19:02:34,088 INFO misc.py line 119 2586773] Train: [36/50][59/376] Data 0.002 (0.002) Batch 0.511 (0.505) Remain 00:47:00 loss: 0.2090 Lr: 0.00088 [2024-11-25 19:02:34,604 INFO misc.py line 119 2586773] Train: [36/50][60/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 00:47:01 loss: 0.2575 Lr: 0.00088 [2024-11-25 19:02:35,127 INFO misc.py line 119 2586773] Train: [36/50][61/376] Data 0.003 (0.002) Batch 0.523 (0.506) Remain 00:47:02 loss: 0.2504 Lr: 0.00088 [2024-11-25 19:02:35,667 INFO misc.py line 119 2586773] Train: [36/50][62/376] Data 0.003 (0.002) Batch 0.540 (0.507) Remain 00:47:05 loss: 0.1791 Lr: 0.00088 [2024-11-25 19:02:36,178 INFO misc.py line 119 2586773] Train: [36/50][63/376] Data 0.003 (0.002) Batch 0.510 (0.507) Remain 00:47:05 loss: 0.3135 Lr: 0.00088 [2024-11-25 19:02:36,645 INFO misc.py line 119 2586773] Train: [36/50][64/376] Data 0.002 (0.002) Batch 0.468 (0.506) Remain 00:47:01 loss: 0.2025 Lr: 0.00088 [2024-11-25 19:02:37,137 INFO misc.py line 119 2586773] Train: [36/50][65/376] Data 0.002 (0.002) Batch 0.492 (0.506) Remain 00:46:59 loss: 0.2202 Lr: 0.00088 [2024-11-25 19:02:37,655 INFO misc.py line 119 2586773] Train: [36/50][66/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 00:46:59 loss: 0.1676 Lr: 0.00088 [2024-11-25 19:02:38,198 INFO misc.py line 119 2586773] Train: [36/50][67/376] Data 0.002 (0.002) Batch 0.543 (0.506) Remain 00:47:02 loss: 0.1845 Lr: 0.00087 [2024-11-25 19:02:38,753 INFO misc.py line 119 2586773] Train: [36/50][68/376] Data 0.003 (0.002) Batch 0.555 (0.507) Remain 00:47:06 loss: 0.2399 Lr: 0.00087 [2024-11-25 19:02:39,231 INFO misc.py line 119 2586773] Train: [36/50][69/376] Data 0.003 (0.002) Batch 0.478 (0.507) Remain 00:47:03 loss: 0.2572 Lr: 0.00087 [2024-11-25 19:02:39,746 INFO misc.py line 119 2586773] Train: [36/50][70/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:47:03 loss: 0.1866 Lr: 0.00087 [2024-11-25 19:02:40,236 INFO misc.py line 119 2586773] Train: [36/50][71/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:47:01 loss: 0.3089 Lr: 0.00087 [2024-11-25 19:02:40,734 INFO misc.py line 119 2586773] Train: [36/50][72/376] Data 0.003 (0.002) Batch 0.498 (0.507) Remain 00:47:00 loss: 0.3114 Lr: 0.00087 [2024-11-25 19:02:41,236 INFO misc.py line 119 2586773] Train: [36/50][73/376] Data 0.003 (0.002) Batch 0.502 (0.506) Remain 00:46:59 loss: 0.1723 Lr: 0.00087 [2024-11-25 19:02:41,718 INFO misc.py line 119 2586773] Train: [36/50][74/376] Data 0.002 (0.002) Batch 0.482 (0.506) Remain 00:46:57 loss: 0.2126 Lr: 0.00087 [2024-11-25 19:02:42,225 INFO misc.py line 119 2586773] Train: [36/50][75/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 00:46:56 loss: 0.1480 Lr: 0.00087 [2024-11-25 19:02:42,706 INFO misc.py line 119 2586773] Train: [36/50][76/376] Data 0.003 (0.002) Batch 0.480 (0.506) Remain 00:46:54 loss: 0.1854 Lr: 0.00087 [2024-11-25 19:02:43,247 INFO misc.py line 119 2586773] Train: [36/50][77/376] Data 0.002 (0.002) Batch 0.541 (0.506) Remain 00:46:56 loss: 0.2170 Lr: 0.00087 [2024-11-25 19:02:43,728 INFO misc.py line 119 2586773] Train: [36/50][78/376] Data 0.003 (0.002) Batch 0.481 (0.506) Remain 00:46:54 loss: 0.1810 Lr: 0.00087 [2024-11-25 19:02:44,207 INFO misc.py line 119 2586773] Train: [36/50][79/376] Data 0.003 (0.002) Batch 0.479 (0.506) Remain 00:46:51 loss: 0.2018 Lr: 0.00087 [2024-11-25 19:02:44,726 INFO misc.py line 119 2586773] Train: [36/50][80/376] Data 0.003 (0.002) Batch 0.519 (0.506) Remain 00:46:52 loss: 0.1977 Lr: 0.00087 [2024-11-25 19:02:45,261 INFO misc.py line 119 2586773] Train: [36/50][81/376] Data 0.003 (0.002) Batch 0.535 (0.506) Remain 00:46:53 loss: 0.2148 Lr: 0.00087 [2024-11-25 19:02:45,755 INFO misc.py line 119 2586773] Train: [36/50][82/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 00:46:52 loss: 0.2248 Lr: 0.00087 [2024-11-25 19:02:46,258 INFO misc.py line 119 2586773] Train: [36/50][83/376] Data 0.003 (0.002) Batch 0.503 (0.506) Remain 00:46:51 loss: 0.1987 Lr: 0.00087 [2024-11-25 19:02:46,725 INFO misc.py line 119 2586773] Train: [36/50][84/376] Data 0.002 (0.002) Batch 0.468 (0.505) Remain 00:46:48 loss: 0.1529 Lr: 0.00087 [2024-11-25 19:02:47,263 INFO misc.py line 119 2586773] Train: 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(0.506) Remain 00:46:47 loss: 0.2078 Lr: 0.00087 [2024-11-25 19:02:50,799 INFO misc.py line 119 2586773] Train: [36/50][92/376] Data 0.003 (0.002) Batch 0.499 (0.506) Remain 00:46:46 loss: 0.2546 Lr: 0.00087 [2024-11-25 19:02:51,309 INFO misc.py line 119 2586773] Train: [36/50][93/376] Data 0.002 (0.002) Batch 0.510 (0.506) Remain 00:46:45 loss: 0.1570 Lr: 0.00087 [2024-11-25 19:02:51,862 INFO misc.py line 119 2586773] Train: [36/50][94/376] Data 0.002 (0.002) Batch 0.554 (0.506) Remain 00:46:48 loss: 0.2154 Lr: 0.00087 [2024-11-25 19:02:52,335 INFO misc.py line 119 2586773] Train: [36/50][95/376] Data 0.003 (0.002) Batch 0.473 (0.506) Remain 00:46:45 loss: 0.2052 Lr: 0.00087 [2024-11-25 19:02:52,816 INFO misc.py line 119 2586773] Train: [36/50][96/376] Data 0.002 (0.002) Batch 0.481 (0.506) Remain 00:46:43 loss: 0.1633 Lr: 0.00087 [2024-11-25 19:02:53,331 INFO misc.py line 119 2586773] Train: [36/50][97/376] Data 0.002 (0.002) Batch 0.515 (0.506) Remain 00:46:43 loss: 0.2047 Lr: 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Batch 0.474 (0.510) Remain 00:46:27 loss: 0.3026 Lr: 0.00085 [2024-11-25 19:03:32,462 INFO misc.py line 119 2586773] Train: [36/50][173/376] Data 0.003 (0.002) Batch 0.512 (0.510) Remain 00:46:27 loss: 0.1542 Lr: 0.00084 [2024-11-25 19:03:32,966 INFO misc.py line 119 2586773] Train: [36/50][174/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 00:46:26 loss: 0.2163 Lr: 0.00084 [2024-11-25 19:03:33,527 INFO misc.py line 119 2586773] Train: [36/50][175/376] Data 0.002 (0.002) Batch 0.562 (0.510) Remain 00:46:27 loss: 0.2091 Lr: 0.00084 [2024-11-25 19:03:34,077 INFO misc.py line 119 2586773] Train: [36/50][176/376] Data 0.002 (0.002) Batch 0.550 (0.510) Remain 00:46:28 loss: 0.2220 Lr: 0.00084 [2024-11-25 19:03:34,572 INFO misc.py line 119 2586773] Train: [36/50][177/376] Data 0.002 (0.002) Batch 0.495 (0.510) Remain 00:46:27 loss: 0.1726 Lr: 0.00084 [2024-11-25 19:03:35,074 INFO misc.py line 119 2586773] Train: [36/50][178/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 00:46:26 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line 119 2586773] Train: [36/50][272/376] Data 0.002 (0.002) Batch 0.485 (0.514) Remain 00:45:57 loss: 0.2095 Lr: 0.00082 [2024-11-25 19:04:24,494 INFO misc.py line 119 2586773] Train: [36/50][273/376] Data 0.002 (0.002) Batch 0.523 (0.514) Remain 00:45:57 loss: 0.2149 Lr: 0.00082 [2024-11-25 19:04:25,033 INFO misc.py line 119 2586773] Train: [36/50][274/376] Data 0.003 (0.002) Batch 0.539 (0.514) Remain 00:45:57 loss: 0.1710 Lr: 0.00082 [2024-11-25 19:04:25,501 INFO misc.py line 119 2586773] Train: [36/50][275/376] Data 0.003 (0.002) Batch 0.468 (0.514) Remain 00:45:55 loss: 0.1900 Lr: 0.00082 [2024-11-25 19:04:25,983 INFO misc.py line 119 2586773] Train: [36/50][276/376] Data 0.002 (0.002) Batch 0.482 (0.514) Remain 00:45:54 loss: 0.2045 Lr: 0.00082 [2024-11-25 19:04:26,510 INFO misc.py line 119 2586773] Train: [36/50][277/376] Data 0.002 (0.002) Batch 0.526 (0.514) Remain 00:45:54 loss: 0.2187 Lr: 0.00082 [2024-11-25 19:04:27,037 INFO misc.py line 119 2586773] Train: 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Batch 0.522 (0.514) Remain 00:45:53 loss: 0.1825 Lr: 0.00081 [2024-11-25 19:04:30,746 INFO misc.py line 119 2586773] Train: [36/50][285/376] Data 0.002 (0.002) Batch 0.523 (0.514) Remain 00:45:52 loss: 0.1709 Lr: 0.00081 [2024-11-25 19:04:31,289 INFO misc.py line 119 2586773] Train: [36/50][286/376] Data 0.002 (0.002) Batch 0.542 (0.514) Remain 00:45:52 loss: 0.1901 Lr: 0.00081 [2024-11-25 19:04:31,840 INFO misc.py line 119 2586773] Train: [36/50][287/376] Data 0.002 (0.002) Batch 0.552 (0.514) Remain 00:45:52 loss: 0.1866 Lr: 0.00081 [2024-11-25 19:04:32,339 INFO misc.py line 119 2586773] Train: [36/50][288/376] Data 0.002 (0.002) Batch 0.499 (0.514) Remain 00:45:52 loss: 0.1782 Lr: 0.00081 [2024-11-25 19:04:32,855 INFO misc.py line 119 2586773] Train: [36/50][289/376] Data 0.002 (0.002) Batch 0.516 (0.514) Remain 00:45:51 loss: 0.2064 Lr: 0.00081 [2024-11-25 19:04:33,379 INFO misc.py line 119 2586773] Train: [36/50][290/376] Data 0.002 (0.002) Batch 0.523 (0.514) Remain 00:45:51 loss: 0.2385 Lr: 0.00081 [2024-11-25 19:04:33,851 INFO misc.py line 119 2586773] Train: [36/50][291/376] Data 0.003 (0.002) Batch 0.473 (0.514) Remain 00:45:50 loss: 0.1674 Lr: 0.00081 [2024-11-25 19:04:34,331 INFO misc.py line 119 2586773] Train: [36/50][292/376] Data 0.003 (0.002) Batch 0.480 (0.514) Remain 00:45:48 loss: 0.1880 Lr: 0.00081 [2024-11-25 19:04:34,836 INFO misc.py line 119 2586773] Train: [36/50][293/376] Data 0.002 (0.002) Batch 0.505 (0.514) Remain 00:45:48 loss: 0.1909 Lr: 0.00081 [2024-11-25 19:04:35,312 INFO misc.py line 119 2586773] Train: [36/50][294/376] Data 0.002 (0.002) Batch 0.475 (0.514) Remain 00:45:47 loss: 0.2227 Lr: 0.00081 [2024-11-25 19:04:35,895 INFO misc.py line 119 2586773] Train: [36/50][295/376] Data 0.002 (0.002) Batch 0.583 (0.514) Remain 00:45:47 loss: 0.1991 Lr: 0.00081 [2024-11-25 19:04:36,395 INFO misc.py line 119 2586773] Train: [36/50][296/376] Data 0.002 (0.002) Batch 0.500 (0.514) Remain 00:45:47 loss: 0.1824 Lr: 0.00081 [2024-11-25 19:04:36,895 INFO misc.py line 119 2586773] Train: [36/50][297/376] Data 0.002 (0.002) Batch 0.499 (0.514) Remain 00:45:46 loss: 0.2068 Lr: 0.00081 [2024-11-25 19:04:37,421 INFO misc.py line 119 2586773] Train: [36/50][298/376] Data 0.002 (0.002) Batch 0.526 (0.514) Remain 00:45:45 loss: 0.2232 Lr: 0.00081 [2024-11-25 19:04:37,910 INFO misc.py line 119 2586773] Train: [36/50][299/376] Data 0.002 (0.002) Batch 0.489 (0.514) Remain 00:45:44 loss: 0.2580 Lr: 0.00081 [2024-11-25 19:04:38,443 INFO misc.py line 119 2586773] Train: [36/50][300/376] Data 0.002 (0.002) Batch 0.533 (0.514) Remain 00:45:44 loss: 0.2148 Lr: 0.00081 [2024-11-25 19:04:38,928 INFO misc.py line 119 2586773] Train: [36/50][301/376] Data 0.002 (0.002) Batch 0.486 (0.514) Remain 00:45:43 loss: 0.1869 Lr: 0.00081 [2024-11-25 19:04:39,445 INFO misc.py line 119 2586773] Train: [36/50][302/376] Data 0.002 (0.002) Batch 0.516 (0.514) Remain 00:45:43 loss: 0.1861 Lr: 0.00081 [2024-11-25 19:04:39,955 INFO misc.py line 119 2586773] Train: [36/50][303/376] Data 0.002 (0.002) Batch 0.511 (0.514) Remain 00:45:42 loss: 0.1689 Lr: 0.00081 [2024-11-25 19:04:40,482 INFO misc.py line 119 2586773] Train: [36/50][304/376] Data 0.002 (0.002) Batch 0.528 (0.514) Remain 00:45:42 loss: 0.1799 Lr: 0.00081 [2024-11-25 19:04:41,027 INFO misc.py line 119 2586773] Train: [36/50][305/376] Data 0.002 (0.002) Batch 0.544 (0.514) Remain 00:45:42 loss: 0.2175 Lr: 0.00081 [2024-11-25 19:04:41,531 INFO misc.py line 119 2586773] Train: [36/50][306/376] Data 0.002 (0.002) Batch 0.504 (0.514) Remain 00:45:41 loss: 0.2510 Lr: 0.00081 [2024-11-25 19:04:42,097 INFO misc.py line 119 2586773] Train: [36/50][307/376] Data 0.002 (0.002) Batch 0.566 (0.514) Remain 00:45:42 loss: 0.1779 Lr: 0.00081 [2024-11-25 19:04:42,626 INFO misc.py line 119 2586773] Train: [36/50][308/376] Data 0.003 (0.002) Batch 0.529 (0.514) Remain 00:45:41 loss: 0.2242 Lr: 0.00081 [2024-11-25 19:04:43,144 INFO misc.py line 119 2586773] Train: [36/50][309/376] Data 0.002 (0.002) Batch 0.518 (0.514) Remain 00:45:41 loss: 0.2088 Lr: 0.00081 [2024-11-25 19:04:43,680 INFO misc.py line 119 2586773] Train: [36/50][310/376] Data 0.002 (0.002) Batch 0.535 (0.514) Remain 00:45:41 loss: 0.1887 Lr: 0.00081 [2024-11-25 19:04:44,192 INFO misc.py line 119 2586773] Train: [36/50][311/376] Data 0.002 (0.002) Batch 0.512 (0.514) Remain 00:45:40 loss: 0.2006 Lr: 0.00081 [2024-11-25 19:04:44,688 INFO misc.py line 119 2586773] Train: [36/50][312/376] Data 0.002 (0.002) Batch 0.496 (0.514) Remain 00:45:39 loss: 0.2149 Lr: 0.00081 [2024-11-25 19:04:45,248 INFO misc.py line 119 2586773] Train: [36/50][313/376] Data 0.002 (0.002) Batch 0.560 (0.514) Remain 00:45:40 loss: 0.2118 Lr: 0.00081 [2024-11-25 19:04:45,747 INFO misc.py line 119 2586773] Train: [36/50][314/376] Data 0.002 (0.002) Batch 0.499 (0.514) Remain 00:45:39 loss: 0.1594 Lr: 0.00081 [2024-11-25 19:04:46,237 INFO misc.py line 119 2586773] Train: [36/50][315/376] Data 0.002 (0.002) Batch 0.490 (0.514) Remain 00:45:38 loss: 0.2806 Lr: 0.00080 [2024-11-25 19:04:46,717 INFO misc.py line 119 2586773] Train: [36/50][316/376] Data 0.002 (0.002) Batch 0.480 (0.514) Remain 00:45:37 loss: 0.1742 Lr: 0.00080 [2024-11-25 19:04:47,266 INFO misc.py line 119 2586773] Train: [36/50][317/376] Data 0.002 (0.002) Batch 0.550 (0.514) Remain 00:45:37 loss: 0.2364 Lr: 0.00080 [2024-11-25 19:04:47,769 INFO misc.py line 119 2586773] Train: [36/50][318/376] Data 0.002 (0.002) Batch 0.502 (0.514) Remain 00:45:36 loss: 0.1612 Lr: 0.00080 [2024-11-25 19:04:48,280 INFO misc.py line 119 2586773] Train: [36/50][319/376] Data 0.002 (0.002) Batch 0.512 (0.514) Remain 00:45:36 loss: 0.1861 Lr: 0.00080 [2024-11-25 19:04:48,773 INFO misc.py line 119 2586773] Train: [36/50][320/376] Data 0.002 (0.002) Batch 0.493 (0.514) Remain 00:45:35 loss: 0.2090 Lr: 0.00080 [2024-11-25 19:04:49,300 INFO misc.py line 119 2586773] Train: [36/50][321/376] Data 0.002 (0.002) Batch 0.527 (0.514) Remain 00:45:35 loss: 0.2418 Lr: 0.00080 [2024-11-25 19:04:49,810 INFO misc.py line 119 2586773] Train: [36/50][322/376] Data 0.002 (0.002) Batch 0.509 (0.514) Remain 00:45:34 loss: 0.2105 Lr: 0.00080 [2024-11-25 19:04:50,339 INFO misc.py line 119 2586773] Train: [36/50][323/376] Data 0.002 (0.002) Batch 0.530 (0.514) Remain 00:45:34 loss: 0.2152 Lr: 0.00080 [2024-11-25 19:04:50,874 INFO misc.py line 119 2586773] Train: [36/50][324/376] Data 0.002 (0.002) Batch 0.534 (0.514) Remain 00:45:34 loss: 0.2210 Lr: 0.00080 [2024-11-25 19:04:51,401 INFO misc.py line 119 2586773] Train: [36/50][325/376] Data 0.002 (0.002) Batch 0.528 (0.514) Remain 00:45:33 loss: 0.1836 Lr: 0.00080 [2024-11-25 19:04:51,966 INFO misc.py line 119 2586773] Train: [36/50][326/376] Data 0.002 (0.002) Batch 0.564 (0.514) Remain 00:45:34 loss: 0.2315 Lr: 0.00080 [2024-11-25 19:04:52,464 INFO misc.py line 119 2586773] Train: [36/50][327/376] Data 0.002 (0.002) Batch 0.498 (0.514) Remain 00:45:33 loss: 0.2411 Lr: 0.00080 [2024-11-25 19:04:53,032 INFO misc.py line 119 2586773] Train: [36/50][328/376] Data 0.002 (0.002) Batch 0.568 (0.515) Remain 00:45:33 loss: 0.1980 Lr: 0.00080 [2024-11-25 19:04:53,578 INFO misc.py line 119 2586773] Train: [36/50][329/376] Data 0.002 (0.002) Batch 0.547 (0.515) Remain 00:45:33 loss: 0.2094 Lr: 0.00080 [2024-11-25 19:04:54,068 INFO misc.py line 119 2586773] Train: [36/50][330/376] Data 0.002 (0.002) Batch 0.490 (0.515) Remain 00:45:32 loss: 0.1703 Lr: 0.00080 [2024-11-25 19:04:54,574 INFO misc.py line 119 2586773] Train: [36/50][331/376] Data 0.002 (0.002) Batch 0.506 (0.515) Remain 00:45:32 loss: 0.2578 Lr: 0.00080 [2024-11-25 19:04:55,104 INFO misc.py line 119 2586773] Train: [36/50][332/376] Data 0.002 (0.002) Batch 0.529 (0.515) Remain 00:45:31 loss: 0.2100 Lr: 0.00080 [2024-11-25 19:04:55,625 INFO misc.py line 119 2586773] Train: [36/50][333/376] Data 0.002 (0.002) Batch 0.522 (0.515) Remain 00:45:31 loss: 0.2049 Lr: 0.00080 [2024-11-25 19:04:56,134 INFO misc.py line 119 2586773] Train: [36/50][334/376] Data 0.002 (0.002) Batch 0.509 (0.515) Remain 00:45:30 loss: 0.1953 Lr: 0.00080 [2024-11-25 19:04:56,653 INFO misc.py line 119 2586773] Train: [36/50][335/376] Data 0.002 (0.002) Batch 0.519 (0.515) Remain 00:45:30 loss: 0.1998 Lr: 0.00080 [2024-11-25 19:04:57,181 INFO misc.py line 119 2586773] Train: [36/50][336/376] Data 0.002 (0.002) Batch 0.528 (0.515) Remain 00:45:30 loss: 0.1974 Lr: 0.00080 [2024-11-25 19:04:57,671 INFO misc.py line 119 2586773] Train: [36/50][337/376] Data 0.002 (0.002) Batch 0.490 (0.515) Remain 00:45:29 loss: 0.2479 Lr: 0.00080 [2024-11-25 19:04:58,168 INFO misc.py line 119 2586773] Train: [36/50][338/376] Data 0.002 (0.002) Batch 0.497 (0.515) Remain 00:45:28 loss: 0.1736 Lr: 0.00080 [2024-11-25 19:04:58,665 INFO misc.py line 119 2586773] Train: [36/50][339/376] Data 0.002 (0.002) Batch 0.497 (0.515) Remain 00:45:27 loss: 0.2101 Lr: 0.00080 [2024-11-25 19:04:59,219 INFO misc.py line 119 2586773] Train: [36/50][340/376] Data 0.002 (0.002) Batch 0.554 (0.515) Remain 00:45:27 loss: 0.1966 Lr: 0.00080 [2024-11-25 19:04:59,747 INFO misc.py line 119 2586773] Train: [36/50][341/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 00:45:27 loss: 0.2232 Lr: 0.00080 [2024-11-25 19:05:00,273 INFO misc.py line 119 2586773] Train: [36/50][342/376] Data 0.002 (0.002) Batch 0.526 (0.515) Remain 00:45:26 loss: 0.1910 Lr: 0.00080 [2024-11-25 19:05:00,827 INFO misc.py line 119 2586773] Train: [36/50][343/376] Data 0.002 (0.002) Batch 0.554 (0.515) Remain 00:45:27 loss: 0.1916 Lr: 0.00080 [2024-11-25 19:05:01,398 INFO misc.py line 119 2586773] Train: [36/50][344/376] Data 0.002 (0.002) Batch 0.570 (0.515) Remain 00:45:27 loss: 0.1678 Lr: 0.00080 [2024-11-25 19:05:01,921 INFO misc.py line 119 2586773] Train: [36/50][345/376] Data 0.002 (0.002) Batch 0.523 (0.515) Remain 00:45:27 loss: 0.1823 Lr: 0.00080 [2024-11-25 19:05:02,395 INFO misc.py line 119 2586773] Train: [36/50][346/376] Data 0.003 (0.002) Batch 0.474 (0.515) Remain 00:45:25 loss: 0.2539 Lr: 0.00080 [2024-11-25 19:05:02,922 INFO misc.py line 119 2586773] Train: [36/50][347/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 00:45:25 loss: 0.1611 Lr: 0.00080 [2024-11-25 19:05:03,399 INFO misc.py line 119 2586773] Train: [36/50][348/376] Data 0.002 (0.002) Batch 0.477 (0.515) Remain 00:45:24 loss: 0.2004 Lr: 0.00080 [2024-11-25 19:05:03,899 INFO misc.py line 119 2586773] Train: [36/50][349/376] Data 0.002 (0.002) Batch 0.501 (0.515) Remain 00:45:23 loss: 0.1773 Lr: 0.00080 [2024-11-25 19:05:04,416 INFO misc.py line 119 2586773] Train: [36/50][350/376] Data 0.002 (0.002) Batch 0.517 (0.515) Remain 00:45:23 loss: 0.1707 Lr: 0.00080 [2024-11-25 19:05:04,954 INFO misc.py line 119 2586773] Train: [36/50][351/376] Data 0.003 (0.002) Batch 0.538 (0.515) Remain 00:45:23 loss: 0.1699 Lr: 0.00079 [2024-11-25 19:05:05,442 INFO misc.py line 119 2586773] Train: [36/50][352/376] Data 0.002 (0.002) Batch 0.488 (0.515) Remain 00:45:22 loss: 0.1936 Lr: 0.00079 [2024-11-25 19:05:05,909 INFO misc.py line 119 2586773] Train: [36/50][353/376] Data 0.002 (0.002) Batch 0.467 (0.515) Remain 00:45:20 loss: 0.3102 Lr: 0.00079 [2024-11-25 19:05:06,437 INFO misc.py line 119 2586773] Train: [36/50][354/376] Data 0.002 (0.002) Batch 0.528 (0.515) Remain 00:45:20 loss: 0.2472 Lr: 0.00079 [2024-11-25 19:05:06,933 INFO misc.py line 119 2586773] Train: [36/50][355/376] Data 0.002 (0.002) Batch 0.496 (0.515) Remain 00:45:19 loss: 0.2770 Lr: 0.00079 [2024-11-25 19:05:07,438 INFO misc.py line 119 2586773] Train: [36/50][356/376] Data 0.002 (0.002) Batch 0.505 (0.515) Remain 00:45:19 loss: 0.1809 Lr: 0.00079 [2024-11-25 19:05:07,967 INFO misc.py line 119 2586773] Train: [36/50][357/376] Data 0.002 (0.002) Batch 0.530 (0.515) Remain 00:45:18 loss: 0.1832 Lr: 0.00079 [2024-11-25 19:05:08,481 INFO misc.py line 119 2586773] Train: [36/50][358/376] Data 0.002 (0.002) Batch 0.513 (0.515) Remain 00:45:18 loss: 0.1893 Lr: 0.00079 [2024-11-25 19:05:08,963 INFO misc.py line 119 2586773] Train: [36/50][359/376] Data 0.002 (0.002) Batch 0.482 (0.515) Remain 00:45:17 loss: 0.1953 Lr: 0.00079 [2024-11-25 19:05:09,468 INFO misc.py line 119 2586773] Train: [36/50][360/376] Data 0.002 (0.002) Batch 0.505 (0.515) Remain 00:45:16 loss: 0.2093 Lr: 0.00079 [2024-11-25 19:05:09,960 INFO misc.py line 119 2586773] Train: [36/50][361/376] Data 0.002 (0.002) Batch 0.492 (0.514) Remain 00:45:15 loss: 0.2740 Lr: 0.00079 [2024-11-25 19:05:10,506 INFO misc.py line 119 2586773] Train: [36/50][362/376] Data 0.002 (0.002) Batch 0.546 (0.515) Remain 00:45:15 loss: 0.1918 Lr: 0.00079 [2024-11-25 19:05:11,039 INFO misc.py line 119 2586773] Train: [36/50][363/376] Data 0.002 (0.002) Batch 0.533 (0.515) Remain 00:45:15 loss: 0.2099 Lr: 0.00079 [2024-11-25 19:05:11,588 INFO misc.py line 119 2586773] Train: [36/50][364/376] Data 0.002 (0.002) Batch 0.549 (0.515) Remain 00:45:15 loss: 0.1978 Lr: 0.00079 [2024-11-25 19:05:12,085 INFO misc.py line 119 2586773] Train: [36/50][365/376] Data 0.002 (0.002) Batch 0.497 (0.515) Remain 00:45:14 loss: 0.1785 Lr: 0.00079 [2024-11-25 19:05:12,613 INFO misc.py line 119 2586773] Train: [36/50][366/376] Data 0.002 (0.002) Batch 0.528 (0.515) Remain 00:45:14 loss: 0.2376 Lr: 0.00079 [2024-11-25 19:05:13,101 INFO misc.py line 119 2586773] Train: [36/50][367/376] Data 0.002 (0.002) Batch 0.488 (0.515) Remain 00:45:13 loss: 0.1599 Lr: 0.00079 [2024-11-25 19:05:13,597 INFO misc.py line 119 2586773] Train: [36/50][368/376] Data 0.002 (0.002) Batch 0.496 (0.515) Remain 00:45:12 loss: 0.2148 Lr: 0.00079 [2024-11-25 19:05:14,100 INFO misc.py line 119 2586773] Train: [36/50][369/376] Data 0.002 (0.002) Batch 0.503 (0.515) Remain 00:45:12 loss: 0.2368 Lr: 0.00079 [2024-11-25 19:05:14,643 INFO misc.py line 119 2586773] Train: [36/50][370/376] Data 0.002 (0.002) Batch 0.543 (0.515) Remain 00:45:11 loss: 0.2180 Lr: 0.00079 [2024-11-25 19:05:15,139 INFO misc.py line 119 2586773] Train: [36/50][371/376] Data 0.002 (0.002) Batch 0.496 (0.515) Remain 00:45:11 loss: 0.1896 Lr: 0.00079 [2024-11-25 19:05:15,669 INFO misc.py line 119 2586773] Train: [36/50][372/376] Data 0.002 (0.002) Batch 0.530 (0.515) Remain 00:45:10 loss: 0.2038 Lr: 0.00079 [2024-11-25 19:05:16,174 INFO misc.py line 119 2586773] Train: [36/50][373/376] Data 0.002 (0.002) Batch 0.505 (0.515) Remain 00:45:10 loss: 0.1826 Lr: 0.00079 [2024-11-25 19:05:16,710 INFO misc.py line 119 2586773] Train: [36/50][374/376] Data 0.002 (0.002) Batch 0.536 (0.515) Remain 00:45:10 loss: 0.1956 Lr: 0.00079 [2024-11-25 19:05:17,216 INFO misc.py line 119 2586773] Train: [36/50][375/376] Data 0.002 (0.002) Batch 0.506 (0.515) Remain 00:45:09 loss: 0.2087 Lr: 0.00079 [2024-11-25 19:05:17,716 INFO misc.py line 119 2586773] Train: [36/50][376/376] Data 0.002 (0.002) Batch 0.500 (0.515) Remain 00:45:08 loss: 0.1871 Lr: 0.00079 [2024-11-25 19:05:17,717 INFO misc.py line 136 2586773] Train result: loss: 0.2068 [2024-11-25 19:05:17,717 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:05:28,697 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1773 [2024-11-25 19:05:28,957 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2140 [2024-11-25 19:05:29,219 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2286 [2024-11-25 19:05:29,442 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1894 [2024-11-25 19:05:29,704 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2656 [2024-11-25 19:05:29,973 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1869 [2024-11-25 19:05:30,195 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2036 [2024-11-25 19:05:30,464 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2411 [2024-11-25 19:05:30,689 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2696 [2024-11-25 19:05:30,951 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2230 [2024-11-25 19:05:31,181 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.1983 [2024-11-25 19:05:31,453 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2460 [2024-11-25 19:05:31,720 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2632 [2024-11-25 19:05:31,988 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2242 [2024-11-25 19:05:32,219 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2307 [2024-11-25 19:05:32,457 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3073 [2024-11-25 19:05:32,724 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2518 [2024-11-25 19:05:32,970 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2115 [2024-11-25 19:05:33,199 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2451 [2024-11-25 19:05:33,459 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2167 [2024-11-25 19:05:33,696 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2622 [2024-11-25 19:05:33,959 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2360 [2024-11-25 19:05:34,197 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.1956 [2024-11-25 19:05:34,462 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2222 [2024-11-25 19:05:34,726 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2120 [2024-11-25 19:05:34,964 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2624 [2024-11-25 19:05:35,215 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2404 [2024-11-25 19:05:35,460 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2161 [2024-11-25 19:05:35,726 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2605 [2024-11-25 19:05:35,982 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2829 [2024-11-25 19:05:36,215 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2433 [2024-11-25 19:05:36,479 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2035 [2024-11-25 19:05:36,705 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2623 [2024-11-25 19:05:36,942 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2272 [2024-11-25 19:05:37,204 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1960 [2024-11-25 19:05:37,448 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2520 [2024-11-25 19:05:37,674 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1930 [2024-11-25 19:05:37,942 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2235 [2024-11-25 19:05:38,172 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2532 [2024-11-25 19:05:38,406 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2082 [2024-11-25 19:05:38,678 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2997 [2024-11-25 19:05:38,928 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2652 [2024-11-25 19:05:39,165 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2291 [2024-11-25 19:05:39,396 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2282 [2024-11-25 19:05:39,634 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2431 [2024-11-25 19:05:39,884 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2181 [2024-11-25 19:05:40,145 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2120 [2024-11-25 19:05:40,395 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2814 [2024-11-25 19:05:40,620 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2036 [2024-11-25 19:05:40,853 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2006 [2024-11-25 19:05:41,074 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2436 [2024-11-25 19:05:41,327 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2101 [2024-11-25 19:05:41,594 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2103 [2024-11-25 19:05:41,856 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3195 [2024-11-25 19:05:42,088 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2322 [2024-11-25 19:05:42,327 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2392 [2024-11-25 19:05:42,582 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2297 [2024-11-25 19:05:42,848 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2644 [2024-11-25 19:05:43,102 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2329 [2024-11-25 19:05:43,364 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2323 [2024-11-25 19:05:43,617 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2099 [2024-11-25 19:05:43,890 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2284 [2024-11-25 19:05:44,118 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2293 [2024-11-25 19:05:44,374 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2382 [2024-11-25 19:05:44,639 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2121 [2024-11-25 19:05:44,905 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1854 [2024-11-25 19:05:45,151 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1969 [2024-11-25 19:05:45,411 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2294 [2024-11-25 19:05:45,680 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2518 [2024-11-25 19:05:45,942 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2790 [2024-11-25 19:05:46,186 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2079 [2024-11-25 19:05:46,422 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2749 [2024-11-25 19:05:46,679 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2389 [2024-11-25 19:05:46,923 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2430 [2024-11-25 19:05:47,145 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2692 [2024-11-25 19:05:47,370 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2033 [2024-11-25 19:05:47,643 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2554 [2024-11-25 19:05:47,880 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2185 [2024-11-25 19:05:48,139 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2283 [2024-11-25 19:05:48,389 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2768 [2024-11-25 19:05:48,631 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2372 [2024-11-25 19:05:48,898 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2651 [2024-11-25 19:05:49,147 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1954 [2024-11-25 19:05:49,396 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2501 [2024-11-25 19:05:49,666 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2616 [2024-11-25 19:05:49,906 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2770 [2024-11-25 19:05:50,173 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2381 [2024-11-25 19:05:50,435 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2167 [2024-11-25 19:05:50,685 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2784 [2024-11-25 19:05:50,932 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2585 [2024-11-25 19:05:51,166 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2439 [2024-11-25 19:05:51,420 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2625 [2024-11-25 19:05:51,690 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2486 [2024-11-25 19:05:51,957 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1847 [2024-11-25 19:05:52,222 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2240 [2024-11-25 19:05:52,469 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2036 [2024-11-25 19:05:52,736 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2131 [2024-11-25 19:05:52,956 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3078 [2024-11-25 19:05:53,230 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2294 [2024-11-25 19:05:53,467 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2432 [2024-11-25 19:05:53,738 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2174 [2024-11-25 19:05:53,997 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2384 [2024-11-25 19:05:54,256 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2089 [2024-11-25 19:05:54,509 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2640 [2024-11-25 19:05:54,733 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2253 [2024-11-25 19:05:54,974 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2240 [2024-11-25 19:05:55,230 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2308 [2024-11-25 19:05:55,499 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2288 [2024-11-25 19:05:55,741 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2299 [2024-11-25 19:05:56,009 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2090 [2024-11-25 19:05:56,272 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2485 [2024-11-25 19:05:56,494 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2186 [2024-11-25 19:05:56,729 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2100 [2024-11-25 19:05:56,951 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2104 [2024-11-25 19:05:57,177 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2059 [2024-11-25 19:05:57,450 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2708 [2024-11-25 19:05:57,709 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2403 [2024-11-25 19:05:57,977 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2344 [2024-11-25 19:05:58,241 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2239 [2024-11-25 19:05:58,503 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2460 [2024-11-25 19:05:58,765 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2843 [2024-11-25 19:05:59,030 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2290 [2024-11-25 19:05:59,285 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2450 [2024-11-25 19:05:59,547 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2104 [2024-11-25 19:05:59,811 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2495 [2024-11-25 19:06:00,061 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2405 [2024-11-25 19:06:00,291 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1971 [2024-11-25 19:06:00,550 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2344 [2024-11-25 19:06:00,784 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2538 [2024-11-25 19:06:01,010 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1736 [2024-11-25 19:06:01,222 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2166 [2024-11-25 19:06:01,438 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1925 [2024-11-25 19:06:02,180 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7791/0.8600/0.9963. [2024-11-25 19:06:02,181 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9981 [2024-11-25 19:06:02,181 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5620/0.7218 [2024-11-25 19:06:02,181 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:06:02,182 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7791 [2024-11-25 19:06:02,182 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7791 [2024-11-25 19:06:02,182 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:06:06,698 INFO misc.py line 119 2586773] Train: [37/50][1/376] Data 0.090 (0.090) Batch 0.578 (0.578) Remain 00:50:40 loss: 0.2008 Lr: 0.00079 [2024-11-25 19:06:07,244 INFO misc.py line 119 2586773] Train: [37/50][2/376] Data 0.002 (0.002) Batch 0.546 (0.546) Remain 00:47:53 loss: 0.1938 Lr: 0.00079 [2024-11-25 19:06:07,757 INFO misc.py line 119 2586773] Train: [37/50][3/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 00:44:55 loss: 0.1797 Lr: 0.00079 [2024-11-25 19:06:08,280 INFO misc.py line 119 2586773] Train: [37/50][4/376] Data 0.002 (0.002) Batch 0.523 (0.523) Remain 00:45:51 loss: 0.1768 Lr: 0.00079 [2024-11-25 19:06:08,817 INFO misc.py line 119 2586773] Train: [37/50][5/376] Data 0.002 (0.002) Batch 0.537 (0.530) Remain 00:46:28 loss: 0.2335 Lr: 0.00079 [2024-11-25 19:06:09,321 INFO misc.py line 119 2586773] Train: [37/50][6/376] Data 0.002 (0.002) Batch 0.503 (0.521) Remain 00:45:41 loss: 0.1621 Lr: 0.00079 [2024-11-25 19:06:09,812 INFO misc.py line 119 2586773] Train: [37/50][7/376] Data 0.002 (0.002) Batch 0.491 (0.514) Remain 00:45:00 loss: 0.2164 Lr: 0.00079 [2024-11-25 19:06:10,349 INFO misc.py line 119 2586773] Train: [37/50][8/376] Data 0.002 (0.002) Batch 0.537 (0.518) Remain 00:45:25 loss: 0.2367 Lr: 0.00079 [2024-11-25 19:06:10,872 INFO misc.py line 119 2586773] Train: [37/50][9/376] Data 0.002 (0.002) Batch 0.523 (0.519) Remain 00:45:28 loss: 0.1942 Lr: 0.00079 [2024-11-25 19:06:11,354 INFO misc.py line 119 2586773] Train: [37/50][10/376] Data 0.002 (0.002) Batch 0.482 (0.514) Remain 00:45:00 loss: 0.1742 Lr: 0.00079 [2024-11-25 19:06:11,842 INFO misc.py line 119 2586773] Train: [37/50][11/376] Data 0.003 (0.002) Batch 0.488 (0.511) Remain 00:44:42 loss: 0.1783 Lr: 0.00078 [2024-11-25 19:06:12,372 INFO misc.py line 119 2586773] Train: [37/50][12/376] Data 0.002 (0.002) Batch 0.530 (0.513) Remain 00:44:53 loss: 0.2362 Lr: 0.00078 [2024-11-25 19:06:12,841 INFO misc.py line 119 2586773] Train: [37/50][13/376] Data 0.003 (0.002) Batch 0.470 (0.508) Remain 00:44:29 loss: 0.1808 Lr: 0.00078 [2024-11-25 19:06:13,330 INFO misc.py line 119 2586773] Train: [37/50][14/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:44:20 loss: 0.2105 Lr: 0.00078 [2024-11-25 19:06:13,824 INFO misc.py line 119 2586773] Train: [37/50][15/376] Data 0.002 (0.002) Batch 0.494 (0.506) Remain 00:44:13 loss: 0.2314 Lr: 0.00078 [2024-11-25 19:06:14,348 INFO misc.py line 119 2586773] Train: [37/50][16/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:44:20 loss: 0.1761 Lr: 0.00078 [2024-11-25 19:06:14,823 INFO misc.py line 119 2586773] Train: [37/50][17/376] Data 0.003 (0.002) Batch 0.475 (0.505) Remain 00:44:08 loss: 0.2108 Lr: 0.00078 [2024-11-25 19:06:15,344 INFO misc.py line 119 2586773] Train: [37/50][18/376] Data 0.002 (0.002) Batch 0.521 (0.506) Remain 00:44:13 loss: 0.2282 Lr: 0.00078 [2024-11-25 19:06:15,829 INFO misc.py line 119 2586773] Train: [37/50][19/376] Data 0.002 (0.002) Batch 0.485 (0.505) Remain 00:44:06 loss: 0.2218 Lr: 0.00078 [2024-11-25 19:06:16,317 INFO misc.py line 119 2586773] Train: [37/50][20/376] Data 0.002 (0.002) Batch 0.488 (0.504) Remain 00:44:00 loss: 0.2157 Lr: 0.00078 [2024-11-25 19:06:16,838 INFO misc.py line 119 2586773] Train: [37/50][21/376] Data 0.002 (0.002) Batch 0.521 (0.505) Remain 00:44:05 loss: 0.2499 Lr: 0.00078 [2024-11-25 19:06:17,294 INFO misc.py line 119 2586773] Train: [37/50][22/376] Data 0.002 (0.002) Batch 0.455 (0.502) Remain 00:43:51 loss: 0.1654 Lr: 0.00078 [2024-11-25 19:06:17,793 INFO misc.py line 119 2586773] Train: [37/50][23/376] Data 0.003 (0.002) Batch 0.500 (0.502) Remain 00:43:50 loss: 0.1651 Lr: 0.00078 [2024-11-25 19:06:18,326 INFO misc.py line 119 2586773] Train: [37/50][24/376] Data 0.002 (0.002) Batch 0.533 (0.503) Remain 00:43:57 loss: 0.1707 Lr: 0.00078 [2024-11-25 19:06:18,812 INFO misc.py line 119 2586773] Train: [37/50][25/376] Data 0.002 (0.002) Batch 0.486 (0.503) Remain 00:43:52 loss: 0.1946 Lr: 0.00078 [2024-11-25 19:06:19,302 INFO misc.py line 119 2586773] Train: [37/50][26/376] Data 0.002 (0.002) Batch 0.490 (0.502) Remain 00:43:49 loss: 0.1763 Lr: 0.00078 [2024-11-25 19:06:19,815 INFO misc.py line 119 2586773] Train: [37/50][27/376] Data 0.002 (0.002) Batch 0.513 (0.502) Remain 00:43:51 loss: 0.2220 Lr: 0.00078 [2024-11-25 19:06:20,317 INFO misc.py line 119 2586773] 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0.002 (0.002) Batch 0.532 (0.508) Remain 00:42:04 loss: 0.1580 Lr: 0.00071 [2024-11-25 19:08:37,136 INFO misc.py line 119 2586773] Train: [37/50][297/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 00:42:03 loss: 0.1963 Lr: 0.00071 [2024-11-25 19:08:37,631 INFO misc.py line 119 2586773] Train: [37/50][298/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:42:02 loss: 0.1878 Lr: 0.00071 [2024-11-25 19:08:38,111 INFO misc.py line 119 2586773] Train: [37/50][299/376] Data 0.003 (0.002) Batch 0.480 (0.508) Remain 00:42:01 loss: 0.2098 Lr: 0.00071 [2024-11-25 19:08:38,607 INFO misc.py line 119 2586773] Train: [37/50][300/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 00:42:01 loss: 0.1595 Lr: 0.00071 [2024-11-25 19:08:39,096 INFO misc.py line 119 2586773] Train: [37/50][301/376] Data 0.003 (0.002) Batch 0.489 (0.508) Remain 00:42:00 loss: 0.1720 Lr: 0.00071 [2024-11-25 19:08:39,575 INFO misc.py line 119 2586773] Train: [37/50][302/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 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[2024-11-25 19:08:42,991 INFO misc.py line 119 2586773] Train: [37/50][309/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:41:53 loss: 0.2409 Lr: 0.00070 [2024-11-25 19:08:43,480 INFO misc.py line 119 2586773] Train: [37/50][310/376] Data 0.003 (0.002) Batch 0.489 (0.507) Remain 00:41:52 loss: 0.2930 Lr: 0.00070 [2024-11-25 19:08:43,995 INFO misc.py line 119 2586773] Train: [37/50][311/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:41:52 loss: 0.1908 Lr: 0.00070 [2024-11-25 19:08:44,525 INFO misc.py line 119 2586773] Train: [37/50][312/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 00:41:52 loss: 0.2641 Lr: 0.00070 [2024-11-25 19:08:45,051 INFO misc.py line 119 2586773] Train: [37/50][313/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:41:52 loss: 0.2065 Lr: 0.00070 [2024-11-25 19:08:45,575 INFO misc.py line 119 2586773] Train: [37/50][314/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:41:51 loss: 0.1668 Lr: 0.00070 [2024-11-25 19:08:46,072 INFO misc.py line 119 2586773] Train: [37/50][315/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:41:51 loss: 0.2259 Lr: 0.00070 [2024-11-25 19:08:46,545 INFO misc.py line 119 2586773] Train: [37/50][316/376] Data 0.002 (0.002) Batch 0.474 (0.507) Remain 00:41:50 loss: 0.1523 Lr: 0.00070 [2024-11-25 19:08:47,058 INFO misc.py line 119 2586773] Train: [37/50][317/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 00:41:49 loss: 0.1951 Lr: 0.00070 [2024-11-25 19:08:47,588 INFO misc.py line 119 2586773] Train: [37/50][318/376] Data 0.003 (0.002) Batch 0.530 (0.507) Remain 00:41:49 loss: 0.2163 Lr: 0.00070 [2024-11-25 19:08:48,109 INFO misc.py line 119 2586773] Train: [37/50][319/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 00:41:49 loss: 0.1883 Lr: 0.00070 [2024-11-25 19:08:48,617 INFO misc.py line 119 2586773] Train: [37/50][320/376] Data 0.002 (0.002) Batch 0.508 (0.507) Remain 00:41:48 loss: 0.2209 Lr: 0.00070 [2024-11-25 19:08:49,107 INFO misc.py line 119 2586773] Train: [37/50][321/376] Data 0.003 (0.002) Batch 0.490 (0.507) Remain 00:41:48 loss: 0.2416 Lr: 0.00070 [2024-11-25 19:08:49,635 INFO misc.py line 119 2586773] Train: [37/50][322/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:41:47 loss: 0.2141 Lr: 0.00070 [2024-11-25 19:08:50,161 INFO misc.py line 119 2586773] Train: [37/50][323/376] Data 0.003 (0.002) Batch 0.526 (0.508) Remain 00:41:47 loss: 0.2353 Lr: 0.00070 [2024-11-25 19:08:50,697 INFO misc.py line 119 2586773] Train: [37/50][324/376] Data 0.002 (0.002) Batch 0.536 (0.508) Remain 00:41:47 loss: 0.1561 Lr: 0.00070 [2024-11-25 19:08:51,225 INFO misc.py line 119 2586773] Train: [37/50][325/376] Data 0.003 (0.002) Batch 0.528 (0.508) Remain 00:41:47 loss: 0.1952 Lr: 0.00070 [2024-11-25 19:08:51,716 INFO misc.py line 119 2586773] Train: [37/50][326/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 00:41:46 loss: 0.2146 Lr: 0.00070 [2024-11-25 19:08:52,224 INFO misc.py line 119 2586773] Train: [37/50][327/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 00:41:46 loss: 0.1965 Lr: 0.00070 [2024-11-25 19:08:52,763 INFO misc.py line 119 2586773] Train: [37/50][328/376] Data 0.003 (0.002) Batch 0.539 (0.508) Remain 00:41:46 loss: 0.1799 Lr: 0.00070 [2024-11-25 19:08:53,263 INFO misc.py line 119 2586773] Train: [37/50][329/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 00:41:45 loss: 0.2086 Lr: 0.00070 [2024-11-25 19:08:53,727 INFO misc.py line 119 2586773] Train: [37/50][330/376] Data 0.002 (0.002) Batch 0.464 (0.508) Remain 00:41:44 loss: 0.1723 Lr: 0.00070 [2024-11-25 19:08:54,195 INFO misc.py line 119 2586773] Train: [37/50][331/376] Data 0.002 (0.002) Batch 0.468 (0.507) Remain 00:41:43 loss: 0.2024 Lr: 0.00070 [2024-11-25 19:08:54,696 INFO misc.py line 119 2586773] Train: [37/50][332/376] Data 0.003 (0.002) Batch 0.501 (0.507) Remain 00:41:42 loss: 0.2247 Lr: 0.00070 [2024-11-25 19:08:55,169 INFO misc.py line 119 2586773] Train: [37/50][333/376] Data 0.003 (0.002) Batch 0.473 (0.507) Remain 00:41:41 loss: 0.2349 Lr: 0.00070 [2024-11-25 19:08:55,728 INFO misc.py line 119 2586773] Train: [37/50][334/376] Data 0.002 (0.002) Batch 0.559 (0.507) Remain 00:41:41 loss: 0.1903 Lr: 0.00070 [2024-11-25 19:08:56,240 INFO misc.py line 119 2586773] Train: [37/50][335/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:41:41 loss: 0.1870 Lr: 0.00070 [2024-11-25 19:08:56,762 INFO misc.py line 119 2586773] Train: [37/50][336/376] Data 0.003 (0.002) Batch 0.522 (0.508) Remain 00:41:41 loss: 0.2315 Lr: 0.00070 [2024-11-25 19:08:57,255 INFO misc.py line 119 2586773] Train: [37/50][337/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:41:40 loss: 0.2856 Lr: 0.00070 [2024-11-25 19:08:57,811 INFO misc.py line 119 2586773] Train: [37/50][338/376] Data 0.003 (0.002) Batch 0.556 (0.508) Remain 00:41:40 loss: 0.1912 Lr: 0.00070 [2024-11-25 19:08:58,281 INFO misc.py line 119 2586773] Train: [37/50][339/376] Data 0.002 (0.002) Batch 0.471 (0.508) Remain 00:41:39 loss: 0.2114 Lr: 0.00070 [2024-11-25 19:08:58,814 INFO misc.py line 119 2586773] Train: [37/50][340/376] Data 0.003 (0.002) Batch 0.532 (0.508) Remain 00:41:39 loss: 0.2291 Lr: 0.00070 [2024-11-25 19:08:59,308 INFO misc.py line 119 2586773] Train: [37/50][341/376] Data 0.003 (0.002) Batch 0.494 (0.508) Remain 00:41:38 loss: 0.1835 Lr: 0.00070 [2024-11-25 19:08:59,839 INFO misc.py line 119 2586773] Train: [37/50][342/376] Data 0.002 (0.002) Batch 0.531 (0.508) Remain 00:41:38 loss: 0.1971 Lr: 0.00070 [2024-11-25 19:09:00,329 INFO misc.py line 119 2586773] Train: [37/50][343/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 00:41:37 loss: 0.2263 Lr: 0.00070 [2024-11-25 19:09:00,805 INFO misc.py line 119 2586773] Train: [37/50][344/376] Data 0.003 (0.002) Batch 0.476 (0.507) Remain 00:41:36 loss: 0.2210 Lr: 0.00070 [2024-11-25 19:09:01,325 INFO misc.py line 119 2586773] Train: [37/50][345/376] Data 0.003 (0.002) Batch 0.520 (0.508) Remain 00:41:36 loss: 0.1872 Lr: 0.00069 [2024-11-25 19:09:01,805 INFO misc.py line 119 2586773] Train: [37/50][346/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:41:35 loss: 0.1776 Lr: 0.00069 [2024-11-25 19:09:02,332 INFO misc.py line 119 2586773] Train: [37/50][347/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:41:35 loss: 0.1492 Lr: 0.00069 [2024-11-25 19:09:02,810 INFO misc.py line 119 2586773] Train: [37/50][348/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:41:34 loss: 0.2123 Lr: 0.00069 [2024-11-25 19:09:03,321 INFO misc.py line 119 2586773] Train: [37/50][349/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 00:41:33 loss: 0.1923 Lr: 0.00069 [2024-11-25 19:09:03,797 INFO misc.py line 119 2586773] Train: [37/50][350/376] Data 0.003 (0.002) Batch 0.476 (0.507) Remain 00:41:32 loss: 0.2109 Lr: 0.00069 [2024-11-25 19:09:04,278 INFO misc.py line 119 2586773] Train: [37/50][351/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 00:41:32 loss: 0.2452 Lr: 0.00069 [2024-11-25 19:09:04,770 INFO misc.py line 119 2586773] Train: [37/50][352/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:41:31 loss: 0.1406 Lr: 0.00069 [2024-11-25 19:09:05,296 INFO misc.py line 119 2586773] Train: [37/50][353/376] Data 0.003 (0.002) Batch 0.527 (0.507) Remain 00:41:31 loss: 0.1589 Lr: 0.00069 [2024-11-25 19:09:05,810 INFO misc.py line 119 2586773] Train: [37/50][354/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 00:41:30 loss: 0.1794 Lr: 0.00069 [2024-11-25 19:09:06,303 INFO misc.py line 119 2586773] Train: [37/50][355/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:41:29 loss: 0.1725 Lr: 0.00069 [2024-11-25 19:09:06,814 INFO misc.py line 119 2586773] Train: [37/50][356/376] Data 0.003 (0.002) Batch 0.511 (0.507) Remain 00:41:29 loss: 0.1968 Lr: 0.00069 [2024-11-25 19:09:07,313 INFO misc.py line 119 2586773] Train: [37/50][357/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:41:28 loss: 0.1972 Lr: 0.00069 [2024-11-25 19:09:07,818 INFO misc.py line 119 2586773] Train: [37/50][358/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:41:28 loss: 0.2068 Lr: 0.00069 [2024-11-25 19:09:08,315 INFO misc.py line 119 2586773] Train: [37/50][359/376] Data 0.003 (0.002) Batch 0.497 (0.507) Remain 00:41:27 loss: 0.1644 Lr: 0.00069 [2024-11-25 19:09:08,785 INFO misc.py line 119 2586773] Train: [37/50][360/376] Data 0.002 (0.002) Batch 0.470 (0.507) Remain 00:41:26 loss: 0.2115 Lr: 0.00069 [2024-11-25 19:09:09,327 INFO misc.py line 119 2586773] Train: [37/50][361/376] Data 0.003 (0.002) Batch 0.542 (0.507) Remain 00:41:26 loss: 0.1792 Lr: 0.00069 [2024-11-25 19:09:09,799 INFO misc.py line 119 2586773] Train: [37/50][362/376] Data 0.003 (0.002) Batch 0.473 (0.507) Remain 00:41:25 loss: 0.1750 Lr: 0.00069 [2024-11-25 19:09:10,286 INFO misc.py line 119 2586773] Train: [37/50][363/376] Data 0.003 (0.002) Batch 0.487 (0.507) Remain 00:41:24 loss: 0.1867 Lr: 0.00069 [2024-11-25 19:09:10,779 INFO misc.py line 119 2586773] Train: [37/50][364/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:41:24 loss: 0.1464 Lr: 0.00069 [2024-11-25 19:09:11,318 INFO misc.py line 119 2586773] Train: [37/50][365/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 00:41:24 loss: 0.2369 Lr: 0.00069 [2024-11-25 19:09:11,822 INFO misc.py line 119 2586773] Train: [37/50][366/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:41:23 loss: 0.2054 Lr: 0.00069 [2024-11-25 19:09:12,340 INFO misc.py line 119 2586773] Train: [37/50][367/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:41:23 loss: 0.1841 Lr: 0.00069 [2024-11-25 19:09:12,827 INFO misc.py line 119 2586773] Train: [37/50][368/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:41:22 loss: 0.1878 Lr: 0.00069 [2024-11-25 19:09:13,331 INFO misc.py line 119 2586773] Train: [37/50][369/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:41:21 loss: 0.2611 Lr: 0.00069 [2024-11-25 19:09:13,821 INFO misc.py line 119 2586773] Train: [37/50][370/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:41:21 loss: 0.2111 Lr: 0.00069 [2024-11-25 19:09:14,307 INFO misc.py line 119 2586773] Train: [37/50][371/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 00:41:20 loss: 0.1800 Lr: 0.00069 [2024-11-25 19:09:14,851 INFO misc.py line 119 2586773] Train: [37/50][372/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 00:41:20 loss: 0.2087 Lr: 0.00069 [2024-11-25 19:09:15,352 INFO misc.py line 119 2586773] Train: [37/50][373/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:41:19 loss: 0.1637 Lr: 0.00069 [2024-11-25 19:09:15,866 INFO misc.py line 119 2586773] Train: [37/50][374/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:41:19 loss: 0.2362 Lr: 0.00069 [2024-11-25 19:09:16,338 INFO misc.py line 119 2586773] Train: [37/50][375/376] Data 0.002 (0.002) Batch 0.472 (0.507) Remain 00:41:18 loss: 0.2402 Lr: 0.00069 [2024-11-25 19:09:16,830 INFO misc.py line 119 2586773] Train: [37/50][376/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:41:17 loss: 0.2017 Lr: 0.00069 [2024-11-25 19:09:16,831 INFO misc.py line 136 2586773] Train result: loss: 0.2014 [2024-11-25 19:09:16,831 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:09:27,802 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1704 [2024-11-25 19:09:28,056 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2074 [2024-11-25 19:09:28,320 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2865 [2024-11-25 19:09:28,542 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2007 [2024-11-25 19:09:28,805 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2804 [2024-11-25 19:09:29,073 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1953 [2024-11-25 19:09:29,295 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1878 [2024-11-25 19:09:29,564 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2050 [2024-11-25 19:09:29,787 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2357 [2024-11-25 19:09:30,052 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2497 [2024-11-25 19:09:30,284 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2184 [2024-11-25 19:09:30,555 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2260 [2024-11-25 19:09:30,824 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2405 [2024-11-25 19:09:31,086 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2346 [2024-11-25 19:09:31,318 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2353 [2024-11-25 19:09:31,562 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2977 [2024-11-25 19:09:31,830 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2855 [2024-11-25 19:09:32,075 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2025 [2024-11-25 19:09:32,304 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.1983 [2024-11-25 19:09:32,571 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2386 [2024-11-25 19:09:32,810 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2211 [2024-11-25 19:09:33,074 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2529 [2024-11-25 19:09:33,309 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2118 [2024-11-25 19:09:33,578 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2616 [2024-11-25 19:09:33,840 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2178 [2024-11-25 19:09:34,076 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2171 [2024-11-25 19:09:34,328 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2506 [2024-11-25 19:09:34,573 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2343 [2024-11-25 19:09:34,846 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2784 [2024-11-25 19:09:35,099 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.3057 [2024-11-25 19:09:35,332 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2682 [2024-11-25 19:09:35,595 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2018 [2024-11-25 19:09:35,814 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2259 [2024-11-25 19:09:36,055 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2013 [2024-11-25 19:09:36,316 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1946 [2024-11-25 19:09:36,560 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2470 [2024-11-25 19:09:36,786 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1800 [2024-11-25 19:09:37,055 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2468 [2024-11-25 19:09:37,287 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2684 [2024-11-25 19:09:37,522 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2497 [2024-11-25 19:09:37,799 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2901 [2024-11-25 19:09:38,049 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2751 [2024-11-25 19:09:38,286 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2454 [2024-11-25 19:09:38,519 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2138 [2024-11-25 19:09:38,756 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2127 [2024-11-25 19:09:39,007 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2286 [2024-11-25 19:09:39,268 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2400 [2024-11-25 19:09:39,517 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.3008 [2024-11-25 19:09:39,740 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2107 [2024-11-25 19:09:39,975 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2134 [2024-11-25 19:09:40,197 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2394 [2024-11-25 19:09:40,449 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2235 [2024-11-25 19:09:40,715 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2259 [2024-11-25 19:09:40,979 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2946 [2024-11-25 19:09:41,209 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2335 [2024-11-25 19:09:41,450 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2157 [2024-11-25 19:09:41,705 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2626 [2024-11-25 19:09:41,972 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2399 [2024-11-25 19:09:42,227 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2657 [2024-11-25 19:09:42,489 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2323 [2024-11-25 19:09:42,744 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2173 [2024-11-25 19:09:43,014 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2444 [2024-11-25 19:09:43,246 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2213 [2024-11-25 19:09:43,504 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2451 [2024-11-25 19:09:43,772 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2733 [2024-11-25 19:09:44,038 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1902 [2024-11-25 19:09:44,281 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1961 [2024-11-25 19:09:44,538 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2672 [2024-11-25 19:09:44,807 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2145 [2024-11-25 19:09:45,070 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2711 [2024-11-25 19:09:45,318 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2023 [2024-11-25 19:09:45,553 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2765 [2024-11-25 19:09:45,808 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2533 [2024-11-25 19:09:46,054 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2520 [2024-11-25 19:09:46,273 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2562 [2024-11-25 19:09:46,492 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2069 [2024-11-25 19:09:46,760 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2663 [2024-11-25 19:09:46,998 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2041 [2024-11-25 19:09:47,256 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2257 [2024-11-25 19:09:47,508 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2954 [2024-11-25 19:09:47,749 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2097 [2024-11-25 19:09:48,012 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2472 [2024-11-25 19:09:48,267 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1850 [2024-11-25 19:09:48,515 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2430 [2024-11-25 19:09:48,784 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2261 [2024-11-25 19:09:49,019 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2449 [2024-11-25 19:09:49,282 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2454 [2024-11-25 19:09:49,543 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2382 [2024-11-25 19:09:49,791 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2384 [2024-11-25 19:09:50,041 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2494 [2024-11-25 19:09:50,275 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2231 [2024-11-25 19:09:50,527 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2671 [2024-11-25 19:09:50,797 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2517 [2024-11-25 19:09:51,067 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1940 [2024-11-25 19:09:51,332 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2102 [2024-11-25 19:09:51,581 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2077 [2024-11-25 19:09:51,850 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2328 [2024-11-25 19:09:52,073 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2683 [2024-11-25 19:09:52,343 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2497 [2024-11-25 19:09:52,581 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2542 [2024-11-25 19:09:52,853 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2044 [2024-11-25 19:09:53,113 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2726 [2024-11-25 19:09:53,372 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2430 [2024-11-25 19:09:53,624 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2882 [2024-11-25 19:09:53,850 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2232 [2024-11-25 19:09:54,086 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2191 [2024-11-25 19:09:54,346 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2034 [2024-11-25 19:09:54,616 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2491 [2024-11-25 19:09:54,849 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2749 [2024-11-25 19:09:55,110 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2374 [2024-11-25 19:09:55,374 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2135 [2024-11-25 19:09:55,596 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2310 [2024-11-25 19:09:55,831 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.1971 [2024-11-25 19:09:56,049 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2039 [2024-11-25 19:09:56,273 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2141 [2024-11-25 19:09:56,544 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.3048 [2024-11-25 19:09:56,802 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2708 [2024-11-25 19:09:57,070 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2641 [2024-11-25 19:09:57,335 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2247 [2024-11-25 19:09:57,597 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3314 [2024-11-25 19:09:57,859 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2866 [2024-11-25 19:09:58,123 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1864 [2024-11-25 19:09:58,378 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2636 [2024-11-25 19:09:58,640 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2689 [2024-11-25 19:09:58,906 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2381 [2024-11-25 19:09:59,155 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2758 [2024-11-25 19:09:59,387 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2039 [2024-11-25 19:09:59,645 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2509 [2024-11-25 19:09:59,880 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2610 [2024-11-25 19:10:00,107 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1913 [2024-11-25 19:10:00,318 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2177 [2024-11-25 19:10:00,533 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1896 [2024-11-25 19:10:01,251 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7809/0.8484/0.9965. [2024-11-25 19:10:01,251 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9965/0.9984 [2024-11-25 19:10:01,251 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5653/0.6983 [2024-11-25 19:10:01,252 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:10:01,252 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7809 [2024-11-25 19:10:01,253 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:10:01,253 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:10:05,536 INFO misc.py line 119 2586773] Train: [38/50][1/376] Data 0.116 (0.116) Batch 0.569 (0.569) Remain 00:46:18 loss: 0.1904 Lr: 0.00069 [2024-11-25 19:10:06,023 INFO misc.py line 119 2586773] Train: [38/50][2/376] Data 0.003 (0.003) Batch 0.487 (0.487) Remain 00:39:40 loss: 0.1898 Lr: 0.00069 [2024-11-25 19:10:06,519 INFO misc.py line 119 2586773] Train: [38/50][3/376] Data 0.002 (0.002) Batch 0.497 (0.497) Remain 00:40:25 loss: 0.1910 Lr: 0.00069 [2024-11-25 19:10:07,044 INFO misc.py line 119 2586773] Train: [38/50][4/376] Data 0.002 (0.002) Batch 0.525 (0.525) Remain 00:42:41 loss: 0.2767 Lr: 0.00069 [2024-11-25 19:10:07,535 INFO misc.py line 119 2586773] Train: [38/50][5/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 00:41:18 loss: 0.2112 Lr: 0.00069 [2024-11-25 19:10:08,004 INFO misc.py line 119 2586773] Train: [38/50][6/376] Data 0.002 (0.002) Batch 0.470 (0.495) Remain 00:40:16 loss: 0.2220 Lr: 0.00069 [2024-11-25 19:10:08,534 INFO misc.py line 119 2586773] Train: [38/50][7/376] Data 0.002 (0.002) Batch 0.530 (0.504) Remain 00:40:58 loss: 0.2001 Lr: 0.00068 [2024-11-25 19:10:09,061 INFO misc.py line 119 2586773] Train: [38/50][8/376] Data 0.003 (0.002) Batch 0.527 (0.508) Remain 00:41:20 loss: 0.1810 Lr: 0.00068 [2024-11-25 19:10:09,546 INFO misc.py line 119 2586773] Train: [38/50][9/376] Data 0.003 (0.002) Batch 0.485 (0.504) Remain 00:41:01 loss: 0.2188 Lr: 0.00068 [2024-11-25 19:10:10,068 INFO misc.py line 119 2586773] Train: [38/50][10/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:41:12 loss: 0.1551 Lr: 0.00068 [2024-11-25 19:10:10,636 INFO misc.py line 119 2586773] Train: [38/50][11/376] Data 0.003 (0.002) Batch 0.568 (0.515) Remain 00:41:49 loss: 0.2227 Lr: 0.00068 [2024-11-25 19:10:11,145 INFO misc.py line 119 2586773] Train: [38/50][12/376] Data 0.003 (0.002) Batch 0.510 (0.514) Remain 00:41:46 loss: 0.1779 Lr: 0.00068 [2024-11-25 19:10:11,635 INFO misc.py line 119 2586773] Train: [38/50][13/376] Data 0.002 (0.002) Batch 0.489 (0.512) Remain 00:41:33 loss: 0.1867 Lr: 0.00068 [2024-11-25 19:10:12,153 INFO misc.py line 119 2586773] Train: [38/50][14/376] Data 0.003 (0.002) Batch 0.517 (0.512) Remain 00:41:35 loss: 0.1634 Lr: 0.00068 [2024-11-25 19:10:12,638 INFO misc.py line 119 2586773] Train: [38/50][15/376] Data 0.003 (0.003) Batch 0.486 (0.510) Remain 00:41:24 loss: 0.1927 Lr: 0.00068 [2024-11-25 19:10:13,133 INFO misc.py line 119 2586773] Train: [38/50][16/376] Data 0.002 (0.003) Batch 0.495 (0.509) Remain 00:41:18 loss: 0.1682 Lr: 0.00068 [2024-11-25 19:10:13,626 INFO misc.py line 119 2586773] Train: [38/50][17/376] Data 0.002 (0.003) Batch 0.493 (0.508) Remain 00:41:12 loss: 0.1760 Lr: 0.00068 [2024-11-25 19:10:14,150 INFO misc.py line 119 2586773] Train: [38/50][18/376] Data 0.003 (0.003) Batch 0.525 (0.509) Remain 00:41:17 loss: 0.1726 Lr: 0.00068 [2024-11-25 19:10:14,673 INFO misc.py line 119 2586773] Train: [38/50][19/376] Data 0.003 (0.003) Batch 0.523 (0.510) Remain 00:41:21 loss: 0.1925 Lr: 0.00068 [2024-11-25 19:10:15,211 INFO misc.py line 119 2586773] Train: [38/50][20/376] Data 0.002 (0.003) Batch 0.539 (0.511) Remain 00:41:28 loss: 0.2027 Lr: 0.00068 [2024-11-25 19:10:15,741 INFO misc.py line 119 2586773] Train: [38/50][21/376] Data 0.003 (0.003) Batch 0.530 (0.512) Remain 00:41:33 loss: 0.1604 Lr: 0.00068 [2024-11-25 19:10:16,241 INFO misc.py line 119 2586773] Train: [38/50][22/376] Data 0.002 (0.003) Batch 0.500 (0.512) Remain 00:41:29 loss: 0.2637 Lr: 0.00068 [2024-11-25 19:10:16,765 INFO misc.py line 119 2586773] Train: [38/50][23/376] Data 0.002 (0.003) Batch 0.525 (0.512) Remain 00:41:32 loss: 0.1984 Lr: 0.00068 [2024-11-25 19:10:17,247 INFO misc.py line 119 2586773] Train: [38/50][24/376] Data 0.002 (0.003) Batch 0.481 (0.511) Remain 00:41:24 loss: 0.2271 Lr: 0.00068 [2024-11-25 19:10:17,731 INFO misc.py line 119 2586773] Train: [38/50][25/376] Data 0.003 (0.003) Batch 0.485 (0.510) Remain 00:41:18 loss: 0.2171 Lr: 0.00068 [2024-11-25 19:10:18,218 INFO misc.py line 119 2586773] Train: [38/50][26/376] Data 0.002 (0.003) Batch 0.487 (0.509) Remain 00:41:13 loss: 0.2059 Lr: 0.00068 [2024-11-25 19:10:18,727 INFO misc.py line 119 2586773] Train: [38/50][27/376] Data 0.002 (0.003) Batch 0.508 (0.509) Remain 00:41:12 loss: 0.1743 Lr: 0.00068 [2024-11-25 19:10:19,281 INFO misc.py line 119 2586773] Train: [38/50][28/376] Data 0.002 (0.003) Batch 0.554 (0.510) Remain 00:41:20 loss: 0.1747 Lr: 0.00068 [2024-11-25 19:10:19,837 INFO misc.py line 119 2586773] Train: [38/50][29/376] Data 0.003 (0.003) Batch 0.556 (0.512) Remain 00:41:28 loss: 0.2087 Lr: 0.00068 [2024-11-25 19:10:20,337 INFO misc.py line 119 2586773] Train: [38/50][30/376] Data 0.002 (0.003) Batch 0.500 (0.512) Remain 00:41:26 loss: 0.1943 Lr: 0.00068 [2024-11-25 19:10:20,869 INFO misc.py line 119 2586773] Train: [38/50][31/376] Data 0.003 (0.003) Batch 0.532 (0.512) Remain 00:41:29 loss: 0.2048 Lr: 0.00068 [2024-11-25 19:10:21,412 INFO misc.py line 119 2586773] Train: [38/50][32/376] Data 0.003 (0.003) Batch 0.543 (0.514) Remain 00:41:33 loss: 0.1862 Lr: 0.00068 [2024-11-25 19:10:21,876 INFO misc.py line 119 2586773] Train: [38/50][33/376] Data 0.002 (0.003) Batch 0.465 (0.512) Remain 00:41:25 loss: 0.2360 Lr: 0.00068 [2024-11-25 19:10:22,394 INFO misc.py line 119 2586773] Train: [38/50][34/376] Data 0.002 (0.002) Batch 0.517 (0.512) Remain 00:41:25 loss: 0.1977 Lr: 0.00068 [2024-11-25 19:10:22,862 INFO misc.py line 119 2586773] Train: [38/50][35/376] Data 0.002 (0.002) Batch 0.468 (0.511) Remain 00:41:18 loss: 0.3790 Lr: 0.00068 [2024-11-25 19:10:23,418 INFO misc.py line 119 2586773] Train: [38/50][36/376] Data 0.002 (0.002) Batch 0.556 (0.512) Remain 00:41:24 loss: 0.1962 Lr: 0.00068 [2024-11-25 19:10:23,906 INFO misc.py line 119 2586773] Train: [38/50][37/376] Data 0.002 (0.002) Batch 0.487 (0.511) Remain 00:41:20 loss: 0.1872 Lr: 0.00068 [2024-11-25 19:10:24,405 INFO misc.py line 119 2586773] Train: [38/50][38/376] Data 0.002 (0.002) Batch 0.500 (0.511) Remain 00:41:18 loss: 0.2508 Lr: 0.00068 [2024-11-25 19:10:24,902 INFO misc.py line 119 2586773] Train: [38/50][39/376] Data 0.002 (0.002) Batch 0.497 (0.511) Remain 00:41:16 loss: 0.2019 Lr: 0.00068 [2024-11-25 19:10:25,407 INFO misc.py line 119 2586773] Train: [38/50][40/376] Data 0.003 (0.002) Batch 0.505 (0.510) Remain 00:41:14 loss: 0.1879 Lr: 0.00068 [2024-11-25 19:10:25,889 INFO misc.py line 119 2586773] Train: [38/50][41/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 00:41:10 loss: 0.1646 Lr: 0.00068 [2024-11-25 19:10:26,379 INFO misc.py line 119 2586773] Train: [38/50][42/376] Data 0.002 (0.002) Batch 0.490 (0.509) Remain 00:41:07 loss: 0.2421 Lr: 0.00068 [2024-11-25 19:10:26,913 INFO misc.py line 119 2586773] Train: [38/50][43/376] Data 0.002 (0.002) Batch 0.534 (0.510) Remain 00:41:10 loss: 0.1724 Lr: 0.00068 [2024-11-25 19:10:27,412 INFO misc.py line 119 2586773] Train: [38/50][44/376] Data 0.002 (0.002) Batch 0.499 (0.510) Remain 00:41:08 loss: 0.1873 Lr: 0.00068 [2024-11-25 19:10:27,903 INFO misc.py line 119 2586773] Train: [38/50][45/376] Data 0.002 (0.002) Batch 0.491 (0.509) Remain 00:41:05 loss: 0.2416 Lr: 0.00067 [2024-11-25 19:10:28,413 INFO misc.py line 119 2586773] Train: [38/50][46/376] Data 0.002 (0.002) Batch 0.510 (0.509) Remain 00:41:05 loss: 0.1897 Lr: 0.00067 [2024-11-25 19:10:28,899 INFO misc.py line 119 2586773] Train: [38/50][47/376] Data 0.003 (0.002) Batch 0.486 (0.509) Remain 00:41:02 loss: 0.1903 Lr: 0.00067 [2024-11-25 19:10:29,445 INFO misc.py line 119 2586773] Train: [38/50][48/376] Data 0.002 (0.002) Batch 0.546 (0.509) Remain 00:41:05 loss: 0.2156 Lr: 0.00067 [2024-11-25 19:10:29,939 INFO misc.py line 119 2586773] Train: [38/50][49/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 00:41:03 loss: 0.1927 Lr: 0.00067 [2024-11-25 19:10:30,496 INFO misc.py line 119 2586773] Train: [38/50][50/376] Data 0.002 (0.002) Batch 0.557 (0.510) Remain 00:41:08 loss: 0.1698 Lr: 0.00067 [2024-11-25 19:10:30,960 INFO misc.py line 119 2586773] Train: [38/50][51/376] Data 0.002 (0.002) Batch 0.464 (0.509) Remain 00:41:02 loss: 0.1611 Lr: 0.00067 [2024-11-25 19:10:31,492 INFO misc.py line 119 2586773] Train: [38/50][52/376] Data 0.002 (0.002) Batch 0.531 (0.510) Remain 00:41:04 loss: 0.1821 Lr: 0.00067 [2024-11-25 19:10:32,032 INFO misc.py line 119 2586773] Train: 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Batch 0.504 (0.507) Remain 00:38:30 loss: 0.2583 Lr: 0.00060 [2024-11-25 19:12:51,135 INFO misc.py line 119 2586773] Train: [38/50][328/376] Data 0.003 (0.002) Batch 0.484 (0.507) Remain 00:38:29 loss: 0.1629 Lr: 0.00060 [2024-11-25 19:12:51,662 INFO misc.py line 119 2586773] Train: [38/50][329/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:38:29 loss: 0.2245 Lr: 0.00060 [2024-11-25 19:12:52,161 INFO misc.py line 119 2586773] Train: [38/50][330/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:38:28 loss: 0.1938 Lr: 0.00060 [2024-11-25 19:12:52,672 INFO misc.py line 119 2586773] Train: [38/50][331/376] Data 0.002 (0.002) Batch 0.511 (0.507) Remain 00:38:28 loss: 0.1687 Lr: 0.00060 [2024-11-25 19:12:53,193 INFO misc.py line 119 2586773] Train: [38/50][332/376] Data 0.002 (0.002) Batch 0.521 (0.507) Remain 00:38:28 loss: 0.1735 Lr: 0.00060 [2024-11-25 19:12:53,711 INFO misc.py line 119 2586773] Train: [38/50][333/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:38:27 loss: 0.2165 Lr: 0.00060 [2024-11-25 19:12:54,201 INFO misc.py line 119 2586773] Train: [38/50][334/376] Data 0.003 (0.002) Batch 0.490 (0.507) Remain 00:38:27 loss: 0.2486 Lr: 0.00060 [2024-11-25 19:12:54,706 INFO misc.py line 119 2586773] Train: [38/50][335/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:38:26 loss: 0.2159 Lr: 0.00060 [2024-11-25 19:12:55,205 INFO misc.py line 119 2586773] Train: [38/50][336/376] Data 0.003 (0.002) Batch 0.498 (0.507) Remain 00:38:25 loss: 0.2231 Lr: 0.00060 [2024-11-25 19:12:55,683 INFO misc.py line 119 2586773] Train: [38/50][337/376] Data 0.002 (0.002) Batch 0.478 (0.506) Remain 00:38:24 loss: 0.1922 Lr: 0.00060 [2024-11-25 19:12:56,240 INFO misc.py line 119 2586773] Train: [38/50][338/376] Data 0.002 (0.002) Batch 0.558 (0.507) Remain 00:38:25 loss: 0.2010 Lr: 0.00060 [2024-11-25 19:12:56,713 INFO misc.py line 119 2586773] Train: [38/50][339/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 00:38:24 loss: 0.1412 Lr: 0.00060 [2024-11-25 19:12:57,251 INFO misc.py line 119 2586773] Train: [38/50][340/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 00:38:24 loss: 0.1788 Lr: 0.00060 [2024-11-25 19:12:57,770 INFO misc.py line 119 2586773] Train: [38/50][341/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:38:23 loss: 0.1974 Lr: 0.00060 [2024-11-25 19:12:58,303 INFO misc.py line 119 2586773] Train: [38/50][342/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:38:23 loss: 0.2294 Lr: 0.00060 [2024-11-25 19:12:58,807 INFO misc.py line 119 2586773] Train: [38/50][343/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:38:23 loss: 0.2000 Lr: 0.00060 [2024-11-25 19:12:59,296 INFO misc.py line 119 2586773] Train: [38/50][344/376] Data 0.003 (0.002) Batch 0.489 (0.507) Remain 00:38:22 loss: 0.1731 Lr: 0.00060 [2024-11-25 19:12:59,794 INFO misc.py line 119 2586773] Train: [38/50][345/376] Data 0.003 (0.002) Batch 0.498 (0.507) Remain 00:38:21 loss: 0.2180 Lr: 0.00060 [2024-11-25 19:13:00,286 INFO misc.py line 119 2586773] Train: [38/50][346/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 00:38:21 loss: 0.2026 Lr: 0.00060 [2024-11-25 19:13:00,846 INFO misc.py line 119 2586773] Train: [38/50][347/376] Data 0.002 (0.002) Batch 0.560 (0.507) Remain 00:38:21 loss: 0.2132 Lr: 0.00060 [2024-11-25 19:13:01,402 INFO misc.py line 119 2586773] Train: [38/50][348/376] Data 0.002 (0.002) Batch 0.556 (0.507) Remain 00:38:21 loss: 0.2730 Lr: 0.00060 [2024-11-25 19:13:01,906 INFO misc.py line 119 2586773] Train: [38/50][349/376] Data 0.003 (0.002) Batch 0.504 (0.507) Remain 00:38:20 loss: 0.1898 Lr: 0.00060 [2024-11-25 19:13:02,441 INFO misc.py line 119 2586773] Train: [38/50][350/376] Data 0.002 (0.002) Batch 0.535 (0.507) Remain 00:38:20 loss: 0.2155 Lr: 0.00060 [2024-11-25 19:13:02,953 INFO misc.py line 119 2586773] Train: [38/50][351/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:38:20 loss: 0.1709 Lr: 0.00060 [2024-11-25 19:13:03,491 INFO misc.py line 119 2586773] Train: [38/50][352/376] Data 0.003 (0.002) Batch 0.538 (0.507) Remain 00:38:20 loss: 0.2090 Lr: 0.00060 [2024-11-25 19:13:03,991 INFO misc.py line 119 2586773] Train: [38/50][353/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:38:19 loss: 0.2161 Lr: 0.00060 [2024-11-25 19:13:04,528 INFO misc.py line 119 2586773] Train: [38/50][354/376] Data 0.003 (0.002) Batch 0.537 (0.507) Remain 00:38:19 loss: 0.1979 Lr: 0.00060 [2024-11-25 19:13:05,077 INFO misc.py line 119 2586773] Train: [38/50][355/376] Data 0.002 (0.002) Batch 0.549 (0.507) Remain 00:38:19 loss: 0.2305 Lr: 0.00060 [2024-11-25 19:13:05,579 INFO misc.py line 119 2586773] Train: [38/50][356/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:38:18 loss: 0.1826 Lr: 0.00060 [2024-11-25 19:13:06,076 INFO misc.py line 119 2586773] Train: [38/50][357/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:38:18 loss: 0.1541 Lr: 0.00060 [2024-11-25 19:13:06,566 INFO misc.py line 119 2586773] Train: [38/50][358/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:38:17 loss: 0.1614 Lr: 0.00060 [2024-11-25 19:13:07,068 INFO misc.py line 119 2586773] Train: [38/50][359/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:38:16 loss: 0.1684 Lr: 0.00060 [2024-11-25 19:13:07,612 INFO misc.py line 119 2586773] Train: [38/50][360/376] Data 0.003 (0.002) Batch 0.545 (0.507) Remain 00:38:16 loss: 0.2225 Lr: 0.00060 [2024-11-25 19:13:08,137 INFO misc.py line 119 2586773] Train: [38/50][361/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 00:38:16 loss: 0.2061 Lr: 0.00059 [2024-11-25 19:13:08,634 INFO misc.py line 119 2586773] Train: [38/50][362/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:38:15 loss: 0.2477 Lr: 0.00059 [2024-11-25 19:13:09,129 INFO misc.py line 119 2586773] Train: [38/50][363/376] Data 0.003 (0.002) Batch 0.495 (0.507) Remain 00:38:15 loss: 0.1977 Lr: 0.00059 [2024-11-25 19:13:09,622 INFO misc.py line 119 2586773] Train: [38/50][364/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:38:14 loss: 0.1775 Lr: 0.00059 [2024-11-25 19:13:10,154 INFO misc.py line 119 2586773] Train: [38/50][365/376] Data 0.002 (0.002) Batch 0.532 (0.507) Remain 00:38:14 loss: 0.2121 Lr: 0.00059 [2024-11-25 19:13:10,674 INFO misc.py line 119 2586773] Train: [38/50][366/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:38:14 loss: 0.3164 Lr: 0.00059 [2024-11-25 19:13:11,207 INFO misc.py line 119 2586773] Train: [38/50][367/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:38:13 loss: 0.2152 Lr: 0.00059 [2024-11-25 19:13:11,746 INFO misc.py line 119 2586773] Train: [38/50][368/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 00:38:13 loss: 0.2252 Lr: 0.00059 [2024-11-25 19:13:12,250 INFO misc.py line 119 2586773] Train: [38/50][369/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:38:13 loss: 0.2065 Lr: 0.00059 [2024-11-25 19:13:12,733 INFO misc.py line 119 2586773] Train: [38/50][370/376] Data 0.002 (0.002) Batch 0.483 (0.507) Remain 00:38:12 loss: 0.2066 Lr: 0.00059 [2024-11-25 19:13:13,232 INFO misc.py line 119 2586773] Train: [38/50][371/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:38:11 loss: 0.1805 Lr: 0.00059 [2024-11-25 19:13:13,748 INFO misc.py line 119 2586773] Train: [38/50][372/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:38:11 loss: 0.1708 Lr: 0.00059 [2024-11-25 19:13:14,268 INFO misc.py line 119 2586773] Train: [38/50][373/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:38:11 loss: 0.1966 Lr: 0.00059 [2024-11-25 19:13:14,763 INFO misc.py line 119 2586773] Train: [38/50][374/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 00:38:10 loss: 0.2070 Lr: 0.00059 [2024-11-25 19:13:15,260 INFO misc.py line 119 2586773] Train: [38/50][375/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:38:09 loss: 0.2607 Lr: 0.00059 [2024-11-25 19:13:15,745 INFO misc.py line 119 2586773] Train: [38/50][376/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:38:08 loss: 0.2323 Lr: 0.00059 [2024-11-25 19:13:15,745 INFO misc.py line 136 2586773] Train result: loss: 0.2036 [2024-11-25 19:13:15,746 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:13:26,753 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1754 [2024-11-25 19:13:27,023 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2304 [2024-11-25 19:13:27,282 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2700 [2024-11-25 19:13:27,507 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2043 [2024-11-25 19:13:27,769 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2900 [2024-11-25 19:13:28,042 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2025 [2024-11-25 19:13:28,266 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2328 [2024-11-25 19:13:28,536 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2151 [2024-11-25 19:13:28,760 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2786 [2024-11-25 19:13:29,021 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2472 [2024-11-25 19:13:29,254 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2028 [2024-11-25 19:13:29,526 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2505 [2024-11-25 19:13:29,798 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2506 [2024-11-25 19:13:30,061 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2348 [2024-11-25 19:13:30,293 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2240 [2024-11-25 19:13:30,536 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3020 [2024-11-25 19:13:30,811 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2820 [2024-11-25 19:13:31,058 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2132 [2024-11-25 19:13:31,292 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2531 [2024-11-25 19:13:31,554 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2291 [2024-11-25 19:13:31,798 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2576 [2024-11-25 19:13:32,066 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2593 [2024-11-25 19:13:32,305 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2088 [2024-11-25 19:13:32,572 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2382 [2024-11-25 19:13:32,834 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2246 [2024-11-25 19:13:33,068 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2531 [2024-11-25 19:13:33,320 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2490 [2024-11-25 19:13:33,566 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2290 [2024-11-25 19:13:33,833 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2699 [2024-11-25 19:13:34,086 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2764 [2024-11-25 19:13:34,321 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2544 [2024-11-25 19:13:34,585 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1985 [2024-11-25 19:13:34,809 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2682 [2024-11-25 19:13:35,049 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2240 [2024-11-25 19:13:35,312 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1967 [2024-11-25 19:13:35,556 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2497 [2024-11-25 19:13:35,783 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1922 [2024-11-25 19:13:36,057 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2321 [2024-11-25 19:13:36,287 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2655 [2024-11-25 19:13:36,528 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2375 [2024-11-25 19:13:36,800 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3104 [2024-11-25 19:13:37,050 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2716 [2024-11-25 19:13:37,287 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2620 [2024-11-25 19:13:37,521 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2232 [2024-11-25 19:13:37,758 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2278 [2024-11-25 19:13:38,004 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2336 [2024-11-25 19:13:38,263 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2340 [2024-11-25 19:13:38,513 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2931 [2024-11-25 19:13:38,736 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2122 [2024-11-25 19:13:38,969 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2178 [2024-11-25 19:13:39,191 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2551 [2024-11-25 19:13:39,444 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2309 [2024-11-25 19:13:39,713 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2268 [2024-11-25 19:13:39,972 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3132 [2024-11-25 19:13:40,205 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2392 [2024-11-25 19:13:40,446 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2314 [2024-11-25 19:13:40,703 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2354 [2024-11-25 19:13:40,969 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2689 [2024-11-25 19:13:41,226 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2458 [2024-11-25 19:13:41,487 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2362 [2024-11-25 19:13:41,740 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2197 [2024-11-25 19:13:42,010 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2353 [2024-11-25 19:13:42,238 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2257 [2024-11-25 19:13:42,497 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2470 [2024-11-25 19:13:42,764 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2513 [2024-11-25 19:13:43,030 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1852 [2024-11-25 19:13:43,274 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1986 [2024-11-25 19:13:43,530 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2621 [2024-11-25 19:13:43,799 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2474 [2024-11-25 19:13:44,061 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2708 [2024-11-25 19:13:44,307 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2031 [2024-11-25 19:13:44,542 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2793 [2024-11-25 19:13:44,797 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2633 [2024-11-25 19:13:45,040 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2626 [2024-11-25 19:13:45,259 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2491 [2024-11-25 19:13:45,480 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2169 [2024-11-25 19:13:45,747 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2474 [2024-11-25 19:13:45,982 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2115 [2024-11-25 19:13:46,243 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2409 [2024-11-25 19:13:46,494 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2952 [2024-11-25 19:13:46,734 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2360 [2024-11-25 19:13:46,995 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2568 [2024-11-25 19:13:47,244 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2074 [2024-11-25 19:13:47,490 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2394 [2024-11-25 19:13:47,760 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2373 [2024-11-25 19:13:47,998 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2571 [2024-11-25 19:13:48,261 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2429 [2024-11-25 19:13:48,521 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2317 [2024-11-25 19:13:48,768 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2631 [2024-11-25 19:13:49,017 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2573 [2024-11-25 19:13:49,252 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2492 [2024-11-25 19:13:49,505 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2664 [2024-11-25 19:13:49,770 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2557 [2024-11-25 19:13:50,035 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1956 [2024-11-25 19:13:50,304 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2199 [2024-11-25 19:13:50,552 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2210 [2024-11-25 19:13:50,823 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2337 [2024-11-25 19:13:51,042 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2883 [2024-11-25 19:13:51,314 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2484 [2024-11-25 19:13:51,556 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2510 [2024-11-25 19:13:51,828 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2072 [2024-11-25 19:13:52,087 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2618 [2024-11-25 19:13:52,345 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2303 [2024-11-25 19:13:52,600 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2634 [2024-11-25 19:13:52,827 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2349 [2024-11-25 19:13:53,063 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2167 [2024-11-25 19:13:53,320 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2209 [2024-11-25 19:13:53,590 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2320 [2024-11-25 19:13:53,825 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2606 [2024-11-25 19:13:54,087 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2152 [2024-11-25 19:13:54,351 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2552 [2024-11-25 19:13:54,573 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2242 [2024-11-25 19:13:54,809 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2041 [2024-11-25 19:13:55,027 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2124 [2024-11-25 19:13:55,252 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2113 [2024-11-25 19:13:55,522 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2989 [2024-11-25 19:13:55,781 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2626 [2024-11-25 19:13:56,048 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2325 [2024-11-25 19:13:56,312 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2109 [2024-11-25 19:13:56,576 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2964 [2024-11-25 19:13:56,833 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2868 [2024-11-25 19:13:57,098 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2303 [2024-11-25 19:13:57,353 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2527 [2024-11-25 19:13:57,620 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2601 [2024-11-25 19:13:57,883 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2517 [2024-11-25 19:13:58,133 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2444 [2024-11-25 19:13:58,363 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1982 [2024-11-25 19:13:58,621 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2576 [2024-11-25 19:13:58,860 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2585 [2024-11-25 19:13:59,087 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1919 [2024-11-25 19:13:59,298 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2285 [2024-11-25 19:13:59,517 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1869 [2024-11-25 19:14:00,231 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7765/0.8544/0.9963. [2024-11-25 19:14:00,231 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 19:14:00,231 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5568/0.7107 [2024-11-25 19:14:00,231 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:14:00,232 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:14:00,232 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:14:02,890 INFO misc.py line 119 2586773] Train: [39/50][1/376] Data 0.090 (0.090) Batch 0.605 (0.605) Remain 00:45:30 loss: 0.1929 Lr: 0.00059 [2024-11-25 19:14:03,343 INFO misc.py line 119 2586773] Train: [39/50][2/376] Data 0.004 (0.004) Batch 0.454 (0.454) Remain 00:34:08 loss: 0.1990 Lr: 0.00059 [2024-11-25 19:14:03,849 INFO misc.py line 119 2586773] Train: [39/50][3/376] Data 0.003 (0.003) Batch 0.507 (0.507) Remain 00:38:03 loss: 0.1990 Lr: 0.00059 [2024-11-25 19:14:04,343 INFO misc.py line 119 2586773] Train: [39/50][4/376] Data 0.002 (0.002) Batch 0.494 (0.494) Remain 00:37:04 loss: 0.1528 Lr: 0.00059 [2024-11-25 19:14:04,847 INFO misc.py line 119 2586773] Train: [39/50][5/376] Data 0.002 (0.002) Batch 0.503 (0.499) Remain 00:37:26 loss: 0.2127 Lr: 0.00059 [2024-11-25 19:14:05,346 INFO misc.py line 119 2586773] Train: [39/50][6/376] Data 0.003 (0.002) Batch 0.499 (0.499) Remain 00:37:26 loss: 0.2715 Lr: 0.00059 [2024-11-25 19:14:05,866 INFO misc.py line 119 2586773] Train: [39/50][7/376] Data 0.002 (0.002) Batch 0.521 (0.504) Remain 00:37:51 loss: 0.1869 Lr: 0.00059 [2024-11-25 19:14:06,375 INFO misc.py line 119 2586773] Train: [39/50][8/376] Data 0.002 (0.002) Batch 0.508 (0.505) Remain 00:37:54 loss: 0.1826 Lr: 0.00059 [2024-11-25 19:14:06,908 INFO misc.py line 119 2586773] Train: [39/50][9/376] Data 0.003 (0.002) Batch 0.533 (0.510) Remain 00:38:15 loss: 0.2209 Lr: 0.00059 [2024-11-25 19:14:07,387 INFO misc.py line 119 2586773] Train: [39/50][10/376] Data 0.003 (0.002) Batch 0.479 (0.505) Remain 00:37:55 loss: 0.2023 Lr: 0.00059 [2024-11-25 19:14:07,894 INFO misc.py line 119 2586773] Train: [39/50][11/376] Data 0.003 (0.002) Batch 0.507 (0.506) Remain 00:37:55 loss: 0.2101 Lr: 0.00059 [2024-11-25 19:14:08,432 INFO misc.py line 119 2586773] Train: [39/50][12/376] Data 0.002 (0.002) Batch 0.538 (0.509) Remain 00:38:11 loss: 0.2182 Lr: 0.00059 [2024-11-25 19:14:08,912 INFO misc.py line 119 2586773] Train: [39/50][13/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 00:37:57 loss: 0.2133 Lr: 0.00059 [2024-11-25 19:14:09,468 INFO misc.py line 119 2586773] Train: [39/50][14/376] Data 0.003 (0.002) Batch 0.556 (0.511) Remain 00:38:17 loss: 0.1804 Lr: 0.00059 [2024-11-25 19:14:10,000 INFO misc.py line 119 2586773] Train: [39/50][15/376] Data 0.002 (0.002) Batch 0.532 (0.513) Remain 00:38:24 loss: 0.2190 Lr: 0.00059 [2024-11-25 19:14:10,512 INFO misc.py line 119 2586773] Train: [39/50][16/376] Data 0.003 (0.002) Batch 0.512 (0.513) Remain 00:38:24 loss: 0.2072 Lr: 0.00059 [2024-11-25 19:14:10,973 INFO misc.py line 119 2586773] Train: [39/50][17/376] Data 0.003 (0.002) Batch 0.461 (0.509) Remain 00:38:07 loss: 0.3542 Lr: 0.00059 [2024-11-25 19:14:11,498 INFO misc.py line 119 2586773] Train: [39/50][18/376] Data 0.002 (0.002) Batch 0.525 (0.510) Remain 00:38:11 loss: 0.2040 Lr: 0.00059 [2024-11-25 19:14:11,984 INFO misc.py line 119 2586773] Train: [39/50][19/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 00:38:04 loss: 0.2142 Lr: 0.00059 [2024-11-25 19:14:12,548 INFO misc.py line 119 2586773] Train: [39/50][20/376] Data 0.002 (0.002) Batch 0.563 (0.512) Remain 00:38:18 loss: 0.2064 Lr: 0.00059 [2024-11-25 19:14:13,037 INFO misc.py line 119 2586773] Train: [39/50][21/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 00:38:12 loss: 0.1739 Lr: 0.00059 [2024-11-25 19:14:13,572 INFO misc.py line 119 2586773] Train: [39/50][22/376] Data 0.002 (0.002) Batch 0.535 (0.512) Remain 00:38:17 loss: 0.1538 Lr: 0.00059 [2024-11-25 19:14:14,112 INFO misc.py line 119 2586773] Train: [39/50][23/376] Data 0.003 (0.002) Batch 0.540 (0.513) Remain 00:38:23 loss: 0.2065 Lr: 0.00059 [2024-11-25 19:14:14,595 INFO misc.py line 119 2586773] Train: [39/50][24/376] Data 0.003 (0.002) Batch 0.483 (0.512) Remain 00:38:16 loss: 0.1914 Lr: 0.00059 [2024-11-25 19:14:15,150 INFO misc.py line 119 2586773] Train: [39/50][25/376] Data 0.002 (0.002) Batch 0.555 (0.514) Remain 00:38:24 loss: 0.2088 Lr: 0.00059 [2024-11-25 19:14:15,630 INFO misc.py line 119 2586773] Train: [39/50][26/376] Data 0.003 (0.002) Batch 0.480 (0.512) Remain 00:38:17 loss: 0.1834 Lr: 0.00058 [2024-11-25 19:14:16,173 INFO misc.py line 119 2586773] Train: [39/50][27/376] Data 0.002 (0.002) Batch 0.543 (0.513) Remain 00:38:22 loss: 0.1654 Lr: 0.00058 [2024-11-25 19:14:16,671 INFO misc.py line 119 2586773] Train: [39/50][28/376] Data 0.002 (0.002) Batch 0.498 (0.513) Remain 00:38:19 loss: 0.2677 Lr: 0.00058 [2024-11-25 19:14:17,189 INFO misc.py line 119 2586773] Train: [39/50][29/376] Data 0.002 (0.002) Batch 0.518 (0.513) Remain 00:38:19 loss: 0.2032 Lr: 0.00058 [2024-11-25 19:14:17,684 INFO misc.py line 119 2586773] Train: [39/50][30/376] Data 0.002 (0.002) Batch 0.495 (0.512) Remain 00:38:16 loss: 0.1687 Lr: 0.00058 [2024-11-25 19:14:18,206 INFO misc.py line 119 2586773] Train: [39/50][31/376] Data 0.002 (0.002) Batch 0.522 (0.513) Remain 00:38:17 loss: 0.2591 Lr: 0.00058 [2024-11-25 19:14:18,721 INFO misc.py line 119 2586773] Train: [39/50][32/376] Data 0.002 (0.002) Batch 0.515 (0.513) Remain 00:38:17 loss: 0.2595 Lr: 0.00058 [2024-11-25 19:14:19,279 INFO misc.py line 119 2586773] Train: [39/50][33/376] Data 0.002 (0.002) Batch 0.558 (0.514) Remain 00:38:23 loss: 0.2099 Lr: 0.00058 [2024-11-25 19:14:19,753 INFO misc.py line 119 2586773] Train: [39/50][34/376] Data 0.003 (0.002) Batch 0.474 (0.513) Remain 00:38:17 loss: 0.2655 Lr: 0.00058 [2024-11-25 19:14:20,266 INFO misc.py line 119 2586773] Train: [39/50][35/376] Data 0.002 (0.002) Batch 0.513 (0.513) Remain 00:38:16 loss: 0.3141 Lr: 0.00058 [2024-11-25 19:14:20,843 INFO misc.py line 119 2586773] Train: [39/50][36/376] Data 0.002 (0.002) Batch 0.578 (0.515) Remain 00:38:24 loss: 0.2266 Lr: 0.00058 [2024-11-25 19:14:21,351 INFO misc.py line 119 2586773] Train: [39/50][37/376] Data 0.002 (0.002) Batch 0.507 (0.515) Remain 00:38:23 loss: 0.2245 Lr: 0.00058 [2024-11-25 19:14:21,888 INFO misc.py line 119 2586773] Train: [39/50][38/376] Data 0.002 (0.002) Batch 0.538 (0.515) Remain 00:38:25 loss: 0.1831 Lr: 0.00058 [2024-11-25 19:14:22,368 INFO misc.py line 119 2586773] Train: [39/50][39/376] Data 0.003 (0.002) Batch 0.479 (0.514) Remain 00:38:20 loss: 0.1937 Lr: 0.00058 [2024-11-25 19:14:22,872 INFO misc.py line 119 2586773] Train: [39/50][40/376] Data 0.003 (0.002) Batch 0.505 (0.514) Remain 00:38:19 loss: 0.2424 Lr: 0.00058 [2024-11-25 19:14:23,349 INFO misc.py line 119 2586773] Train: [39/50][41/376] Data 0.002 (0.002) Batch 0.477 (0.513) Remain 00:38:14 loss: 0.1670 Lr: 0.00058 [2024-11-25 19:14:23,833 INFO misc.py line 119 2586773] Train: [39/50][42/376] Data 0.002 (0.002) Batch 0.483 (0.512) Remain 00:38:10 loss: 0.1777 Lr: 0.00058 [2024-11-25 19:14:24,360 INFO misc.py line 119 2586773] Train: [39/50][43/376] Data 0.002 (0.002) Batch 0.527 (0.513) Remain 00:38:11 loss: 0.2746 Lr: 0.00058 [2024-11-25 19:14:24,839 INFO misc.py line 119 2586773] Train: [39/50][44/376] Data 0.003 (0.002) Batch 0.479 (0.512) Remain 00:38:07 loss: 0.2111 Lr: 0.00058 [2024-11-25 19:14:25,351 INFO misc.py line 119 2586773] Train: [39/50][45/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 00:38:06 loss: 0.1695 Lr: 0.00058 [2024-11-25 19:14:25,842 INFO misc.py line 119 2586773] Train: [39/50][46/376] Data 0.002 (0.002) Batch 0.491 (0.511) Remain 00:38:04 loss: 0.1648 Lr: 0.00058 [2024-11-25 19:14:26,388 INFO misc.py line 119 2586773] Train: [39/50][47/376] Data 0.003 (0.002) Batch 0.546 (0.512) Remain 00:38:07 loss: 0.1846 Lr: 0.00058 [2024-11-25 19:14:26,883 INFO misc.py line 119 2586773] Train: [39/50][48/376] Data 0.002 (0.002) Batch 0.495 (0.512) Remain 00:38:04 loss: 0.2462 Lr: 0.00058 [2024-11-25 19:14:27,417 INFO misc.py line 119 2586773] Train: [39/50][49/376] Data 0.002 (0.002) Batch 0.534 (0.512) Remain 00:38:06 loss: 0.1999 Lr: 0.00058 [2024-11-25 19:14:27,966 INFO misc.py line 119 2586773] Train: [39/50][50/376] Data 0.002 (0.002) Batch 0.550 (0.513) Remain 00:38:09 loss: 0.1968 Lr: 0.00058 [2024-11-25 19:14:28,455 INFO misc.py line 119 2586773] Train: [39/50][51/376] Data 0.002 (0.002) Batch 0.489 (0.513) Remain 00:38:06 loss: 0.2075 Lr: 0.00058 [2024-11-25 19:14:28,910 INFO misc.py line 119 2586773] Train: [39/50][52/376] Data 0.002 (0.002) Batch 0.456 (0.511) Remain 00:38:01 loss: 0.2023 Lr: 0.00058 [2024-11-25 19:14:29,421 INFO misc.py line 119 2586773] Train: [39/50][53/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 00:38:00 loss: 0.1888 Lr: 0.00058 [2024-11-25 19:14:29,986 INFO misc.py line 119 2586773] Train: [39/50][54/376] Data 0.002 (0.002) Batch 0.565 (0.512) Remain 00:38:04 loss: 0.2020 Lr: 0.00058 [2024-11-25 19:14:30,528 INFO misc.py line 119 2586773] Train: [39/50][55/376] Data 0.002 (0.002) Batch 0.542 (0.513) Remain 00:38:06 loss: 0.2102 Lr: 0.00058 [2024-11-25 19:14:31,026 INFO misc.py line 119 2586773] Train: [39/50][56/376] Data 0.003 (0.002) Batch 0.498 (0.513) Remain 00:38:04 loss: 0.1897 Lr: 0.00058 [2024-11-25 19:14:31,484 INFO misc.py line 119 2586773] Train: [39/50][57/376] Data 0.003 (0.002) Batch 0.458 (0.512) Remain 00:37:59 loss: 0.1778 Lr: 0.00058 [2024-11-25 19:14:32,008 INFO misc.py line 119 2586773] Train: [39/50][58/376] Data 0.002 (0.002) Batch 0.523 (0.512) Remain 00:38:00 loss: 0.2553 Lr: 0.00058 [2024-11-25 19:14:32,486 INFO misc.py line 119 2586773] Train: [39/50][59/376] Data 0.002 (0.002) Batch 0.478 (0.511) Remain 00:37:57 loss: 0.1733 Lr: 0.00058 [2024-11-25 19:14:32,997 INFO misc.py line 119 2586773] Train: [39/50][60/376] Data 0.003 (0.002) Batch 0.510 (0.511) Remain 00:37:56 loss: 0.2216 Lr: 0.00058 [2024-11-25 19:14:33,492 INFO misc.py line 119 2586773] Train: [39/50][61/376] Data 0.002 (0.002) Batch 0.495 (0.511) Remain 00:37:54 loss: 0.2445 Lr: 0.00058 [2024-11-25 19:14:34,038 INFO misc.py line 119 2586773] Train: [39/50][62/376] Data 0.002 (0.002) Batch 0.546 (0.512) Remain 00:37:56 loss: 0.2052 Lr: 0.00058 [2024-11-25 19:14:34,519 INFO misc.py line 119 2586773] Train: [39/50][63/376] Data 0.002 (0.002) Batch 0.481 (0.511) Remain 00:37:54 loss: 0.3495 Lr: 0.00058 [2024-11-25 19:14:34,983 INFO misc.py line 119 2586773] Train: [39/50][64/376] Data 0.003 (0.002) Batch 0.464 (0.510) Remain 00:37:50 loss: 0.1720 Lr: 0.00058 [2024-11-25 19:14:35,541 INFO misc.py line 119 2586773] Train: [39/50][65/376] Data 0.002 (0.002) Batch 0.558 (0.511) Remain 00:37:53 loss: 0.1912 Lr: 0.00058 [2024-11-25 19:14:36,080 INFO misc.py line 119 2586773] Train: [39/50][66/376] Data 0.002 (0.002) Batch 0.538 (0.512) Remain 00:37:54 loss: 0.2282 Lr: 0.00058 [2024-11-25 19:14:36,578 INFO misc.py line 119 2586773] Train: [39/50][67/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 00:37:53 loss: 0.2106 Lr: 0.00057 [2024-11-25 19:14:37,086 INFO misc.py line 119 2586773] Train: [39/50][68/376] Data 0.002 (0.002) Batch 0.507 (0.511) Remain 00:37:52 loss: 0.1824 Lr: 0.00057 [2024-11-25 19:14:37,630 INFO misc.py line 119 2586773] Train: [39/50][69/376] Data 0.002 (0.002) Batch 0.545 (0.512) Remain 00:37:54 loss: 0.1844 Lr: 0.00057 [2024-11-25 19:14:38,142 INFO misc.py line 119 2586773] Train: [39/50][70/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 00:37:53 loss: 0.1601 Lr: 0.00057 [2024-11-25 19:14:38,645 INFO misc.py line 119 2586773] Train: [39/50][71/376] Data 0.002 (0.002) Batch 0.503 (0.512) Remain 00:37:52 loss: 0.1828 Lr: 0.00057 [2024-11-25 19:14:39,119 INFO misc.py line 119 2586773] Train: [39/50][72/376] Data 0.002 (0.002) Batch 0.474 (0.511) Remain 00:37:49 loss: 0.1843 Lr: 0.00057 [2024-11-25 19:14:39,621 INFO misc.py line 119 2586773] Train: [39/50][73/376] Data 0.002 (0.002) Batch 0.502 (0.511) Remain 00:37:48 loss: 0.2075 Lr: 0.00057 [2024-11-25 19:14:40,122 INFO misc.py line 119 2586773] Train: [39/50][74/376] Data 0.002 (0.002) Batch 0.501 (0.511) Remain 00:37:47 loss: 0.2450 Lr: 0.00057 [2024-11-25 19:14:40,641 INFO misc.py line 119 2586773] Train: [39/50][75/376] Data 0.002 (0.002) Batch 0.520 (0.511) Remain 00:37:47 loss: 0.1953 Lr: 0.00057 [2024-11-25 19:14:41,133 INFO misc.py line 119 2586773] Train: [39/50][76/376] Data 0.003 (0.002) Batch 0.491 (0.511) Remain 00:37:45 loss: 0.1769 Lr: 0.00057 [2024-11-25 19:14:41,680 INFO misc.py line 119 2586773] Train: [39/50][77/376] Data 0.002 (0.002) Batch 0.547 (0.511) Remain 00:37:47 loss: 0.2448 Lr: 0.00057 [2024-11-25 19:14:42,178 INFO misc.py line 119 2586773] Train: [39/50][78/376] Data 0.002 (0.002) Batch 0.498 (0.511) Remain 00:37:45 loss: 0.2388 Lr: 0.00057 [2024-11-25 19:14:42,707 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loss: 0.2127 Lr: 0.00051 [2024-11-25 19:16:58,916 INFO misc.py line 119 2586773] Train: [39/50][347/376] Data 0.002 (0.002) Batch 0.470 (0.509) Remain 00:35:19 loss: 0.1806 Lr: 0.00051 [2024-11-25 19:16:59,387 INFO misc.py line 119 2586773] Train: [39/50][348/376] Data 0.002 (0.002) Batch 0.471 (0.509) Remain 00:35:18 loss: 0.1538 Lr: 0.00051 [2024-11-25 19:16:59,905 INFO misc.py line 119 2586773] Train: [39/50][349/376] Data 0.003 (0.002) Batch 0.518 (0.509) Remain 00:35:18 loss: 0.1630 Lr: 0.00051 [2024-11-25 19:17:00,434 INFO misc.py line 119 2586773] Train: [39/50][350/376] Data 0.002 (0.002) Batch 0.529 (0.509) Remain 00:35:17 loss: 0.2056 Lr: 0.00051 [2024-11-25 19:17:00,955 INFO misc.py line 119 2586773] Train: [39/50][351/376] Data 0.002 (0.002) Batch 0.521 (0.509) Remain 00:35:17 loss: 0.1989 Lr: 0.00051 [2024-11-25 19:17:01,433 INFO misc.py line 119 2586773] Train: [39/50][352/376] Data 0.002 (0.002) Batch 0.478 (0.509) Remain 00:35:16 loss: 0.1910 Lr: 0.00051 [2024-11-25 19:17:01,936 INFO misc.py line 119 2586773] Train: [39/50][353/376] Data 0.002 (0.002) Batch 0.503 (0.509) Remain 00:35:16 loss: 0.1652 Lr: 0.00051 [2024-11-25 19:17:02,405 INFO misc.py line 119 2586773] Train: [39/50][354/376] Data 0.003 (0.002) Batch 0.469 (0.509) Remain 00:35:15 loss: 0.2003 Lr: 0.00051 [2024-11-25 19:17:02,901 INFO misc.py line 119 2586773] Train: [39/50][355/376] Data 0.002 (0.002) Batch 0.496 (0.509) Remain 00:35:14 loss: 0.3042 Lr: 0.00051 [2024-11-25 19:17:03,384 INFO misc.py line 119 2586773] Train: [39/50][356/376] Data 0.002 (0.002) Batch 0.483 (0.509) Remain 00:35:13 loss: 0.1768 Lr: 0.00051 [2024-11-25 19:17:03,920 INFO misc.py line 119 2586773] Train: [39/50][357/376] Data 0.002 (0.002) Batch 0.536 (0.509) Remain 00:35:13 loss: 0.1931 Lr: 0.00051 [2024-11-25 19:17:04,451 INFO misc.py line 119 2586773] Train: [39/50][358/376] Data 0.003 (0.002) Batch 0.531 (0.509) Remain 00:35:13 loss: 0.1504 Lr: 0.00051 [2024-11-25 19:17:04,934 INFO misc.py line 119 2586773] Train: [39/50][359/376] Data 0.002 (0.002) Batch 0.483 (0.509) Remain 00:35:12 loss: 0.1925 Lr: 0.00051 [2024-11-25 19:17:05,447 INFO misc.py line 119 2586773] Train: [39/50][360/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 00:35:12 loss: 0.1375 Lr: 0.00051 [2024-11-25 19:17:05,972 INFO misc.py line 119 2586773] Train: [39/50][361/376] Data 0.002 (0.002) Batch 0.525 (0.509) Remain 00:35:11 loss: 0.2604 Lr: 0.00051 [2024-11-25 19:17:06,491 INFO misc.py line 119 2586773] Train: [39/50][362/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 00:35:11 loss: 0.2371 Lr: 0.00050 [2024-11-25 19:17:06,967 INFO misc.py line 119 2586773] Train: [39/50][363/376] Data 0.002 (0.002) Batch 0.476 (0.509) Remain 00:35:10 loss: 0.1850 Lr: 0.00050 [2024-11-25 19:17:07,463 INFO misc.py line 119 2586773] Train: [39/50][364/376] Data 0.002 (0.002) Batch 0.496 (0.509) Remain 00:35:09 loss: 0.1882 Lr: 0.00050 [2024-11-25 19:17:08,010 INFO misc.py line 119 2586773] Train: [39/50][365/376] Data 0.002 (0.002) Batch 0.546 (0.509) Remain 00:35:09 loss: 0.1659 Lr: 0.00050 [2024-11-25 19:17:08,523 INFO misc.py line 119 2586773] Train: [39/50][366/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 00:35:09 loss: 0.2005 Lr: 0.00050 [2024-11-25 19:17:09,036 INFO misc.py line 119 2586773] Train: [39/50][367/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 00:35:08 loss: 0.2226 Lr: 0.00050 [2024-11-25 19:17:09,498 INFO misc.py line 119 2586773] Train: [39/50][368/376] Data 0.002 (0.002) Batch 0.462 (0.509) Remain 00:35:07 loss: 0.2056 Lr: 0.00050 [2024-11-25 19:17:09,995 INFO misc.py line 119 2586773] Train: [39/50][369/376] Data 0.002 (0.002) Batch 0.497 (0.509) Remain 00:35:07 loss: 0.1694 Lr: 0.00050 [2024-11-25 19:17:10,505 INFO misc.py line 119 2586773] Train: [39/50][370/376] Data 0.002 (0.002) Batch 0.510 (0.509) Remain 00:35:06 loss: 0.2055 Lr: 0.00050 [2024-11-25 19:17:11,043 INFO misc.py line 119 2586773] Train: [39/50][371/376] Data 0.002 (0.002) Batch 0.538 (0.509) Remain 00:35:06 loss: 0.2326 Lr: 0.00050 [2024-11-25 19:17:11,540 INFO misc.py line 119 2586773] Train: [39/50][372/376] Data 0.002 (0.002) Batch 0.497 (0.509) Remain 00:35:05 loss: 0.2008 Lr: 0.00050 [2024-11-25 19:17:12,053 INFO misc.py line 119 2586773] Train: [39/50][373/376] Data 0.002 (0.002) Batch 0.513 (0.509) Remain 00:35:05 loss: 0.2247 Lr: 0.00050 [2024-11-25 19:17:12,530 INFO misc.py line 119 2586773] Train: [39/50][374/376] Data 0.002 (0.002) Batch 0.477 (0.509) Remain 00:35:04 loss: 0.2402 Lr: 0.00050 [2024-11-25 19:17:13,044 INFO misc.py line 119 2586773] Train: [39/50][375/376] Data 0.002 (0.002) Batch 0.514 (0.509) Remain 00:35:04 loss: 0.2685 Lr: 0.00050 [2024-11-25 19:17:13,580 INFO misc.py line 119 2586773] Train: [39/50][376/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 00:35:03 loss: 0.2301 Lr: 0.00050 [2024-11-25 19:17:13,581 INFO misc.py line 136 2586773] Train result: loss: 0.2028 [2024-11-25 19:17:13,581 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:17:24,436 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1703 [2024-11-25 19:17:24,700 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2029 [2024-11-25 19:17:24,962 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2699 [2024-11-25 19:17:25,186 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1950 [2024-11-25 19:17:25,450 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2893 [2024-11-25 19:17:25,719 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2023 [2024-11-25 19:17:25,942 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2160 [2024-11-25 19:17:26,212 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2224 [2024-11-25 19:17:26,438 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2630 [2024-11-25 19:17:26,698 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2554 [2024-11-25 19:17:26,928 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2060 [2024-11-25 19:17:27,200 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2261 [2024-11-25 19:17:27,467 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2338 [2024-11-25 19:17:27,730 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2431 [2024-11-25 19:17:27,963 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2335 [2024-11-25 19:17:28,202 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.2893 [2024-11-25 19:17:28,477 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2730 [2024-11-25 19:17:28,732 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2062 [2024-11-25 19:17:28,963 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2253 [2024-11-25 19:17:29,222 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2476 [2024-11-25 19:17:29,459 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2341 [2024-11-25 19:17:29,725 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2634 [2024-11-25 19:17:29,962 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2154 [2024-11-25 19:17:30,229 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2403 [2024-11-25 19:17:30,495 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2193 [2024-11-25 19:17:30,731 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2381 [2024-11-25 19:17:30,981 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2709 [2024-11-25 19:17:31,228 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2286 [2024-11-25 19:17:31,496 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2689 [2024-11-25 19:17:31,750 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2948 [2024-11-25 19:17:31,983 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2671 [2024-11-25 19:17:32,248 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1980 [2024-11-25 19:17:32,467 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2508 [2024-11-25 19:17:32,707 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2078 [2024-11-25 19:17:32,967 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2031 [2024-11-25 19:17:33,212 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2427 [2024-11-25 19:17:33,437 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1907 [2024-11-25 19:17:33,710 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2355 [2024-11-25 19:17:33,946 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2631 [2024-11-25 19:17:34,189 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2474 [2024-11-25 19:17:34,461 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3019 [2024-11-25 19:17:34,718 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2711 [2024-11-25 19:17:34,956 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2489 [2024-11-25 19:17:35,188 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2113 [2024-11-25 19:17:35,425 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2294 [2024-11-25 19:17:35,672 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2288 [2024-11-25 19:17:35,938 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2241 [2024-11-25 19:17:36,190 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2953 [2024-11-25 19:17:36,414 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2152 [2024-11-25 19:17:36,647 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2166 [2024-11-25 19:17:36,867 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2478 [2024-11-25 19:17:37,125 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2227 [2024-11-25 19:17:37,392 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2184 [2024-11-25 19:17:37,653 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3055 [2024-11-25 19:17:37,885 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2421 [2024-11-25 19:17:38,127 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2107 [2024-11-25 19:17:38,385 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2595 [2024-11-25 19:17:38,661 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2616 [2024-11-25 19:17:38,918 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2444 [2024-11-25 19:17:39,177 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2540 [2024-11-25 19:17:39,433 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2223 [2024-11-25 19:17:39,703 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2430 [2024-11-25 19:17:39,932 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2277 [2024-11-25 19:17:40,190 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2497 [2024-11-25 19:17:40,456 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2749 [2024-11-25 19:17:40,723 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.2017 [2024-11-25 19:17:40,967 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1924 [2024-11-25 19:17:41,222 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2566 [2024-11-25 19:17:41,492 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2257 [2024-11-25 19:17:41,755 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2712 [2024-11-25 19:17:41,998 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.1906 [2024-11-25 19:17:42,236 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2709 [2024-11-25 19:17:42,494 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2608 [2024-11-25 19:17:42,738 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2606 [2024-11-25 19:17:42,963 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2579 [2024-11-25 19:17:43,192 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2130 [2024-11-25 19:17:43,460 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2482 [2024-11-25 19:17:43,704 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2012 [2024-11-25 19:17:43,984 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2171 [2024-11-25 19:17:44,235 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2944 [2024-11-25 19:17:44,483 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2328 [2024-11-25 19:17:44,745 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2515 [2024-11-25 19:17:44,994 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1780 [2024-11-25 19:17:45,243 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2525 [2024-11-25 19:17:45,517 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2374 [2024-11-25 19:17:45,751 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2491 [2024-11-25 19:17:46,013 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2596 [2024-11-25 19:17:46,271 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2424 [2024-11-25 19:17:46,519 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2641 [2024-11-25 19:17:46,774 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2367 [2024-11-25 19:17:47,016 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2350 [2024-11-25 19:17:47,268 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2608 [2024-11-25 19:17:47,534 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2426 [2024-11-25 19:17:47,808 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1926 [2024-11-25 19:17:48,074 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2235 [2024-11-25 19:17:48,323 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2100 [2024-11-25 19:17:48,595 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2275 [2024-11-25 19:17:48,820 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2915 [2024-11-25 19:17:49,091 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2386 [2024-11-25 19:17:49,330 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2608 [2024-11-25 19:17:49,599 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2077 [2024-11-25 19:17:49,869 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2746 [2024-11-25 19:17:50,129 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2411 [2024-11-25 19:17:50,382 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2758 [2024-11-25 19:17:50,605 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2453 [2024-11-25 19:17:50,849 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2299 [2024-11-25 19:17:51,105 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2120 [2024-11-25 19:17:51,377 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2415 [2024-11-25 19:17:51,614 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2618 [2024-11-25 19:17:51,874 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2151 [2024-11-25 19:17:52,137 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2307 [2024-11-25 19:17:52,362 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2357 [2024-11-25 19:17:52,629 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2052 [2024-11-25 19:17:52,908 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2096 [2024-11-25 19:17:53,133 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2092 [2024-11-25 19:17:53,406 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2958 [2024-11-25 19:17:53,664 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2622 [2024-11-25 19:17:53,932 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2438 [2024-11-25 19:17:54,199 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2202 [2024-11-25 19:17:54,463 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3133 [2024-11-25 19:17:54,721 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2820 [2024-11-25 19:17:54,986 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1806 [2024-11-25 19:17:55,243 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2541 [2024-11-25 19:17:55,508 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2553 [2024-11-25 19:17:55,771 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2570 [2024-11-25 19:17:56,022 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2753 [2024-11-25 19:17:56,254 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2078 [2024-11-25 19:17:56,513 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2505 [2024-11-25 19:17:56,749 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2643 [2024-11-25 19:17:56,976 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1938 [2024-11-25 19:17:57,189 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2259 [2024-11-25 19:17:57,407 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1914 [2024-11-25 19:17:57,983 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7782/0.8468/0.9964. [2024-11-25 19:17:57,984 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9964/0.9984 [2024-11-25 19:17:57,984 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5599/0.6953 [2024-11-25 19:17:57,984 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:17:57,985 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:17:57,985 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:18:00,742 INFO misc.py line 119 2586773] Train: [40/50][1/376] Data 0.119 (0.119) Batch 0.593 (0.593) Remain 00:40:50 loss: 0.1724 Lr: 0.00050 [2024-11-25 19:18:01,233 INFO misc.py line 119 2586773] Train: [40/50][2/376] Data 0.003 (0.003) Batch 0.491 (0.491) Remain 00:33:50 loss: 0.1894 Lr: 0.00050 [2024-11-25 19:18:01,768 INFO misc.py line 119 2586773] Train: [40/50][3/376] Data 0.002 (0.002) Batch 0.535 (0.535) Remain 00:36:51 loss: 0.2005 Lr: 0.00050 [2024-11-25 19:18:02,265 INFO misc.py line 119 2586773] Train: [40/50][4/376] Data 0.002 (0.002) Batch 0.497 (0.497) Remain 00:34:13 loss: 0.1596 Lr: 0.00050 [2024-11-25 19:18:02,770 INFO misc.py line 119 2586773] Train: [40/50][5/376] Data 0.002 (0.002) Batch 0.505 (0.501) Remain 00:34:28 loss: 0.1749 Lr: 0.00050 [2024-11-25 19:18:03,288 INFO misc.py line 119 2586773] Train: [40/50][6/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:34:52 loss: 0.1710 Lr: 0.00050 [2024-11-25 19:18:03,787 INFO misc.py line 119 2586773] Train: [40/50][7/376] Data 0.003 (0.003) Batch 0.499 (0.505) Remain 00:34:44 loss: 0.1664 Lr: 0.00050 [2024-11-25 19:18:04,329 INFO misc.py line 119 2586773] Train: [40/50][8/376] Data 0.003 (0.003) Batch 0.542 (0.512) Remain 00:35:14 loss: 0.2350 Lr: 0.00050 [2024-11-25 19:18:04,847 INFO misc.py line 119 2586773] Train: [40/50][9/376] Data 0.002 (0.002) Batch 0.518 (0.513) Remain 00:35:18 loss: 0.1797 Lr: 0.00050 [2024-11-25 19:18:05,363 INFO misc.py line 119 2586773] Train: [40/50][10/376] Data 0.002 (0.002) Batch 0.515 (0.514) Remain 00:35:18 loss: 0.1498 Lr: 0.00050 [2024-11-25 19:18:05,902 INFO misc.py line 119 2586773] Train: [40/50][11/376] Data 0.002 (0.002) Batch 0.540 (0.517) Remain 00:35:31 loss: 0.2480 Lr: 0.00050 [2024-11-25 19:18:06,417 INFO misc.py line 119 2586773] Train: [40/50][12/376] Data 0.002 (0.002) Batch 0.515 (0.517) Remain 00:35:30 loss: 0.1851 Lr: 0.00050 [2024-11-25 19:18:06,918 INFO misc.py line 119 2586773] Train: [40/50][13/376] Data 0.002 (0.002) Batch 0.501 (0.515) Remain 00:35:23 loss: 0.1457 Lr: 0.00050 [2024-11-25 19:18:07,460 INFO misc.py line 119 2586773] Train: [40/50][14/376] Data 0.002 (0.002) Batch 0.541 (0.517) Remain 00:35:32 loss: 0.1855 Lr: 0.00050 [2024-11-25 19:18:07,943 INFO misc.py line 119 2586773] Train: [40/50][15/376] Data 0.002 (0.002) Batch 0.483 (0.515) Remain 00:35:20 loss: 0.2026 Lr: 0.00050 [2024-11-25 19:18:08,497 INFO misc.py line 119 2586773] Train: [40/50][16/376] Data 0.002 (0.002) Batch 0.555 (0.518) Remain 00:35:32 loss: 0.2198 Lr: 0.00050 [2024-11-25 19:18:09,000 INFO misc.py line 119 2586773] Train: [40/50][17/376] Data 0.003 (0.002) Batch 0.503 (0.517) Remain 00:35:27 loss: 0.1898 Lr: 0.00050 [2024-11-25 19:18:09,536 INFO misc.py line 119 2586773] Train: [40/50][18/376] Data 0.002 (0.002) Batch 0.536 (0.518) Remain 00:35:32 loss: 0.1968 Lr: 0.00050 [2024-11-25 19:18:10,036 INFO misc.py line 119 2586773] Train: [40/50][19/376] Data 0.002 (0.002) Batch 0.500 (0.517) Remain 00:35:27 loss: 0.1914 Lr: 0.00050 [2024-11-25 19:18:10,553 INFO misc.py line 119 2586773] Train: [40/50][20/376] Data 0.002 (0.002) Batch 0.518 (0.517) Remain 00:35:27 loss: 0.2029 Lr: 0.00050 [2024-11-25 19:18:11,058 INFO misc.py line 119 2586773] Train: [40/50][21/376] Data 0.002 (0.002) Batch 0.505 (0.516) Remain 00:35:23 loss: 0.1715 Lr: 0.00050 [2024-11-25 19:18:11,576 INFO misc.py line 119 2586773] Train: [40/50][22/376] Data 0.002 (0.002) Batch 0.517 (0.516) Remain 00:35:23 loss: 0.2014 Lr: 0.00050 [2024-11-25 19:18:12,046 INFO misc.py line 119 2586773] Train: [40/50][23/376] Data 0.002 (0.002) Batch 0.470 (0.514) Remain 00:35:13 loss: 0.1484 Lr: 0.00050 [2024-11-25 19:18:12,550 INFO misc.py line 119 2586773] Train: [40/50][24/376] Data 0.002 (0.002) Batch 0.504 (0.513) Remain 00:35:11 loss: 0.1792 Lr: 0.00050 [2024-11-25 19:18:13,019 INFO misc.py line 119 2586773] Train: [40/50][25/376] Data 0.002 (0.002) Batch 0.468 (0.511) Remain 00:35:02 loss: 0.2541 Lr: 0.00050 [2024-11-25 19:18:13,504 INFO misc.py line 119 2586773] Train: [40/50][26/376] Data 0.002 (0.002) Batch 0.485 (0.510) Remain 00:34:57 loss: 0.3348 Lr: 0.00050 [2024-11-25 19:18:14,014 INFO misc.py line 119 2586773] Train: [40/50][27/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:34:56 loss: 0.1809 Lr: 0.00050 [2024-11-25 19:18:14,543 INFO misc.py line 119 2586773] Train: [40/50][28/376] Data 0.002 (0.002) Batch 0.529 (0.511) Remain 00:34:59 loss: 0.1679 Lr: 0.00050 [2024-11-25 19:18:15,100 INFO misc.py line 119 2586773] Train: [40/50][29/376] Data 0.002 (0.002) Batch 0.557 (0.513) Remain 00:35:05 loss: 0.2044 Lr: 0.00050 [2024-11-25 19:18:15,639 INFO misc.py line 119 2586773] Train: [40/50][30/376] Data 0.002 (0.002) Batch 0.540 (0.514) Remain 00:35:09 loss: 0.1750 Lr: 0.00049 [2024-11-25 19:18:16,156 INFO misc.py line 119 2586773] Train: [40/50][31/376] Data 0.002 (0.002) Batch 0.517 (0.514) Remain 00:35:09 loss: 0.2088 Lr: 0.00049 [2024-11-25 19:18:16,655 INFO misc.py line 119 2586773] Train: [40/50][32/376] Data 0.002 (0.002) Batch 0.499 (0.513) Remain 00:35:06 loss: 0.1650 Lr: 0.00049 [2024-11-25 19:18:17,194 INFO misc.py line 119 2586773] Train: [40/50][33/376] Data 0.002 (0.002) Batch 0.539 (0.514) Remain 00:35:09 loss: 0.1827 Lr: 0.00049 [2024-11-25 19:18:17,717 INFO misc.py line 119 2586773] Train: [40/50][34/376] Data 0.002 (0.002) Batch 0.523 (0.514) Remain 00:35:10 loss: 0.1678 Lr: 0.00049 [2024-11-25 19:18:18,212 INFO misc.py line 119 2586773] Train: [40/50][35/376] Data 0.002 (0.002) Batch 0.495 (0.514) Remain 00:35:07 loss: 0.1965 Lr: 0.00049 [2024-11-25 19:18:18,732 INFO misc.py line 119 2586773] Train: [40/50][36/376] Data 0.002 (0.002) Batch 0.520 (0.514) Remain 00:35:07 loss: 0.2216 Lr: 0.00049 [2024-11-25 19:18:19,227 INFO misc.py line 119 2586773] Train: [40/50][37/376] Data 0.002 (0.002) Batch 0.495 (0.513) Remain 00:35:04 loss: 0.1791 Lr: 0.00049 [2024-11-25 19:18:19,700 INFO misc.py line 119 2586773] Train: [40/50][38/376] Data 0.002 (0.002) Batch 0.473 (0.512) Remain 00:34:59 loss: 0.1824 Lr: 0.00049 [2024-11-25 19:18:20,166 INFO misc.py line 119 2586773] Train: [40/50][39/376] Data 0.002 (0.002) Batch 0.466 (0.511) Remain 00:34:53 loss: 0.2004 Lr: 0.00049 [2024-11-25 19:18:20,677 INFO misc.py line 119 2586773] Train: [40/50][40/376] Data 0.003 (0.002) Batch 0.511 (0.511) Remain 00:34:53 loss: 0.2368 Lr: 0.00049 [2024-11-25 19:18:21,196 INFO misc.py line 119 2586773] Train: [40/50][41/376] Data 0.002 (0.002) Batch 0.519 (0.511) Remain 00:34:53 loss: 0.2550 Lr: 0.00049 [2024-11-25 19:18:21,762 INFO misc.py line 119 2586773] Train: [40/50][42/376] Data 0.002 (0.002) Batch 0.566 (0.513) Remain 00:34:58 loss: 0.2276 Lr: 0.00049 [2024-11-25 19:18:22,281 INFO misc.py line 119 2586773] Train: [40/50][43/376] Data 0.002 (0.002) Batch 0.519 (0.513) Remain 00:34:58 loss: 0.1638 Lr: 0.00049 [2024-11-25 19:18:22,751 INFO misc.py line 119 2586773] Train: [40/50][44/376] Data 0.002 (0.002) Batch 0.470 (0.512) Remain 00:34:54 loss: 0.1814 Lr: 0.00049 [2024-11-25 19:18:23,248 INFO misc.py line 119 2586773] Train: [40/50][45/376] Data 0.002 (0.002) Batch 0.497 (0.511) Remain 00:34:52 loss: 0.1876 Lr: 0.00049 [2024-11-25 19:18:23,775 INFO misc.py line 119 2586773] Train: [40/50][46/376] Data 0.003 (0.002) Batch 0.527 (0.512) Remain 00:34:53 loss: 0.1838 Lr: 0.00049 [2024-11-25 19:18:24,259 INFO misc.py line 119 2586773] Train: [40/50][47/376] Data 0.002 (0.002) Batch 0.484 (0.511) Remain 00:34:50 loss: 0.1377 Lr: 0.00049 [2024-11-25 19:18:24,762 INFO misc.py line 119 2586773] Train: [40/50][48/376] Data 0.002 (0.002) Batch 0.503 (0.511) Remain 00:34:48 loss: 0.1896 Lr: 0.00049 [2024-11-25 19:18:25,262 INFO misc.py line 119 2586773] Train: [40/50][49/376] Data 0.002 (0.002) Batch 0.500 (0.511) Remain 00:34:47 loss: 0.1655 Lr: 0.00049 [2024-11-25 19:18:25,766 INFO misc.py line 119 2586773] Train: [40/50][50/376] Data 0.002 (0.002) Batch 0.504 (0.511) Remain 00:34:46 loss: 0.1844 Lr: 0.00049 [2024-11-25 19:18:26,252 INFO misc.py line 119 2586773] Train: [40/50][51/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 00:34:43 loss: 0.2211 Lr: 0.00049 [2024-11-25 19:18:26,732 INFO misc.py line 119 2586773] Train: [40/50][52/376] Data 0.002 (0.002) Batch 0.479 (0.509) Remain 00:34:40 loss: 0.1628 Lr: 0.00049 [2024-11-25 19:18:27,240 INFO misc.py line 119 2586773] Train: [40/50][53/376] Data 0.003 (0.002) Batch 0.508 (0.509) Remain 00:34:40 loss: 0.1957 Lr: 0.00049 [2024-11-25 19:18:27,749 INFO misc.py line 119 2586773] Train: [40/50][54/376] Data 0.003 (0.002) Batch 0.509 (0.509) Remain 00:34:39 loss: 0.2062 Lr: 0.00049 [2024-11-25 19:18:28,307 INFO misc.py line 119 2586773] Train: [40/50][55/376] Data 0.002 (0.002) Batch 0.558 (0.510) Remain 00:34:42 loss: 0.1995 Lr: 0.00049 [2024-11-25 19:18:28,802 INFO misc.py line 119 2586773] Train: [40/50][56/376] Data 0.002 (0.002) Batch 0.495 (0.510) Remain 00:34:41 loss: 0.2299 Lr: 0.00049 [2024-11-25 19:18:29,314 INFO misc.py line 119 2586773] Train: [40/50][57/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 00:34:40 loss: 0.1762 Lr: 0.00049 [2024-11-25 19:18:29,779 INFO misc.py line 119 2586773] Train: [40/50][58/376] Data 0.002 (0.002) Batch 0.466 (0.509) Remain 00:34:36 loss: 0.2961 Lr: 0.00049 [2024-11-25 19:18:30,267 INFO misc.py line 119 2586773] Train: [40/50][59/376] Data 0.002 (0.002) Batch 0.488 (0.509) Remain 00:34:34 loss: 0.1675 Lr: 0.00049 [2024-11-25 19:18:30,807 INFO misc.py line 119 2586773] Train: [40/50][60/376] Data 0.002 (0.002) Batch 0.540 (0.509) Remain 00:34:36 loss: 0.2312 Lr: 0.00049 [2024-11-25 19:18:31,317 INFO misc.py line 119 2586773] Train: [40/50][61/376] Data 0.002 (0.002) Batch 0.510 (0.509) Remain 00:34:36 loss: 0.2198 Lr: 0.00049 [2024-11-25 19:18:31,856 INFO misc.py line 119 2586773] Train: [40/50][62/376] Data 0.002 (0.002) Batch 0.540 (0.510) Remain 00:34:37 loss: 0.2127 Lr: 0.00049 [2024-11-25 19:18:32,347 INFO misc.py line 119 2586773] Train: [40/50][63/376] Data 0.002 (0.002) Batch 0.491 (0.510) Remain 00:34:35 loss: 0.2096 Lr: 0.00049 [2024-11-25 19:18:32,879 INFO misc.py line 119 2586773] Train: [40/50][64/376] Data 0.002 (0.002) Batch 0.532 (0.510) Remain 00:34:36 loss: 0.1758 Lr: 0.00049 [2024-11-25 19:18:33,324 INFO misc.py line 119 2586773] Train: [40/50][65/376] Data 0.002 (0.002) Batch 0.445 (0.509) Remain 00:34:31 loss: 0.2425 Lr: 0.00049 [2024-11-25 19:18:33,842 INFO misc.py line 119 2586773] Train: [40/50][66/376] Data 0.002 (0.002) Batch 0.518 (0.509) Remain 00:34:32 loss: 0.2452 Lr: 0.00049 [2024-11-25 19:18:34,353 INFO misc.py line 119 2586773] Train: [40/50][67/376] Data 0.002 (0.002) Batch 0.511 (0.509) Remain 00:34:31 loss: 0.2162 Lr: 0.00049 [2024-11-25 19:18:34,884 INFO misc.py line 119 2586773] Train: [40/50][68/376] Data 0.002 (0.002) Batch 0.532 (0.509) Remain 00:34:32 loss: 0.2247 Lr: 0.00049 [2024-11-25 19:18:35,392 INFO misc.py line 119 2586773] Train: [40/50][69/376] Data 0.002 (0.002) Batch 0.508 (0.509) Remain 00:34:31 loss: 0.1820 Lr: 0.00049 [2024-11-25 19:18:35,885 INFO misc.py line 119 2586773] Train: [40/50][70/376] Data 0.002 (0.002) Batch 0.493 (0.509) Remain 00:34:30 loss: 0.2246 Lr: 0.00049 [2024-11-25 19:18:36,462 INFO misc.py line 119 2586773] Train: [40/50][71/376] Data 0.002 (0.002) Batch 0.576 (0.510) Remain 00:34:33 loss: 0.2379 Lr: 0.00049 [2024-11-25 19:18:36,955 INFO misc.py line 119 2586773] Train: [40/50][72/376] Data 0.003 (0.002) Batch 0.493 (0.510) Remain 00:34:32 loss: 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INFO misc.py line 119 2586773] Train: [40/50][79/376] Data 0.002 (0.002) Batch 0.495 (0.509) Remain 00:34:23 loss: 0.2607 Lr: 0.00048 [2024-11-25 19:18:40,891 INFO misc.py line 119 2586773] Train: [40/50][80/376] Data 0.002 (0.002) Batch 0.463 (0.508) Remain 00:34:20 loss: 0.1840 Lr: 0.00048 [2024-11-25 19:18:41,437 INFO misc.py line 119 2586773] Train: [40/50][81/376] Data 0.002 (0.002) Batch 0.545 (0.509) Remain 00:34:22 loss: 0.1868 Lr: 0.00048 [2024-11-25 19:18:41,990 INFO misc.py line 119 2586773] Train: [40/50][82/376] Data 0.002 (0.002) Batch 0.553 (0.509) Remain 00:34:24 loss: 0.1800 Lr: 0.00048 [2024-11-25 19:18:42,546 INFO misc.py line 119 2586773] Train: [40/50][83/376] Data 0.002 (0.002) Batch 0.556 (0.510) Remain 00:34:25 loss: 0.2170 Lr: 0.00048 [2024-11-25 19:18:43,051 INFO misc.py line 119 2586773] Train: [40/50][84/376] Data 0.002 (0.002) Batch 0.505 (0.510) Remain 00:34:25 loss: 0.2568 Lr: 0.00048 [2024-11-25 19:18:43,538 INFO misc.py line 119 2586773] Train: 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line 119 2586773] Train: [40/50][272/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:32:41 loss: 0.1430 Lr: 0.00044 [2024-11-25 19:20:18,884 INFO misc.py line 119 2586773] Train: [40/50][273/376] Data 0.003 (0.002) Batch 0.540 (0.508) Remain 00:32:41 loss: 0.2188 Lr: 0.00044 [2024-11-25 19:20:19,408 INFO misc.py line 119 2586773] Train: [40/50][274/376] Data 0.003 (0.002) Batch 0.524 (0.508) Remain 00:32:41 loss: 0.2137 Lr: 0.00044 [2024-11-25 19:20:19,909 INFO misc.py line 119 2586773] Train: [40/50][275/376] Data 0.003 (0.002) Batch 0.501 (0.508) Remain 00:32:40 loss: 0.2491 Lr: 0.00044 [2024-11-25 19:20:20,382 INFO misc.py line 119 2586773] Train: [40/50][276/376] Data 0.002 (0.002) Batch 0.473 (0.508) Remain 00:32:39 loss: 0.2366 Lr: 0.00044 [2024-11-25 19:20:20,904 INFO misc.py line 119 2586773] Train: [40/50][277/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 00:32:39 loss: 0.1996 Lr: 0.00044 [2024-11-25 19:20:21,393 INFO misc.py line 119 2586773] Train: 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Batch 0.520 (0.508) Remain 00:32:35 loss: 0.2614 Lr: 0.00044 [2024-11-25 19:20:24,969 INFO misc.py line 119 2586773] Train: [40/50][285/376] Data 0.002 (0.002) Batch 0.542 (0.508) Remain 00:32:35 loss: 0.1598 Lr: 0.00044 [2024-11-25 19:20:25,501 INFO misc.py line 119 2586773] Train: [40/50][286/376] Data 0.002 (0.002) Batch 0.533 (0.508) Remain 00:32:35 loss: 0.2032 Lr: 0.00044 [2024-11-25 19:20:26,036 INFO misc.py line 119 2586773] Train: [40/50][287/376] Data 0.002 (0.002) Batch 0.535 (0.508) Remain 00:32:35 loss: 0.2526 Lr: 0.00044 [2024-11-25 19:20:26,513 INFO misc.py line 119 2586773] Train: [40/50][288/376] Data 0.002 (0.002) Batch 0.477 (0.508) Remain 00:32:34 loss: 0.2151 Lr: 0.00044 [2024-11-25 19:20:26,996 INFO misc.py line 119 2586773] Train: [40/50][289/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 00:32:33 loss: 0.1849 Lr: 0.00044 [2024-11-25 19:20:27,492 INFO misc.py line 119 2586773] Train: [40/50][290/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 00:32:32 loss: 0.1792 Lr: 0.00044 [2024-11-25 19:20:27,967 INFO misc.py line 119 2586773] Train: [40/50][291/376] Data 0.002 (0.002) Batch 0.475 (0.508) Remain 00:32:31 loss: 0.2440 Lr: 0.00044 [2024-11-25 19:20:28,469 INFO misc.py line 119 2586773] Train: [40/50][292/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 00:32:31 loss: 0.1689 Lr: 0.00044 [2024-11-25 19:20:28,972 INFO misc.py line 119 2586773] Train: [40/50][293/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 00:32:30 loss: 0.2716 Lr: 0.00044 [2024-11-25 19:20:29,507 INFO misc.py line 119 2586773] Train: [40/50][294/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 00:32:30 loss: 0.1568 Lr: 0.00044 [2024-11-25 19:20:30,000 INFO misc.py line 119 2586773] Train: [40/50][295/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 00:32:29 loss: 0.1987 Lr: 0.00044 [2024-11-25 19:20:30,502 INFO misc.py line 119 2586773] Train: [40/50][296/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:32:29 loss: 0.1713 Lr: 0.00044 [2024-11-25 19:20:30,971 INFO misc.py line 119 2586773] Train: [40/50][297/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:32:28 loss: 0.1738 Lr: 0.00044 [2024-11-25 19:20:31,513 INFO misc.py line 119 2586773] Train: [40/50][298/376] Data 0.002 (0.002) Batch 0.542 (0.508) Remain 00:32:28 loss: 0.1802 Lr: 0.00044 [2024-11-25 19:20:32,028 INFO misc.py line 119 2586773] Train: [40/50][299/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:32:27 loss: 0.1806 Lr: 0.00044 [2024-11-25 19:20:32,516 INFO misc.py line 119 2586773] Train: [40/50][300/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 00:32:27 loss: 0.1923 Lr: 0.00044 [2024-11-25 19:20:33,039 INFO misc.py line 119 2586773] Train: [40/50][301/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 00:32:26 loss: 0.2205 Lr: 0.00043 [2024-11-25 19:20:33,586 INFO misc.py line 119 2586773] Train: [40/50][302/376] Data 0.002 (0.002) Batch 0.547 (0.508) Remain 00:32:26 loss: 0.2505 Lr: 0.00043 [2024-11-25 19:20:34,099 INFO misc.py line 119 2586773] Train: [40/50][303/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 00:32:26 loss: 0.1846 Lr: 0.00043 [2024-11-25 19:20:34,606 INFO misc.py line 119 2586773] Train: [40/50][304/376] Data 0.003 (0.002) Batch 0.507 (0.508) Remain 00:32:25 loss: 0.1992 Lr: 0.00043 [2024-11-25 19:20:35,100 INFO misc.py line 119 2586773] Train: [40/50][305/376] Data 0.002 (0.002) Batch 0.493 (0.508) Remain 00:32:25 loss: 0.3098 Lr: 0.00043 [2024-11-25 19:20:35,627 INFO misc.py line 119 2586773] Train: [40/50][306/376] Data 0.002 (0.002) Batch 0.527 (0.508) Remain 00:32:24 loss: 0.1753 Lr: 0.00043 [2024-11-25 19:20:36,124 INFO misc.py line 119 2586773] Train: [40/50][307/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 00:32:24 loss: 0.2337 Lr: 0.00043 [2024-11-25 19:20:36,633 INFO misc.py line 119 2586773] Train: [40/50][308/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:32:23 loss: 0.2135 Lr: 0.00043 [2024-11-25 19:20:37,165 INFO misc.py line 119 2586773] Train: [40/50][309/376] Data 0.002 (0.002) Batch 0.532 (0.508) Remain 00:32:23 loss: 0.2551 Lr: 0.00043 [2024-11-25 19:20:37,679 INFO misc.py line 119 2586773] Train: [40/50][310/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:32:23 loss: 0.1649 Lr: 0.00043 [2024-11-25 19:20:38,176 INFO misc.py line 119 2586773] Train: [40/50][311/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 00:32:22 loss: 0.1677 Lr: 0.00043 [2024-11-25 19:20:38,722 INFO misc.py line 119 2586773] Train: [40/50][312/376] Data 0.002 (0.002) Batch 0.546 (0.508) Remain 00:32:22 loss: 0.1729 Lr: 0.00043 [2024-11-25 19:20:39,251 INFO misc.py line 119 2586773] Train: [40/50][313/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 00:32:22 loss: 0.1957 Lr: 0.00043 [2024-11-25 19:20:39,715 INFO misc.py line 119 2586773] Train: [40/50][314/376] Data 0.002 (0.002) Batch 0.464 (0.508) Remain 00:32:21 loss: 0.1899 Lr: 0.00043 [2024-11-25 19:20:40,217 INFO misc.py line 119 2586773] Train: [40/50][315/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 00:32:20 loss: 0.2016 Lr: 0.00043 [2024-11-25 19:20:40,726 INFO misc.py line 119 2586773] Train: [40/50][316/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:32:19 loss: 0.1732 Lr: 0.00043 [2024-11-25 19:20:41,239 INFO misc.py line 119 2586773] Train: [40/50][317/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 00:32:19 loss: 0.2233 Lr: 0.00043 [2024-11-25 19:20:41,751 INFO misc.py line 119 2586773] Train: [40/50][318/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 00:32:19 loss: 0.2439 Lr: 0.00043 [2024-11-25 19:20:42,236 INFO misc.py line 119 2586773] Train: [40/50][319/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 00:32:18 loss: 0.2294 Lr: 0.00043 [2024-11-25 19:20:42,726 INFO misc.py line 119 2586773] Train: [40/50][320/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 00:32:17 loss: 0.1932 Lr: 0.00043 [2024-11-25 19:20:43,240 INFO misc.py line 119 2586773] Train: [40/50][321/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:32:17 loss: 0.1478 Lr: 0.00043 [2024-11-25 19:20:43,764 INFO misc.py line 119 2586773] Train: [40/50][322/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 00:32:16 loss: 0.2055 Lr: 0.00043 [2024-11-25 19:20:44,303 INFO misc.py line 119 2586773] Train: [40/50][323/376] Data 0.002 (0.002) Batch 0.539 (0.508) Remain 00:32:16 loss: 0.2542 Lr: 0.00043 [2024-11-25 19:20:44,783 INFO misc.py line 119 2586773] Train: [40/50][324/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 00:32:15 loss: 0.2536 Lr: 0.00043 [2024-11-25 19:20:45,322 INFO misc.py line 119 2586773] Train: [40/50][325/376] Data 0.002 (0.002) Batch 0.538 (0.508) Remain 00:32:15 loss: 0.2169 Lr: 0.00043 [2024-11-25 19:20:45,822 INFO misc.py line 119 2586773] Train: [40/50][326/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 00:32:15 loss: 0.1734 Lr: 0.00043 [2024-11-25 19:20:46,320 INFO misc.py line 119 2586773] Train: [40/50][327/376] Data 0.003 (0.002) Batch 0.498 (0.508) Remain 00:32:14 loss: 0.2234 Lr: 0.00043 [2024-11-25 19:20:46,823 INFO misc.py line 119 2586773] Train: [40/50][328/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:32:13 loss: 0.1884 Lr: 0.00043 [2024-11-25 19:20:47,303 INFO misc.py line 119 2586773] Train: [40/50][329/376] Data 0.003 (0.002) Batch 0.480 (0.508) Remain 00:32:13 loss: 0.2066 Lr: 0.00043 [2024-11-25 19:20:47,832 INFO misc.py line 119 2586773] Train: [40/50][330/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 00:32:12 loss: 0.1876 Lr: 0.00043 [2024-11-25 19:20:48,338 INFO misc.py line 119 2586773] Train: [40/50][331/376] Data 0.002 (0.002) Batch 0.506 (0.508) Remain 00:32:12 loss: 0.1923 Lr: 0.00043 [2024-11-25 19:20:48,858 INFO misc.py line 119 2586773] Train: [40/50][332/376] Data 0.002 (0.002) Batch 0.520 (0.508) Remain 00:32:11 loss: 0.1705 Lr: 0.00043 [2024-11-25 19:20:49,393 INFO misc.py line 119 2586773] Train: [40/50][333/376] Data 0.002 (0.002) Batch 0.535 (0.508) Remain 00:32:11 loss: 0.2539 Lr: 0.00043 [2024-11-25 19:20:49,893 INFO misc.py line 119 2586773] Train: [40/50][334/376] Data 0.003 (0.002) Batch 0.500 (0.508) Remain 00:32:11 loss: 0.1779 Lr: 0.00043 [2024-11-25 19:20:50,404 INFO misc.py line 119 2586773] Train: [40/50][335/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:32:10 loss: 0.1964 Lr: 0.00043 [2024-11-25 19:20:50,861 INFO misc.py line 119 2586773] Train: [40/50][336/376] Data 0.002 (0.002) Batch 0.457 (0.508) Remain 00:32:09 loss: 0.2001 Lr: 0.00043 [2024-11-25 19:20:51,350 INFO misc.py line 119 2586773] Train: [40/50][337/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:32:08 loss: 0.2068 Lr: 0.00043 [2024-11-25 19:20:51,805 INFO misc.py line 119 2586773] Train: [40/50][338/376] Data 0.002 (0.002) Batch 0.455 (0.508) Remain 00:32:07 loss: 0.2114 Lr: 0.00043 [2024-11-25 19:20:52,302 INFO misc.py line 119 2586773] Train: [40/50][339/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 00:32:07 loss: 0.1898 Lr: 0.00043 [2024-11-25 19:20:52,797 INFO misc.py line 119 2586773] Train: [40/50][340/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:32:06 loss: 0.1734 Lr: 0.00043 [2024-11-25 19:20:53,251 INFO misc.py line 119 2586773] Train: [40/50][341/376] Data 0.002 (0.002) Batch 0.455 (0.507) Remain 00:32:05 loss: 0.2152 Lr: 0.00043 [2024-11-25 19:20:53,778 INFO misc.py line 119 2586773] Train: [40/50][342/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 00:32:05 loss: 0.1691 Lr: 0.00043 [2024-11-25 19:20:54,294 INFO misc.py line 119 2586773] Train: [40/50][343/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:32:04 loss: 0.2490 Lr: 0.00043 [2024-11-25 19:20:54,818 INFO misc.py line 119 2586773] Train: [40/50][344/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 00:32:04 loss: 0.1782 Lr: 0.00043 [2024-11-25 19:20:55,298 INFO misc.py line 119 2586773] Train: [40/50][345/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:32:03 loss: 0.2850 Lr: 0.00043 [2024-11-25 19:20:55,811 INFO misc.py line 119 2586773] Train: [40/50][346/376] Data 0.002 (0.002) Batch 0.513 (0.507) Remain 00:32:03 loss: 0.1709 Lr: 0.00043 [2024-11-25 19:20:56,353 INFO misc.py line 119 2586773] Train: [40/50][347/376] Data 0.002 (0.002) Batch 0.542 (0.508) Remain 00:32:02 loss: 0.1729 Lr: 0.00042 [2024-11-25 19:20:56,831 INFO misc.py line 119 2586773] Train: [40/50][348/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 00:32:02 loss: 0.1812 Lr: 0.00042 [2024-11-25 19:20:57,348 INFO misc.py line 119 2586773] Train: [40/50][349/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 00:32:01 loss: 0.2338 Lr: 0.00042 [2024-11-25 19:20:57,837 INFO misc.py line 119 2586773] Train: [40/50][350/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:32:01 loss: 0.1810 Lr: 0.00042 [2024-11-25 19:20:58,365 INFO misc.py line 119 2586773] Train: [40/50][351/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:32:00 loss: 0.1828 Lr: 0.00042 [2024-11-25 19:20:58,870 INFO misc.py line 119 2586773] Train: [40/50][352/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:32:00 loss: 0.1577 Lr: 0.00042 [2024-11-25 19:20:59,365 INFO misc.py line 119 2586773] Train: [40/50][353/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:31:59 loss: 0.1633 Lr: 0.00042 [2024-11-25 19:20:59,875 INFO misc.py line 119 2586773] Train: [40/50][354/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 00:31:59 loss: 0.2120 Lr: 0.00042 [2024-11-25 19:21:00,382 INFO misc.py line 119 2586773] Train: [40/50][355/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 00:31:58 loss: 0.1943 Lr: 0.00042 [2024-11-25 19:21:00,910 INFO misc.py line 119 2586773] Train: [40/50][356/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:31:58 loss: 0.2266 Lr: 0.00042 [2024-11-25 19:21:01,471 INFO misc.py line 119 2586773] Train: [40/50][357/376] Data 0.002 (0.002) Batch 0.561 (0.508) Remain 00:31:58 loss: 0.2138 Lr: 0.00042 [2024-11-25 19:21:01,923 INFO misc.py line 119 2586773] Train: [40/50][358/376] Data 0.002 (0.002) Batch 0.452 (0.507) Remain 00:31:57 loss: 0.2492 Lr: 0.00042 [2024-11-25 19:21:02,424 INFO misc.py line 119 2586773] Train: [40/50][359/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:31:56 loss: 0.1926 Lr: 0.00042 [2024-11-25 19:21:02,930 INFO misc.py line 119 2586773] Train: [40/50][360/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:31:56 loss: 0.1840 Lr: 0.00042 [2024-11-25 19:21:03,453 INFO misc.py line 119 2586773] Train: [40/50][361/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:31:55 loss: 0.1714 Lr: 0.00042 [2024-11-25 19:21:04,014 INFO misc.py line 119 2586773] Train: [40/50][362/376] Data 0.002 (0.002) Batch 0.561 (0.508) Remain 00:31:55 loss: 0.2141 Lr: 0.00042 [2024-11-25 19:21:04,542 INFO misc.py line 119 2586773] Train: [40/50][363/376] Data 0.002 (0.002) Batch 0.528 (0.508) Remain 00:31:55 loss: 0.2088 Lr: 0.00042 [2024-11-25 19:21:05,010 INFO misc.py line 119 2586773] Train: [40/50][364/376] Data 0.002 (0.002) Batch 0.468 (0.508) Remain 00:31:54 loss: 0.2515 Lr: 0.00042 [2024-11-25 19:21:05,480 INFO misc.py line 119 2586773] Train: [40/50][365/376] Data 0.002 (0.002) Batch 0.470 (0.507) Remain 00:31:53 loss: 0.2578 Lr: 0.00042 [2024-11-25 19:21:05,992 INFO misc.py line 119 2586773] Train: [40/50][366/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 00:31:53 loss: 0.1908 Lr: 0.00042 [2024-11-25 19:21:06,474 INFO misc.py line 119 2586773] Train: [40/50][367/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 00:31:52 loss: 0.1584 Lr: 0.00042 [2024-11-25 19:21:06,994 INFO misc.py line 119 2586773] Train: [40/50][368/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:31:52 loss: 0.2614 Lr: 0.00042 [2024-11-25 19:21:07,520 INFO misc.py line 119 2586773] Train: [40/50][369/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 00:31:51 loss: 0.1780 Lr: 0.00042 [2024-11-25 19:21:08,000 INFO misc.py line 119 2586773] Train: [40/50][370/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:31:51 loss: 0.1904 Lr: 0.00042 [2024-11-25 19:21:08,476 INFO misc.py line 119 2586773] Train: [40/50][371/376] Data 0.002 (0.002) Batch 0.476 (0.507) Remain 00:31:50 loss: 0.1939 Lr: 0.00042 [2024-11-25 19:21:08,961 INFO misc.py line 119 2586773] Train: [40/50][372/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:31:49 loss: 0.1902 Lr: 0.00042 [2024-11-25 19:21:09,440 INFO misc.py line 119 2586773] Train: [40/50][373/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:31:48 loss: 0.1972 Lr: 0.00042 [2024-11-25 19:21:09,935 INFO misc.py line 119 2586773] Train: [40/50][374/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:31:48 loss: 0.1740 Lr: 0.00042 [2024-11-25 19:21:10,390 INFO misc.py line 119 2586773] Train: [40/50][375/376] Data 0.002 (0.002) Batch 0.455 (0.507) Remain 00:31:47 loss: 0.1606 Lr: 0.00042 [2024-11-25 19:21:10,918 INFO misc.py line 119 2586773] Train: [40/50][376/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:31:46 loss: 0.2026 Lr: 0.00042 [2024-11-25 19:21:10,919 INFO misc.py line 136 2586773] Train result: loss: 0.2011 [2024-11-25 19:21:10,919 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:21:21,698 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1716 [2024-11-25 19:21:21,954 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2000 [2024-11-25 19:21:22,216 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2836 [2024-11-25 19:21:22,440 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2009 [2024-11-25 19:21:22,702 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2851 [2024-11-25 19:21:22,976 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1934 [2024-11-25 19:21:23,199 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2009 [2024-11-25 19:21:23,468 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2269 [2024-11-25 19:21:23,692 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2641 [2024-11-25 19:21:23,954 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2349 [2024-11-25 19:21:24,193 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2116 [2024-11-25 19:21:24,465 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2391 [2024-11-25 19:21:24,730 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2465 [2024-11-25 19:21:24,994 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2338 [2024-11-25 19:21:25,226 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2307 [2024-11-25 19:21:25,467 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3094 [2024-11-25 19:21:25,744 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2794 [2024-11-25 19:21:25,990 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1895 [2024-11-25 19:21:26,221 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2469 [2024-11-25 19:21:26,480 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2202 [2024-11-25 19:21:26,715 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2476 [2024-11-25 19:21:26,984 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2372 [2024-11-25 19:21:27,224 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2200 [2024-11-25 19:21:27,490 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2392 [2024-11-25 19:21:27,753 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2192 [2024-11-25 19:21:27,992 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2458 [2024-11-25 19:21:28,254 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2509 [2024-11-25 19:21:28,500 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2142 [2024-11-25 19:21:28,768 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2640 [2024-11-25 19:21:29,021 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2877 [2024-11-25 19:21:29,254 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2607 [2024-11-25 19:21:29,518 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2071 [2024-11-25 19:21:29,744 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2707 [2024-11-25 19:21:29,983 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2328 [2024-11-25 19:21:30,245 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1959 [2024-11-25 19:21:30,489 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2503 [2024-11-25 19:21:30,716 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1888 [2024-11-25 19:21:30,983 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2358 [2024-11-25 19:21:31,213 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2542 [2024-11-25 19:21:31,449 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2281 [2024-11-25 19:21:31,719 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.2936 [2024-11-25 19:21:31,972 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2632 [2024-11-25 19:21:32,217 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2384 [2024-11-25 19:21:32,448 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2190 [2024-11-25 19:21:32,684 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2415 [2024-11-25 19:21:32,932 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2333 [2024-11-25 19:21:33,188 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2221 [2024-11-25 19:21:33,442 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2840 [2024-11-25 19:21:33,666 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2157 [2024-11-25 19:21:33,899 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2127 [2024-11-25 19:21:34,123 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2374 [2024-11-25 19:21:34,376 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2303 [2024-11-25 19:21:34,641 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2252 [2024-11-25 19:21:34,901 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.2990 [2024-11-25 19:21:35,133 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2352 [2024-11-25 19:21:35,377 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2248 [2024-11-25 19:21:35,634 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2476 [2024-11-25 19:21:35,901 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2687 [2024-11-25 19:21:36,156 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2372 [2024-11-25 19:21:36,417 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2450 [2024-11-25 19:21:36,668 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2163 [2024-11-25 19:21:36,938 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2339 [2024-11-25 19:21:37,167 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2338 [2024-11-25 19:21:37,425 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2451 [2024-11-25 19:21:37,693 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2465 [2024-11-25 19:21:37,964 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1900 [2024-11-25 19:21:38,214 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1891 [2024-11-25 19:21:38,470 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2484 [2024-11-25 19:21:38,739 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2447 [2024-11-25 19:21:39,002 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2595 [2024-11-25 19:21:39,246 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2202 [2024-11-25 19:21:39,480 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2742 [2024-11-25 19:21:39,735 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2644 [2024-11-25 19:21:39,979 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2441 [2024-11-25 19:21:40,203 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2550 [2024-11-25 19:21:40,425 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2060 [2024-11-25 19:21:40,695 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2498 [2024-11-25 19:21:40,932 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2115 [2024-11-25 19:21:41,187 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2257 [2024-11-25 19:21:41,438 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2831 [2024-11-25 19:21:41,681 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2121 [2024-11-25 19:21:41,943 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2580 [2024-11-25 19:21:42,196 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1917 [2024-11-25 19:21:42,442 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2375 [2024-11-25 19:21:42,713 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2555 [2024-11-25 19:21:42,954 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2519 [2024-11-25 19:21:43,217 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2477 [2024-11-25 19:21:43,477 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2314 [2024-11-25 19:21:43,725 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2639 [2024-11-25 19:21:43,973 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2719 [2024-11-25 19:21:44,215 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2342 [2024-11-25 19:21:44,470 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2647 [2024-11-25 19:21:44,738 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2331 [2024-11-25 19:21:45,004 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1885 [2024-11-25 19:21:45,268 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2257 [2024-11-25 19:21:45,517 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2208 [2024-11-25 19:21:45,787 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2194 [2024-11-25 19:21:46,005 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2834 [2024-11-25 19:21:46,282 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2275 [2024-11-25 19:21:46,518 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2424 [2024-11-25 19:21:46,789 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2035 [2024-11-25 19:21:47,051 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2505 [2024-11-25 19:21:47,319 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2306 [2024-11-25 19:21:47,574 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2691 [2024-11-25 19:21:47,795 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2316 [2024-11-25 19:21:48,033 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2178 [2024-11-25 19:21:48,291 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2085 [2024-11-25 19:21:48,559 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2387 [2024-11-25 19:21:48,792 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2521 [2024-11-25 19:21:49,054 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2075 [2024-11-25 19:21:49,317 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2408 [2024-11-25 19:21:49,541 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2171 [2024-11-25 19:21:49,777 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2144 [2024-11-25 19:21:49,997 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2169 [2024-11-25 19:21:50,223 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2249 [2024-11-25 19:21:50,496 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2957 [2024-11-25 19:21:50,761 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2525 [2024-11-25 19:21:51,030 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2468 [2024-11-25 19:21:51,293 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2102 [2024-11-25 19:21:51,555 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3094 [2024-11-25 19:21:51,818 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2773 [2024-11-25 19:21:52,080 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1977 [2024-11-25 19:21:52,335 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2410 [2024-11-25 19:21:52,597 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2442 [2024-11-25 19:21:52,860 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2476 [2024-11-25 19:21:53,111 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2485 [2024-11-25 19:21:53,342 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1899 [2024-11-25 19:21:53,600 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2477 [2024-11-25 19:21:53,835 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2522 [2024-11-25 19:21:54,062 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.2009 [2024-11-25 19:21:54,275 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2266 [2024-11-25 19:21:54,491 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1935 [2024-11-25 19:21:55,139 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7786/0.8499/0.9964. [2024-11-25 19:21:55,139 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9964/0.9983 [2024-11-25 19:21:55,139 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5608/0.7015 [2024-11-25 19:21:55,140 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:21:55,141 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:21:55,141 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:21:57,817 INFO misc.py line 119 2586773] Train: [41/50][1/376] Data 0.086 (0.086) Batch 0.593 (0.593) Remain 00:37:10 loss: 0.1946 Lr: 0.00042 [2024-11-25 19:21:58,315 INFO misc.py line 119 2586773] Train: [41/50][2/376] Data 0.002 (0.002) Batch 0.498 (0.498) Remain 00:31:12 loss: 0.2143 Lr: 0.00042 [2024-11-25 19:21:58,818 INFO misc.py line 119 2586773] Train: [41/50][3/376] Data 0.002 (0.002) Batch 0.502 (0.502) Remain 00:31:26 loss: 0.2245 Lr: 0.00042 [2024-11-25 19:21:59,315 INFO misc.py line 119 2586773] Train: [41/50][4/376] Data 0.003 (0.003) Batch 0.497 (0.497) Remain 00:31:06 loss: 0.1890 Lr: 0.00042 [2024-11-25 19:21:59,811 INFO misc.py line 119 2586773] Train: [41/50][5/376] Data 0.002 (0.002) Batch 0.496 (0.496) Remain 00:31:04 loss: 0.1516 Lr: 0.00042 [2024-11-25 19:22:00,330 INFO misc.py line 119 2586773] Train: [41/50][6/376] Data 0.002 (0.002) Batch 0.519 (0.504) Remain 00:31:32 loss: 0.2275 Lr: 0.00042 [2024-11-25 19:22:00,866 INFO misc.py line 119 2586773] Train: [41/50][7/376] Data 0.002 (0.002) Batch 0.536 (0.512) Remain 00:32:01 loss: 0.2309 Lr: 0.00042 [2024-11-25 19:22:01,386 INFO misc.py line 119 2586773] Train: [41/50][8/376] Data 0.002 (0.002) Batch 0.520 (0.514) Remain 00:32:07 loss: 0.2528 Lr: 0.00042 [2024-11-25 19:22:01,902 INFO misc.py line 119 2586773] Train: [41/50][9/376] Data 0.002 (0.002) Batch 0.516 (0.514) Remain 00:32:08 loss: 0.1767 Lr: 0.00042 [2024-11-25 19:22:02,434 INFO misc.py line 119 2586773] Train: [41/50][10/376] Data 0.002 (0.002) Batch 0.532 (0.517) Remain 00:32:17 loss: 0.1972 Lr: 0.00042 [2024-11-25 19:22:02,968 INFO misc.py line 119 2586773] Train: [41/50][11/376] Data 0.002 (0.002) Batch 0.534 (0.519) Remain 00:32:24 loss: 0.1684 Lr: 0.00042 [2024-11-25 19:22:03,467 INFO misc.py line 119 2586773] Train: [41/50][12/376] Data 0.002 (0.002) Batch 0.499 (0.517) Remain 00:32:16 loss: 0.1759 Lr: 0.00042 [2024-11-25 19:22:03,929 INFO misc.py line 119 2586773] Train: [41/50][13/376] Data 0.003 (0.002) Batch 0.462 (0.511) Remain 00:31:55 loss: 0.1645 Lr: 0.00042 [2024-11-25 19:22:04,471 INFO misc.py line 119 2586773] Train: [41/50][14/376] Data 0.002 (0.002) Batch 0.542 (0.514) Remain 00:32:05 loss: 0.2288 Lr: 0.00042 [2024-11-25 19:22:05,006 INFO misc.py line 119 2586773] Train: [41/50][15/376] Data 0.002 (0.002) Batch 0.535 (0.516) Remain 00:32:11 loss: 0.3415 Lr: 0.00042 [2024-11-25 19:22:05,513 INFO misc.py line 119 2586773] Train: [41/50][16/376] Data 0.002 (0.002) Batch 0.508 (0.515) Remain 00:32:08 loss: 0.3535 Lr: 0.00042 [2024-11-25 19:22:05,975 INFO misc.py line 119 2586773] Train: [41/50][17/376] Data 0.002 (0.002) Batch 0.461 (0.511) Remain 00:31:53 loss: 0.1629 Lr: 0.00042 [2024-11-25 19:22:06,462 INFO misc.py line 119 2586773] Train: [41/50][18/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 00:31:46 loss: 0.1942 Lr: 0.00041 [2024-11-25 19:22:06,960 INFO misc.py line 119 2586773] Train: [41/50][19/376] Data 0.002 (0.002) Batch 0.498 (0.509) Remain 00:31:43 loss: 0.1910 Lr: 0.00041 [2024-11-25 19:22:07,452 INFO misc.py line 119 2586773] Train: [41/50][20/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 00:31:39 loss: 0.1604 Lr: 0.00041 [2024-11-25 19:22:07,966 INFO misc.py line 119 2586773] Train: [41/50][21/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:31:40 loss: 0.1797 Lr: 0.00041 [2024-11-25 19:22:08,448 INFO misc.py line 119 2586773] Train: [41/50][22/376] Data 0.003 (0.002) Batch 0.482 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loss: 0.1458 Lr: 0.00036 [2024-11-25 19:24:25,095 INFO misc.py line 119 2586773] Train: [41/50][291/376] Data 0.003 (0.002) Batch 0.511 (0.508) Remain 00:29:21 loss: 0.1647 Lr: 0.00036 [2024-11-25 19:24:25,598 INFO misc.py line 119 2586773] Train: [41/50][292/376] Data 0.002 (0.002) Batch 0.503 (0.508) Remain 00:29:21 loss: 0.3546 Lr: 0.00036 [2024-11-25 19:24:26,071 INFO misc.py line 119 2586773] Train: [41/50][293/376] Data 0.002 (0.002) Batch 0.472 (0.508) Remain 00:29:20 loss: 0.2083 Lr: 0.00036 [2024-11-25 19:24:26,607 INFO misc.py line 119 2586773] Train: [41/50][294/376] Data 0.003 (0.002) Batch 0.537 (0.508) Remain 00:29:20 loss: 0.2048 Lr: 0.00036 [2024-11-25 19:24:27,106 INFO misc.py line 119 2586773] Train: [41/50][295/376] Data 0.003 (0.002) Batch 0.499 (0.508) Remain 00:29:19 loss: 0.1744 Lr: 0.00036 [2024-11-25 19:24:27,582 INFO misc.py line 119 2586773] Train: [41/50][296/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 00:29:18 loss: 0.3097 Lr: 0.00036 [2024-11-25 19:24:28,106 INFO misc.py line 119 2586773] Train: [41/50][297/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 00:29:18 loss: 0.1993 Lr: 0.00036 [2024-11-25 19:24:28,594 INFO misc.py line 119 2586773] Train: [41/50][298/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:29:17 loss: 0.1748 Lr: 0.00036 [2024-11-25 19:24:29,070 INFO misc.py line 119 2586773] Train: [41/50][299/376] Data 0.002 (0.002) Batch 0.476 (0.508) Remain 00:29:16 loss: 0.1601 Lr: 0.00036 [2024-11-25 19:24:29,541 INFO misc.py line 119 2586773] Train: [41/50][300/376] Data 0.002 (0.002) Batch 0.471 (0.507) Remain 00:29:15 loss: 0.1923 Lr: 0.00036 [2024-11-25 19:24:30,051 INFO misc.py line 119 2586773] Train: [41/50][301/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:29:15 loss: 0.2282 Lr: 0.00036 [2024-11-25 19:24:30,540 INFO misc.py line 119 2586773] Train: [41/50][302/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:29:14 loss: 0.2374 Lr: 0.00036 [2024-11-25 19:24:31,076 INFO misc.py line 119 2586773] Train: [41/50][303/376] Data 0.002 (0.002) Batch 0.537 (0.508) Remain 00:29:14 loss: 0.2200 Lr: 0.00036 [2024-11-25 19:24:31,584 INFO misc.py line 119 2586773] Train: [41/50][304/376] Data 0.002 (0.002) Batch 0.507 (0.508) Remain 00:29:14 loss: 0.1948 Lr: 0.00036 [2024-11-25 19:24:32,105 INFO misc.py line 119 2586773] Train: [41/50][305/376] Data 0.003 (0.002) Batch 0.521 (0.508) Remain 00:29:13 loss: 0.1788 Lr: 0.00036 [2024-11-25 19:24:32,615 INFO misc.py line 119 2586773] Train: [41/50][306/376] Data 0.002 (0.002) Batch 0.510 (0.508) Remain 00:29:13 loss: 0.2363 Lr: 0.00036 [2024-11-25 19:24:33,142 INFO misc.py line 119 2586773] Train: [41/50][307/376] Data 0.003 (0.002) Batch 0.528 (0.508) Remain 00:29:12 loss: 0.2204 Lr: 0.00036 [2024-11-25 19:24:33,678 INFO misc.py line 119 2586773] Train: [41/50][308/376] Data 0.002 (0.002) Batch 0.535 (0.508) Remain 00:29:12 loss: 0.2148 Lr: 0.00036 [2024-11-25 19:24:34,171 INFO misc.py line 119 2586773] Train: [41/50][309/376] Data 0.003 (0.002) Batch 0.494 (0.508) Remain 00:29:12 loss: 0.1786 Lr: 0.00036 [2024-11-25 19:24:34,716 INFO misc.py line 119 2586773] Train: [41/50][310/376] Data 0.002 (0.002) Batch 0.545 (0.508) Remain 00:29:11 loss: 0.2003 Lr: 0.00036 [2024-11-25 19:24:35,194 INFO misc.py line 119 2586773] Train: [41/50][311/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 00:29:11 loss: 0.1630 Lr: 0.00036 [2024-11-25 19:24:35,695 INFO misc.py line 119 2586773] Train: [41/50][312/376] Data 0.002 (0.002) Batch 0.500 (0.508) Remain 00:29:10 loss: 0.2836 Lr: 0.00035 [2024-11-25 19:24:36,200 INFO misc.py line 119 2586773] Train: [41/50][313/376] Data 0.002 (0.002) Batch 0.506 (0.508) Remain 00:29:09 loss: 0.1985 Lr: 0.00035 [2024-11-25 19:24:36,685 INFO misc.py line 119 2586773] Train: [41/50][314/376] Data 0.003 (0.002) Batch 0.485 (0.508) Remain 00:29:09 loss: 0.1863 Lr: 0.00035 [2024-11-25 19:24:37,189 INFO misc.py line 119 2586773] Train: [41/50][315/376] Data 0.003 (0.002) Batch 0.504 (0.508) Remain 00:29:08 loss: 0.1965 Lr: 0.00035 [2024-11-25 19:24:37,721 INFO misc.py line 119 2586773] Train: [41/50][316/376] Data 0.003 (0.002) Batch 0.532 (0.508) Remain 00:29:08 loss: 0.1949 Lr: 0.00035 [2024-11-25 19:24:38,269 INFO misc.py line 119 2586773] Train: [41/50][317/376] Data 0.003 (0.002) Batch 0.548 (0.508) Remain 00:29:08 loss: 0.2002 Lr: 0.00035 [2024-11-25 19:24:38,801 INFO misc.py line 119 2586773] Train: [41/50][318/376] Data 0.003 (0.002) Batch 0.531 (0.508) Remain 00:29:08 loss: 0.2393 Lr: 0.00035 [2024-11-25 19:24:39,279 INFO misc.py line 119 2586773] Train: [41/50][319/376] Data 0.003 (0.002) Batch 0.479 (0.508) Remain 00:29:07 loss: 0.3559 Lr: 0.00035 [2024-11-25 19:24:39,787 INFO misc.py line 119 2586773] Train: [41/50][320/376] Data 0.002 (0.002) Batch 0.507 (0.508) Remain 00:29:06 loss: 0.1802 Lr: 0.00035 [2024-11-25 19:24:40,298 INFO misc.py line 119 2586773] Train: [41/50][321/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 00:29:06 loss: 0.1565 Lr: 0.00035 [2024-11-25 19:24:40,783 INFO misc.py line 119 2586773] Train: [41/50][322/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:29:05 loss: 0.2998 Lr: 0.00035 [2024-11-25 19:24:41,312 INFO misc.py line 119 2586773] Train: [41/50][323/376] Data 0.003 (0.002) Batch 0.530 (0.508) Remain 00:29:05 loss: 0.1664 Lr: 0.00035 [2024-11-25 19:24:41,770 INFO misc.py line 119 2586773] Train: [41/50][324/376] Data 0.002 (0.002) Batch 0.457 (0.508) Remain 00:29:04 loss: 0.1806 Lr: 0.00035 [2024-11-25 19:24:42,285 INFO misc.py line 119 2586773] Train: [41/50][325/376] Data 0.003 (0.002) Batch 0.515 (0.508) Remain 00:29:03 loss: 0.1855 Lr: 0.00035 [2024-11-25 19:24:42,774 INFO misc.py line 119 2586773] Train: [41/50][326/376] Data 0.003 (0.002) Batch 0.489 (0.508) Remain 00:29:03 loss: 0.1722 Lr: 0.00035 [2024-11-25 19:24:43,295 INFO misc.py line 119 2586773] Train: [41/50][327/376] Data 0.003 (0.002) Batch 0.521 (0.508) Remain 00:29:02 loss: 0.2514 Lr: 0.00035 [2024-11-25 19:24:43,799 INFO misc.py line 119 2586773] Train: [41/50][328/376] Data 0.003 (0.002) Batch 0.504 (0.508) Remain 00:29:02 loss: 0.1946 Lr: 0.00035 [2024-11-25 19:24:44,285 INFO misc.py line 119 2586773] Train: [41/50][329/376] Data 0.003 (0.002) Batch 0.486 (0.508) Remain 00:29:01 loss: 0.2024 Lr: 0.00035 [2024-11-25 19:24:44,804 INFO misc.py line 119 2586773] Train: [41/50][330/376] Data 0.003 (0.002) Batch 0.518 (0.508) Remain 00:29:01 loss: 0.2254 Lr: 0.00035 [2024-11-25 19:24:45,307 INFO misc.py line 119 2586773] Train: [41/50][331/376] Data 0.003 (0.002) Batch 0.503 (0.508) Remain 00:29:00 loss: 0.1791 Lr: 0.00035 [2024-11-25 19:24:45,803 INFO misc.py line 119 2586773] Train: [41/50][332/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 00:28:59 loss: 0.1993 Lr: 0.00035 [2024-11-25 19:24:46,321 INFO misc.py line 119 2586773] Train: [41/50][333/376] Data 0.003 (0.002) Batch 0.519 (0.508) Remain 00:28:59 loss: 0.2048 Lr: 0.00035 [2024-11-25 19:24:46,806 INFO misc.py line 119 2586773] Train: [41/50][334/376] Data 0.003 (0.002) Batch 0.485 (0.508) Remain 00:28:58 loss: 0.1811 Lr: 0.00035 [2024-11-25 19:24:47,342 INFO misc.py line 119 2586773] Train: [41/50][335/376] Data 0.003 (0.002) Batch 0.536 (0.508) Remain 00:28:58 loss: 0.1898 Lr: 0.00035 [2024-11-25 19:24:47,874 INFO misc.py line 119 2586773] Train: [41/50][336/376] Data 0.003 (0.002) Batch 0.532 (0.508) Remain 00:28:58 loss: 0.1681 Lr: 0.00035 [2024-11-25 19:24:48,345 INFO misc.py line 119 2586773] Train: [41/50][337/376] Data 0.003 (0.002) Batch 0.471 (0.508) Remain 00:28:57 loss: 0.2223 Lr: 0.00035 [2024-11-25 19:24:48,859 INFO misc.py line 119 2586773] Train: [41/50][338/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:28:56 loss: 0.1556 Lr: 0.00035 [2024-11-25 19:24:49,345 INFO misc.py line 119 2586773] Train: [41/50][339/376] Data 0.003 (0.002) Batch 0.485 (0.508) Remain 00:28:56 loss: 0.2023 Lr: 0.00035 [2024-11-25 19:24:49,876 INFO misc.py line 119 2586773] Train: [41/50][340/376] Data 0.002 (0.002) Batch 0.531 (0.508) Remain 00:28:55 loss: 0.2985 Lr: 0.00035 [2024-11-25 19:24:50,346 INFO misc.py line 119 2586773] Train: [41/50][341/376] Data 0.003 (0.002) Batch 0.470 (0.507) Remain 00:28:55 loss: 0.2160 Lr: 0.00035 [2024-11-25 19:24:50,867 INFO misc.py line 119 2586773] Train: [41/50][342/376] Data 0.003 (0.002) Batch 0.521 (0.508) Remain 00:28:54 loss: 0.1893 Lr: 0.00035 [2024-11-25 19:24:51,357 INFO misc.py line 119 2586773] Train: [41/50][343/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:28:54 loss: 0.2329 Lr: 0.00035 [2024-11-25 19:24:51,821 INFO misc.py line 119 2586773] Train: [41/50][344/376] Data 0.002 (0.002) Batch 0.464 (0.507) Remain 00:28:53 loss: 0.2187 Lr: 0.00035 [2024-11-25 19:24:52,361 INFO misc.py line 119 2586773] Train: [41/50][345/376] Data 0.003 (0.002) Batch 0.540 (0.507) Remain 00:28:52 loss: 0.1843 Lr: 0.00035 [2024-11-25 19:24:52,852 INFO misc.py line 119 2586773] Train: [41/50][346/376] Data 0.003 (0.002) Batch 0.491 (0.507) Remain 00:28:52 loss: 0.1928 Lr: 0.00035 [2024-11-25 19:24:53,367 INFO misc.py line 119 2586773] Train: [41/50][347/376] Data 0.003 (0.002) Batch 0.515 (0.507) Remain 00:28:51 loss: 0.1848 Lr: 0.00035 [2024-11-25 19:24:53,871 INFO misc.py line 119 2586773] Train: [41/50][348/376] Data 0.003 (0.002) Batch 0.504 (0.507) Remain 00:28:51 loss: 0.2524 Lr: 0.00035 [2024-11-25 19:24:54,357 INFO misc.py line 119 2586773] Train: [41/50][349/376] Data 0.003 (0.002) Batch 0.486 (0.507) Remain 00:28:50 loss: 0.1666 Lr: 0.00035 [2024-11-25 19:24:54,846 INFO misc.py line 119 2586773] Train: [41/50][350/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:28:49 loss: 0.1440 Lr: 0.00035 [2024-11-25 19:24:55,364 INFO misc.py line 119 2586773] Train: [41/50][351/376] Data 0.002 (0.002) Batch 0.517 (0.507) Remain 00:28:49 loss: 0.1799 Lr: 0.00035 [2024-11-25 19:24:55,842 INFO misc.py line 119 2586773] Train: [41/50][352/376] Data 0.003 (0.002) Batch 0.479 (0.507) Remain 00:28:48 loss: 0.1633 Lr: 0.00035 [2024-11-25 19:24:56,358 INFO misc.py line 119 2586773] Train: [41/50][353/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:28:48 loss: 0.1797 Lr: 0.00035 [2024-11-25 19:24:56,852 INFO misc.py line 119 2586773] Train: [41/50][354/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:28:47 loss: 0.1919 Lr: 0.00035 [2024-11-25 19:24:57,340 INFO misc.py line 119 2586773] Train: [41/50][355/376] Data 0.003 (0.002) Batch 0.488 (0.507) Remain 00:28:46 loss: 0.2355 Lr: 0.00035 [2024-11-25 19:24:57,865 INFO misc.py line 119 2586773] Train: [41/50][356/376] Data 0.003 (0.002) Batch 0.525 (0.507) Remain 00:28:46 loss: 0.1845 Lr: 0.00035 [2024-11-25 19:24:58,363 INFO misc.py line 119 2586773] Train: [41/50][357/376] Data 0.003 (0.002) Batch 0.498 (0.507) Remain 00:28:45 loss: 0.1962 Lr: 0.00035 [2024-11-25 19:24:58,894 INFO misc.py line 119 2586773] Train: [41/50][358/376] Data 0.003 (0.002) Batch 0.530 (0.507) Remain 00:28:45 loss: 0.1666 Lr: 0.00035 [2024-11-25 19:24:59,425 INFO misc.py line 119 2586773] Train: [41/50][359/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 00:28:45 loss: 0.2926 Lr: 0.00035 [2024-11-25 19:24:59,919 INFO misc.py line 119 2586773] Train: [41/50][360/376] Data 0.003 (0.002) Batch 0.494 (0.507) Remain 00:28:44 loss: 0.1994 Lr: 0.00035 [2024-11-25 19:25:00,389 INFO misc.py line 119 2586773] Train: [41/50][361/376] Data 0.002 (0.002) Batch 0.470 (0.507) Remain 00:28:43 loss: 0.1871 Lr: 0.00035 [2024-11-25 19:25:00,907 INFO misc.py line 119 2586773] Train: [41/50][362/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:28:43 loss: 0.1949 Lr: 0.00035 [2024-11-25 19:25:01,445 INFO misc.py line 119 2586773] Train: [41/50][363/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 00:28:43 loss: 0.1852 Lr: 0.00034 [2024-11-25 19:25:01,914 INFO misc.py line 119 2586773] Train: [41/50][364/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:28:42 loss: 0.1932 Lr: 0.00034 [2024-11-25 19:25:02,422 INFO misc.py line 119 2586773] Train: [41/50][365/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:28:41 loss: 0.1760 Lr: 0.00034 [2024-11-25 19:25:02,974 INFO misc.py line 119 2586773] Train: [41/50][366/376] Data 0.002 (0.002) Batch 0.552 (0.507) Remain 00:28:41 loss: 0.2170 Lr: 0.00034 [2024-11-25 19:25:03,459 INFO misc.py line 119 2586773] Train: [41/50][367/376] Data 0.002 (0.002) Batch 0.484 (0.507) Remain 00:28:41 loss: 0.1756 Lr: 0.00034 [2024-11-25 19:25:03,924 INFO misc.py line 119 2586773] Train: [41/50][368/376] Data 0.002 (0.002) Batch 0.465 (0.507) Remain 00:28:40 loss: 0.2464 Lr: 0.00034 [2024-11-25 19:25:04,440 INFO misc.py line 119 2586773] Train: [41/50][369/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:28:39 loss: 0.1720 Lr: 0.00034 [2024-11-25 19:25:04,970 INFO misc.py line 119 2586773] Train: [41/50][370/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 00:28:39 loss: 0.1802 Lr: 0.00034 [2024-11-25 19:25:05,472 INFO misc.py line 119 2586773] Train: [41/50][371/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:28:38 loss: 0.1692 Lr: 0.00034 [2024-11-25 19:25:05,988 INFO misc.py line 119 2586773] Train: [41/50][372/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:28:38 loss: 0.1626 Lr: 0.00034 [2024-11-25 19:25:06,485 INFO misc.py line 119 2586773] Train: [41/50][373/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:28:37 loss: 0.1775 Lr: 0.00034 [2024-11-25 19:25:06,946 INFO misc.py line 119 2586773] Train: [41/50][374/376] Data 0.002 (0.002) Batch 0.461 (0.507) Remain 00:28:36 loss: 0.1752 Lr: 0.00034 [2024-11-25 19:25:07,425 INFO misc.py line 119 2586773] Train: [41/50][375/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:28:36 loss: 0.1715 Lr: 0.00034 [2024-11-25 19:25:07,918 INFO misc.py line 119 2586773] Train: [41/50][376/376] Data 0.003 (0.002) Batch 0.493 (0.507) Remain 00:28:35 loss: 0.2061 Lr: 0.00034 [2024-11-25 19:25:07,919 INFO misc.py line 136 2586773] Train result: loss: 0.2012 [2024-11-25 19:25:07,919 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:25:18,730 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1824 [2024-11-25 19:25:18,994 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2339 [2024-11-25 19:25:19,260 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2559 [2024-11-25 19:25:19,485 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1972 [2024-11-25 19:25:19,747 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2801 [2024-11-25 19:25:20,012 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1939 [2024-11-25 19:25:20,235 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2163 [2024-11-25 19:25:20,506 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2341 [2024-11-25 19:25:20,729 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2631 [2024-11-25 19:25:20,990 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2319 [2024-11-25 19:25:21,220 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2050 [2024-11-25 19:25:21,493 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2464 [2024-11-25 19:25:21,760 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2594 [2024-11-25 19:25:22,022 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2320 [2024-11-25 19:25:22,254 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2301 [2024-11-25 19:25:22,492 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3085 [2024-11-25 19:25:22,759 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2682 [2024-11-25 19:25:23,005 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2153 [2024-11-25 19:25:23,236 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2446 [2024-11-25 19:25:23,498 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2211 [2024-11-25 19:25:23,734 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2500 [2024-11-25 19:25:24,003 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2521 [2024-11-25 19:25:24,240 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2157 [2024-11-25 19:25:24,508 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2318 [2024-11-25 19:25:24,767 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2318 [2024-11-25 19:25:25,001 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2506 [2024-11-25 19:25:25,255 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2572 [2024-11-25 19:25:25,501 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2223 [2024-11-25 19:25:25,769 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2670 [2024-11-25 19:25:26,022 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2834 [2024-11-25 19:25:26,256 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2570 [2024-11-25 19:25:26,519 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2029 [2024-11-25 19:25:26,738 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2770 [2024-11-25 19:25:26,987 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2411 [2024-11-25 19:25:27,247 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2060 [2024-11-25 19:25:27,492 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2406 [2024-11-25 19:25:27,720 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1941 [2024-11-25 19:25:27,989 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2197 [2024-11-25 19:25:28,221 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2605 [2024-11-25 19:25:28,455 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2356 [2024-11-25 19:25:28,724 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3112 [2024-11-25 19:25:28,973 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2696 [2024-11-25 19:25:29,211 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2516 [2024-11-25 19:25:29,445 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2216 [2024-11-25 19:25:29,682 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2301 [2024-11-25 19:25:29,935 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2262 [2024-11-25 19:25:30,197 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2158 [2024-11-25 19:25:30,447 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2847 [2024-11-25 19:25:30,671 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2098 [2024-11-25 19:25:30,904 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2078 [2024-11-25 19:25:31,128 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2568 [2024-11-25 19:25:31,381 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2277 [2024-11-25 19:25:31,647 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2176 [2024-11-25 19:25:31,908 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3097 [2024-11-25 19:25:32,138 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2226 [2024-11-25 19:25:32,378 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2334 [2024-11-25 19:25:32,634 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2476 [2024-11-25 19:25:32,904 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2761 [2024-11-25 19:25:33,160 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2403 [2024-11-25 19:25:33,421 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2258 [2024-11-25 19:25:33,672 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2151 [2024-11-25 19:25:33,939 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2291 [2024-11-25 19:25:34,168 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2391 [2024-11-25 19:25:34,427 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2393 [2024-11-25 19:25:34,692 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2731 [2024-11-25 19:25:34,961 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1818 [2024-11-25 19:25:35,205 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1963 [2024-11-25 19:25:35,461 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2596 [2024-11-25 19:25:35,730 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2502 [2024-11-25 19:25:35,992 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2683 [2024-11-25 19:25:36,236 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2033 [2024-11-25 19:25:36,472 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2829 [2024-11-25 19:25:36,731 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2492 [2024-11-25 19:25:36,984 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2481 [2024-11-25 19:25:37,205 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2647 [2024-11-25 19:25:37,432 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2119 [2024-11-25 19:25:37,701 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2559 [2024-11-25 19:25:37,938 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2160 [2024-11-25 19:25:38,196 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2320 [2024-11-25 19:25:38,452 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2857 [2024-11-25 19:25:38,693 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2257 [2024-11-25 19:25:38,962 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2588 [2024-11-25 19:25:39,220 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2044 [2024-11-25 19:25:39,468 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2346 [2024-11-25 19:25:39,741 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2545 [2024-11-25 19:25:39,984 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2673 [2024-11-25 19:25:40,247 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2446 [2024-11-25 19:25:40,506 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2343 [2024-11-25 19:25:40,755 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2622 [2024-11-25 19:25:41,003 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2513 [2024-11-25 19:25:41,236 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2442 [2024-11-25 19:25:41,489 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2623 [2024-11-25 19:25:41,757 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2447 [2024-11-25 19:25:42,021 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1852 [2024-11-25 19:25:42,288 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2348 [2024-11-25 19:25:42,535 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2156 [2024-11-25 19:25:42,804 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2235 [2024-11-25 19:25:43,025 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2973 [2024-11-25 19:25:43,302 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2455 [2024-11-25 19:25:43,547 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2478 [2024-11-25 19:25:43,829 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2043 [2024-11-25 19:25:44,088 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2594 [2024-11-25 19:25:44,345 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2318 [2024-11-25 19:25:44,600 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2675 [2024-11-25 19:25:44,839 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2418 [2024-11-25 19:25:45,072 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2185 [2024-11-25 19:25:45,328 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2296 [2024-11-25 19:25:45,598 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2303 [2024-11-25 19:25:45,835 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2475 [2024-11-25 19:25:46,102 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2070 [2024-11-25 19:25:46,366 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2440 [2024-11-25 19:25:46,594 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2234 [2024-11-25 19:25:46,829 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2010 [2024-11-25 19:25:47,049 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2073 [2024-11-25 19:25:47,274 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2143 [2024-11-25 19:25:47,545 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2901 [2024-11-25 19:25:47,804 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2533 [2024-11-25 19:25:48,072 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2243 [2024-11-25 19:25:48,335 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2123 [2024-11-25 19:25:48,599 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2544 [2024-11-25 19:25:48,859 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2860 [2024-11-25 19:25:49,123 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2354 [2024-11-25 19:25:49,382 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2429 [2024-11-25 19:25:49,643 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2418 [2024-11-25 19:25:49,911 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2535 [2024-11-25 19:25:50,162 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2509 [2024-11-25 19:25:50,392 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2009 [2024-11-25 19:25:50,651 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2368 [2024-11-25 19:25:50,886 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2617 [2024-11-25 19:25:51,112 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1949 [2024-11-25 19:25:51,323 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2248 [2024-11-25 19:25:51,539 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1862 [2024-11-25 19:25:52,210 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7762/0.8539/0.9963. [2024-11-25 19:25:52,210 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 19:25:52,210 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5562/0.7097 [2024-11-25 19:25:52,211 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:25:52,212 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:25:52,212 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:25:55,013 INFO misc.py line 119 2586773] Train: [42/50][1/376] Data 0.119 (0.119) Batch 0.610 (0.610) Remain 00:34:22 loss: 0.1838 Lr: 0.00034 [2024-11-25 19:25:55,470 INFO misc.py line 119 2586773] Train: [42/50][2/376] Data 0.003 (0.003) Batch 0.456 (0.456) Remain 00:25:43 loss: 0.1494 Lr: 0.00034 [2024-11-25 19:25:55,986 INFO misc.py line 119 2586773] Train: [42/50][3/376] Data 0.002 (0.002) Batch 0.517 (0.517) Remain 00:29:08 loss: 0.1860 Lr: 0.00034 [2024-11-25 19:25:56,473 INFO misc.py line 119 2586773] Train: [42/50][4/376] Data 0.002 (0.002) Batch 0.487 (0.487) Remain 00:27:26 loss: 0.1951 Lr: 0.00034 [2024-11-25 19:25:56,947 INFO misc.py line 119 2586773] Train: [42/50][5/376] Data 0.002 (0.002) Batch 0.473 (0.480) Remain 00:27:02 loss: 0.1903 Lr: 0.00034 [2024-11-25 19:25:57,418 INFO misc.py line 119 2586773] Train: [42/50][6/376] Data 0.002 (0.002) Batch 0.471 (0.477) Remain 00:26:51 loss: 0.1855 Lr: 0.00034 [2024-11-25 19:25:57,944 INFO misc.py line 119 2586773] Train: [42/50][7/376] Data 0.002 (0.002) Batch 0.527 (0.490) Remain 00:27:33 loss: 0.1843 Lr: 0.00034 [2024-11-25 19:25:58,415 INFO misc.py line 119 2586773] Train: [42/50][8/376] Data 0.002 (0.002) Batch 0.471 (0.486) Remain 00:27:20 loss: 0.1673 Lr: 0.00034 [2024-11-25 19:25:58,916 INFO misc.py line 119 2586773] Train: [42/50][9/376] Data 0.002 (0.002) Batch 0.500 (0.488) Remain 00:27:27 loss: 0.2129 Lr: 0.00034 [2024-11-25 19:25:59,432 INFO misc.py line 119 2586773] Train: [42/50][10/376] Data 0.002 (0.002) Batch 0.516 (0.492) Remain 00:27:40 loss: 0.2172 Lr: 0.00034 [2024-11-25 19:25:59,941 INFO misc.py line 119 2586773] Train: [42/50][11/376] Data 0.002 (0.002) Batch 0.509 (0.494) Remain 00:27:47 loss: 0.1872 Lr: 0.00034 [2024-11-25 19:26:00,482 INFO misc.py line 119 2586773] Train: [42/50][12/376] Data 0.002 (0.002) Batch 0.540 (0.499) Remain 00:28:04 loss: 0.1932 Lr: 0.00034 [2024-11-25 19:26:01,009 INFO misc.py line 119 2586773] Train: [42/50][13/376] Data 0.003 (0.002) Batch 0.527 (0.502) Remain 00:28:13 loss: 0.1722 Lr: 0.00034 [2024-11-25 19:26:01,485 INFO misc.py line 119 2586773] Train: [42/50][14/376] Data 0.002 (0.002) Batch 0.477 (0.500) Remain 00:28:04 loss: 0.1799 Lr: 0.00034 [2024-11-25 19:26:01,990 INFO misc.py line 119 2586773] Train: [42/50][15/376] Data 0.002 (0.002) Batch 0.505 (0.500) Remain 00:28:05 loss: 0.2074 Lr: 0.00034 [2024-11-25 19:26:02,494 INFO misc.py line 119 2586773] Train: [42/50][16/376] Data 0.003 (0.002) Batch 0.504 (0.501) Remain 00:28:06 loss: 0.1601 Lr: 0.00034 [2024-11-25 19:26:03,046 INFO misc.py line 119 2586773] Train: [42/50][17/376] Data 0.002 (0.002) Batch 0.551 (0.504) Remain 00:28:17 loss: 0.1858 Lr: 0.00034 [2024-11-25 19:26:03,560 INFO misc.py line 119 2586773] Train: [42/50][18/376] Data 0.002 (0.002) Batch 0.514 (0.505) Remain 00:28:19 loss: 0.1626 Lr: 0.00034 [2024-11-25 19:26:04,179 INFO misc.py line 119 2586773] Train: [42/50][19/376] Data 0.003 (0.002) Batch 0.619 (0.512) Remain 00:28:43 loss: 0.1740 Lr: 0.00034 [2024-11-25 19:26:04,683 INFO misc.py line 119 2586773] Train: [42/50][20/376] Data 0.003 (0.002) Batch 0.504 (0.512) Remain 00:28:40 loss: 0.1409 Lr: 0.00034 [2024-11-25 19:26:05,179 INFO misc.py line 119 2586773] Train: [42/50][21/376] Data 0.002 (0.002) Batch 0.496 (0.511) Remain 00:28:37 loss: 0.1613 Lr: 0.00034 [2024-11-25 19:26:05,698 INFO misc.py line 119 2586773] Train: [42/50][22/376] Data 0.003 (0.002) Batch 0.519 (0.511) Remain 00:28:38 loss: 0.2610 Lr: 0.00034 [2024-11-25 19:26:06,229 INFO misc.py line 119 2586773] Train: [42/50][23/376] Data 0.002 (0.002) Batch 0.530 (0.512) Remain 00:28:41 loss: 0.1793 Lr: 0.00034 [2024-11-25 19:26:06,702 INFO misc.py line 119 2586773] Train: [42/50][24/376] Data 0.002 (0.002) Batch 0.473 (0.510) Remain 00:28:34 loss: 0.1894 Lr: 0.00034 [2024-11-25 19:26:07,171 INFO misc.py line 119 2586773] Train: [42/50][25/376] Data 0.002 (0.002) Batch 0.469 (0.508) Remain 00:28:27 loss: 0.2133 Lr: 0.00034 [2024-11-25 19:26:07,648 INFO misc.py line 119 2586773] Train: [42/50][26/376] Data 0.002 (0.002) Batch 0.477 (0.507) Remain 00:28:22 loss: 0.1588 Lr: 0.00034 [2024-11-25 19:26:08,167 INFO misc.py line 119 2586773] Train: [42/50][27/376] Data 0.003 (0.002) Batch 0.519 (0.508) Remain 00:28:23 loss: 0.1938 Lr: 0.00034 [2024-11-25 19:26:08,661 INFO misc.py line 119 2586773] Train: [42/50][28/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:28:21 loss: 0.1746 Lr: 0.00034 [2024-11-25 19:26:09,222 INFO misc.py line 119 2586773] Train: [42/50][29/376] Data 0.002 (0.002) Batch 0.560 (0.509) Remain 00:28:27 loss: 0.2146 Lr: 0.00034 [2024-11-25 19:26:09,715 INFO misc.py line 119 2586773] Train: [42/50][30/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 00:28:25 loss: 0.1723 Lr: 0.00034 [2024-11-25 19:26:10,205 INFO misc.py line 119 2586773] Train: [42/50][31/376] Data 0.003 (0.002) Batch 0.489 (0.508) Remain 00:28:22 loss: 0.2522 Lr: 0.00034 [2024-11-25 19:26:10,742 INFO misc.py line 119 2586773] Train: [42/50][32/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 00:28:25 loss: 0.2178 Lr: 0.00034 [2024-11-25 19:26:11,305 INFO misc.py line 119 2586773] Train: [42/50][33/376] Data 0.002 (0.002) Batch 0.563 (0.511) Remain 00:28:31 loss: 0.1922 Lr: 0.00034 [2024-11-25 19:26:11,840 INFO misc.py line 119 2586773] Train: [42/50][34/376] Data 0.003 (0.002) Batch 0.535 (0.511) Remain 00:28:33 loss: 0.3111 Lr: 0.00034 [2024-11-25 19:26:12,333 INFO misc.py line 119 2586773] Train: [42/50][35/376] Data 0.003 (0.002) Batch 0.493 (0.511) Remain 00:28:30 loss: 0.1999 Lr: 0.00034 [2024-11-25 19:26:12,862 INFO misc.py line 119 2586773] Train: [42/50][36/376] Data 0.003 (0.002) Batch 0.530 (0.511) Remain 00:28:32 loss: 0.2151 Lr: 0.00034 [2024-11-25 19:26:13,410 INFO misc.py line 119 2586773] Train: [42/50][37/376] Data 0.002 (0.002) Batch 0.547 (0.512) Remain 00:28:35 loss: 0.2144 Lr: 0.00034 [2024-11-25 19:26:13,910 INFO misc.py line 119 2586773] Train: [42/50][38/376] Data 0.002 (0.002) Batch 0.501 (0.512) Remain 00:28:33 loss: 0.2051 Lr: 0.00034 [2024-11-25 19:26:14,419 INFO misc.py line 119 2586773] Train: [42/50][39/376] Data 0.002 (0.002) Batch 0.508 (0.512) Remain 00:28:32 loss: 0.1597 Lr: 0.00033 [2024-11-25 19:26:14,971 INFO misc.py line 119 2586773] Train: [42/50][40/376] Data 0.003 (0.002) Batch 0.552 (0.513) Remain 00:28:35 loss: 0.2103 Lr: 0.00033 [2024-11-25 19:26:15,504 INFO misc.py line 119 2586773] Train: [42/50][41/376] Data 0.002 (0.002) Batch 0.533 (0.514) Remain 00:28:37 loss: 0.1565 Lr: 0.00033 [2024-11-25 19:26:16,020 INFO misc.py line 119 2586773] Train: [42/50][42/376] Data 0.002 (0.002) Batch 0.516 (0.514) Remain 00:28:36 loss: 0.2507 Lr: 0.00033 [2024-11-25 19:26:16,589 INFO misc.py line 119 2586773] Train: [42/50][43/376] Data 0.002 (0.002) Batch 0.570 (0.515) Remain 00:28:40 loss: 0.1772 Lr: 0.00033 [2024-11-25 19:26:17,098 INFO misc.py line 119 2586773] Train: [42/50][44/376] Data 0.003 (0.002) Batch 0.509 (0.515) Remain 00:28:39 loss: 0.1717 Lr: 0.00033 [2024-11-25 19:26:17,625 INFO misc.py line 119 2586773] Train: [42/50][45/376] Data 0.002 (0.002) Batch 0.527 (0.515) Remain 00:28:40 loss: 0.1861 Lr: 0.00033 [2024-11-25 19:26:18,117 INFO misc.py line 119 2586773] Train: [42/50][46/376] Data 0.002 (0.002) Batch 0.492 (0.515) Remain 00:28:37 loss: 0.1969 Lr: 0.00033 [2024-11-25 19:26:18,653 INFO misc.py line 119 2586773] Train: [42/50][47/376] Data 0.002 (0.002) Batch 0.536 (0.515) Remain 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Train: [42/50][60/376] Data 0.002 (0.002) Batch 0.518 (0.514) Remain 00:28:30 loss: 0.1656 Lr: 0.00033 [2024-11-25 19:26:25,808 INFO misc.py line 119 2586773] Train: [42/50][61/376] Data 0.003 (0.002) Batch 0.498 (0.514) Remain 00:28:28 loss: 0.2076 Lr: 0.00033 [2024-11-25 19:26:26,288 INFO misc.py line 119 2586773] Train: [42/50][62/376] Data 0.003 (0.002) Batch 0.481 (0.514) Remain 00:28:26 loss: 0.1819 Lr: 0.00033 [2024-11-25 19:26:26,778 INFO misc.py line 119 2586773] Train: [42/50][63/376] Data 0.003 (0.002) Batch 0.490 (0.513) Remain 00:28:24 loss: 0.1797 Lr: 0.00033 [2024-11-25 19:26:27,273 INFO misc.py line 119 2586773] Train: [42/50][64/376] Data 0.002 (0.002) Batch 0.495 (0.513) Remain 00:28:22 loss: 0.2462 Lr: 0.00033 [2024-11-25 19:26:27,786 INFO misc.py line 119 2586773] Train: [42/50][65/376] Data 0.003 (0.002) Batch 0.513 (0.513) Remain 00:28:22 loss: 0.1639 Lr: 0.00033 [2024-11-25 19:26:28,295 INFO misc.py line 119 2586773] Train: [42/50][66/376] Data 0.002 (0.002) 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[2024-11-25 19:28:37,420 INFO misc.py line 119 2586773] Train: [42/50][322/376] Data 0.002 (0.002) Batch 0.490 (0.506) Remain 00:25:49 loss: 0.1787 Lr: 0.00028 [2024-11-25 19:28:37,938 INFO misc.py line 119 2586773] Train: [42/50][323/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 00:25:49 loss: 0.2121 Lr: 0.00028 [2024-11-25 19:28:38,443 INFO misc.py line 119 2586773] Train: [42/50][324/376] Data 0.002 (0.002) Batch 0.505 (0.506) Remain 00:25:48 loss: 0.1760 Lr: 0.00028 [2024-11-25 19:28:38,946 INFO misc.py line 119 2586773] Train: [42/50][325/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 00:25:48 loss: 0.1741 Lr: 0.00028 [2024-11-25 19:28:39,447 INFO misc.py line 119 2586773] Train: [42/50][326/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 00:25:47 loss: 0.1934 Lr: 0.00028 [2024-11-25 19:28:39,978 INFO misc.py line 119 2586773] Train: [42/50][327/376] Data 0.002 (0.002) Batch 0.531 (0.506) Remain 00:25:47 loss: 0.2083 Lr: 0.00028 [2024-11-25 19:28:40,516 INFO misc.py line 119 2586773] Train: [42/50][328/376] Data 0.003 (0.002) Batch 0.539 (0.506) Remain 00:25:47 loss: 0.1706 Lr: 0.00028 [2024-11-25 19:28:41,008 INFO misc.py line 119 2586773] Train: [42/50][329/376] Data 0.003 (0.002) Batch 0.491 (0.506) Remain 00:25:46 loss: 0.1834 Lr: 0.00028 [2024-11-25 19:28:41,550 INFO misc.py line 119 2586773] Train: [42/50][330/376] Data 0.002 (0.002) Batch 0.543 (0.506) Remain 00:25:46 loss: 0.1662 Lr: 0.00028 [2024-11-25 19:28:42,094 INFO misc.py line 119 2586773] Train: [42/50][331/376] Data 0.003 (0.002) Batch 0.543 (0.506) Remain 00:25:46 loss: 0.2356 Lr: 0.00028 [2024-11-25 19:28:42,580 INFO misc.py line 119 2586773] Train: [42/50][332/376] Data 0.003 (0.002) Batch 0.486 (0.506) Remain 00:25:45 loss: 0.1795 Lr: 0.00028 [2024-11-25 19:28:43,070 INFO misc.py line 119 2586773] Train: [42/50][333/376] Data 0.002 (0.002) Batch 0.490 (0.506) Remain 00:25:44 loss: 0.2030 Lr: 0.00028 [2024-11-25 19:28:43,555 INFO misc.py line 119 2586773] Train: [42/50][334/376] Data 0.002 (0.002) Batch 0.485 (0.506) Remain 00:25:44 loss: 0.1583 Lr: 0.00028 [2024-11-25 19:28:44,113 INFO misc.py line 119 2586773] Train: [42/50][335/376] Data 0.003 (0.002) Batch 0.558 (0.506) Remain 00:25:44 loss: 0.2113 Lr: 0.00028 [2024-11-25 19:28:44,590 INFO misc.py line 119 2586773] Train: [42/50][336/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 00:25:43 loss: 0.1709 Lr: 0.00028 [2024-11-25 19:28:45,089 INFO misc.py line 119 2586773] Train: [42/50][337/376] Data 0.003 (0.002) Batch 0.499 (0.506) Remain 00:25:42 loss: 0.2539 Lr: 0.00028 [2024-11-25 19:28:45,578 INFO misc.py line 119 2586773] Train: [42/50][338/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 00:25:42 loss: 0.2076 Lr: 0.00028 [2024-11-25 19:28:46,088 INFO misc.py line 119 2586773] Train: [42/50][339/376] Data 0.003 (0.002) Batch 0.510 (0.506) Remain 00:25:41 loss: 0.1817 Lr: 0.00028 [2024-11-25 19:28:46,604 INFO misc.py line 119 2586773] Train: [42/50][340/376] Data 0.002 (0.002) Batch 0.515 (0.506) Remain 00:25:41 loss: 0.1726 Lr: 0.00028 [2024-11-25 19:28:47,126 INFO misc.py line 119 2586773] Train: [42/50][341/376] Data 0.002 (0.002) Batch 0.523 (0.506) Remain 00:25:40 loss: 0.2076 Lr: 0.00028 [2024-11-25 19:28:47,631 INFO misc.py line 119 2586773] Train: [42/50][342/376] Data 0.002 (0.002) Batch 0.504 (0.506) Remain 00:25:40 loss: 0.1598 Lr: 0.00028 [2024-11-25 19:28:48,098 INFO misc.py line 119 2586773] Train: [42/50][343/376] Data 0.002 (0.002) Batch 0.467 (0.506) Remain 00:25:39 loss: 0.1592 Lr: 0.00028 [2024-11-25 19:28:48,620 INFO misc.py line 119 2586773] Train: [42/50][344/376] Data 0.003 (0.002) Batch 0.522 (0.506) Remain 00:25:39 loss: 0.1564 Lr: 0.00028 [2024-11-25 19:28:49,120 INFO misc.py line 119 2586773] Train: [42/50][345/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 00:25:38 loss: 0.1959 Lr: 0.00028 [2024-11-25 19:28:49,636 INFO misc.py line 119 2586773] Train: [42/50][346/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 00:25:38 loss: 0.2089 Lr: 0.00028 [2024-11-25 19:28:50,119 INFO misc.py line 119 2586773] Train: [42/50][347/376] Data 0.003 (0.002) Batch 0.483 (0.506) Remain 00:25:37 loss: 0.1815 Lr: 0.00028 [2024-11-25 19:28:50,639 INFO misc.py line 119 2586773] Train: [42/50][348/376] Data 0.003 (0.002) Batch 0.520 (0.506) Remain 00:25:36 loss: 0.2110 Lr: 0.00028 [2024-11-25 19:28:51,186 INFO misc.py line 119 2586773] Train: [42/50][349/376] Data 0.003 (0.002) Batch 0.547 (0.506) Remain 00:25:36 loss: 0.2039 Lr: 0.00028 [2024-11-25 19:28:51,728 INFO misc.py line 119 2586773] Train: [42/50][350/376] Data 0.003 (0.002) Batch 0.542 (0.506) Remain 00:25:36 loss: 0.1941 Lr: 0.00028 [2024-11-25 19:28:52,264 INFO misc.py line 119 2586773] Train: [42/50][351/376] Data 0.003 (0.002) Batch 0.536 (0.507) Remain 00:25:36 loss: 0.2129 Lr: 0.00028 [2024-11-25 19:28:52,766 INFO misc.py line 119 2586773] Train: [42/50][352/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:25:35 loss: 0.2129 Lr: 0.00028 [2024-11-25 19:28:53,247 INFO misc.py line 119 2586773] Train: [42/50][353/376] Data 0.003 (0.002) Batch 0.481 (0.506) Remain 00:25:35 loss: 0.1603 Lr: 0.00028 [2024-11-25 19:28:53,758 INFO misc.py line 119 2586773] Train: [42/50][354/376] Data 0.003 (0.002) Batch 0.510 (0.506) Remain 00:25:34 loss: 0.2106 Lr: 0.00028 [2024-11-25 19:28:54,281 INFO misc.py line 119 2586773] Train: [42/50][355/376] Data 0.003 (0.002) Batch 0.524 (0.507) Remain 00:25:34 loss: 0.1902 Lr: 0.00028 [2024-11-25 19:28:54,788 INFO misc.py line 119 2586773] Train: [42/50][356/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 00:25:33 loss: 0.1735 Lr: 0.00028 [2024-11-25 19:28:55,268 INFO misc.py line 119 2586773] Train: [42/50][357/376] Data 0.003 (0.002) Batch 0.480 (0.506) Remain 00:25:33 loss: 0.1659 Lr: 0.00028 [2024-11-25 19:28:55,810 INFO misc.py line 119 2586773] Train: [42/50][358/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 00:25:32 loss: 0.2199 Lr: 0.00028 [2024-11-25 19:28:56,313 INFO misc.py line 119 2586773] Train: [42/50][359/376] Data 0.003 (0.002) Batch 0.503 (0.507) Remain 00:25:32 loss: 0.1896 Lr: 0.00028 [2024-11-25 19:28:56,804 INFO misc.py line 119 2586773] Train: [42/50][360/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 00:25:31 loss: 0.1703 Lr: 0.00028 [2024-11-25 19:28:57,314 INFO misc.py line 119 2586773] Train: [42/50][361/376] Data 0.002 (0.002) Batch 0.510 (0.507) Remain 00:25:31 loss: 0.1738 Lr: 0.00028 [2024-11-25 19:28:57,823 INFO misc.py line 119 2586773] Train: [42/50][362/376] Data 0.002 (0.002) Batch 0.509 (0.507) Remain 00:25:30 loss: 0.1885 Lr: 0.00028 [2024-11-25 19:28:58,339 INFO misc.py line 119 2586773] Train: [42/50][363/376] Data 0.003 (0.002) Batch 0.516 (0.507) Remain 00:25:30 loss: 0.2298 Lr: 0.00028 [2024-11-25 19:28:58,812 INFO misc.py line 119 2586773] Train: [42/50][364/376] Data 0.002 (0.002) Batch 0.473 (0.506) Remain 00:25:29 loss: 0.1890 Lr: 0.00028 [2024-11-25 19:28:59,305 INFO misc.py line 119 2586773] Train: [42/50][365/376] Data 0.003 (0.002) Batch 0.493 (0.506) Remain 00:25:28 loss: 0.1833 Lr: 0.00028 [2024-11-25 19:28:59,804 INFO misc.py line 119 2586773] Train: [42/50][366/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 00:25:28 loss: 0.2053 Lr: 0.00027 [2024-11-25 19:29:00,302 INFO misc.py line 119 2586773] Train: [42/50][367/376] Data 0.002 (0.002) Batch 0.499 (0.506) Remain 00:25:27 loss: 0.1755 Lr: 0.00027 [2024-11-25 19:29:00,784 INFO misc.py line 119 2586773] Train: [42/50][368/376] Data 0.003 (0.002) Batch 0.481 (0.506) Remain 00:25:26 loss: 0.1825 Lr: 0.00027 [2024-11-25 19:29:01,264 INFO misc.py line 119 2586773] Train: [42/50][369/376] Data 0.002 (0.002) Batch 0.481 (0.506) Remain 00:25:26 loss: 0.2615 Lr: 0.00027 [2024-11-25 19:29:01,784 INFO misc.py line 119 2586773] Train: [42/50][370/376] Data 0.002 (0.002) Batch 0.520 (0.506) Remain 00:25:25 loss: 0.1565 Lr: 0.00027 [2024-11-25 19:29:02,273 INFO misc.py line 119 2586773] Train: [42/50][371/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 00:25:25 loss: 0.1947 Lr: 0.00027 [2024-11-25 19:29:02,774 INFO misc.py line 119 2586773] Train: [42/50][372/376] Data 0.002 (0.002) Batch 0.500 (0.506) Remain 00:25:24 loss: 0.1641 Lr: 0.00027 [2024-11-25 19:29:03,290 INFO misc.py line 119 2586773] Train: [42/50][373/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 00:25:24 loss: 0.2286 Lr: 0.00027 [2024-11-25 19:29:03,808 INFO misc.py line 119 2586773] Train: [42/50][374/376] Data 0.002 (0.002) Batch 0.518 (0.506) Remain 00:25:23 loss: 0.1746 Lr: 0.00027 [2024-11-25 19:29:04,302 INFO misc.py line 119 2586773] Train: [42/50][375/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 00:25:23 loss: 0.1899 Lr: 0.00027 [2024-11-25 19:29:04,832 INFO misc.py line 119 2586773] Train: [42/50][376/376] Data 0.002 (0.002) Batch 0.530 (0.506) Remain 00:25:22 loss: 0.1897 Lr: 0.00027 [2024-11-25 19:29:04,832 INFO misc.py line 136 2586773] Train result: loss: 0.1977 [2024-11-25 19:29:04,833 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:29:15,731 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1751 [2024-11-25 19:29:15,987 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2040 [2024-11-25 19:29:16,248 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2721 [2024-11-25 19:29:16,471 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2063 [2024-11-25 19:29:16,734 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2863 [2024-11-25 19:29:17,002 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2000 [2024-11-25 19:29:17,224 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2126 [2024-11-25 19:29:17,493 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2476 [2024-11-25 19:29:17,716 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2666 [2024-11-25 19:29:17,978 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2453 [2024-11-25 19:29:18,208 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2150 [2024-11-25 19:29:18,480 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2487 [2024-11-25 19:29:18,747 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2630 [2024-11-25 19:29:19,011 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2385 [2024-11-25 19:29:19,243 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2353 [2024-11-25 19:29:19,481 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3067 [2024-11-25 19:29:19,746 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2860 [2024-11-25 19:29:19,992 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1975 [2024-11-25 19:29:20,223 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2485 [2024-11-25 19:29:20,483 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2237 [2024-11-25 19:29:20,720 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2434 [2024-11-25 19:29:20,985 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2427 [2024-11-25 19:29:21,222 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2156 [2024-11-25 19:29:21,488 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2379 [2024-11-25 19:29:21,749 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2255 [2024-11-25 19:29:21,987 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2526 [2024-11-25 19:29:22,238 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2566 [2024-11-25 19:29:22,485 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2044 [2024-11-25 19:29:22,753 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2657 [2024-11-25 19:29:23,005 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2827 [2024-11-25 19:29:23,238 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2699 [2024-11-25 19:29:23,502 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2037 [2024-11-25 19:29:23,721 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2640 [2024-11-25 19:29:23,962 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2326 [2024-11-25 19:29:24,223 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1883 [2024-11-25 19:29:24,468 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2513 [2024-11-25 19:29:24,696 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1852 [2024-11-25 19:29:24,964 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2375 [2024-11-25 19:29:25,196 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2652 [2024-11-25 19:29:25,429 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2362 [2024-11-25 19:29:25,700 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3003 [2024-11-25 19:29:25,952 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2646 [2024-11-25 19:29:26,189 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2474 [2024-11-25 19:29:26,420 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2190 [2024-11-25 19:29:26,656 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2400 [2024-11-25 19:29:26,903 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2265 [2024-11-25 19:29:27,162 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2333 [2024-11-25 19:29:27,411 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2914 [2024-11-25 19:29:27,635 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2066 [2024-11-25 19:29:27,868 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2192 [2024-11-25 19:29:28,089 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2468 [2024-11-25 19:29:28,343 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2158 [2024-11-25 19:29:28,608 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2124 [2024-11-25 19:29:28,870 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3101 [2024-11-25 19:29:29,100 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2363 [2024-11-25 19:29:29,343 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2307 [2024-11-25 19:29:29,599 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2510 [2024-11-25 19:29:29,870 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2726 [2024-11-25 19:29:30,126 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2439 [2024-11-25 19:29:30,385 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2339 [2024-11-25 19:29:30,638 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2204 [2024-11-25 19:29:30,909 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2377 [2024-11-25 19:29:31,138 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2404 [2024-11-25 19:29:31,398 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2477 [2024-11-25 19:29:31,667 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2650 [2024-11-25 19:29:31,934 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1883 [2024-11-25 19:29:32,179 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1855 [2024-11-25 19:29:32,437 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2557 [2024-11-25 19:29:32,706 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2482 [2024-11-25 19:29:32,967 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2700 [2024-11-25 19:29:33,211 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2126 [2024-11-25 19:29:33,445 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2812 [2024-11-25 19:29:33,705 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2586 [2024-11-25 19:29:33,951 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2531 [2024-11-25 19:29:34,173 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2594 [2024-11-25 19:29:34,400 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2095 [2024-11-25 19:29:34,672 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2548 [2024-11-25 19:29:34,908 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.1932 [2024-11-25 19:29:35,166 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2214 [2024-11-25 19:29:35,419 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2876 [2024-11-25 19:29:35,660 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2276 [2024-11-25 19:29:35,931 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2541 [2024-11-25 19:29:36,189 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1891 [2024-11-25 19:29:36,439 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2470 [2024-11-25 19:29:36,711 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2540 [2024-11-25 19:29:36,958 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2579 [2024-11-25 19:29:37,218 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2463 [2024-11-25 19:29:37,477 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2279 [2024-11-25 19:29:37,724 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2770 [2024-11-25 19:29:37,972 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2700 [2024-11-25 19:29:38,206 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2411 [2024-11-25 19:29:38,460 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2632 [2024-11-25 19:29:38,728 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2338 [2024-11-25 19:29:38,993 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1887 [2024-11-25 19:29:39,262 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2128 [2024-11-25 19:29:39,510 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2211 [2024-11-25 19:29:39,782 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2282 [2024-11-25 19:29:40,001 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2933 [2024-11-25 19:29:40,273 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2488 [2024-11-25 19:29:40,511 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2539 [2024-11-25 19:29:40,782 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2061 [2024-11-25 19:29:41,043 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2515 [2024-11-25 19:29:41,305 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2361 [2024-11-25 19:29:41,557 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2685 [2024-11-25 19:29:41,779 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2382 [2024-11-25 19:29:42,017 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2215 [2024-11-25 19:29:42,273 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2188 [2024-11-25 19:29:42,541 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2483 [2024-11-25 19:29:42,773 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2484 [2024-11-25 19:29:43,035 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.1949 [2024-11-25 19:29:43,299 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2357 [2024-11-25 19:29:43,523 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2247 [2024-11-25 19:29:43,759 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2037 [2024-11-25 19:29:43,979 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2205 [2024-11-25 19:29:44,204 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2190 [2024-11-25 19:29:44,478 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2960 [2024-11-25 19:29:44,738 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2519 [2024-11-25 19:29:45,005 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2422 [2024-11-25 19:29:45,270 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2275 [2024-11-25 19:29:45,532 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2818 [2024-11-25 19:29:45,792 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2789 [2024-11-25 19:29:46,056 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2163 [2024-11-25 19:29:46,312 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2460 [2024-11-25 19:29:46,574 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2502 [2024-11-25 19:29:46,837 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2392 [2024-11-25 19:29:47,088 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2581 [2024-11-25 19:29:47,318 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2079 [2024-11-25 19:29:47,580 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2406 [2024-11-25 19:29:47,814 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2571 [2024-11-25 19:29:48,041 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1969 [2024-11-25 19:29:48,253 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2149 [2024-11-25 19:29:48,468 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1912 [2024-11-25 19:29:49,143 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7750/0.8470/0.9963. [2024-11-25 19:29:49,143 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9983 [2024-11-25 19:29:49,143 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5537/0.6957 [2024-11-25 19:29:49,144 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:29:49,144 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:29:49,144 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:29:52,036 INFO misc.py line 119 2586773] Train: [43/50][1/376] Data 0.130 (0.130) Batch 0.613 (0.613) Remain 00:30:43 loss: 0.1464 Lr: 0.00027 [2024-11-25 19:29:52,548 INFO misc.py line 119 2586773] Train: [43/50][2/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 00:25:38 loss: 0.2406 Lr: 0.00027 [2024-11-25 19:29:53,039 INFO misc.py line 119 2586773] Train: [43/50][3/376] Data 0.003 (0.003) Batch 0.491 (0.491) Remain 00:24:35 loss: 0.1866 Lr: 0.00027 [2024-11-25 19:29:53,542 INFO misc.py line 119 2586773] Train: [43/50][4/376] Data 0.003 (0.003) Batch 0.503 (0.503) Remain 00:25:11 loss: 0.1507 Lr: 0.00027 [2024-11-25 19:29:54,047 INFO misc.py line 119 2586773] Train: [43/50][5/376] Data 0.002 (0.002) Batch 0.506 (0.504) Remain 00:25:14 loss: 0.2087 Lr: 0.00027 [2024-11-25 19:29:54,598 INFO misc.py line 119 2586773] Train: [43/50][6/376] Data 0.002 (0.002) Batch 0.551 (0.520) Remain 00:26:00 loss: 0.1805 Lr: 0.00027 [2024-11-25 19:29:55,137 INFO misc.py line 119 2586773] Train: [43/50][7/376] Data 0.002 (0.002) Batch 0.539 (0.525) Remain 00:26:14 loss: 0.2531 Lr: 0.00027 [2024-11-25 19:29:55,609 INFO misc.py line 119 2586773] Train: [43/50][8/376] Data 0.003 (0.002) Batch 0.471 (0.514) Remain 00:25:41 loss: 0.1704 Lr: 0.00027 [2024-11-25 19:29:56,083 INFO misc.py line 119 2586773] Train: [43/50][9/376] Data 0.003 (0.003) Batch 0.475 (0.507) Remain 00:25:21 loss: 0.1760 Lr: 0.00027 [2024-11-25 19:29:56,594 INFO misc.py line 119 2586773] Train: [43/50][10/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:25:22 loss: 0.2162 Lr: 0.00027 [2024-11-25 19:29:57,074 INFO misc.py line 119 2586773] Train: [43/50][11/376] Data 0.002 (0.002) Batch 0.479 (0.504) Remain 00:25:11 loss: 0.2099 Lr: 0.00027 [2024-11-25 19:29:57,562 INFO misc.py line 119 2586773] Train: [43/50][12/376] Data 0.003 (0.002) Batch 0.488 (0.503) Remain 00:25:05 loss: 0.1899 Lr: 0.00027 [2024-11-25 19:29:58,094 INFO misc.py line 119 2586773] Train: [43/50][13/376] Data 0.003 (0.003) Batch 0.533 (0.506) Remain 00:25:14 loss: 0.1614 Lr: 0.00027 [2024-11-25 19:29:58,565 INFO misc.py line 119 2586773] Train: [43/50][14/376] Data 0.003 (0.003) Batch 0.471 (0.502) Remain 00:25:04 loss: 0.1387 Lr: 0.00027 [2024-11-25 19:29:59,072 INFO misc.py line 119 2586773] Train: [43/50][15/376] Data 0.002 (0.002) Batch 0.506 (0.503) Remain 00:25:04 loss: 0.1689 Lr: 0.00027 [2024-11-25 19:29:59,603 INFO misc.py line 119 2586773] Train: [43/50][16/376] Data 0.002 (0.002) Batch 0.531 (0.505) Remain 00:25:10 loss: 0.2213 Lr: 0.00027 [2024-11-25 19:30:00,151 INFO misc.py line 119 2586773] Train: [43/50][17/376] Data 0.002 (0.002) Batch 0.548 (0.508) Remain 00:25:19 loss: 0.2294 Lr: 0.00027 [2024-11-25 19:30:00,614 INFO misc.py line 119 2586773] Train: [43/50][18/376] Data 0.003 (0.002) Batch 0.463 (0.505) Remain 00:25:10 loss: 0.2047 Lr: 0.00027 [2024-11-25 19:30:01,124 INFO misc.py line 119 2586773] Train: [43/50][19/376] Data 0.003 (0.003) Batch 0.510 (0.505) Remain 00:25:10 loss: 0.1776 Lr: 0.00027 [2024-11-25 19:30:01,653 INFO misc.py line 119 2586773] Train: [43/50][20/376] Data 0.003 (0.003) Batch 0.529 (0.507) Remain 00:25:14 loss: 0.1949 Lr: 0.00027 [2024-11-25 19:30:02,141 INFO misc.py line 119 2586773] Train: [43/50][21/376] Data 0.003 (0.003) Batch 0.489 (0.506) Remain 00:25:10 loss: 0.1995 Lr: 0.00027 [2024-11-25 19:30:02,625 INFO misc.py line 119 2586773] Train: [43/50][22/376] Data 0.002 (0.003) Batch 0.483 (0.505) Remain 00:25:06 loss: 0.2086 Lr: 0.00027 [2024-11-25 19:30:03,151 INFO misc.py line 119 2586773] Train: [43/50][23/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 00:25:09 loss: 0.2383 Lr: 0.00027 [2024-11-25 19:30:03,703 INFO misc.py line 119 2586773] Train: [43/50][24/376] Data 0.003 (0.003) Batch 0.552 (0.508) Remain 00:25:15 loss: 0.2109 Lr: 0.00027 [2024-11-25 19:30:04,223 INFO misc.py line 119 2586773] Train: [43/50][25/376] Data 0.003 (0.003) Batch 0.520 (0.508) Remain 00:25:16 loss: 0.2403 Lr: 0.00027 [2024-11-25 19:30:04,740 INFO misc.py line 119 2586773] Train: [43/50][26/376] Data 0.002 (0.003) Batch 0.518 (0.509) Remain 00:25:17 loss: 0.2117 Lr: 0.00027 [2024-11-25 19:30:05,298 INFO misc.py line 119 2586773] Train: [43/50][27/376] Data 0.003 (0.003) Batch 0.558 (0.511) Remain 00:25:22 loss: 0.1907 Lr: 0.00027 [2024-11-25 19:30:05,802 INFO misc.py line 119 2586773] Train: [43/50][28/376] Data 0.003 (0.003) Batch 0.504 (0.511) Remain 00:25:21 loss: 0.1672 Lr: 0.00027 [2024-11-25 19:30:06,308 INFO misc.py line 119 2586773] Train: [43/50][29/376] Data 0.003 (0.003) Batch 0.506 (0.510) Remain 00:25:20 loss: 0.1756 Lr: 0.00027 [2024-11-25 19:30:06,814 INFO misc.py line 119 2586773] Train: [43/50][30/376] Data 0.003 (0.003) Batch 0.506 (0.510) Remain 00:25:19 loss: 0.2134 Lr: 0.00027 [2024-11-25 19:30:07,350 INFO misc.py line 119 2586773] Train: [43/50][31/376] Data 0.003 (0.003) Batch 0.536 (0.511) Remain 00:25:21 loss: 0.2131 Lr: 0.00027 [2024-11-25 19:30:07,867 INFO misc.py line 119 2586773] Train: [43/50][32/376] Data 0.003 (0.003) Batch 0.517 (0.511) Remain 00:25:21 loss: 0.1990 Lr: 0.00027 [2024-11-25 19:30:08,351 INFO misc.py line 119 2586773] Train: [43/50][33/376] Data 0.003 (0.003) Batch 0.485 (0.510) Remain 00:25:18 loss: 0.1583 Lr: 0.00027 [2024-11-25 19:30:08,875 INFO misc.py line 119 2586773] Train: [43/50][34/376] Data 0.003 (0.003) Batch 0.523 (0.511) Remain 00:25:19 loss: 0.1858 Lr: 0.00027 [2024-11-25 19:30:09,366 INFO misc.py line 119 2586773] Train: [43/50][35/376] Data 0.003 (0.003) Batch 0.492 (0.510) Remain 00:25:16 loss: 0.2006 Lr: 0.00027 [2024-11-25 19:30:09,894 INFO misc.py line 119 2586773] Train: [43/50][36/376] Data 0.002 (0.003) Batch 0.527 (0.511) Remain 00:25:17 loss: 0.2243 Lr: 0.00027 [2024-11-25 19:30:10,367 INFO misc.py line 119 2586773] Train: [43/50][37/376] Data 0.003 (0.003) Batch 0.474 (0.510) Remain 00:25:14 loss: 0.1869 Lr: 0.00027 [2024-11-25 19:30:10,885 INFO misc.py line 119 2586773] Train: [43/50][38/376] Data 0.003 (0.003) Batch 0.517 (0.510) Remain 00:25:14 loss: 0.1737 Lr: 0.00027 [2024-11-25 19:30:11,407 INFO misc.py line 119 2586773] Train: [43/50][39/376] Data 0.003 (0.003) Batch 0.522 (0.510) Remain 00:25:14 loss: 0.1991 Lr: 0.00027 [2024-11-25 19:30:11,908 INFO misc.py line 119 2586773] Train: [43/50][40/376] Data 0.002 (0.003) Batch 0.501 (0.510) Remain 00:25:13 loss: 0.1799 Lr: 0.00027 [2024-11-25 19:30:12,428 INFO misc.py line 119 2586773] Train: [43/50][41/376] Data 0.002 (0.003) Batch 0.520 (0.510) Remain 00:25:13 loss: 0.1771 Lr: 0.00027 [2024-11-25 19:30:12,937 INFO misc.py line 119 2586773] Train: [43/50][42/376] Data 0.003 (0.003) Batch 0.509 (0.510) Remain 00:25:13 loss: 0.2093 Lr: 0.00027 [2024-11-25 19:30:13,445 INFO misc.py line 119 2586773] Train: [43/50][43/376] Data 0.002 (0.003) Batch 0.509 (0.510) Remain 00:25:12 loss: 0.1655 Lr: 0.00027 [2024-11-25 19:30:14,016 INFO misc.py line 119 2586773] Train: [43/50][44/376] Data 0.003 (0.003) Batch 0.571 (0.512) Remain 00:25:16 loss: 0.1905 Lr: 0.00027 [2024-11-25 19:30:14,508 INFO misc.py line 119 2586773] Train: [43/50][45/376] Data 0.003 (0.003) Batch 0.492 (0.511) Remain 00:25:14 loss: 0.2636 Lr: 0.00027 [2024-11-25 19:30:15,045 INFO misc.py line 119 2586773] Train: [43/50][46/376] Data 0.003 (0.003) Batch 0.537 (0.512) Remain 00:25:15 loss: 0.1914 Lr: 0.00027 [2024-11-25 19:30:15,537 INFO misc.py line 119 2586773] Train: [43/50][47/376] Data 0.002 (0.003) Batch 0.492 (0.511) Remain 00:25:14 loss: 0.2269 Lr: 0.00027 [2024-11-25 19:30:16,061 INFO misc.py line 119 2586773] Train: [43/50][48/376] Data 0.003 (0.003) Batch 0.524 (0.512) Remain 00:25:14 loss: 0.2146 Lr: 0.00026 [2024-11-25 19:30:16,586 INFO misc.py line 119 2586773] Train: [43/50][49/376] Data 0.002 (0.003) Batch 0.525 (0.512) Remain 00:25:14 loss: 0.1644 Lr: 0.00026 [2024-11-25 19:30:17,113 INFO misc.py line 119 2586773] Train: [43/50][50/376] Data 0.003 (0.003) Batch 0.527 (0.512) Remain 00:25:15 loss: 0.1620 Lr: 0.00026 [2024-11-25 19:30:17,586 INFO misc.py line 119 2586773] Train: [43/50][51/376] Data 0.002 (0.003) Batch 0.472 (0.511) Remain 00:25:12 loss: 0.1502 Lr: 0.00026 [2024-11-25 19:30:18,053 INFO misc.py line 119 2586773] Train: [43/50][52/376] Data 0.002 (0.003) Batch 0.467 (0.510) Remain 00:25:09 loss: 0.2153 Lr: 0.00026 [2024-11-25 19:30:18,558 INFO misc.py line 119 2586773] Train: [43/50][53/376] Data 0.002 (0.003) Batch 0.506 (0.510) Remain 00:25:08 loss: 0.1767 Lr: 0.00026 [2024-11-25 19:30:19,105 INFO misc.py line 119 2586773] Train: [43/50][54/376] Data 0.002 (0.003) Batch 0.547 (0.511) Remain 00:25:09 loss: 0.2573 Lr: 0.00026 [2024-11-25 19:30:19,603 INFO misc.py line 119 2586773] Train: [43/50][55/376] Data 0.002 (0.003) Batch 0.498 (0.511) Remain 00:25:08 loss: 0.2054 Lr: 0.00026 [2024-11-25 19:30:20,076 INFO misc.py line 119 2586773] Train: [43/50][56/376] Data 0.002 (0.002) Batch 0.473 (0.510) Remain 00:25:05 loss: 0.1832 Lr: 0.00026 [2024-11-25 19:30:20,635 INFO misc.py line 119 2586773] Train: [43/50][57/376] Data 0.002 (0.002) Batch 0.558 (0.511) Remain 00:25:08 loss: 0.1867 Lr: 0.00026 [2024-11-25 19:30:21,125 INFO misc.py line 119 2586773] Train: [43/50][58/376] Data 0.003 (0.003) Batch 0.491 (0.511) Remain 00:25:06 loss: 0.2456 Lr: 0.00026 [2024-11-25 19:30:21,685 INFO misc.py line 119 2586773] Train: [43/50][59/376] Data 0.002 (0.003) Batch 0.559 (0.512) Remain 00:25:08 loss: 0.1866 Lr: 0.00026 [2024-11-25 19:30:22,238 INFO misc.py line 119 2586773] Train: [43/50][60/376] Data 0.003 (0.003) Batch 0.553 (0.512) Remain 00:25:10 loss: 0.1662 Lr: 0.00026 [2024-11-25 19:30:22,720 INFO misc.py line 119 2586773] Train: [43/50][61/376] Data 0.002 (0.002) Batch 0.482 (0.512) Remain 00:25:08 loss: 0.2147 Lr: 0.00026 [2024-11-25 19:30:23,243 INFO misc.py line 119 2586773] Train: [43/50][62/376] Data 0.002 (0.002) Batch 0.523 (0.512) Remain 00:25:08 loss: 0.2097 Lr: 0.00026 [2024-11-25 19:30:23,722 INFO misc.py line 119 2586773] Train: [43/50][63/376] Data 0.003 (0.002) Batch 0.479 (0.511) Remain 00:25:06 loss: 0.2464 Lr: 0.00026 [2024-11-25 19:30:24,198 INFO misc.py line 119 2586773] Train: [43/50][64/376] Data 0.002 (0.002) Batch 0.476 (0.511) Remain 00:25:03 loss: 0.2565 Lr: 0.00026 [2024-11-25 19:30:24,698 INFO misc.py line 119 2586773] Train: [43/50][65/376] Data 0.002 (0.002) Batch 0.500 (0.511) Remain 00:25:02 loss: 0.1931 Lr: 0.00026 [2024-11-25 19:30:25,220 INFO misc.py line 119 2586773] Train: [43/50][66/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 00:25:02 loss: 0.1679 Lr: 0.00026 [2024-11-25 19:30:25,719 INFO misc.py line 119 2586773] Train: [43/50][67/376] Data 0.002 (0.002) Batch 0.499 (0.511) Remain 00:25:01 loss: 0.2003 Lr: 0.00026 [2024-11-25 19:30:26,170 INFO misc.py line 119 2586773] Train: [43/50][68/376] Data 0.002 (0.002) Batch 0.451 (0.510) Remain 00:24:58 loss: 0.1904 Lr: 0.00026 [2024-11-25 19:30:26,677 INFO misc.py line 119 2586773] Train: [43/50][69/376] Data 0.002 (0.002) Batch 0.508 (0.510) Remain 00:24:57 loss: 0.1821 Lr: 0.00026 [2024-11-25 19:30:27,166 INFO misc.py line 119 2586773] Train: [43/50][70/376] Data 0.003 (0.002) Batch 0.488 (0.509) Remain 00:24:56 loss: 0.1729 Lr: 0.00026 [2024-11-25 19:30:27,729 INFO misc.py line 119 2586773] Train: [43/50][71/376] Data 0.002 (0.002) Batch 0.563 (0.510) Remain 00:24:58 loss: 0.2326 Lr: 0.00026 [2024-11-25 19:30:28,251 INFO misc.py line 119 2586773] Train: [43/50][72/376] Data 0.002 (0.002) Batch 0.523 (0.510) Remain 00:24:58 loss: 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Batch 0.485 (0.507) Remain 00:24:27 loss: 0.2220 Lr: 0.00025 [2024-11-25 19:30:50,893 INFO misc.py line 119 2586773] Train: [43/50][117/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 00:24:27 loss: 0.1990 Lr: 0.00025 [2024-11-25 19:30:51,406 INFO misc.py line 119 2586773] Train: [43/50][118/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:24:26 loss: 0.2110 Lr: 0.00025 [2024-11-25 19:30:51,902 INFO misc.py line 119 2586773] Train: [43/50][119/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 00:24:25 loss: 0.1790 Lr: 0.00025 [2024-11-25 19:30:52,436 INFO misc.py line 119 2586773] Train: [43/50][120/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 00:24:26 loss: 0.1859 Lr: 0.00025 [2024-11-25 19:30:52,914 INFO misc.py line 119 2586773] Train: [43/50][121/376] Data 0.002 (0.002) Batch 0.478 (0.507) Remain 00:24:24 loss: 0.1727 Lr: 0.00025 [2024-11-25 19:30:53,393 INFO misc.py line 119 2586773] Train: [43/50][122/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:24:23 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Batch 0.513 (0.509) Remain 00:24:03 loss: 0.1645 Lr: 0.00024 [2024-11-25 19:31:19,528 INFO misc.py line 119 2586773] Train: [43/50][173/376] Data 0.002 (0.002) Batch 0.479 (0.509) Remain 00:24:02 loss: 0.1983 Lr: 0.00024 [2024-11-25 19:31:20,067 INFO misc.py line 119 2586773] Train: [43/50][174/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 00:24:02 loss: 0.2217 Lr: 0.00024 [2024-11-25 19:31:20,575 INFO misc.py line 119 2586773] Train: [43/50][175/376] Data 0.002 (0.002) Batch 0.508 (0.509) Remain 00:24:01 loss: 0.1633 Lr: 0.00024 [2024-11-25 19:31:21,110 INFO misc.py line 119 2586773] Train: [43/50][176/376] Data 0.003 (0.002) Batch 0.535 (0.509) Remain 00:24:01 loss: 0.2217 Lr: 0.00024 [2024-11-25 19:31:21,687 INFO misc.py line 119 2586773] Train: [43/50][177/376] Data 0.002 (0.002) Batch 0.577 (0.509) Remain 00:24:02 loss: 0.1965 Lr: 0.00024 [2024-11-25 19:31:22,192 INFO misc.py line 119 2586773] Train: [43/50][178/376] Data 0.003 (0.002) Batch 0.505 (0.509) Remain 00:24:01 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19:31:25,982 INFO misc.py line 119 2586773] Train: [43/50][185/376] Data 0.002 (0.002) Batch 0.561 (0.511) Remain 00:24:01 loss: 0.1567 Lr: 0.00024 [2024-11-25 19:31:26,452 INFO misc.py line 119 2586773] Train: [43/50][186/376] Data 0.003 (0.002) Batch 0.470 (0.510) Remain 00:24:00 loss: 0.1647 Lr: 0.00024 [2024-11-25 19:31:26,936 INFO misc.py line 119 2586773] Train: [43/50][187/376] Data 0.002 (0.002) Batch 0.484 (0.510) Remain 00:23:59 loss: 0.1938 Lr: 0.00024 [2024-11-25 19:31:27,465 INFO misc.py line 119 2586773] Train: [43/50][188/376] Data 0.002 (0.002) Batch 0.529 (0.510) Remain 00:23:59 loss: 0.1751 Lr: 0.00024 [2024-11-25 19:31:27,991 INFO misc.py line 119 2586773] Train: [43/50][189/376] Data 0.002 (0.002) Batch 0.526 (0.510) Remain 00:23:59 loss: 0.1661 Lr: 0.00024 [2024-11-25 19:31:28,548 INFO misc.py line 119 2586773] Train: [43/50][190/376] Data 0.002 (0.002) Batch 0.557 (0.511) Remain 00:23:59 loss: 0.1884 Lr: 0.00024 [2024-11-25 19:31:29,083 INFO misc.py line 119 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Batch 0.462 (0.512) Remain 00:23:42 loss: 0.2277 Lr: 0.00023 [2024-11-25 19:31:48,693 INFO misc.py line 119 2586773] Train: [43/50][229/376] Data 0.002 (0.002) Batch 0.553 (0.512) Remain 00:23:42 loss: 0.2013 Lr: 0.00023 [2024-11-25 19:31:49,241 INFO misc.py line 119 2586773] Train: [43/50][230/376] Data 0.002 (0.002) Batch 0.548 (0.512) Remain 00:23:42 loss: 0.1570 Lr: 0.00023 [2024-11-25 19:31:49,743 INFO misc.py line 119 2586773] Train: [43/50][231/376] Data 0.003 (0.002) Batch 0.502 (0.512) Remain 00:23:41 loss: 0.2309 Lr: 0.00023 [2024-11-25 19:31:50,256 INFO misc.py line 119 2586773] Train: [43/50][232/376] Data 0.002 (0.002) Batch 0.513 (0.512) Remain 00:23:40 loss: 0.1829 Lr: 0.00023 [2024-11-25 19:31:50,770 INFO misc.py line 119 2586773] Train: [43/50][233/376] Data 0.002 (0.002) Batch 0.514 (0.512) Remain 00:23:40 loss: 0.2044 Lr: 0.00023 [2024-11-25 19:31:51,297 INFO misc.py line 119 2586773] Train: [43/50][234/376] Data 0.003 (0.002) Batch 0.527 (0.512) Remain 00:23:40 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Batch 0.509 (0.510) Remain 00:22:39 loss: 0.1667 Lr: 0.00022 [2024-11-25 19:32:45,260 INFO misc.py line 119 2586773] Train: [43/50][341/376] Data 0.002 (0.002) Batch 0.496 (0.510) Remain 00:22:38 loss: 0.1541 Lr: 0.00022 [2024-11-25 19:32:45,737 INFO misc.py line 119 2586773] Train: [43/50][342/376] Data 0.002 (0.002) Batch 0.477 (0.509) Remain 00:22:38 loss: 0.1889 Lr: 0.00022 [2024-11-25 19:32:46,260 INFO misc.py line 119 2586773] Train: [43/50][343/376] Data 0.002 (0.002) Batch 0.523 (0.509) Remain 00:22:37 loss: 0.1973 Lr: 0.00022 [2024-11-25 19:32:46,749 INFO misc.py line 119 2586773] Train: [43/50][344/376] Data 0.002 (0.002) Batch 0.488 (0.509) Remain 00:22:37 loss: 0.1981 Lr: 0.00022 [2024-11-25 19:32:47,245 INFO misc.py line 119 2586773] Train: [43/50][345/376] Data 0.003 (0.002) Batch 0.497 (0.509) Remain 00:22:36 loss: 0.1665 Lr: 0.00022 [2024-11-25 19:32:47,751 INFO misc.py line 119 2586773] Train: [43/50][346/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 00:22:35 loss: 0.2416 Lr: 0.00022 [2024-11-25 19:32:48,251 INFO misc.py line 119 2586773] Train: [43/50][347/376] Data 0.002 (0.002) Batch 0.500 (0.509) Remain 00:22:35 loss: 0.1591 Lr: 0.00022 [2024-11-25 19:32:48,772 INFO misc.py line 119 2586773] Train: [43/50][348/376] Data 0.003 (0.002) Batch 0.521 (0.509) Remain 00:22:34 loss: 0.3078 Lr: 0.00022 [2024-11-25 19:32:49,314 INFO misc.py line 119 2586773] Train: [43/50][349/376] Data 0.002 (0.002) Batch 0.542 (0.509) Remain 00:22:34 loss: 0.2017 Lr: 0.00022 [2024-11-25 19:32:49,848 INFO misc.py line 119 2586773] Train: [43/50][350/376] Data 0.002 (0.002) Batch 0.534 (0.510) Remain 00:22:34 loss: 0.1842 Lr: 0.00022 [2024-11-25 19:32:50,342 INFO misc.py line 119 2586773] Train: [43/50][351/376] Data 0.003 (0.002) Batch 0.494 (0.509) Remain 00:22:33 loss: 0.3329 Lr: 0.00022 [2024-11-25 19:32:50,827 INFO misc.py line 119 2586773] Train: [43/50][352/376] Data 0.003 (0.002) Batch 0.485 (0.509) Remain 00:22:33 loss: 0.2308 Lr: 0.00022 [2024-11-25 19:32:51,351 INFO misc.py line 119 2586773] Train: [43/50][353/376] Data 0.003 (0.002) Batch 0.524 (0.509) Remain 00:22:32 loss: 0.2095 Lr: 0.00021 [2024-11-25 19:32:51,889 INFO misc.py line 119 2586773] Train: [43/50][354/376] Data 0.002 (0.002) Batch 0.538 (0.510) Remain 00:22:32 loss: 0.1706 Lr: 0.00021 [2024-11-25 19:32:52,441 INFO misc.py line 119 2586773] Train: [43/50][355/376] Data 0.003 (0.002) Batch 0.552 (0.510) Remain 00:22:32 loss: 0.1690 Lr: 0.00021 [2024-11-25 19:32:52,992 INFO misc.py line 119 2586773] Train: [43/50][356/376] Data 0.002 (0.002) Batch 0.551 (0.510) Remain 00:22:31 loss: 0.2325 Lr: 0.00021 [2024-11-25 19:32:53,491 INFO misc.py line 119 2586773] Train: [43/50][357/376] Data 0.002 (0.002) Batch 0.499 (0.510) Remain 00:22:31 loss: 0.2111 Lr: 0.00021 [2024-11-25 19:32:54,019 INFO misc.py line 119 2586773] Train: [43/50][358/376] Data 0.002 (0.002) Batch 0.527 (0.510) Remain 00:22:30 loss: 0.2110 Lr: 0.00021 [2024-11-25 19:32:54,545 INFO misc.py line 119 2586773] Train: [43/50][359/376] Data 0.002 (0.002) Batch 0.526 (0.510) Remain 00:22:30 loss: 0.1753 Lr: 0.00021 [2024-11-25 19:32:55,046 INFO misc.py line 119 2586773] Train: [43/50][360/376] Data 0.003 (0.002) Batch 0.501 (0.510) Remain 00:22:30 loss: 0.1677 Lr: 0.00021 [2024-11-25 19:32:55,518 INFO misc.py line 119 2586773] Train: [43/50][361/376] Data 0.002 (0.002) Batch 0.473 (0.510) Remain 00:22:29 loss: 0.1605 Lr: 0.00021 [2024-11-25 19:32:55,988 INFO misc.py line 119 2586773] Train: [43/50][362/376] Data 0.002 (0.002) Batch 0.470 (0.510) Remain 00:22:28 loss: 0.2450 Lr: 0.00021 [2024-11-25 19:32:56,498 INFO misc.py line 119 2586773] Train: [43/50][363/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:22:27 loss: 0.1870 Lr: 0.00021 [2024-11-25 19:32:57,017 INFO misc.py line 119 2586773] Train: [43/50][364/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 00:22:27 loss: 0.1695 Lr: 0.00021 [2024-11-25 19:32:57,538 INFO misc.py line 119 2586773] Train: [43/50][365/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 00:22:27 loss: 0.3120 Lr: 0.00021 [2024-11-25 19:32:58,056 INFO misc.py line 119 2586773] Train: [43/50][366/376] Data 0.002 (0.002) Batch 0.518 (0.510) Remain 00:22:26 loss: 0.2256 Lr: 0.00021 [2024-11-25 19:32:58,598 INFO misc.py line 119 2586773] Train: [43/50][367/376] Data 0.002 (0.002) Batch 0.542 (0.510) Remain 00:22:26 loss: 0.2285 Lr: 0.00021 [2024-11-25 19:32:59,119 INFO misc.py line 119 2586773] Train: [43/50][368/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 00:22:25 loss: 0.1909 Lr: 0.00021 [2024-11-25 19:32:59,619 INFO misc.py line 119 2586773] Train: [43/50][369/376] Data 0.002 (0.002) Batch 0.501 (0.510) Remain 00:22:25 loss: 0.1807 Lr: 0.00021 [2024-11-25 19:33:00,135 INFO misc.py line 119 2586773] Train: [43/50][370/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 00:22:24 loss: 0.1656 Lr: 0.00021 [2024-11-25 19:33:00,648 INFO misc.py line 119 2586773] Train: [43/50][371/376] Data 0.003 (0.002) Batch 0.513 (0.510) Remain 00:22:24 loss: 0.2006 Lr: 0.00021 [2024-11-25 19:33:01,131 INFO misc.py line 119 2586773] Train: [43/50][372/376] Data 0.003 (0.002) Batch 0.483 (0.510) Remain 00:22:23 loss: 0.2441 Lr: 0.00021 [2024-11-25 19:33:01,661 INFO misc.py line 119 2586773] Train: [43/50][373/376] Data 0.002 (0.002) Batch 0.530 (0.510) Remain 00:22:23 loss: 0.3439 Lr: 0.00021 [2024-11-25 19:33:02,229 INFO misc.py line 119 2586773] Train: [43/50][374/376] Data 0.003 (0.002) Batch 0.568 (0.510) Remain 00:22:23 loss: 0.1931 Lr: 0.00021 [2024-11-25 19:33:02,747 INFO misc.py line 119 2586773] Train: [43/50][375/376] Data 0.002 (0.002) Batch 0.518 (0.510) Remain 00:22:22 loss: 0.1803 Lr: 0.00021 [2024-11-25 19:33:03,257 INFO misc.py line 119 2586773] Train: [43/50][376/376] Data 0.002 (0.002) Batch 0.511 (0.510) Remain 00:22:22 loss: 0.2059 Lr: 0.00021 [2024-11-25 19:33:03,258 INFO misc.py line 136 2586773] Train result: loss: 0.1987 [2024-11-25 19:33:03,258 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:33:14,181 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1753 [2024-11-25 19:33:14,444 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2172 [2024-11-25 19:33:14,707 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2500 [2024-11-25 19:33:14,930 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2015 [2024-11-25 19:33:15,192 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2830 [2024-11-25 19:33:15,461 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.2010 [2024-11-25 19:33:15,685 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2258 [2024-11-25 19:33:15,956 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2162 [2024-11-25 19:33:16,180 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2597 [2024-11-25 19:33:16,440 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2461 [2024-11-25 19:33:16,674 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2061 [2024-11-25 19:33:16,946 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2444 [2024-11-25 19:33:17,212 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2409 [2024-11-25 19:33:17,475 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2218 [2024-11-25 19:33:17,708 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2316 [2024-11-25 19:33:17,949 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3120 [2024-11-25 19:33:18,214 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2621 [2024-11-25 19:33:18,473 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2159 [2024-11-25 19:33:18,706 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2356 [2024-11-25 19:33:18,967 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2259 [2024-11-25 19:33:19,201 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2597 [2024-11-25 19:33:19,467 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2515 [2024-11-25 19:33:19,705 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2050 [2024-11-25 19:33:19,972 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2386 [2024-11-25 19:33:20,234 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2282 [2024-11-25 19:33:20,476 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2511 [2024-11-25 19:33:20,730 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2519 [2024-11-25 19:33:20,979 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2151 [2024-11-25 19:33:21,245 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2653 [2024-11-25 19:33:21,498 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2830 [2024-11-25 19:33:21,731 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2631 [2024-11-25 19:33:21,998 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1983 [2024-11-25 19:33:22,218 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2729 [2024-11-25 19:33:22,457 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2369 [2024-11-25 19:33:22,724 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1930 [2024-11-25 19:33:22,970 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2441 [2024-11-25 19:33:23,205 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1885 [2024-11-25 19:33:23,474 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2425 [2024-11-25 19:33:23,705 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2696 [2024-11-25 19:33:23,941 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2296 [2024-11-25 19:33:24,216 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3088 [2024-11-25 19:33:24,473 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2627 [2024-11-25 19:33:24,710 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2492 [2024-11-25 19:33:24,941 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2131 [2024-11-25 19:33:25,179 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2431 [2024-11-25 19:33:25,430 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2221 [2024-11-25 19:33:25,690 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2166 [2024-11-25 19:33:25,940 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2773 [2024-11-25 19:33:26,162 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2150 [2024-11-25 19:33:26,397 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2193 [2024-11-25 19:33:26,619 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2473 [2024-11-25 19:33:26,871 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2217 [2024-11-25 19:33:27,138 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2167 [2024-11-25 19:33:27,399 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3041 [2024-11-25 19:33:27,632 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2250 [2024-11-25 19:33:27,872 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2283 [2024-11-25 19:33:28,129 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2534 [2024-11-25 19:33:28,403 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2698 [2024-11-25 19:33:28,660 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2492 [2024-11-25 19:33:28,926 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2213 [2024-11-25 19:33:29,187 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2141 [2024-11-25 19:33:29,464 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2349 [2024-11-25 19:33:29,694 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2310 [2024-11-25 19:33:29,960 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2428 [2024-11-25 19:33:30,225 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2648 [2024-11-25 19:33:30,504 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1921 [2024-11-25 19:33:30,755 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1912 [2024-11-25 19:33:31,019 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2521 [2024-11-25 19:33:31,286 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2562 [2024-11-25 19:33:31,546 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2751 [2024-11-25 19:33:31,791 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2056 [2024-11-25 19:33:32,026 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2785 [2024-11-25 19:33:32,285 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2674 [2024-11-25 19:33:32,529 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2567 [2024-11-25 19:33:32,749 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2695 [2024-11-25 19:33:32,969 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2057 [2024-11-25 19:33:33,237 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2494 [2024-11-25 19:33:33,473 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2055 [2024-11-25 19:33:33,730 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2385 [2024-11-25 19:33:33,985 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2880 [2024-11-25 19:33:34,226 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2461 [2024-11-25 19:33:34,488 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2514 [2024-11-25 19:33:34,736 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1944 [2024-11-25 19:33:34,984 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2544 [2024-11-25 19:33:35,255 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2535 [2024-11-25 19:33:35,494 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2651 [2024-11-25 19:33:35,757 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2376 [2024-11-25 19:33:36,018 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2382 [2024-11-25 19:33:36,266 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2734 [2024-11-25 19:33:36,516 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2678 [2024-11-25 19:33:36,749 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2464 [2024-11-25 19:33:37,002 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2659 [2024-11-25 19:33:37,270 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2430 [2024-11-25 19:33:37,538 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.2054 [2024-11-25 19:33:37,804 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2329 [2024-11-25 19:33:38,052 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2167 [2024-11-25 19:33:38,322 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2295 [2024-11-25 19:33:38,543 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2976 [2024-11-25 19:33:38,814 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2388 [2024-11-25 19:33:39,053 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2478 [2024-11-25 19:33:39,322 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2076 [2024-11-25 19:33:39,581 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2532 [2024-11-25 19:33:39,840 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2325 [2024-11-25 19:33:40,096 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2644 [2024-11-25 19:33:40,318 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2514 [2024-11-25 19:33:40,552 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2304 [2024-11-25 19:33:40,810 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2286 [2024-11-25 19:33:41,085 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2332 [2024-11-25 19:33:41,319 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2493 [2024-11-25 19:33:41,580 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2012 [2024-11-25 19:33:41,844 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2424 [2024-11-25 19:33:42,066 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2241 [2024-11-25 19:33:42,301 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2024 [2024-11-25 19:33:42,524 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2112 [2024-11-25 19:33:42,750 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2146 [2024-11-25 19:33:43,025 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2884 [2024-11-25 19:33:43,285 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2487 [2024-11-25 19:33:43,553 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2390 [2024-11-25 19:33:43,818 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2211 [2024-11-25 19:33:44,081 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2637 [2024-11-25 19:33:44,340 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2788 [2024-11-25 19:33:44,604 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2390 [2024-11-25 19:33:44,860 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2485 [2024-11-25 19:33:45,123 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2438 [2024-11-25 19:33:45,388 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2484 [2024-11-25 19:33:45,639 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2502 [2024-11-25 19:33:45,869 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2050 [2024-11-25 19:33:46,129 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2432 [2024-11-25 19:33:46,364 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2593 [2024-11-25 19:33:46,590 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1920 [2024-11-25 19:33:46,804 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2247 [2024-11-25 19:33:47,021 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1898 [2024-11-25 19:33:47,761 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7739/0.8510/0.9962. [2024-11-25 19:33:47,761 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9962/0.9982 [2024-11-25 19:33:47,761 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5516/0.7039 [2024-11-25 19:33:47,761 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:33:47,762 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7809 [2024-11-25 19:33:47,762 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:33:50,427 INFO misc.py line 119 2586773] Train: [44/50][1/376] Data 0.094 (0.094) Batch 0.565 (0.565) Remain 00:24:46 loss: 0.1884 Lr: 0.00021 [2024-11-25 19:33:50,916 INFO misc.py line 119 2586773] Train: [44/50][2/376] Data 0.002 (0.002) Batch 0.489 (0.489) Remain 00:21:26 loss: 0.2003 Lr: 0.00021 [2024-11-25 19:33:51,407 INFO misc.py line 119 2586773] Train: [44/50][3/376] Data 0.003 (0.003) Batch 0.491 (0.491) Remain 00:21:30 loss: 0.2017 Lr: 0.00021 [2024-11-25 19:33:51,880 INFO misc.py line 119 2586773] Train: [44/50][4/376] Data 0.002 (0.002) Batch 0.474 (0.474) Remain 00:20:44 loss: 0.1646 Lr: 0.00021 [2024-11-25 19:33:52,394 INFO misc.py line 119 2586773] Train: [44/50][5/376] Data 0.002 (0.002) Batch 0.514 (0.494) Remain 00:21:36 loss: 0.1560 Lr: 0.00021 [2024-11-25 19:33:52,935 INFO misc.py line 119 2586773] Train: [44/50][6/376] Data 0.002 (0.002) Batch 0.541 (0.509) Remain 00:22:17 loss: 0.1744 Lr: 0.00021 [2024-11-25 19:33:53,430 INFO misc.py line 119 2586773] Train: [44/50][7/376] Data 0.002 (0.002) Batch 0.494 (0.506) Remain 00:22:07 loss: 0.1560 Lr: 0.00021 [2024-11-25 19:33:53,958 INFO misc.py line 119 2586773] Train: [44/50][8/376] Data 0.002 (0.002) Batch 0.529 (0.510) Remain 00:22:18 loss: 0.2176 Lr: 0.00021 [2024-11-25 19:33:54,429 INFO misc.py line 119 2586773] Train: [44/50][9/376] Data 0.003 (0.002) Batch 0.470 (0.504) Remain 00:22:00 loss: 0.2083 Lr: 0.00021 [2024-11-25 19:33:54,906 INFO misc.py line 119 2586773] Train: [44/50][10/376] Data 0.002 (0.002) Batch 0.478 (0.500) Remain 00:21:50 loss: 0.2020 Lr: 0.00021 [2024-11-25 19:33:55,409 INFO misc.py line 119 2586773] Train: [44/50][11/376] Data 0.002 (0.002) Batch 0.502 (0.500) Remain 00:21:51 loss: 0.1639 Lr: 0.00021 [2024-11-25 19:33:55,904 INFO misc.py line 119 2586773] Train: [44/50][12/376] Data 0.002 (0.002) Batch 0.496 (0.500) Remain 00:21:49 loss: 0.2028 Lr: 0.00021 [2024-11-25 19:33:56,395 INFO misc.py line 119 2586773] Train: [44/50][13/376] Data 0.002 (0.002) Batch 0.491 (0.499) Remain 00:21:46 loss: 0.2612 Lr: 0.00021 [2024-11-25 19:33:56,867 INFO misc.py line 119 2586773] Train: [44/50][14/376] Data 0.003 (0.002) Batch 0.472 (0.496) Remain 00:21:39 loss: 0.1878 Lr: 0.00021 [2024-11-25 19:33:57,386 INFO misc.py line 119 2586773] Train: [44/50][15/376] Data 0.003 (0.002) Batch 0.519 (0.498) Remain 00:21:43 loss: 0.1570 Lr: 0.00021 [2024-11-25 19:33:57,882 INFO misc.py line 119 2586773] Train: [44/50][16/376] Data 0.002 (0.002) Batch 0.496 (0.498) Remain 00:21:43 loss: 0.2178 Lr: 0.00021 [2024-11-25 19:33:58,395 INFO misc.py line 119 2586773] Train: [44/50][17/376] Data 0.003 (0.002) Batch 0.512 (0.499) Remain 00:21:45 loss: 0.1501 Lr: 0.00021 [2024-11-25 19:33:58,909 INFO misc.py line 119 2586773] Train: [44/50][18/376] Data 0.002 (0.002) Batch 0.514 (0.500) Remain 00:21:47 loss: 0.1612 Lr: 0.00021 [2024-11-25 19:33:59,403 INFO misc.py line 119 2586773] Train: [44/50][19/376] Data 0.002 (0.002) Batch 0.495 (0.500) Remain 00:21:45 loss: 0.2037 Lr: 0.00021 [2024-11-25 19:33:59,929 INFO misc.py line 119 2586773] Train: [44/50][20/376] Data 0.003 (0.002) Batch 0.526 (0.501) Remain 00:21:49 loss: 0.1827 Lr: 0.00021 [2024-11-25 19:34:00,377 INFO misc.py line 119 2586773] Train: [44/50][21/376] Data 0.002 (0.002) Batch 0.447 (0.498) Remain 00:21:41 loss: 0.1876 Lr: 0.00021 [2024-11-25 19:34:00,877 INFO misc.py line 119 2586773] Train: [44/50][22/376] Data 0.003 (0.002) Batch 0.501 (0.498) Remain 00:21:40 loss: 0.2478 Lr: 0.00021 [2024-11-25 19:34:01,367 INFO misc.py line 119 2586773] Train: [44/50][23/376] Data 0.002 (0.002) Batch 0.490 (0.498) Remain 00:21:39 loss: 0.1570 Lr: 0.00021 [2024-11-25 19:34:01,880 INFO misc.py line 119 2586773] Train: [44/50][24/376] Data 0.002 (0.002) Batch 0.513 (0.499) Remain 00:21:40 loss: 0.1920 Lr: 0.00021 [2024-11-25 19:34:02,367 INFO misc.py line 119 2586773] Train: [44/50][25/376] Data 0.002 (0.002) Batch 0.488 (0.498) Remain 00:21:38 loss: 0.1954 Lr: 0.00021 [2024-11-25 19:34:02,841 INFO misc.py line 119 2586773] Train: [44/50][26/376] Data 0.003 (0.002) Batch 0.474 (0.497) Remain 00:21:35 loss: 0.2724 Lr: 0.00021 [2024-11-25 19:34:03,341 INFO misc.py line 119 2586773] Train: [44/50][27/376] Data 0.002 (0.002) Batch 0.500 (0.497) Remain 00:21:35 loss: 0.1552 Lr: 0.00021 [2024-11-25 19:34:03,854 INFO misc.py line 119 2586773] Train: [44/50][28/376] Data 0.002 (0.002) Batch 0.513 (0.498) Remain 00:21:36 loss: 0.1615 Lr: 0.00021 [2024-11-25 19:34:04,379 INFO misc.py line 119 2586773] Train: [44/50][29/376] Data 0.002 (0.002) Batch 0.525 (0.499) Remain 00:21:38 loss: 0.2029 Lr: 0.00021 [2024-11-25 19:34:04,880 INFO misc.py line 119 2586773] Train: [44/50][30/376] Data 0.002 (0.002) Batch 0.500 (0.499) Remain 00:21:38 loss: 0.2220 Lr: 0.00021 [2024-11-25 19:34:05,365 INFO misc.py line 119 2586773] Train: [44/50][31/376] Data 0.002 (0.002) Batch 0.485 (0.499) Remain 00:21:36 loss: 0.1771 Lr: 0.00021 [2024-11-25 19:34:05,834 INFO misc.py line 119 2586773] Train: [44/50][32/376] Data 0.002 (0.002) Batch 0.469 (0.498) Remain 00:21:33 loss: 0.2093 Lr: 0.00021 [2024-11-25 19:34:06,373 INFO misc.py line 119 2586773] Train: [44/50][33/376] Data 0.002 (0.002) Batch 0.538 (0.499) Remain 00:21:36 loss: 0.2144 Lr: 0.00021 [2024-11-25 19:34:06,863 INFO misc.py line 119 2586773] Train: [44/50][34/376] Data 0.002 (0.002) Batch 0.490 (0.499) Remain 00:21:35 loss: 0.2036 Lr: 0.00021 [2024-11-25 19:34:07,373 INFO misc.py line 119 2586773] Train: [44/50][35/376] Data 0.002 (0.002) Batch 0.510 (0.499) Remain 00:21:35 loss: 0.1943 Lr: 0.00021 [2024-11-25 19:34:07,875 INFO misc.py line 119 2586773] Train: [44/50][36/376] Data 0.003 (0.002) Batch 0.502 (0.499) Remain 00:21:35 loss: 0.1871 Lr: 0.00021 [2024-11-25 19:34:08,401 INFO misc.py line 119 2586773] Train: [44/50][37/376] Data 0.002 (0.002) Batch 0.526 (0.500) Remain 00:21:37 loss: 0.1814 Lr: 0.00021 [2024-11-25 19:34:08,889 INFO misc.py line 119 2586773] Train: [44/50][38/376] Data 0.002 (0.002) Batch 0.489 (0.499) Remain 00:21:35 loss: 0.1679 Lr: 0.00021 [2024-11-25 19:34:09,382 INFO misc.py line 119 2586773] Train: [44/50][39/376] Data 0.002 (0.002) Batch 0.492 (0.499) Remain 00:21:34 loss: 0.2121 Lr: 0.00021 [2024-11-25 19:34:09,866 INFO misc.py line 119 2586773] Train: [44/50][40/376] Data 0.002 (0.002) Batch 0.484 (0.499) Remain 00:21:33 loss: 0.2033 Lr: 0.00021 [2024-11-25 19:34:10,367 INFO misc.py line 119 2586773] Train: [44/50][41/376] Data 0.002 (0.002) Batch 0.501 (0.499) Remain 00:21:32 loss: 0.2184 Lr: 0.00021 [2024-11-25 19:34:10,883 INFO misc.py line 119 2586773] Train: [44/50][42/376] Data 0.002 (0.002) Batch 0.517 (0.499) Remain 00:21:33 loss: 0.2416 Lr: 0.00021 [2024-11-25 19:34:11,436 INFO misc.py line 119 2586773] Train: [44/50][43/376] Data 0.002 (0.002) Batch 0.552 (0.501) Remain 00:21:36 loss: 0.1754 Lr: 0.00020 [2024-11-25 19:34:11,955 INFO misc.py line 119 2586773] Train: [44/50][44/376] Data 0.003 (0.002) Batch 0.519 (0.501) Remain 00:21:37 loss: 0.2033 Lr: 0.00020 [2024-11-25 19:34:12,446 INFO misc.py line 119 2586773] Train: [44/50][45/376] Data 0.002 (0.002) Batch 0.491 (0.501) Remain 00:21:35 loss: 0.1561 Lr: 0.00020 [2024-11-25 19:34:12,924 INFO misc.py line 119 2586773] Train: [44/50][46/376] Data 0.002 (0.002) Batch 0.478 (0.500) Remain 00:21:34 loss: 0.2271 Lr: 0.00020 [2024-11-25 19:34:13,438 INFO misc.py line 119 2586773] Train: [44/50][47/376] Data 0.002 (0.002) Batch 0.514 (0.501) Remain 00:21:34 loss: 0.2395 Lr: 0.00020 [2024-11-25 19:34:13,949 INFO misc.py line 119 2586773] Train: [44/50][48/376] Data 0.003 (0.002) Batch 0.512 (0.501) Remain 00:21:34 loss: 0.2353 Lr: 0.00020 [2024-11-25 19:34:14,436 INFO misc.py line 119 2586773] Train: [44/50][49/376] Data 0.002 (0.002) Batch 0.487 (0.501) Remain 00:21:33 loss: 0.1950 Lr: 0.00020 [2024-11-25 19:34:14,920 INFO misc.py line 119 2586773] Train: [44/50][50/376] Data 0.002 (0.002) Batch 0.484 (0.500) Remain 00:21:31 loss: 0.1538 Lr: 0.00020 [2024-11-25 19:34:15,455 INFO misc.py line 119 2586773] Train: [44/50][51/376] Data 0.002 (0.002) Batch 0.535 (0.501) Remain 00:21:33 loss: 0.1724 Lr: 0.00020 [2024-11-25 19:34:15,969 INFO misc.py line 119 2586773] Train: [44/50][52/376] Data 0.003 (0.002) Batch 0.514 (0.501) Remain 00:21:33 loss: 0.2384 Lr: 0.00020 [2024-11-25 19:34:16,438 INFO misc.py line 119 2586773] Train: [44/50][53/376] Data 0.002 (0.002) Batch 0.469 (0.501) Remain 00:21:31 loss: 0.1675 Lr: 0.00020 [2024-11-25 19:34:16,949 INFO misc.py line 119 2586773] Train: [44/50][54/376] Data 0.003 (0.002) Batch 0.511 (0.501) Remain 00:21:31 loss: 0.2306 Lr: 0.00020 [2024-11-25 19:34:17,447 INFO misc.py line 119 2586773] Train: [44/50][55/376] Data 0.002 (0.002) Batch 0.498 (0.501) Remain 00:21:30 loss: 0.1796 Lr: 0.00020 [2024-11-25 19:34:17,977 INFO misc.py line 119 2586773] Train: [44/50][56/376] Data 0.002 (0.002) Batch 0.529 (0.501) Remain 00:21:31 loss: 0.1751 Lr: 0.00020 [2024-11-25 19:34:18,505 INFO misc.py line 119 2586773] Train: [44/50][57/376] Data 0.003 (0.002) Batch 0.529 (0.502) Remain 00:21:32 loss: 0.1495 Lr: 0.00020 [2024-11-25 19:34:18,978 INFO misc.py line 119 2586773] Train: [44/50][58/376] Data 0.002 (0.002) Batch 0.473 (0.501) Remain 00:21:30 loss: 0.2020 Lr: 0.00020 [2024-11-25 19:34:19,499 INFO misc.py line 119 2586773] Train: [44/50][59/376] Data 0.002 (0.002) Batch 0.520 (0.502) Remain 00:21:30 loss: 0.2252 Lr: 0.00020 [2024-11-25 19:34:20,049 INFO misc.py line 119 2586773] Train: [44/50][60/376] Data 0.002 (0.002) Batch 0.550 (0.502) Remain 00:21:32 loss: 0.1984 Lr: 0.00020 [2024-11-25 19:34:20,525 INFO misc.py line 119 2586773] Train: [44/50][61/376] Data 0.002 (0.002) Batch 0.476 (0.502) Remain 00:21:30 loss: 0.2363 Lr: 0.00020 [2024-11-25 19:34:21,003 INFO misc.py line 119 2586773] Train: [44/50][62/376] Data 0.002 (0.002) Batch 0.478 (0.502) Remain 00:21:29 loss: 0.1914 Lr: 0.00020 [2024-11-25 19:34:21,537 INFO misc.py line 119 2586773] Train: [44/50][63/376] Data 0.002 (0.002) Batch 0.533 (0.502) Remain 00:21:30 loss: 0.1630 Lr: 0.00020 [2024-11-25 19:34:22,022 INFO misc.py line 119 2586773] Train: [44/50][64/376] Data 0.002 (0.002) Batch 0.485 (0.502) Remain 00:21:28 loss: 0.1926 Lr: 0.00020 [2024-11-25 19:34:22,512 INFO misc.py line 119 2586773] Train: [44/50][65/376] Data 0.002 (0.002) Batch 0.490 (0.502) Remain 00:21:27 loss: 0.1680 Lr: 0.00020 [2024-11-25 19:34:23,020 INFO misc.py line 119 2586773] Train: [44/50][66/376] Data 0.002 (0.002) Batch 0.508 (0.502) Remain 00:21:27 loss: 0.2910 Lr: 0.00020 [2024-11-25 19:34:23,586 INFO misc.py line 119 2586773] Train: [44/50][67/376] Data 0.002 (0.002) Batch 0.566 (0.503) Remain 00:21:29 loss: 0.1900 Lr: 0.00020 [2024-11-25 19:34:24,075 INFO misc.py line 119 2586773] Train: [44/50][68/376] Data 0.002 (0.002) Batch 0.489 (0.503) Remain 00:21:28 loss: 0.1832 Lr: 0.00020 [2024-11-25 19:34:24,585 INFO misc.py line 119 2586773] Train: [44/50][69/376] Data 0.002 (0.002) Batch 0.510 (0.503) Remain 00:21:28 loss: 0.1640 Lr: 0.00020 [2024-11-25 19:34:25,061 INFO misc.py line 119 2586773] Train: [44/50][70/376] Data 0.002 (0.002) Batch 0.476 (0.502) Remain 00:21:26 loss: 0.2385 Lr: 0.00020 [2024-11-25 19:34:25,555 INFO misc.py line 119 2586773] Train: [44/50][71/376] Data 0.002 (0.002) Batch 0.494 (0.502) Remain 00:21:26 loss: 0.1969 Lr: 0.00020 [2024-11-25 19:34:26,038 INFO misc.py line 119 2586773] Train: [44/50][72/376] Data 0.002 (0.002) Batch 0.484 (0.502) Remain 00:21:24 loss: 0.1923 Lr: 0.00020 [2024-11-25 19:34:26,572 INFO misc.py line 119 2586773] Train: [44/50][73/376] Data 0.002 (0.002) Batch 0.533 (0.502) Remain 00:21:25 loss: 0.2367 Lr: 0.00020 [2024-11-25 19:34:27,028 INFO misc.py line 119 2586773] Train: [44/50][74/376] Data 0.002 (0.002) Batch 0.456 (0.502) Remain 00:21:23 loss: 0.2561 Lr: 0.00020 [2024-11-25 19:34:27,557 INFO misc.py line 119 2586773] Train: [44/50][75/376] Data 0.002 (0.002) Batch 0.530 (0.502) Remain 00:21:23 loss: 0.2569 Lr: 0.00020 [2024-11-25 19:34:28,051 INFO misc.py line 119 2586773] Train: [44/50][76/376] Data 0.002 (0.002) Batch 0.494 (0.502) Remain 00:21:23 loss: 0.2417 Lr: 0.00020 [2024-11-25 19:34:28,558 INFO misc.py line 119 2586773] Train: [44/50][77/376] Data 0.002 (0.002) Batch 0.507 (0.502) Remain 00:21:22 loss: 0.1781 Lr: 0.00020 [2024-11-25 19:34:29,074 INFO misc.py line 119 2586773] Train: [44/50][78/376] Data 0.002 (0.002) Batch 0.515 (0.502) Remain 00:21:22 loss: 0.2117 Lr: 0.00020 [2024-11-25 19:34:29,579 INFO misc.py line 119 2586773] Train: [44/50][79/376] Data 0.003 (0.002) Batch 0.506 (0.502) Remain 00:21:22 loss: 0.1506 Lr: 0.00020 [2024-11-25 19:34:30,056 INFO misc.py line 119 2586773] Train: [44/50][80/376] Data 0.002 (0.002) Batch 0.477 (0.502) Remain 00:21:20 loss: 0.1492 Lr: 0.00020 [2024-11-25 19:34:30,582 INFO misc.py line 119 2586773] Train: [44/50][81/376] Data 0.002 (0.002) Batch 0.526 (0.502) Remain 00:21:21 loss: 0.1665 Lr: 0.00020 [2024-11-25 19:34:31,100 INFO misc.py line 119 2586773] Train: [44/50][82/376] Data 0.002 (0.002) Batch 0.517 (0.502) Remain 00:21:21 loss: 0.2352 Lr: 0.00020 [2024-11-25 19:34:31,592 INFO misc.py line 119 2586773] Train: [44/50][83/376] Data 0.003 (0.002) Batch 0.492 (0.502) Remain 00:21:20 loss: 0.1755 Lr: 0.00020 [2024-11-25 19:34:32,093 INFO misc.py line 119 2586773] Train: [44/50][84/376] Data 0.003 (0.002) Batch 0.501 (0.502) Remain 00:21:19 loss: 0.1966 Lr: 0.00020 [2024-11-25 19:34:32,611 INFO misc.py line 119 2586773] Train: 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(0.503) Remain 00:21:19 loss: 0.1614 Lr: 0.00020 [2024-11-25 19:34:36,212 INFO misc.py line 119 2586773] Train: [44/50][92/376] Data 0.002 (0.002) Batch 0.502 (0.503) Remain 00:21:18 loss: 0.1605 Lr: 0.00020 [2024-11-25 19:34:36,699 INFO misc.py line 119 2586773] Train: [44/50][93/376] Data 0.002 (0.002) Batch 0.487 (0.503) Remain 00:21:17 loss: 0.1320 Lr: 0.00020 [2024-11-25 19:34:37,182 INFO misc.py line 119 2586773] Train: [44/50][94/376] Data 0.002 (0.002) Batch 0.484 (0.503) Remain 00:21:16 loss: 0.1671 Lr: 0.00020 [2024-11-25 19:34:37,677 INFO misc.py line 119 2586773] Train: [44/50][95/376] Data 0.002 (0.002) Batch 0.495 (0.503) Remain 00:21:15 loss: 0.2200 Lr: 0.00020 [2024-11-25 19:34:38,183 INFO misc.py line 119 2586773] Train: [44/50][96/376] Data 0.002 (0.002) Batch 0.506 (0.503) Remain 00:21:15 loss: 0.1902 Lr: 0.00020 [2024-11-25 19:34:38,686 INFO misc.py line 119 2586773] Train: [44/50][97/376] Data 0.002 (0.002) Batch 0.503 (0.503) Remain 00:21:15 loss: 0.2065 Lr: 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line 119 2586773] Train: [44/50][272/376] Data 0.002 (0.002) Batch 0.534 (0.507) Remain 00:19:55 loss: 0.1731 Lr: 0.00017 [2024-11-25 19:36:08,213 INFO misc.py line 119 2586773] Train: [44/50][273/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:19:55 loss: 0.1685 Lr: 0.00017 [2024-11-25 19:36:08,733 INFO misc.py line 119 2586773] Train: [44/50][274/376] Data 0.002 (0.002) Batch 0.520 (0.507) Remain 00:19:54 loss: 0.1704 Lr: 0.00017 [2024-11-25 19:36:09,204 INFO misc.py line 119 2586773] Train: [44/50][275/376] Data 0.003 (0.002) Batch 0.471 (0.507) Remain 00:19:54 loss: 0.1644 Lr: 0.00017 [2024-11-25 19:36:09,685 INFO misc.py line 119 2586773] Train: [44/50][276/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 00:19:53 loss: 0.1994 Lr: 0.00017 [2024-11-25 19:36:10,247 INFO misc.py line 119 2586773] Train: [44/50][277/376] Data 0.002 (0.002) Batch 0.563 (0.507) Remain 00:19:53 loss: 0.1957 Lr: 0.00017 [2024-11-25 19:36:10,742 INFO misc.py line 119 2586773] Train: 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Batch 0.513 (0.507) Remain 00:19:49 loss: 0.1736 Lr: 0.00017 [2024-11-25 19:36:14,315 INFO misc.py line 119 2586773] Train: [44/50][285/376] Data 0.002 (0.002) Batch 0.551 (0.507) Remain 00:19:49 loss: 0.1923 Lr: 0.00017 [2024-11-25 19:36:14,830 INFO misc.py line 119 2586773] Train: [44/50][286/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:19:48 loss: 0.1899 Lr: 0.00017 [2024-11-25 19:36:15,331 INFO misc.py line 119 2586773] Train: [44/50][287/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:19:48 loss: 0.2416 Lr: 0.00017 [2024-11-25 19:36:15,850 INFO misc.py line 119 2586773] Train: [44/50][288/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:19:47 loss: 0.1869 Lr: 0.00017 [2024-11-25 19:36:16,337 INFO misc.py line 119 2586773] Train: [44/50][289/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:19:47 loss: 0.1876 Lr: 0.00017 [2024-11-25 19:36:16,832 INFO misc.py line 119 2586773] Train: [44/50][290/376] Data 0.002 (0.002) Batch 0.496 (0.507) Remain 00:19:46 loss: 0.1668 Lr: 0.00017 [2024-11-25 19:36:17,306 INFO misc.py line 119 2586773] Train: [44/50][291/376] Data 0.002 (0.002) Batch 0.474 (0.507) Remain 00:19:45 loss: 0.3413 Lr: 0.00017 [2024-11-25 19:36:17,776 INFO misc.py line 119 2586773] Train: [44/50][292/376] Data 0.002 (0.002) Batch 0.470 (0.506) Remain 00:19:45 loss: 0.2071 Lr: 0.00017 [2024-11-25 19:36:18,282 INFO misc.py line 119 2586773] Train: [44/50][293/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 00:19:44 loss: 0.2041 Lr: 0.00017 [2024-11-25 19:36:18,821 INFO misc.py line 119 2586773] Train: [44/50][294/376] Data 0.002 (0.002) Batch 0.538 (0.507) Remain 00:19:44 loss: 0.2379 Lr: 0.00017 [2024-11-25 19:36:19,289 INFO misc.py line 119 2586773] Train: [44/50][295/376] Data 0.002 (0.002) Batch 0.469 (0.506) Remain 00:19:43 loss: 0.1997 Lr: 0.00017 [2024-11-25 19:36:19,812 INFO misc.py line 119 2586773] Train: [44/50][296/376] Data 0.002 (0.002) Batch 0.522 (0.506) Remain 00:19:43 loss: 0.2007 Lr: 0.00017 [2024-11-25 19:36:20,337 INFO misc.py line 119 2586773] Train: [44/50][297/376] Data 0.002 (0.002) Batch 0.526 (0.507) Remain 00:19:42 loss: 0.2289 Lr: 0.00017 [2024-11-25 19:36:20,844 INFO misc.py line 119 2586773] Train: [44/50][298/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:19:42 loss: 0.2751 Lr: 0.00017 [2024-11-25 19:36:21,345 INFO misc.py line 119 2586773] Train: [44/50][299/376] Data 0.002 (0.002) Batch 0.501 (0.507) Remain 00:19:41 loss: 0.1975 Lr: 0.00017 [2024-11-25 19:36:21,834 INFO misc.py line 119 2586773] Train: [44/50][300/376] Data 0.002 (0.002) Batch 0.489 (0.506) Remain 00:19:41 loss: 0.2410 Lr: 0.00017 [2024-11-25 19:36:22,311 INFO misc.py line 119 2586773] Train: [44/50][301/376] Data 0.002 (0.002) Batch 0.476 (0.506) Remain 00:19:40 loss: 0.1898 Lr: 0.00017 [2024-11-25 19:36:22,797 INFO misc.py line 119 2586773] Train: [44/50][302/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 00:19:39 loss: 0.1713 Lr: 0.00017 [2024-11-25 19:36:23,332 INFO misc.py line 119 2586773] Train: [44/50][303/376] Data 0.002 (0.002) Batch 0.534 (0.506) Remain 00:19:39 loss: 0.2570 Lr: 0.00017 [2024-11-25 19:36:23,837 INFO misc.py line 119 2586773] Train: [44/50][304/376] Data 0.002 (0.002) Batch 0.505 (0.506) Remain 00:19:38 loss: 0.2332 Lr: 0.00017 [2024-11-25 19:36:24,367 INFO misc.py line 119 2586773] Train: [44/50][305/376] Data 0.002 (0.002) Batch 0.530 (0.506) Remain 00:19:38 loss: 0.1495 Lr: 0.00017 [2024-11-25 19:36:24,858 INFO misc.py line 119 2586773] Train: [44/50][306/376] Data 0.003 (0.002) Batch 0.491 (0.506) Remain 00:19:37 loss: 0.1932 Lr: 0.00017 [2024-11-25 19:36:25,334 INFO misc.py line 119 2586773] Train: [44/50][307/376] Data 0.002 (0.002) Batch 0.475 (0.506) Remain 00:19:37 loss: 0.2250 Lr: 0.00017 [2024-11-25 19:36:25,871 INFO misc.py line 119 2586773] Train: [44/50][308/376] Data 0.003 (0.002) Batch 0.537 (0.506) Remain 00:19:36 loss: 0.1988 Lr: 0.00017 [2024-11-25 19:36:26,336 INFO misc.py line 119 2586773] Train: [44/50][309/376] Data 0.002 (0.002) Batch 0.465 (0.506) Remain 00:19:36 loss: 0.1658 Lr: 0.00017 [2024-11-25 19:36:26,805 INFO misc.py line 119 2586773] Train: [44/50][310/376] Data 0.002 (0.002) Batch 0.469 (0.506) Remain 00:19:35 loss: 0.1649 Lr: 0.00017 [2024-11-25 19:36:27,297 INFO misc.py line 119 2586773] Train: [44/50][311/376] Data 0.003 (0.002) Batch 0.493 (0.506) Remain 00:19:34 loss: 0.1874 Lr: 0.00017 [2024-11-25 19:36:27,806 INFO misc.py line 119 2586773] Train: [44/50][312/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 00:19:34 loss: 0.1855 Lr: 0.00017 [2024-11-25 19:36:28,309 INFO misc.py line 119 2586773] Train: [44/50][313/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 00:19:33 loss: 0.1796 Lr: 0.00017 [2024-11-25 19:36:28,822 INFO misc.py line 119 2586773] Train: [44/50][314/376] Data 0.002 (0.002) Batch 0.513 (0.506) Remain 00:19:33 loss: 0.1830 Lr: 0.00017 [2024-11-25 19:36:29,388 INFO misc.py line 119 2586773] Train: [44/50][315/376] Data 0.002 (0.002) Batch 0.566 (0.506) Remain 00:19:33 loss: 0.1847 Lr: 0.00017 [2024-11-25 19:36:29,928 INFO misc.py line 119 2586773] Train: [44/50][316/376] Data 0.002 (0.002) Batch 0.541 (0.506) Remain 00:19:32 loss: 0.2521 Lr: 0.00017 [2024-11-25 19:36:30,412 INFO misc.py line 119 2586773] Train: [44/50][317/376] Data 0.003 (0.002) Batch 0.483 (0.506) Remain 00:19:32 loss: 0.2342 Lr: 0.00017 [2024-11-25 19:36:30,885 INFO misc.py line 119 2586773] Train: [44/50][318/376] Data 0.003 (0.002) Batch 0.473 (0.506) Remain 00:19:31 loss: 0.1759 Lr: 0.00017 [2024-11-25 19:36:31,394 INFO misc.py line 119 2586773] Train: [44/50][319/376] Data 0.003 (0.002) Batch 0.509 (0.506) Remain 00:19:31 loss: 0.1642 Lr: 0.00016 [2024-11-25 19:36:31,875 INFO misc.py line 119 2586773] Train: [44/50][320/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 00:19:30 loss: 0.2132 Lr: 0.00016 [2024-11-25 19:36:32,357 INFO misc.py line 119 2586773] Train: [44/50][321/376] Data 0.002 (0.002) Batch 0.483 (0.506) Remain 00:19:29 loss: 0.1792 Lr: 0.00016 [2024-11-25 19:36:32,837 INFO misc.py line 119 2586773] Train: [44/50][322/376] Data 0.002 (0.002) Batch 0.479 (0.506) Remain 00:19:28 loss: 0.2756 Lr: 0.00016 [2024-11-25 19:36:33,340 INFO misc.py line 119 2586773] Train: [44/50][323/376] Data 0.002 (0.002) Batch 0.504 (0.506) Remain 00:19:28 loss: 0.1796 Lr: 0.00016 [2024-11-25 19:36:33,874 INFO misc.py line 119 2586773] Train: [44/50][324/376] Data 0.002 (0.002) Batch 0.533 (0.506) Remain 00:19:28 loss: 0.1704 Lr: 0.00016 [2024-11-25 19:36:34,437 INFO misc.py line 119 2586773] Train: [44/50][325/376] Data 0.002 (0.002) Batch 0.563 (0.506) Remain 00:19:28 loss: 0.1732 Lr: 0.00016 [2024-11-25 19:36:34,964 INFO misc.py line 119 2586773] Train: [44/50][326/376] Data 0.003 (0.002) Batch 0.527 (0.506) Remain 00:19:27 loss: 0.1512 Lr: 0.00016 [2024-11-25 19:36:35,488 INFO misc.py line 119 2586773] Train: [44/50][327/376] Data 0.002 (0.002) Batch 0.525 (0.506) Remain 00:19:27 loss: 0.2158 Lr: 0.00016 [2024-11-25 19:36:35,958 INFO misc.py line 119 2586773] Train: [44/50][328/376] Data 0.003 (0.002) Batch 0.470 (0.506) Remain 00:19:26 loss: 0.1921 Lr: 0.00016 [2024-11-25 19:36:36,495 INFO misc.py line 119 2586773] Train: [44/50][329/376] Data 0.002 (0.002) Batch 0.537 (0.506) Remain 00:19:26 loss: 0.1913 Lr: 0.00016 [2024-11-25 19:36:37,010 INFO misc.py line 119 2586773] Train: [44/50][330/376] Data 0.002 (0.002) Batch 0.515 (0.506) Remain 00:19:25 loss: 0.1815 Lr: 0.00016 [2024-11-25 19:36:37,478 INFO misc.py line 119 2586773] Train: [44/50][331/376] Data 0.002 (0.002) Batch 0.467 (0.506) Remain 00:19:25 loss: 0.1702 Lr: 0.00016 [2024-11-25 19:36:37,968 INFO misc.py line 119 2586773] Train: [44/50][332/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 00:19:24 loss: 0.1961 Lr: 0.00016 [2024-11-25 19:36:38,471 INFO misc.py line 119 2586773] Train: [44/50][333/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 00:19:23 loss: 0.1552 Lr: 0.00016 [2024-11-25 19:36:39,012 INFO misc.py line 119 2586773] Train: [44/50][334/376] Data 0.002 (0.002) Batch 0.542 (0.506) Remain 00:19:23 loss: 0.1950 Lr: 0.00016 [2024-11-25 19:36:39,543 INFO misc.py line 119 2586773] Train: [44/50][335/376] Data 0.002 (0.002) Batch 0.530 (0.506) Remain 00:19:23 loss: 0.2390 Lr: 0.00016 [2024-11-25 19:36:40,019 INFO misc.py line 119 2586773] Train: [44/50][336/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 00:19:22 loss: 0.1916 Lr: 0.00016 [2024-11-25 19:36:40,547 INFO misc.py line 119 2586773] Train: [44/50][337/376] Data 0.002 (0.002) Batch 0.527 (0.506) Remain 00:19:22 loss: 0.1736 Lr: 0.00016 [2024-11-25 19:36:41,073 INFO misc.py line 119 2586773] Train: [44/50][338/376] Data 0.002 (0.002) Batch 0.526 (0.506) Remain 00:19:21 loss: 0.1759 Lr: 0.00016 [2024-11-25 19:36:41,576 INFO misc.py line 119 2586773] Train: [44/50][339/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 00:19:21 loss: 0.1840 Lr: 0.00016 [2024-11-25 19:36:42,091 INFO misc.py line 119 2586773] Train: [44/50][340/376] Data 0.002 (0.002) Batch 0.515 (0.506) Remain 00:19:20 loss: 0.2003 Lr: 0.00016 [2024-11-25 19:36:42,577 INFO misc.py line 119 2586773] Train: [44/50][341/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 00:19:20 loss: 0.2181 Lr: 0.00016 [2024-11-25 19:36:43,101 INFO misc.py line 119 2586773] Train: [44/50][342/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 00:19:19 loss: 0.1654 Lr: 0.00016 [2024-11-25 19:36:43,628 INFO misc.py line 119 2586773] Train: [44/50][343/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:19:19 loss: 0.2140 Lr: 0.00016 [2024-11-25 19:36:44,166 INFO misc.py line 119 2586773] Train: [44/50][344/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 00:19:19 loss: 0.2627 Lr: 0.00016 [2024-11-25 19:36:44,690 INFO misc.py line 119 2586773] Train: [44/50][345/376] Data 0.003 (0.002) Batch 0.525 (0.507) Remain 00:19:18 loss: 0.1862 Lr: 0.00016 [2024-11-25 19:36:45,213 INFO misc.py line 119 2586773] Train: [44/50][346/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:19:18 loss: 0.2005 Lr: 0.00016 [2024-11-25 19:36:45,692 INFO misc.py line 119 2586773] Train: [44/50][347/376] Data 0.003 (0.002) Batch 0.479 (0.507) Remain 00:19:17 loss: 0.1771 Lr: 0.00016 [2024-11-25 19:36:46,251 INFO misc.py line 119 2586773] Train: [44/50][348/376] Data 0.002 (0.002) Batch 0.559 (0.507) Remain 00:19:17 loss: 0.2049 Lr: 0.00016 [2024-11-25 19:36:46,766 INFO misc.py line 119 2586773] Train: [44/50][349/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:19:17 loss: 0.1714 Lr: 0.00016 [2024-11-25 19:36:47,270 INFO misc.py line 119 2586773] Train: [44/50][350/376] Data 0.003 (0.002) Batch 0.504 (0.507) Remain 00:19:16 loss: 0.2826 Lr: 0.00016 [2024-11-25 19:36:47,748 INFO misc.py line 119 2586773] Train: [44/50][351/376] Data 0.003 (0.002) Batch 0.478 (0.507) Remain 00:19:15 loss: 0.1724 Lr: 0.00016 [2024-11-25 19:36:48,276 INFO misc.py line 119 2586773] Train: [44/50][352/376] Data 0.002 (0.002) Batch 0.527 (0.507) Remain 00:19:15 loss: 0.1873 Lr: 0.00016 [2024-11-25 19:36:48,809 INFO misc.py line 119 2586773] Train: [44/50][353/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:19:15 loss: 0.2003 Lr: 0.00016 [2024-11-25 19:36:49,321 INFO misc.py line 119 2586773] Train: [44/50][354/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:19:14 loss: 0.2886 Lr: 0.00016 [2024-11-25 19:36:49,854 INFO misc.py line 119 2586773] Train: [44/50][355/376] Data 0.003 (0.002) Batch 0.533 (0.507) Remain 00:19:14 loss: 0.2003 Lr: 0.00016 [2024-11-25 19:36:50,331 INFO misc.py line 119 2586773] Train: [44/50][356/376] Data 0.003 (0.002) Batch 0.477 (0.507) Remain 00:19:13 loss: 0.2685 Lr: 0.00016 [2024-11-25 19:36:50,874 INFO misc.py line 119 2586773] Train: [44/50][357/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 00:19:13 loss: 0.1843 Lr: 0.00016 [2024-11-25 19:36:51,374 INFO misc.py line 119 2586773] Train: [44/50][358/376] Data 0.003 (0.002) Batch 0.500 (0.507) Remain 00:19:12 loss: 0.2426 Lr: 0.00016 [2024-11-25 19:36:51,913 INFO misc.py line 119 2586773] Train: [44/50][359/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 00:19:12 loss: 0.2016 Lr: 0.00016 [2024-11-25 19:36:52,413 INFO misc.py line 119 2586773] Train: [44/50][360/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:19:11 loss: 0.1771 Lr: 0.00016 [2024-11-25 19:36:52,917 INFO misc.py line 119 2586773] Train: [44/50][361/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:19:11 loss: 0.1583 Lr: 0.00016 [2024-11-25 19:36:53,445 INFO misc.py line 119 2586773] Train: [44/50][362/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:19:11 loss: 0.2476 Lr: 0.00016 [2024-11-25 19:36:53,961 INFO misc.py line 119 2586773] Train: [44/50][363/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:19:10 loss: 0.2049 Lr: 0.00016 [2024-11-25 19:36:54,453 INFO misc.py line 119 2586773] Train: [44/50][364/376] Data 0.002 (0.002) Batch 0.492 (0.507) Remain 00:19:09 loss: 0.1954 Lr: 0.00016 [2024-11-25 19:36:54,935 INFO misc.py line 119 2586773] Train: [44/50][365/376] Data 0.002 (0.002) Batch 0.482 (0.507) Remain 00:19:09 loss: 0.2948 Lr: 0.00016 [2024-11-25 19:36:55,439 INFO misc.py line 119 2586773] Train: [44/50][366/376] Data 0.002 (0.002) Batch 0.504 (0.507) Remain 00:19:08 loss: 0.2534 Lr: 0.00016 [2024-11-25 19:36:55,984 INFO misc.py line 119 2586773] Train: [44/50][367/376] Data 0.002 (0.002) Batch 0.545 (0.507) Remain 00:19:08 loss: 0.1842 Lr: 0.00016 [2024-11-25 19:36:56,484 INFO misc.py line 119 2586773] Train: [44/50][368/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:19:07 loss: 0.1471 Lr: 0.00016 [2024-11-25 19:36:56,953 INFO misc.py line 119 2586773] Train: [44/50][369/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:19:07 loss: 0.2689 Lr: 0.00016 [2024-11-25 19:36:57,452 INFO misc.py line 119 2586773] Train: [44/50][370/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:19:06 loss: 0.1750 Lr: 0.00016 [2024-11-25 19:36:57,975 INFO misc.py line 119 2586773] Train: [44/50][371/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:19:06 loss: 0.1922 Lr: 0.00016 [2024-11-25 19:36:58,461 INFO misc.py line 119 2586773] Train: [44/50][372/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 00:19:05 loss: 0.1738 Lr: 0.00016 [2024-11-25 19:36:58,927 INFO misc.py line 119 2586773] Train: [44/50][373/376] Data 0.002 (0.002) Batch 0.466 (0.507) Remain 00:19:04 loss: 0.1648 Lr: 0.00016 [2024-11-25 19:36:59,407 INFO misc.py line 119 2586773] Train: [44/50][374/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:19:04 loss: 0.1763 Lr: 0.00016 [2024-11-25 19:36:59,902 INFO misc.py line 119 2586773] Train: [44/50][375/376] Data 0.002 (0.002) Batch 0.495 (0.507) Remain 00:19:03 loss: 0.1493 Lr: 0.00016 [2024-11-25 19:37:00,418 INFO misc.py line 119 2586773] Train: [44/50][376/376] Data 0.002 (0.002) Batch 0.516 (0.507) Remain 00:19:03 loss: 0.3111 Lr: 0.00016 [2024-11-25 19:37:00,419 INFO misc.py line 136 2586773] Train result: loss: 0.1981 [2024-11-25 19:37:00,419 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:37:11,131 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1728 [2024-11-25 19:37:11,405 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2170 [2024-11-25 19:37:11,668 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2580 [2024-11-25 19:37:11,891 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1858 [2024-11-25 19:37:12,154 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2684 [2024-11-25 19:37:12,425 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1826 [2024-11-25 19:37:12,647 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2044 [2024-11-25 19:37:12,915 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2057 [2024-11-25 19:37:13,140 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2752 [2024-11-25 19:37:13,405 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2200 [2024-11-25 19:37:13,638 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.1980 [2024-11-25 19:37:13,919 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2446 [2024-11-25 19:37:14,187 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2442 [2024-11-25 19:37:14,450 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2275 [2024-11-25 19:37:14,682 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2255 [2024-11-25 19:37:14,922 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3075 [2024-11-25 19:37:15,188 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2512 [2024-11-25 19:37:15,433 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2012 [2024-11-25 19:37:15,663 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2382 [2024-11-25 19:37:15,926 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2111 [2024-11-25 19:37:16,167 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2532 [2024-11-25 19:37:16,430 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2411 [2024-11-25 19:37:16,670 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2050 [2024-11-25 19:37:16,937 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2277 [2024-11-25 19:37:17,199 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2166 [2024-11-25 19:37:17,437 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2519 [2024-11-25 19:37:17,688 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2419 [2024-11-25 19:37:17,935 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2030 [2024-11-25 19:37:18,201 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2619 [2024-11-25 19:37:18,454 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2740 [2024-11-25 19:37:18,689 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2523 [2024-11-25 19:37:18,954 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1933 [2024-11-25 19:37:19,172 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2598 [2024-11-25 19:37:19,412 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2330 [2024-11-25 19:37:19,674 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1917 [2024-11-25 19:37:19,919 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2504 [2024-11-25 19:37:20,144 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1872 [2024-11-25 19:37:20,411 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2205 [2024-11-25 19:37:20,641 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2558 [2024-11-25 19:37:20,875 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2272 [2024-11-25 19:37:21,144 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3030 [2024-11-25 19:37:21,395 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2603 [2024-11-25 19:37:21,633 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2384 [2024-11-25 19:37:21,864 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2096 [2024-11-25 19:37:22,101 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2320 [2024-11-25 19:37:22,348 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2277 [2024-11-25 19:37:22,609 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2101 [2024-11-25 19:37:22,860 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2739 [2024-11-25 19:37:23,084 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2044 [2024-11-25 19:37:23,319 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2103 [2024-11-25 19:37:23,539 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2386 [2024-11-25 19:37:23,792 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2172 [2024-11-25 19:37:24,057 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2098 [2024-11-25 19:37:24,319 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3000 [2024-11-25 19:37:24,552 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2289 [2024-11-25 19:37:24,790 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2256 [2024-11-25 19:37:25,048 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2320 [2024-11-25 19:37:25,314 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2695 [2024-11-25 19:37:25,571 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2253 [2024-11-25 19:37:25,830 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2209 [2024-11-25 19:37:26,081 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2027 [2024-11-25 19:37:26,356 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2298 [2024-11-25 19:37:26,585 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2268 [2024-11-25 19:37:26,843 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2424 [2024-11-25 19:37:27,106 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2343 [2024-11-25 19:37:27,375 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1871 [2024-11-25 19:37:27,619 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1845 [2024-11-25 19:37:27,875 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2467 [2024-11-25 19:37:28,146 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2452 [2024-11-25 19:37:28,408 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2654 [2024-11-25 19:37:28,651 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2058 [2024-11-25 19:37:28,885 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2809 [2024-11-25 19:37:29,142 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2489 [2024-11-25 19:37:29,386 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2490 [2024-11-25 19:37:29,603 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2631 [2024-11-25 19:37:29,826 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2030 [2024-11-25 19:37:30,094 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2519 [2024-11-25 19:37:30,330 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2057 [2024-11-25 19:37:30,591 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2293 [2024-11-25 19:37:30,845 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2854 [2024-11-25 19:37:31,086 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2128 [2024-11-25 19:37:31,352 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2554 [2024-11-25 19:37:31,604 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1852 [2024-11-25 19:37:31,853 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2380 [2024-11-25 19:37:32,124 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2452 [2024-11-25 19:37:32,364 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2605 [2024-11-25 19:37:32,625 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2425 [2024-11-25 19:37:32,884 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2254 [2024-11-25 19:37:33,131 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2662 [2024-11-25 19:37:33,377 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2601 [2024-11-25 19:37:33,610 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2475 [2024-11-25 19:37:33,867 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2630 [2024-11-25 19:37:34,135 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2377 [2024-11-25 19:37:34,404 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1813 [2024-11-25 19:37:34,669 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2225 [2024-11-25 19:37:34,917 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2069 [2024-11-25 19:37:35,186 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2115 [2024-11-25 19:37:35,408 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2981 [2024-11-25 19:37:35,678 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2300 [2024-11-25 19:37:35,916 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2427 [2024-11-25 19:37:36,185 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2002 [2024-11-25 19:37:36,445 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2451 [2024-11-25 19:37:36,703 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2154 [2024-11-25 19:37:36,957 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2626 [2024-11-25 19:37:37,180 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2279 [2024-11-25 19:37:37,420 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2175 [2024-11-25 19:37:37,681 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2246 [2024-11-25 19:37:37,958 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2250 [2024-11-25 19:37:38,193 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2332 [2024-11-25 19:37:38,452 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.1926 [2024-11-25 19:37:38,733 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2282 [2024-11-25 19:37:38,963 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2184 [2024-11-25 19:37:39,208 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2012 [2024-11-25 19:37:39,432 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2090 [2024-11-25 19:37:39,656 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2093 [2024-11-25 19:37:39,929 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2819 [2024-11-25 19:37:40,192 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2466 [2024-11-25 19:37:40,459 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2342 [2024-11-25 19:37:40,731 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2244 [2024-11-25 19:37:40,992 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2693 [2024-11-25 19:37:41,252 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2751 [2024-11-25 19:37:41,516 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2212 [2024-11-25 19:37:41,771 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2411 [2024-11-25 19:37:42,039 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2293 [2024-11-25 19:37:42,305 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2531 [2024-11-25 19:37:42,557 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2430 [2024-11-25 19:37:42,788 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1950 [2024-11-25 19:37:43,055 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2303 [2024-11-25 19:37:43,288 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2481 [2024-11-25 19:37:43,522 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1884 [2024-11-25 19:37:43,740 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2171 [2024-11-25 19:37:43,963 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1837 [2024-11-25 19:37:44,638 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7815/0.8604/0.9964. [2024-11-25 19:37:44,639 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9964/0.9982 [2024-11-25 19:37:44,639 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5666/0.7225 [2024-11-25 19:37:44,639 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:37:44,640 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7815 [2024-11-25 19:37:44,640 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7815 [2024-11-25 19:37:44,640 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:37:49,197 INFO misc.py line 119 2586773] Train: [45/50][1/376] Data 0.134 (0.134) Batch 0.607 (0.607) Remain 00:22:47 loss: 0.2769 Lr: 0.00016 [2024-11-25 19:37:49,724 INFO misc.py line 119 2586773] Train: [45/50][2/376] Data 0.003 (0.003) Batch 0.527 (0.527) Remain 00:19:47 loss: 0.2239 Lr: 0.00016 [2024-11-25 19:37:50,239 INFO misc.py line 119 2586773] Train: [45/50][3/376] Data 0.002 (0.002) Batch 0.515 (0.515) Remain 00:19:20 loss: 0.2200 Lr: 0.00016 [2024-11-25 19:37:50,764 INFO misc.py line 119 2586773] Train: [45/50][4/376] Data 0.003 (0.003) Batch 0.524 (0.524) Remain 00:19:40 loss: 0.2034 Lr: 0.00016 [2024-11-25 19:37:51,251 INFO misc.py line 119 2586773] Train: [45/50][5/376] Data 0.003 (0.003) Batch 0.488 (0.506) Remain 00:18:58 loss: 0.1626 Lr: 0.00016 [2024-11-25 19:37:51,738 INFO misc.py line 119 2586773] Train: [45/50][6/376] Data 0.002 (0.002) Batch 0.487 (0.500) Remain 00:18:44 loss: 0.2017 Lr: 0.00016 [2024-11-25 19:37:52,224 INFO misc.py line 119 2586773] Train: [45/50][7/376] Data 0.002 (0.002) Batch 0.485 (0.496) Remain 00:18:35 loss: 0.1677 Lr: 0.00016 [2024-11-25 19:37:52,692 INFO misc.py line 119 2586773] Train: [45/50][8/376] Data 0.002 (0.002) Batch 0.468 (0.490) Remain 00:18:22 loss: 0.2524 Lr: 0.00016 [2024-11-25 19:37:53,206 INFO misc.py line 119 2586773] Train: [45/50][9/376] Data 0.002 (0.002) Batch 0.514 (0.494) Remain 00:18:30 loss: 0.1890 Lr: 0.00016 [2024-11-25 19:37:53,691 INFO misc.py line 119 2586773] Train: [45/50][10/376] Data 0.002 (0.002) Batch 0.485 (0.493) Remain 00:18:27 loss: 0.1662 Lr: 0.00016 [2024-11-25 19:37:54,185 INFO misc.py line 119 2586773] Train: [45/50][11/376] Data 0.002 (0.002) Batch 0.493 (0.493) Remain 00:18:27 loss: 0.1518 Lr: 0.00016 [2024-11-25 19:37:54,687 INFO misc.py line 119 2586773] Train: [45/50][12/376] Data 0.002 (0.002) Batch 0.502 (0.494) Remain 00:18:28 loss: 0.1650 Lr: 0.00016 [2024-11-25 19:37:55,217 INFO misc.py line 119 2586773] Train: [45/50][13/376] Data 0.002 (0.002) Batch 0.530 (0.498) Remain 00:18:36 loss: 0.2059 Lr: 0.00016 [2024-11-25 19:37:55,703 INFO misc.py line 119 2586773] Train: [45/50][14/376] Data 0.002 (0.002) Batch 0.486 (0.497) Remain 00:18:33 loss: 0.1570 Lr: 0.00016 [2024-11-25 19:37:56,185 INFO misc.py line 119 2586773] Train: [45/50][15/376] Data 0.002 (0.002) Batch 0.482 (0.495) Remain 00:18:30 loss: 0.1984 Lr: 0.00016 [2024-11-25 19:37:56,685 INFO misc.py line 119 2586773] Train: [45/50][16/376] Data 0.002 (0.002) Batch 0.500 (0.496) Remain 00:18:30 loss: 0.1906 Lr: 0.00016 [2024-11-25 19:37:57,152 INFO misc.py line 119 2586773] Train: [45/50][17/376] Data 0.003 (0.002) Batch 0.466 (0.494) Remain 00:18:25 loss: 0.1976 Lr: 0.00016 [2024-11-25 19:37:57,671 INFO misc.py line 119 2586773] Train: [45/50][18/376] Data 0.002 (0.002) Batch 0.520 (0.495) Remain 00:18:28 loss: 0.1991 Lr: 0.00015 [2024-11-25 19:37:58,180 INFO misc.py line 119 2586773] Train: [45/50][19/376] Data 0.002 (0.002) Batch 0.509 (0.496) Remain 00:18:30 loss: 0.2120 Lr: 0.00015 [2024-11-25 19:37:58,695 INFO misc.py line 119 2586773] Train: [45/50][20/376] Data 0.002 (0.002) Batch 0.514 (0.497) Remain 00:18:32 loss: 0.1659 Lr: 0.00015 [2024-11-25 19:37:59,204 INFO misc.py line 119 2586773] Train: [45/50][21/376] Data 0.002 (0.002) Batch 0.510 (0.498) Remain 00:18:33 loss: 0.1752 Lr: 0.00015 [2024-11-25 19:37:59,722 INFO misc.py line 119 2586773] Train: [45/50][22/376] Data 0.002 (0.002) Batch 0.518 (0.499) Remain 00:18:34 loss: 0.1935 Lr: 0.00015 [2024-11-25 19:38:00,218 INFO misc.py line 119 2586773] Train: [45/50][23/376] Data 0.002 (0.002) Batch 0.495 (0.499) Remain 00:18:34 loss: 0.1823 Lr: 0.00015 [2024-11-25 19:38:00,739 INFO misc.py line 119 2586773] Train: [45/50][24/376] Data 0.002 (0.002) Batch 0.521 (0.500) Remain 00:18:35 loss: 0.2217 Lr: 0.00015 [2024-11-25 19:38:01,266 INFO misc.py line 119 2586773] Train: [45/50][25/376] Data 0.002 (0.002) Batch 0.527 (0.501) Remain 00:18:38 loss: 0.1694 Lr: 0.00015 [2024-11-25 19:38:01,755 INFO misc.py line 119 2586773] Train: [45/50][26/376] Data 0.003 (0.002) Batch 0.490 (0.501) Remain 00:18:36 loss: 0.1895 Lr: 0.00015 [2024-11-25 19:38:02,274 INFO misc.py line 119 2586773] Train: [45/50][27/376] Data 0.002 (0.002) Batch 0.518 (0.501) Remain 00:18:37 loss: 0.2076 Lr: 0.00015 [2024-11-25 19:38:02,820 INFO misc.py line 119 2586773] 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0.002 (0.002) Batch 0.560 (0.513) Remain 00:16:45 loss: 0.1920 Lr: 0.00012 [2024-11-25 19:40:20,990 INFO misc.py line 119 2586773] Train: [45/50][297/376] Data 0.003 (0.002) Batch 0.472 (0.513) Remain 00:16:44 loss: 0.1688 Lr: 0.00012 [2024-11-25 19:40:21,508 INFO misc.py line 119 2586773] Train: [45/50][298/376] Data 0.002 (0.002) Batch 0.518 (0.513) Remain 00:16:44 loss: 0.3037 Lr: 0.00012 [2024-11-25 19:40:22,037 INFO misc.py line 119 2586773] Train: [45/50][299/376] Data 0.003 (0.002) Batch 0.529 (0.513) Remain 00:16:43 loss: 0.1992 Lr: 0.00012 [2024-11-25 19:40:22,564 INFO misc.py line 119 2586773] Train: [45/50][300/376] Data 0.002 (0.002) Batch 0.526 (0.513) Remain 00:16:43 loss: 0.2226 Lr: 0.00012 [2024-11-25 19:40:23,065 INFO misc.py line 119 2586773] Train: [45/50][301/376] Data 0.002 (0.002) Batch 0.501 (0.513) Remain 00:16:42 loss: 0.2047 Lr: 0.00012 [2024-11-25 19:40:23,573 INFO misc.py line 119 2586773] Train: [45/50][302/376] Data 0.002 (0.002) Batch 0.509 (0.513) Remain 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[2024-11-25 19:40:27,195 INFO misc.py line 119 2586773] Train: [45/50][309/376] Data 0.002 (0.002) Batch 0.479 (0.513) Remain 00:16:38 loss: 0.2234 Lr: 0.00012 [2024-11-25 19:40:27,686 INFO misc.py line 119 2586773] Train: [45/50][310/376] Data 0.002 (0.002) Batch 0.492 (0.513) Remain 00:16:38 loss: 0.1564 Lr: 0.00012 [2024-11-25 19:40:28,170 INFO misc.py line 119 2586773] Train: [45/50][311/376] Data 0.002 (0.002) Batch 0.484 (0.513) Remain 00:16:37 loss: 0.2008 Lr: 0.00012 [2024-11-25 19:40:28,686 INFO misc.py line 119 2586773] Train: [45/50][312/376] Data 0.003 (0.002) Batch 0.516 (0.513) Remain 00:16:36 loss: 0.1975 Lr: 0.00012 [2024-11-25 19:40:29,196 INFO misc.py line 119 2586773] Train: [45/50][313/376] Data 0.002 (0.002) Batch 0.510 (0.513) Remain 00:16:36 loss: 0.1732 Lr: 0.00012 [2024-11-25 19:40:29,710 INFO misc.py line 119 2586773] Train: [45/50][314/376] Data 0.002 (0.002) Batch 0.514 (0.513) Remain 00:16:35 loss: 0.2061 Lr: 0.00012 [2024-11-25 19:40:30,184 INFO misc.py line 119 2586773] Train: [45/50][315/376] Data 0.002 (0.002) Batch 0.474 (0.513) Remain 00:16:35 loss: 0.1740 Lr: 0.00012 [2024-11-25 19:40:30,702 INFO misc.py line 119 2586773] Train: [45/50][316/376] Data 0.002 (0.002) Batch 0.519 (0.513) Remain 00:16:34 loss: 0.1592 Lr: 0.00012 [2024-11-25 19:40:31,197 INFO misc.py line 119 2586773] Train: [45/50][317/376] Data 0.002 (0.002) Batch 0.494 (0.513) Remain 00:16:33 loss: 0.1673 Lr: 0.00012 [2024-11-25 19:40:31,695 INFO misc.py line 119 2586773] Train: [45/50][318/376] Data 0.002 (0.002) Batch 0.498 (0.513) Remain 00:16:33 loss: 0.1795 Lr: 0.00012 [2024-11-25 19:40:32,259 INFO misc.py line 119 2586773] Train: [45/50][319/376] Data 0.002 (0.002) Batch 0.564 (0.513) Remain 00:16:33 loss: 0.1958 Lr: 0.00012 [2024-11-25 19:40:32,755 INFO misc.py line 119 2586773] Train: [45/50][320/376] Data 0.002 (0.002) Batch 0.496 (0.513) Remain 00:16:32 loss: 0.2029 Lr: 0.00012 [2024-11-25 19:40:33,241 INFO misc.py line 119 2586773] Train: [45/50][321/376] Data 0.002 (0.002) Batch 0.486 (0.513) Remain 00:16:31 loss: 0.2548 Lr: 0.00012 [2024-11-25 19:40:33,757 INFO misc.py line 119 2586773] Train: [45/50][322/376] Data 0.002 (0.002) Batch 0.516 (0.513) Remain 00:16:31 loss: 0.1555 Lr: 0.00012 [2024-11-25 19:40:34,248 INFO misc.py line 119 2586773] Train: [45/50][323/376] Data 0.002 (0.002) Batch 0.491 (0.513) Remain 00:16:30 loss: 0.1745 Lr: 0.00012 [2024-11-25 19:40:34,758 INFO misc.py line 119 2586773] Train: [45/50][324/376] Data 0.002 (0.002) Batch 0.510 (0.513) Remain 00:16:30 loss: 0.2066 Lr: 0.00012 [2024-11-25 19:40:35,264 INFO misc.py line 119 2586773] Train: [45/50][325/376] Data 0.002 (0.002) Batch 0.506 (0.512) Remain 00:16:29 loss: 0.1790 Lr: 0.00012 [2024-11-25 19:40:35,745 INFO misc.py line 119 2586773] Train: [45/50][326/376] Data 0.002 (0.002) Batch 0.481 (0.512) Remain 00:16:28 loss: 0.1996 Lr: 0.00012 [2024-11-25 19:40:36,254 INFO misc.py line 119 2586773] Train: [45/50][327/376] Data 0.003 (0.002) Batch 0.509 (0.512) Remain 00:16:28 loss: 0.2200 Lr: 0.00012 [2024-11-25 19:40:36,732 INFO misc.py line 119 2586773] Train: [45/50][328/376] Data 0.002 (0.002) Batch 0.478 (0.512) Remain 00:16:27 loss: 0.1824 Lr: 0.00012 [2024-11-25 19:40:37,225 INFO misc.py line 119 2586773] Train: [45/50][329/376] Data 0.002 (0.002) Batch 0.493 (0.512) Remain 00:16:27 loss: 0.1809 Lr: 0.00012 [2024-11-25 19:40:37,725 INFO misc.py line 119 2586773] Train: [45/50][330/376] Data 0.003 (0.002) Batch 0.500 (0.512) Remain 00:16:26 loss: 0.1955 Lr: 0.00012 [2024-11-25 19:40:38,245 INFO misc.py line 119 2586773] Train: [45/50][331/376] Data 0.002 (0.002) Batch 0.520 (0.512) Remain 00:16:26 loss: 0.2262 Lr: 0.00012 [2024-11-25 19:40:38,759 INFO misc.py line 119 2586773] Train: [45/50][332/376] Data 0.002 (0.002) Batch 0.513 (0.512) Remain 00:16:25 loss: 0.1857 Lr: 0.00012 [2024-11-25 19:40:39,229 INFO misc.py line 119 2586773] Train: [45/50][333/376] Data 0.002 (0.002) Batch 0.471 (0.512) Remain 00:16:24 loss: 0.2983 Lr: 0.00012 [2024-11-25 19:40:39,729 INFO misc.py line 119 2586773] Train: [45/50][334/376] Data 0.002 (0.002) Batch 0.500 (0.512) Remain 00:16:24 loss: 0.1929 Lr: 0.00012 [2024-11-25 19:40:40,240 INFO misc.py line 119 2586773] Train: [45/50][335/376] Data 0.002 (0.002) Batch 0.510 (0.512) Remain 00:16:23 loss: 0.2091 Lr: 0.00012 [2024-11-25 19:40:40,744 INFO misc.py line 119 2586773] Train: [45/50][336/376] Data 0.003 (0.002) Batch 0.504 (0.512) Remain 00:16:23 loss: 0.1740 Lr: 0.00012 [2024-11-25 19:40:41,281 INFO misc.py line 119 2586773] Train: [45/50][337/376] Data 0.002 (0.002) Batch 0.537 (0.512) Remain 00:16:22 loss: 0.1928 Lr: 0.00012 [2024-11-25 19:40:41,842 INFO misc.py line 119 2586773] Train: [45/50][338/376] Data 0.002 (0.002) Batch 0.561 (0.512) Remain 00:16:22 loss: 0.2429 Lr: 0.00012 [2024-11-25 19:40:42,339 INFO misc.py line 119 2586773] Train: [45/50][339/376] Data 0.003 (0.002) Batch 0.497 (0.512) Remain 00:16:21 loss: 0.1899 Lr: 0.00012 [2024-11-25 19:40:42,826 INFO misc.py line 119 2586773] Train: [45/50][340/376] Data 0.002 (0.002) Batch 0.486 (0.512) Remain 00:16:21 loss: 0.1887 Lr: 0.00012 [2024-11-25 19:40:43,323 INFO misc.py line 119 2586773] Train: [45/50][341/376] Data 0.002 (0.002) Batch 0.498 (0.512) Remain 00:16:20 loss: 0.1843 Lr: 0.00012 [2024-11-25 19:40:43,867 INFO misc.py line 119 2586773] Train: [45/50][342/376] Data 0.002 (0.002) Batch 0.543 (0.512) Remain 00:16:20 loss: 0.1844 Lr: 0.00011 [2024-11-25 19:40:44,361 INFO misc.py line 119 2586773] Train: [45/50][343/376] Data 0.002 (0.002) Batch 0.495 (0.512) Remain 00:16:19 loss: 0.1712 Lr: 0.00011 [2024-11-25 19:40:44,849 INFO misc.py line 119 2586773] Train: [45/50][344/376] Data 0.002 (0.002) Batch 0.488 (0.512) Remain 00:16:19 loss: 0.1759 Lr: 0.00011 [2024-11-25 19:40:45,368 INFO misc.py line 119 2586773] Train: [45/50][345/376] Data 0.002 (0.002) Batch 0.518 (0.512) Remain 00:16:18 loss: 0.1769 Lr: 0.00011 [2024-11-25 19:40:45,834 INFO misc.py line 119 2586773] Train: [45/50][346/376] Data 0.002 (0.002) Batch 0.467 (0.512) Remain 00:16:17 loss: 0.1655 Lr: 0.00011 [2024-11-25 19:40:46,318 INFO misc.py line 119 2586773] Train: [45/50][347/376] Data 0.002 (0.002) Batch 0.483 (0.512) Remain 00:16:17 loss: 0.2339 Lr: 0.00011 [2024-11-25 19:40:46,789 INFO misc.py line 119 2586773] Train: [45/50][348/376] Data 0.002 (0.002) Batch 0.471 (0.512) Remain 00:16:16 loss: 0.1537 Lr: 0.00011 [2024-11-25 19:40:47,340 INFO misc.py line 119 2586773] Train: [45/50][349/376] Data 0.002 (0.002) Batch 0.551 (0.512) Remain 00:16:16 loss: 0.2436 Lr: 0.00011 [2024-11-25 19:40:47,845 INFO misc.py line 119 2586773] Train: [45/50][350/376] Data 0.002 (0.002) Batch 0.505 (0.512) Remain 00:16:15 loss: 0.1791 Lr: 0.00011 [2024-11-25 19:40:48,314 INFO misc.py line 119 2586773] Train: [45/50][351/376] Data 0.002 (0.002) Batch 0.469 (0.512) Remain 00:16:14 loss: 0.2165 Lr: 0.00011 [2024-11-25 19:40:48,845 INFO misc.py line 119 2586773] Train: [45/50][352/376] Data 0.002 (0.002) Batch 0.530 (0.512) Remain 00:16:14 loss: 0.1844 Lr: 0.00011 [2024-11-25 19:40:49,357 INFO misc.py line 119 2586773] Train: [45/50][353/376] Data 0.002 (0.002) Batch 0.512 (0.512) Remain 00:16:13 loss: 0.1741 Lr: 0.00011 [2024-11-25 19:40:49,856 INFO misc.py line 119 2586773] Train: [45/50][354/376] Data 0.003 (0.002) Batch 0.499 (0.512) Remain 00:16:13 loss: 0.1804 Lr: 0.00011 [2024-11-25 19:40:50,359 INFO misc.py line 119 2586773] Train: [45/50][355/376] Data 0.002 (0.002) Batch 0.502 (0.512) Remain 00:16:12 loss: 0.1635 Lr: 0.00011 [2024-11-25 19:40:50,827 INFO misc.py line 119 2586773] Train: [45/50][356/376] Data 0.002 (0.002) Batch 0.468 (0.512) Remain 00:16:11 loss: 0.1823 Lr: 0.00011 [2024-11-25 19:40:51,308 INFO misc.py line 119 2586773] Train: [45/50][357/376] Data 0.002 (0.002) Batch 0.481 (0.511) Remain 00:16:11 loss: 0.1564 Lr: 0.00011 [2024-11-25 19:40:51,828 INFO misc.py line 119 2586773] Train: [45/50][358/376] Data 0.002 (0.002) Batch 0.520 (0.512) Remain 00:16:10 loss: 0.2075 Lr: 0.00011 [2024-11-25 19:40:52,356 INFO misc.py line 119 2586773] Train: [45/50][359/376] Data 0.002 (0.002) Batch 0.528 (0.512) Remain 00:16:10 loss: 0.2141 Lr: 0.00011 [2024-11-25 19:40:52,854 INFO misc.py line 119 2586773] Train: [45/50][360/376] Data 0.002 (0.002) Batch 0.497 (0.512) Remain 00:16:09 loss: 0.1830 Lr: 0.00011 [2024-11-25 19:40:53,332 INFO misc.py line 119 2586773] Train: [45/50][361/376] Data 0.002 (0.002) Batch 0.478 (0.511) Remain 00:16:09 loss: 0.1639 Lr: 0.00011 [2024-11-25 19:40:53,803 INFO misc.py line 119 2586773] Train: [45/50][362/376] Data 0.002 (0.002) Batch 0.471 (0.511) Remain 00:16:08 loss: 0.1753 Lr: 0.00011 [2024-11-25 19:40:54,292 INFO misc.py line 119 2586773] Train: [45/50][363/376] Data 0.002 (0.002) Batch 0.488 (0.511) Remain 00:16:07 loss: 0.1968 Lr: 0.00011 [2024-11-25 19:40:54,830 INFO misc.py line 119 2586773] Train: [45/50][364/376] Data 0.002 (0.002) Batch 0.539 (0.511) Remain 00:16:07 loss: 0.2140 Lr: 0.00011 [2024-11-25 19:40:55,309 INFO misc.py line 119 2586773] Train: [45/50][365/376] Data 0.002 (0.002) Batch 0.479 (0.511) Remain 00:16:06 loss: 0.1367 Lr: 0.00011 [2024-11-25 19:40:55,844 INFO misc.py line 119 2586773] Train: [45/50][366/376] Data 0.002 (0.002) Batch 0.534 (0.511) Remain 00:16:06 loss: 0.1948 Lr: 0.00011 [2024-11-25 19:40:56,316 INFO misc.py line 119 2586773] Train: [45/50][367/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 00:16:05 loss: 0.2152 Lr: 0.00011 [2024-11-25 19:40:56,832 INFO misc.py line 119 2586773] Train: [45/50][368/376] Data 0.002 (0.002) Batch 0.516 (0.511) Remain 00:16:05 loss: 0.2216 Lr: 0.00011 [2024-11-25 19:40:57,348 INFO misc.py line 119 2586773] Train: [45/50][369/376] Data 0.002 (0.002) Batch 0.516 (0.511) Remain 00:16:04 loss: 0.1742 Lr: 0.00011 [2024-11-25 19:40:57,905 INFO misc.py line 119 2586773] Train: [45/50][370/376] Data 0.002 (0.002) Batch 0.557 (0.511) Remain 00:16:04 loss: 0.1744 Lr: 0.00011 [2024-11-25 19:40:58,390 INFO misc.py line 119 2586773] Train: [45/50][371/376] Data 0.002 (0.002) Batch 0.485 (0.511) Remain 00:16:03 loss: 0.1803 Lr: 0.00011 [2024-11-25 19:40:58,928 INFO misc.py line 119 2586773] Train: [45/50][372/376] Data 0.002 (0.002) Batch 0.538 (0.511) Remain 00:16:03 loss: 0.1822 Lr: 0.00011 [2024-11-25 19:40:59,452 INFO misc.py line 119 2586773] Train: [45/50][373/376] Data 0.002 (0.002) Batch 0.524 (0.511) Remain 00:16:02 loss: 0.2192 Lr: 0.00011 [2024-11-25 19:40:59,977 INFO misc.py line 119 2586773] Train: [45/50][374/376] Data 0.002 (0.002) Batch 0.525 (0.511) Remain 00:16:02 loss: 0.1932 Lr: 0.00011 [2024-11-25 19:41:00,449 INFO misc.py line 119 2586773] Train: [45/50][375/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 00:16:01 loss: 0.1893 Lr: 0.00011 [2024-11-25 19:41:00,945 INFO misc.py line 119 2586773] Train: [45/50][376/376] Data 0.002 (0.002) Batch 0.496 (0.511) Remain 00:16:01 loss: 0.1723 Lr: 0.00011 [2024-11-25 19:41:00,946 INFO misc.py line 136 2586773] Train result: loss: 0.1946 [2024-11-25 19:41:00,946 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:41:11,897 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1708 [2024-11-25 19:41:12,154 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2138 [2024-11-25 19:41:12,417 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2346 [2024-11-25 19:41:12,639 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1932 [2024-11-25 19:41:12,901 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2785 [2024-11-25 19:41:13,168 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1836 [2024-11-25 19:41:13,391 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1873 [2024-11-25 19:41:13,662 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2173 [2024-11-25 19:41:13,887 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2628 [2024-11-25 19:41:14,147 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2322 [2024-11-25 19:41:14,377 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2029 [2024-11-25 19:41:14,648 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2449 [2024-11-25 19:41:14,915 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2393 [2024-11-25 19:41:15,179 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2184 [2024-11-25 19:41:15,413 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2293 [2024-11-25 19:41:15,659 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3121 [2024-11-25 19:41:15,927 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2562 [2024-11-25 19:41:16,173 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1963 [2024-11-25 19:41:16,402 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2313 [2024-11-25 19:41:16,663 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2067 [2024-11-25 19:41:16,901 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2452 [2024-11-25 19:41:17,169 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2414 [2024-11-25 19:41:17,408 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2033 [2024-11-25 19:41:17,682 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2210 [2024-11-25 19:41:17,945 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2148 [2024-11-25 19:41:18,181 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2508 [2024-11-25 19:41:18,436 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2463 [2024-11-25 19:41:18,684 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2166 [2024-11-25 19:41:18,951 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2523 [2024-11-25 19:41:19,204 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2795 [2024-11-25 19:41:19,437 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2538 [2024-11-25 19:41:19,703 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1958 [2024-11-25 19:41:19,921 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2513 [2024-11-25 19:41:20,159 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2059 [2024-11-25 19:41:20,420 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2009 [2024-11-25 19:41:20,664 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2368 [2024-11-25 19:41:20,892 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1840 [2024-11-25 19:41:21,162 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2260 [2024-11-25 19:41:21,393 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2561 [2024-11-25 19:41:21,634 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2229 [2024-11-25 19:41:21,904 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3015 [2024-11-25 19:41:22,156 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2599 [2024-11-25 19:41:22,392 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2395 [2024-11-25 19:41:22,626 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2254 [2024-11-25 19:41:22,863 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2301 [2024-11-25 19:41:23,110 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2207 [2024-11-25 19:41:23,367 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2238 [2024-11-25 19:41:23,618 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2732 [2024-11-25 19:41:23,840 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2090 [2024-11-25 19:41:24,074 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2099 [2024-11-25 19:41:24,294 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2426 [2024-11-25 19:41:24,550 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2134 [2024-11-25 19:41:24,818 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2072 [2024-11-25 19:41:25,082 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3074 [2024-11-25 19:41:25,313 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2218 [2024-11-25 19:41:25,556 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2327 [2024-11-25 19:41:25,812 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2358 [2024-11-25 19:41:26,081 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2686 [2024-11-25 19:41:26,336 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2337 [2024-11-25 19:41:26,598 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2195 [2024-11-25 19:41:26,849 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.1929 [2024-11-25 19:41:27,117 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2269 [2024-11-25 19:41:27,347 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2276 [2024-11-25 19:41:27,610 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2414 [2024-11-25 19:41:27,878 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2340 [2024-11-25 19:41:28,148 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1821 [2024-11-25 19:41:28,391 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1845 [2024-11-25 19:41:28,647 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2481 [2024-11-25 19:41:28,915 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2338 [2024-11-25 19:41:29,175 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2688 [2024-11-25 19:41:29,421 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2027 [2024-11-25 19:41:29,659 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2780 [2024-11-25 19:41:29,915 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2503 [2024-11-25 19:41:30,159 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2456 [2024-11-25 19:41:30,376 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2544 [2024-11-25 19:41:30,599 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2095 [2024-11-25 19:41:30,866 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2435 [2024-11-25 19:41:31,101 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.1965 [2024-11-25 19:41:31,363 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2279 [2024-11-25 19:41:31,617 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2767 [2024-11-25 19:41:31,858 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2132 [2024-11-25 19:41:32,118 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2578 [2024-11-25 19:41:32,368 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1941 [2024-11-25 19:41:32,616 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2368 [2024-11-25 19:41:32,884 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2465 [2024-11-25 19:41:33,120 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2535 [2024-11-25 19:41:33,393 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2413 [2024-11-25 19:41:33,656 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2284 [2024-11-25 19:41:33,902 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2698 [2024-11-25 19:41:34,148 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2456 [2024-11-25 19:41:34,382 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2404 [2024-11-25 19:41:34,636 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2639 [2024-11-25 19:41:34,905 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2301 [2024-11-25 19:41:35,172 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1787 [2024-11-25 19:41:35,438 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2115 [2024-11-25 19:41:35,686 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.1985 [2024-11-25 19:41:35,953 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2115 [2024-11-25 19:41:36,180 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2924 [2024-11-25 19:41:36,452 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2349 [2024-11-25 19:41:36,689 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2416 [2024-11-25 19:41:36,963 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1995 [2024-11-25 19:41:37,223 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2420 [2024-11-25 19:41:37,481 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2259 [2024-11-25 19:41:37,740 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2631 [2024-11-25 19:41:37,964 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2349 [2024-11-25 19:41:38,198 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2136 [2024-11-25 19:41:38,455 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2211 [2024-11-25 19:41:38,725 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2204 [2024-11-25 19:41:38,958 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2318 [2024-11-25 19:41:39,219 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.1961 [2024-11-25 19:41:39,485 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2229 [2024-11-25 19:41:39,709 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2133 [2024-11-25 19:41:39,946 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.1987 [2024-11-25 19:41:40,165 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2060 [2024-11-25 19:41:40,390 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2058 [2024-11-25 19:41:40,662 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2869 [2024-11-25 19:41:40,922 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2470 [2024-11-25 19:41:41,189 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2329 [2024-11-25 19:41:41,457 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2188 [2024-11-25 19:41:41,717 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2741 [2024-11-25 19:41:41,979 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2835 [2024-11-25 19:41:42,245 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2092 [2024-11-25 19:41:42,499 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2384 [2024-11-25 19:41:42,764 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2205 [2024-11-25 19:41:43,030 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2464 [2024-11-25 19:41:43,284 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2276 [2024-11-25 19:41:43,516 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1905 [2024-11-25 19:41:43,774 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2293 [2024-11-25 19:41:44,009 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2548 [2024-11-25 19:41:44,236 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1847 [2024-11-25 19:41:44,450 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2154 [2024-11-25 19:41:44,666 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1827 [2024-11-25 19:41:45,420 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7829/0.8608/0.9964. [2024-11-25 19:41:45,420 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9964/0.9982 [2024-11-25 19:41:45,420 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5694/0.7234 [2024-11-25 19:41:45,420 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:41:45,421 INFO misc.py line 160 2586773] Best validation mIoU updated to: 0.7829 [2024-11-25 19:41:45,421 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 19:41:45,422 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:41:49,983 INFO misc.py line 119 2586773] Train: [46/50][1/376] Data 0.111 (0.111) Batch 0.615 (0.615) Remain 00:19:14 loss: 0.2109 Lr: 0.00011 [2024-11-25 19:41:50,490 INFO misc.py line 119 2586773] Train: [46/50][2/376] Data 0.002 (0.002) Batch 0.507 (0.507) Remain 00:15:52 loss: 0.2206 Lr: 0.00011 [2024-11-25 19:41:51,021 INFO misc.py line 119 2586773] Train: [46/50][3/376] Data 0.002 (0.002) Batch 0.531 (0.531) Remain 00:16:35 loss: 0.1837 Lr: 0.00011 [2024-11-25 19:41:51,531 INFO misc.py line 119 2586773] Train: [46/50][4/376] Data 0.002 (0.002) Batch 0.511 (0.511) Remain 00:15:57 loss: 0.1852 Lr: 0.00011 [2024-11-25 19:41:51,993 INFO misc.py line 119 2586773] Train: [46/50][5/376] Data 0.002 (0.002) Batch 0.462 (0.486) Remain 00:15:11 loss: 0.1854 Lr: 0.00011 [2024-11-25 19:41:52,487 INFO misc.py line 119 2586773] Train: [46/50][6/376] Data 0.002 (0.002) Batch 0.494 (0.489) Remain 00:15:15 loss: 0.1706 Lr: 0.00011 [2024-11-25 19:41:52,985 INFO misc.py line 119 2586773] Train: [46/50][7/376] Data 0.002 (0.002) Batch 0.498 (0.491) Remain 00:15:19 loss: 0.2389 Lr: 0.00011 [2024-11-25 19:41:53,478 INFO misc.py line 119 2586773] Train: [46/50][8/376] Data 0.002 (0.002) Batch 0.493 (0.491) Remain 00:15:20 loss: 0.1796 Lr: 0.00011 [2024-11-25 19:41:53,987 INFO misc.py line 119 2586773] Train: [46/50][9/376] Data 0.002 (0.002) Batch 0.509 (0.494) Remain 00:15:25 loss: 0.2139 Lr: 0.00011 [2024-11-25 19:41:54,504 INFO misc.py line 119 2586773] Train: [46/50][10/376] Data 0.002 (0.002) Batch 0.517 (0.498) Remain 00:15:30 loss: 0.2283 Lr: 0.00011 [2024-11-25 19:41:55,047 INFO misc.py line 119 2586773] Train: [46/50][11/376] Data 0.002 (0.002) Batch 0.543 (0.503) Remain 00:15:40 loss: 0.1729 Lr: 0.00011 [2024-11-25 19:41:55,570 INFO misc.py line 119 2586773] Train: [46/50][12/376] Data 0.002 (0.002) Batch 0.523 (0.505) Remain 00:15:44 loss: 0.2444 Lr: 0.00011 [2024-11-25 19:41:56,115 INFO misc.py line 119 2586773] Train: [46/50][13/376] Data 0.002 (0.002) Batch 0.546 (0.509) Remain 00:15:51 loss: 0.2488 Lr: 0.00011 [2024-11-25 19:41:56,601 INFO misc.py line 119 2586773] Train: [46/50][14/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:15:46 loss: 0.1796 Lr: 0.00011 [2024-11-25 19:41:57,106 INFO misc.py line 119 2586773] Train: [46/50][15/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:15:45 loss: 0.2114 Lr: 0.00011 [2024-11-25 19:41:57,622 INFO misc.py line 119 2586773] Train: [46/50][16/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:15:46 loss: 0.2106 Lr: 0.00011 [2024-11-25 19:41:58,128 INFO misc.py line 119 2586773] Train: [46/50][17/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 00:15:45 loss: 0.2055 Lr: 0.00011 [2024-11-25 19:41:58,654 INFO misc.py line 119 2586773] Train: [46/50][18/376] Data 0.002 (0.002) Batch 0.527 (0.509) Remain 00:15:47 loss: 0.2224 Lr: 0.00011 [2024-11-25 19:41:59,139 INFO misc.py line 119 2586773] Train: [46/50][19/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:15:44 loss: 0.1797 Lr: 0.00011 [2024-11-25 19:41:59,637 INFO misc.py line 119 2586773] Train: [46/50][20/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:15:42 loss: 0.1648 Lr: 0.00011 [2024-11-25 19:42:00,131 INFO misc.py line 119 2586773] Train: [46/50][21/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 00:15:40 loss: 0.1968 Lr: 0.00011 [2024-11-25 19:42:00,606 INFO misc.py line 119 2586773] Train: [46/50][22/376] Data 0.002 (0.002) Batch 0.475 (0.505) Remain 00:15:37 loss: 0.1900 Lr: 0.00011 [2024-11-25 19:42:01,105 INFO misc.py line 119 2586773] Train: [46/50][23/376] Data 0.002 (0.002) Batch 0.499 (0.504) Remain 00:15:36 loss: 0.2102 Lr: 0.00011 [2024-11-25 19:42:01,668 INFO misc.py line 119 2586773] Train: [46/50][24/376] Data 0.002 (0.002) Batch 0.563 (0.507) Remain 00:15:41 loss: 0.1821 Lr: 0.00011 [2024-11-25 19:42:02,173 INFO misc.py line 119 2586773] Train: [46/50][25/376] Data 0.003 (0.002) Batch 0.505 (0.507) Remain 00:15:40 loss: 0.2064 Lr: 0.00011 [2024-11-25 19:42:02,653 INFO misc.py line 119 2586773] Train: [46/50][26/376] Data 0.002 (0.002) Batch 0.480 (0.506) Remain 00:15:37 loss: 0.1836 Lr: 0.00011 [2024-11-25 19:42:03,168 INFO misc.py line 119 2586773] Train: [46/50][27/376] Data 0.002 (0.002) Batch 0.514 (0.506) Remain 00:15:37 loss: 0.2105 Lr: 0.00011 [2024-11-25 19:42:03,677 INFO misc.py line 119 2586773] Train: [46/50][28/376] Data 0.004 (0.002) Batch 0.510 (0.506) Remain 00:15:37 loss: 0.1742 Lr: 0.00011 [2024-11-25 19:42:04,206 INFO misc.py line 119 2586773] Train: [46/50][29/376] Data 0.003 (0.002) Batch 0.529 (0.507) Remain 00:15:38 loss: 0.1800 Lr: 0.00011 [2024-11-25 19:42:04,661 INFO misc.py line 119 2586773] Train: [46/50][30/376] Data 0.002 (0.002) Batch 0.455 (0.505) Remain 00:15:34 loss: 0.2066 Lr: 0.00011 [2024-11-25 19:42:05,149 INFO misc.py line 119 2586773] Train: [46/50][31/376] Data 0.002 (0.002) Batch 0.488 (0.505) Remain 00:15:32 loss: 0.1463 Lr: 0.00011 [2024-11-25 19:42:05,645 INFO misc.py line 119 2586773] Train: [46/50][32/376] Data 0.002 (0.002) Batch 0.496 (0.504) Remain 00:15:31 loss: 0.1675 Lr: 0.00011 [2024-11-25 19:42:06,149 INFO misc.py line 119 2586773] Train: [46/50][33/376] Data 0.002 (0.002) Batch 0.504 (0.504) Remain 00:15:31 loss: 0.1810 Lr: 0.00011 [2024-11-25 19:42:06,655 INFO misc.py line 119 2586773] Train: [46/50][34/376] Data 0.002 (0.002) Batch 0.506 (0.504) Remain 00:15:31 loss: 0.1868 Lr: 0.00011 [2024-11-25 19:42:07,132 INFO misc.py line 119 2586773] Train: [46/50][35/376] Data 0.002 (0.002) Batch 0.477 (0.503) Remain 00:15:28 loss: 0.1502 Lr: 0.00011 [2024-11-25 19:42:07,699 INFO misc.py line 119 2586773] Train: [46/50][36/376] Data 0.003 (0.002) Batch 0.567 (0.505) Remain 00:15:31 loss: 0.1608 Lr: 0.00011 [2024-11-25 19:42:08,174 INFO misc.py line 119 2586773] Train: [46/50][37/376] Data 0.003 (0.002) Batch 0.475 (0.505) Remain 00:15:29 loss: 0.1972 Lr: 0.00011 [2024-11-25 19:42:08,714 INFO misc.py line 119 2586773] Train: [46/50][38/376] Data 0.003 (0.002) Batch 0.540 (0.506) Remain 00:15:31 loss: 0.1993 Lr: 0.00011 [2024-11-25 19:42:09,268 INFO misc.py line 119 2586773] Train: [46/50][39/376] Data 0.002 (0.002) Batch 0.554 (0.507) Remain 00:15:33 loss: 0.2427 Lr: 0.00011 [2024-11-25 19:42:09,810 INFO misc.py line 119 2586773] Train: [46/50][40/376] Data 0.002 (0.002) Batch 0.543 (0.508) Remain 00:15:34 loss: 0.1879 Lr: 0.00011 [2024-11-25 19:42:10,278 INFO misc.py line 119 2586773] Train: [46/50][41/376] Data 0.002 (0.002) Batch 0.468 (0.507) Remain 00:15:31 loss: 0.1949 Lr: 0.00011 [2024-11-25 19:42:10,766 INFO misc.py line 119 2586773] Train: [46/50][42/376] Data 0.002 (0.002) Batch 0.488 (0.506) Remain 00:15:30 loss: 0.1586 Lr: 0.00011 [2024-11-25 19:42:11,288 INFO misc.py line 119 2586773] Train: [46/50][43/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:15:30 loss: 0.1670 Lr: 0.00011 [2024-11-25 19:42:11,758 INFO misc.py line 119 2586773] Train: [46/50][44/376] Data 0.002 (0.002) Batch 0.470 (0.506) Remain 00:15:28 loss: 0.1803 Lr: 0.00011 [2024-11-25 19:42:12,229 INFO misc.py line 119 2586773] Train: [46/50][45/376] Data 0.002 (0.002) Batch 0.471 (0.505) Remain 00:15:26 loss: 0.1900 Lr: 0.00011 [2024-11-25 19:42:12,751 INFO misc.py line 119 2586773] Train: [46/50][46/376] Data 0.002 (0.002) Batch 0.522 (0.505) Remain 00:15:26 loss: 0.1993 Lr: 0.00011 [2024-11-25 19:42:13,273 INFO misc.py line 119 2586773] Train: [46/50][47/376] Data 0.002 (0.002) Batch 0.522 (0.506) Remain 00:15:26 loss: 0.1897 Lr: 0.00011 [2024-11-25 19:42:13,762 INFO misc.py line 119 2586773] Train: [46/50][48/376] Data 0.002 (0.002) Batch 0.490 (0.505) Remain 00:15:25 loss: 0.2250 Lr: 0.00011 [2024-11-25 19:42:14,220 INFO misc.py line 119 2586773] Train: [46/50][49/376] Data 0.002 (0.002) Batch 0.458 (0.504) Remain 00:15:23 loss: 0.1940 Lr: 0.00011 [2024-11-25 19:42:14,739 INFO misc.py line 119 2586773] Train: [46/50][50/376] Data 0.002 (0.002) Batch 0.519 (0.505) Remain 00:15:23 loss: 0.2598 Lr: 0.00011 [2024-11-25 19:42:15,217 INFO misc.py line 119 2586773] Train: [46/50][51/376] Data 0.002 (0.002) Batch 0.478 (0.504) Remain 00:15:21 loss: 0.2276 Lr: 0.00011 [2024-11-25 19:42:15,721 INFO misc.py line 119 2586773] Train: [46/50][52/376] Data 0.002 (0.002) Batch 0.504 (0.504) Remain 00:15:21 loss: 0.1834 Lr: 0.00011 [2024-11-25 19:42:16,246 INFO misc.py line 119 2586773] Train: 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Batch 0.515 (0.506) Remain 00:13:34 loss: 0.2000 Lr: 0.00008 [2024-11-25 19:44:07,151 INFO misc.py line 119 2586773] Train: [46/50][272/376] Data 0.002 (0.002) Batch 0.501 (0.506) Remain 00:13:33 loss: 0.3069 Lr: 0.00008 [2024-11-25 19:44:07,637 INFO misc.py line 119 2586773] Train: [46/50][273/376] Data 0.002 (0.002) Batch 0.486 (0.506) Remain 00:13:33 loss: 0.1946 Lr: 0.00008 [2024-11-25 19:44:08,125 INFO misc.py line 119 2586773] Train: [46/50][274/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 00:13:32 loss: 0.2547 Lr: 0.00008 [2024-11-25 19:44:08,611 INFO misc.py line 119 2586773] Train: [46/50][275/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 00:13:31 loss: 0.1810 Lr: 0.00008 [2024-11-25 19:44:09,073 INFO misc.py line 119 2586773] Train: [46/50][276/376] Data 0.002 (0.002) Batch 0.462 (0.506) Remain 00:13:31 loss: 0.1696 Lr: 0.00008 [2024-11-25 19:44:09,557 INFO misc.py line 119 2586773] Train: [46/50][277/376] Data 0.002 (0.002) Batch 0.483 (0.506) Remain 00:13:30 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Batch 0.496 (0.506) Remain 00:13:06 loss: 0.1999 Lr: 0.00008 [2024-11-25 19:44:35,580 INFO misc.py line 119 2586773] Train: [46/50][328/376] Data 0.002 (0.002) Batch 0.464 (0.506) Remain 00:13:05 loss: 0.1640 Lr: 0.00008 [2024-11-25 19:44:36,104 INFO misc.py line 119 2586773] Train: [46/50][329/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 00:13:05 loss: 0.2151 Lr: 0.00008 [2024-11-25 19:44:36,625 INFO misc.py line 119 2586773] Train: [46/50][330/376] Data 0.002 (0.002) Batch 0.521 (0.506) Remain 00:13:04 loss: 0.1663 Lr: 0.00008 [2024-11-25 19:44:37,149 INFO misc.py line 119 2586773] Train: [46/50][331/376] Data 0.002 (0.002) Batch 0.524 (0.506) Remain 00:13:04 loss: 0.3013 Lr: 0.00008 [2024-11-25 19:44:37,682 INFO misc.py line 119 2586773] Train: [46/50][332/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:13:04 loss: 0.1946 Lr: 0.00008 [2024-11-25 19:44:38,194 INFO misc.py line 119 2586773] Train: [46/50][333/376] Data 0.002 (0.002) Batch 0.512 (0.507) Remain 00:13:03 loss: 0.1682 Lr: 0.00008 [2024-11-25 19:44:38,697 INFO misc.py line 119 2586773] Train: [46/50][334/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:13:03 loss: 0.1765 Lr: 0.00008 [2024-11-25 19:44:39,145 INFO misc.py line 119 2586773] Train: [46/50][335/376] Data 0.002 (0.002) Batch 0.447 (0.506) Remain 00:13:02 loss: 0.2740 Lr: 0.00008 [2024-11-25 19:44:39,657 INFO misc.py line 119 2586773] Train: [46/50][336/376] Data 0.002 (0.002) Batch 0.512 (0.506) Remain 00:13:01 loss: 0.1706 Lr: 0.00008 [2024-11-25 19:44:40,207 INFO misc.py line 119 2586773] Train: [46/50][337/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 00:13:01 loss: 0.2038 Lr: 0.00008 [2024-11-25 19:44:40,707 INFO misc.py line 119 2586773] Train: [46/50][338/376] Data 0.002 (0.002) Batch 0.499 (0.507) Remain 00:13:01 loss: 0.1698 Lr: 0.00008 [2024-11-25 19:44:41,229 INFO misc.py line 119 2586773] Train: [46/50][339/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:13:00 loss: 0.1727 Lr: 0.00008 [2024-11-25 19:44:41,722 INFO misc.py line 119 2586773] Train: [46/50][340/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:13:00 loss: 0.1965 Lr: 0.00008 [2024-11-25 19:44:42,237 INFO misc.py line 119 2586773] Train: [46/50][341/376] Data 0.003 (0.002) Batch 0.515 (0.507) Remain 00:12:59 loss: 0.1916 Lr: 0.00008 [2024-11-25 19:44:42,727 INFO misc.py line 119 2586773] Train: [46/50][342/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:12:59 loss: 0.1917 Lr: 0.00008 [2024-11-25 19:44:43,214 INFO misc.py line 119 2586773] Train: [46/50][343/376] Data 0.002 (0.002) Batch 0.487 (0.506) Remain 00:12:58 loss: 0.1803 Lr: 0.00008 [2024-11-25 19:44:43,692 INFO misc.py line 119 2586773] Train: [46/50][344/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 00:12:57 loss: 0.1452 Lr: 0.00008 [2024-11-25 19:44:44,213 INFO misc.py line 119 2586773] Train: [46/50][345/376] Data 0.002 (0.002) Batch 0.521 (0.506) Remain 00:12:57 loss: 0.1853 Lr: 0.00008 [2024-11-25 19:44:44,695 INFO misc.py line 119 2586773] Train: [46/50][346/376] Data 0.002 (0.002) Batch 0.483 (0.506) Remain 00:12:56 loss: 0.2143 Lr: 0.00008 [2024-11-25 19:44:45,250 INFO misc.py line 119 2586773] Train: [46/50][347/376] Data 0.002 (0.002) Batch 0.555 (0.506) Remain 00:12:56 loss: 0.1954 Lr: 0.00008 [2024-11-25 19:44:45,727 INFO misc.py line 119 2586773] Train: [46/50][348/376] Data 0.002 (0.002) Batch 0.477 (0.506) Remain 00:12:55 loss: 0.1731 Lr: 0.00008 [2024-11-25 19:44:46,222 INFO misc.py line 119 2586773] Train: [46/50][349/376] Data 0.002 (0.002) Batch 0.495 (0.506) Remain 00:12:55 loss: 0.1911 Lr: 0.00008 [2024-11-25 19:44:46,719 INFO misc.py line 119 2586773] Train: [46/50][350/376] Data 0.002 (0.002) Batch 0.497 (0.506) Remain 00:12:54 loss: 0.1866 Lr: 0.00008 [2024-11-25 19:44:47,183 INFO misc.py line 119 2586773] Train: [46/50][351/376] Data 0.003 (0.002) Batch 0.464 (0.506) Remain 00:12:53 loss: 0.2138 Lr: 0.00008 [2024-11-25 19:44:47,737 INFO misc.py line 119 2586773] Train: [46/50][352/376] Data 0.002 (0.002) Batch 0.554 (0.506) Remain 00:12:53 loss: 0.2252 Lr: 0.00008 [2024-11-25 19:44:48,241 INFO misc.py line 119 2586773] Train: [46/50][353/376] Data 0.002 (0.002) Batch 0.504 (0.506) Remain 00:12:53 loss: 0.1749 Lr: 0.00007 [2024-11-25 19:44:48,763 INFO misc.py line 119 2586773] Train: [46/50][354/376] Data 0.002 (0.002) Batch 0.522 (0.506) Remain 00:12:52 loss: 0.1983 Lr: 0.00007 [2024-11-25 19:44:49,293 INFO misc.py line 119 2586773] Train: [46/50][355/376] Data 0.002 (0.002) Batch 0.530 (0.506) Remain 00:12:52 loss: 0.2131 Lr: 0.00007 [2024-11-25 19:44:49,759 INFO misc.py line 119 2586773] Train: [46/50][356/376] Data 0.002 (0.002) Batch 0.466 (0.506) Remain 00:12:51 loss: 0.1577 Lr: 0.00007 [2024-11-25 19:44:50,271 INFO misc.py line 119 2586773] Train: [46/50][357/376] Data 0.002 (0.002) Batch 0.513 (0.506) Remain 00:12:51 loss: 0.1907 Lr: 0.00007 [2024-11-25 19:44:50,820 INFO misc.py line 119 2586773] Train: [46/50][358/376] Data 0.002 (0.002) Batch 0.548 (0.506) Remain 00:12:50 loss: 0.2304 Lr: 0.00007 [2024-11-25 19:44:51,323 INFO misc.py line 119 2586773] Train: [46/50][359/376] Data 0.002 (0.002) Batch 0.503 (0.506) Remain 00:12:50 loss: 0.1803 Lr: 0.00007 [2024-11-25 19:44:51,854 INFO misc.py line 119 2586773] Train: [46/50][360/376] Data 0.002 (0.002) Batch 0.531 (0.507) Remain 00:12:49 loss: 0.2134 Lr: 0.00007 [2024-11-25 19:44:52,345 INFO misc.py line 119 2586773] Train: [46/50][361/376] Data 0.002 (0.002) Batch 0.491 (0.506) Remain 00:12:49 loss: 0.1517 Lr: 0.00007 [2024-11-25 19:44:52,851 INFO misc.py line 119 2586773] Train: [46/50][362/376] Data 0.002 (0.002) Batch 0.506 (0.506) Remain 00:12:48 loss: 0.1900 Lr: 0.00007 [2024-11-25 19:44:53,384 INFO misc.py line 119 2586773] Train: [46/50][363/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:12:48 loss: 0.2102 Lr: 0.00007 [2024-11-25 19:44:53,863 INFO misc.py line 119 2586773] Train: [46/50][364/376] Data 0.002 (0.002) Batch 0.479 (0.506) Remain 00:12:47 loss: 0.1892 Lr: 0.00007 [2024-11-25 19:44:54,410 INFO misc.py line 119 2586773] Train: [46/50][365/376] Data 0.002 (0.002) Batch 0.548 (0.507) Remain 00:12:47 loss: 0.1702 Lr: 0.00007 [2024-11-25 19:44:54,876 INFO misc.py line 119 2586773] Train: [46/50][366/376] Data 0.002 (0.002) Batch 0.466 (0.506) Remain 00:12:46 loss: 0.1924 Lr: 0.00007 [2024-11-25 19:44:55,395 INFO misc.py line 119 2586773] Train: [46/50][367/376] Data 0.002 (0.002) Batch 0.519 (0.507) Remain 00:12:46 loss: 0.2906 Lr: 0.00007 [2024-11-25 19:44:55,871 INFO misc.py line 119 2586773] Train: [46/50][368/376] Data 0.002 (0.002) Batch 0.475 (0.506) Remain 00:12:45 loss: 0.1788 Lr: 0.00007 [2024-11-25 19:44:56,379 INFO misc.py line 119 2586773] Train: [46/50][369/376] Data 0.002 (0.002) Batch 0.508 (0.506) Remain 00:12:45 loss: 0.1802 Lr: 0.00007 [2024-11-25 19:44:56,869 INFO misc.py line 119 2586773] Train: [46/50][370/376] Data 0.002 (0.002) Batch 0.490 (0.506) Remain 00:12:44 loss: 0.1719 Lr: 0.00007 [2024-11-25 19:44:57,352 INFO misc.py line 119 2586773] Train: [46/50][371/376] Data 0.002 (0.002) Batch 0.483 (0.506) Remain 00:12:44 loss: 0.1844 Lr: 0.00007 [2024-11-25 19:44:57,880 INFO misc.py line 119 2586773] Train: [46/50][372/376] Data 0.002 (0.002) Batch 0.529 (0.506) Remain 00:12:43 loss: 0.1835 Lr: 0.00007 [2024-11-25 19:44:58,434 INFO misc.py line 119 2586773] Train: [46/50][373/376] Data 0.002 (0.002) Batch 0.554 (0.507) Remain 00:12:43 loss: 0.2211 Lr: 0.00007 [2024-11-25 19:44:58,974 INFO misc.py line 119 2586773] Train: [46/50][374/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 00:12:42 loss: 0.2520 Lr: 0.00007 [2024-11-25 19:44:59,496 INFO misc.py line 119 2586773] Train: [46/50][375/376] Data 0.002 (0.002) Batch 0.522 (0.507) Remain 00:12:42 loss: 0.2206 Lr: 0.00007 [2024-11-25 19:45:00,050 INFO misc.py line 119 2586773] Train: [46/50][376/376] Data 0.002 (0.002) Batch 0.555 (0.507) Remain 00:12:42 loss: 0.2419 Lr: 0.00007 [2024-11-25 19:45:00,051 INFO misc.py line 136 2586773] Train result: loss: 0.1949 [2024-11-25 19:45:00,051 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:45:10,796 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1835 [2024-11-25 19:45:11,058 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2318 [2024-11-25 19:45:11,326 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2356 [2024-11-25 19:45:11,551 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1898 [2024-11-25 19:45:11,816 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2827 [2024-11-25 19:45:12,086 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1907 [2024-11-25 19:45:12,309 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2136 [2024-11-25 19:45:12,578 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2132 [2024-11-25 19:45:12,802 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2541 [2024-11-25 19:45:13,065 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2291 [2024-11-25 19:45:13,295 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2005 [2024-11-25 19:45:13,564 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2447 [2024-11-25 19:45:13,833 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2434 [2024-11-25 19:45:14,095 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2273 [2024-11-25 19:45:14,329 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2231 [2024-11-25 19:45:14,569 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3020 [2024-11-25 19:45:14,834 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2549 [2024-11-25 19:45:15,088 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2201 [2024-11-25 19:45:15,319 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2403 [2024-11-25 19:45:15,581 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2040 [2024-11-25 19:45:15,816 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2541 [2024-11-25 19:45:16,082 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2439 [2024-11-25 19:45:16,318 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2037 [2024-11-25 19:45:16,587 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2266 [2024-11-25 19:45:16,850 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2133 [2024-11-25 19:45:17,086 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2480 [2024-11-25 19:45:17,341 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2481 [2024-11-25 19:45:17,587 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2165 [2024-11-25 19:45:17,853 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2593 [2024-11-25 19:45:18,105 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2651 [2024-11-25 19:45:18,340 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2556 [2024-11-25 19:45:18,604 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1964 [2024-11-25 19:45:18,822 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2628 [2024-11-25 19:45:19,065 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2267 [2024-11-25 19:45:19,326 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2000 [2024-11-25 19:45:19,573 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2468 [2024-11-25 19:45:19,801 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1909 [2024-11-25 19:45:20,070 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2260 [2024-11-25 19:45:20,300 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2597 [2024-11-25 19:45:20,534 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2203 [2024-11-25 19:45:20,809 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3122 [2024-11-25 19:45:21,068 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2599 [2024-11-25 19:45:21,305 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2383 [2024-11-25 19:45:21,537 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2150 [2024-11-25 19:45:21,774 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2253 [2024-11-25 19:45:22,021 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2160 [2024-11-25 19:45:22,280 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2199 [2024-11-25 19:45:22,531 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2783 [2024-11-25 19:45:22,754 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2059 [2024-11-25 19:45:22,989 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2102 [2024-11-25 19:45:23,208 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2560 [2024-11-25 19:45:23,461 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2188 [2024-11-25 19:45:23,728 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2171 [2024-11-25 19:45:23,988 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3103 [2024-11-25 19:45:24,220 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2317 [2024-11-25 19:45:24,458 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2252 [2024-11-25 19:45:24,715 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2433 [2024-11-25 19:45:24,987 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2753 [2024-11-25 19:45:25,242 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2386 [2024-11-25 19:45:25,502 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2256 [2024-11-25 19:45:25,759 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2073 [2024-11-25 19:45:26,031 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2252 [2024-11-25 19:45:26,261 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2298 [2024-11-25 19:45:26,519 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2390 [2024-11-25 19:45:26,786 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2340 [2024-11-25 19:45:27,059 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1903 [2024-11-25 19:45:27,304 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1864 [2024-11-25 19:45:27,562 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2448 [2024-11-25 19:45:27,831 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2468 [2024-11-25 19:45:28,094 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2622 [2024-11-25 19:45:28,338 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2031 [2024-11-25 19:45:28,571 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2863 [2024-11-25 19:45:28,829 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2454 [2024-11-25 19:45:29,075 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2453 [2024-11-25 19:45:29,292 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2618 [2024-11-25 19:45:29,513 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2168 [2024-11-25 19:45:29,783 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2354 [2024-11-25 19:45:30,020 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2171 [2024-11-25 19:45:30,277 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2328 [2024-11-25 19:45:30,529 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2761 [2024-11-25 19:45:30,774 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2436 [2024-11-25 19:45:31,032 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2613 [2024-11-25 19:45:31,284 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1873 [2024-11-25 19:45:31,532 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2364 [2024-11-25 19:45:31,803 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2514 [2024-11-25 19:45:32,040 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2565 [2024-11-25 19:45:32,302 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2386 [2024-11-25 19:45:32,560 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2250 [2024-11-25 19:45:32,807 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2707 [2024-11-25 19:45:33,053 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2564 [2024-11-25 19:45:33,288 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2511 [2024-11-25 19:45:33,542 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2674 [2024-11-25 19:45:33,811 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2396 [2024-11-25 19:45:34,078 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1811 [2024-11-25 19:45:34,343 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2287 [2024-11-25 19:45:34,592 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2033 [2024-11-25 19:45:34,860 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2054 [2024-11-25 19:45:35,081 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2970 [2024-11-25 19:45:35,354 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2395 [2024-11-25 19:45:35,595 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2420 [2024-11-25 19:45:35,867 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1958 [2024-11-25 19:45:36,127 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2528 [2024-11-25 19:45:36,388 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2183 [2024-11-25 19:45:36,639 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2580 [2024-11-25 19:45:36,865 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2391 [2024-11-25 19:45:37,100 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2231 [2024-11-25 19:45:37,354 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2277 [2024-11-25 19:45:37,622 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2327 [2024-11-25 19:45:37,857 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2446 [2024-11-25 19:45:38,119 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2000 [2024-11-25 19:45:38,380 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2412 [2024-11-25 19:45:38,605 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2269 [2024-11-25 19:45:38,843 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.1968 [2024-11-25 19:45:39,061 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2077 [2024-11-25 19:45:39,286 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2091 [2024-11-25 19:45:39,559 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2897 [2024-11-25 19:45:39,818 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2434 [2024-11-25 19:45:40,085 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2368 [2024-11-25 19:45:40,350 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2128 [2024-11-25 19:45:40,612 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2464 [2024-11-25 19:45:40,871 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2829 [2024-11-25 19:45:41,138 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2243 [2024-11-25 19:45:41,396 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2481 [2024-11-25 19:45:41,659 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2272 [2024-11-25 19:45:41,926 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2512 [2024-11-25 19:45:42,176 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2335 [2024-11-25 19:45:42,407 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2068 [2024-11-25 19:45:42,665 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2377 [2024-11-25 19:45:42,902 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2530 [2024-11-25 19:45:43,128 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1929 [2024-11-25 19:45:43,338 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2237 [2024-11-25 19:45:43,558 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1837 [2024-11-25 19:45:44,290 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7793/0.8586/0.9963. [2024-11-25 19:45:44,290 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 19:45:44,290 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5624/0.7191 [2024-11-25 19:45:44,291 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:45:44,292 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 19:45:44,292 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:45:47,040 INFO misc.py line 119 2586773] Train: [47/50][1/376] Data 0.088 (0.088) Batch 0.572 (0.572) Remain 00:14:19 loss: 0.1545 Lr: 0.00007 [2024-11-25 19:45:47,597 INFO misc.py line 119 2586773] Train: [47/50][2/376] Data 0.002 (0.002) Batch 0.558 (0.558) Remain 00:13:57 loss: 0.2418 Lr: 0.00007 [2024-11-25 19:45:48,146 INFO misc.py line 119 2586773] Train: [47/50][3/376] Data 0.002 (0.002) Batch 0.548 (0.548) Remain 00:13:43 loss: 0.2072 Lr: 0.00007 [2024-11-25 19:45:48,680 INFO misc.py line 119 2586773] Train: [47/50][4/376] Data 0.002 (0.002) Batch 0.534 (0.534) Remain 00:13:21 loss: 0.2612 Lr: 0.00007 [2024-11-25 19:45:49,227 INFO misc.py line 119 2586773] Train: [47/50][5/376] Data 0.002 (0.002) Batch 0.547 (0.541) Remain 00:13:30 loss: 0.2038 Lr: 0.00007 [2024-11-25 19:45:49,723 INFO misc.py line 119 2586773] Train: [47/50][6/376] Data 0.002 (0.002) Batch 0.496 (0.526) Remain 00:13:07 loss: 0.1992 Lr: 0.00007 [2024-11-25 19:45:50,288 INFO misc.py line 119 2586773] Train: [47/50][7/376] Data 0.003 (0.002) Batch 0.564 (0.535) Remain 00:13:21 loss: 0.2003 Lr: 0.00007 [2024-11-25 19:45:50,782 INFO misc.py line 119 2586773] Train: [47/50][8/376] Data 0.003 (0.002) Batch 0.494 (0.527) Remain 00:13:08 loss: 0.1839 Lr: 0.00007 [2024-11-25 19:45:51,293 INFO misc.py line 119 2586773] Train: [47/50][9/376] Data 0.003 (0.002) Batch 0.511 (0.525) Remain 00:13:04 loss: 0.1508 Lr: 0.00007 [2024-11-25 19:45:51,783 INFO misc.py line 119 2586773] Train: [47/50][10/376] Data 0.002 (0.002) Batch 0.490 (0.520) Remain 00:12:56 loss: 0.2276 Lr: 0.00007 [2024-11-25 19:45:52,296 INFO misc.py line 119 2586773] Train: [47/50][11/376] Data 0.003 (0.002) Batch 0.514 (0.519) Remain 00:12:54 loss: 0.1797 Lr: 0.00007 [2024-11-25 19:45:52,814 INFO misc.py line 119 2586773] Train: [47/50][12/376] Data 0.002 (0.002) Batch 0.518 (0.519) Remain 00:12:53 loss: 0.1874 Lr: 0.00007 [2024-11-25 19:45:53,308 INFO misc.py line 119 2586773] Train: [47/50][13/376] Data 0.003 (0.002) Batch 0.494 (0.516) Remain 00:12:49 loss: 0.2073 Lr: 0.00007 [2024-11-25 19:45:53,789 INFO misc.py line 119 2586773] Train: [47/50][14/376] Data 0.002 (0.002) Batch 0.482 (0.513) Remain 00:12:44 loss: 0.1902 Lr: 0.00007 [2024-11-25 19:45:54,257 INFO misc.py line 119 2586773] Train: [47/50][15/376] Data 0.002 (0.002) Batch 0.468 (0.509) Remain 00:12:38 loss: 0.3418 Lr: 0.00007 [2024-11-25 19:45:54,764 INFO misc.py line 119 2586773] Train: [47/50][16/376] Data 0.002 (0.002) Batch 0.507 (0.509) Remain 00:12:37 loss: 0.1632 Lr: 0.00007 [2024-11-25 19:45:55,248 INFO misc.py line 119 2586773] Train: [47/50][17/376] Data 0.003 (0.002) Batch 0.484 (0.507) Remain 00:12:34 loss: 0.1712 Lr: 0.00007 [2024-11-25 19:45:55,726 INFO misc.py line 119 2586773] Train: [47/50][18/376] Data 0.003 (0.002) Batch 0.478 (0.505) Remain 00:12:30 loss: 0.1687 Lr: 0.00007 [2024-11-25 19:45:56,219 INFO misc.py line 119 2586773] Train: [47/50][19/376] Data 0.003 (0.002) Batch 0.493 (0.505) Remain 00:12:29 loss: 0.1633 Lr: 0.00007 [2024-11-25 19:45:56,698 INFO misc.py line 119 2586773] Train: [47/50][20/376] Data 0.002 (0.002) Batch 0.479 (0.503) Remain 00:12:26 loss: 0.1739 Lr: 0.00007 [2024-11-25 19:45:57,215 INFO misc.py line 119 2586773] Train: [47/50][21/376] Data 0.002 (0.002) Batch 0.517 (0.504) Remain 00:12:27 loss: 0.2051 Lr: 0.00007 [2024-11-25 19:45:57,755 INFO misc.py line 119 2586773] Train: [47/50][22/376] Data 0.003 (0.002) Batch 0.540 (0.506) Remain 00:12:29 loss: 0.1940 Lr: 0.00007 [2024-11-25 19:45:58,297 INFO misc.py line 119 2586773] Train: [47/50][23/376] Data 0.003 (0.002) Batch 0.542 (0.508) Remain 00:12:31 loss: 0.1956 Lr: 0.00007 [2024-11-25 19:45:58,868 INFO misc.py line 119 2586773] Train: [47/50][24/376] Data 0.002 (0.002) Batch 0.570 (0.511) Remain 00:12:35 loss: 0.2086 Lr: 0.00007 [2024-11-25 19:45:59,356 INFO misc.py line 119 2586773] Train: [47/50][25/376] Data 0.002 (0.002) Batch 0.489 (0.510) Remain 00:12:33 loss: 0.1647 Lr: 0.00007 [2024-11-25 19:45:59,858 INFO misc.py line 119 2586773] Train: [47/50][26/376] Data 0.002 (0.002) Batch 0.501 (0.509) Remain 00:12:32 loss: 0.2036 Lr: 0.00007 [2024-11-25 19:46:00,366 INFO misc.py line 119 2586773] Train: [47/50][27/376] Data 0.002 (0.002) Batch 0.508 (0.509) Remain 00:12:32 loss: 0.1559 Lr: 0.00007 [2024-11-25 19:46:00,898 INFO misc.py line 119 2586773] Train: [47/50][28/376] Data 0.003 (0.002) Batch 0.532 (0.510) Remain 00:12:32 loss: 0.2114 Lr: 0.00007 [2024-11-25 19:46:01,418 INFO misc.py line 119 2586773] Train: [47/50][29/376] Data 0.002 (0.002) Batch 0.519 (0.510) Remain 00:12:32 loss: 0.1496 Lr: 0.00007 [2024-11-25 19:46:01,936 INFO misc.py line 119 2586773] Train: [47/50][30/376] Data 0.002 (0.002) Batch 0.518 (0.511) Remain 00:12:32 loss: 0.1981 Lr: 0.00007 [2024-11-25 19:46:02,468 INFO misc.py line 119 2586773] Train: [47/50][31/376] Data 0.003 (0.002) Batch 0.532 (0.512) Remain 00:12:33 loss: 0.2024 Lr: 0.00007 [2024-11-25 19:46:02,972 INFO misc.py line 119 2586773] Train: [47/50][32/376] Data 0.002 (0.002) Batch 0.504 (0.511) Remain 00:12:32 loss: 0.1903 Lr: 0.00007 [2024-11-25 19:46:03,476 INFO misc.py line 119 2586773] Train: [47/50][33/376] Data 0.002 (0.002) Batch 0.503 (0.511) Remain 00:12:31 loss: 0.2034 Lr: 0.00007 [2024-11-25 19:46:03,954 INFO misc.py line 119 2586773] Train: [47/50][34/376] Data 0.002 (0.002) Batch 0.478 (0.510) Remain 00:12:29 loss: 0.1953 Lr: 0.00007 [2024-11-25 19:46:04,453 INFO misc.py line 119 2586773] Train: [47/50][35/376] Data 0.002 (0.002) Batch 0.499 (0.510) Remain 00:12:28 loss: 0.2029 Lr: 0.00007 [2024-11-25 19:46:04,955 INFO misc.py line 119 2586773] Train: [47/50][36/376] Data 0.003 (0.002) Batch 0.502 (0.509) Remain 00:12:27 loss: 0.1753 Lr: 0.00007 [2024-11-25 19:46:05,487 INFO misc.py line 119 2586773] Train: [47/50][37/376] Data 0.002 (0.002) Batch 0.531 (0.510) Remain 00:12:28 loss: 0.1642 Lr: 0.00007 [2024-11-25 19:46:06,030 INFO misc.py line 119 2586773] Train: [47/50][38/376] Data 0.003 (0.002) Batch 0.544 (0.511) Remain 00:12:29 loss: 0.1890 Lr: 0.00007 [2024-11-25 19:46:06,586 INFO misc.py line 119 2586773] Train: [47/50][39/376] Data 0.002 (0.002) Batch 0.556 (0.512) Remain 00:12:30 loss: 0.1952 Lr: 0.00007 [2024-11-25 19:46:07,067 INFO misc.py line 119 2586773] Train: [47/50][40/376] Data 0.003 (0.002) Batch 0.481 (0.511) Remain 00:12:28 loss: 0.1611 Lr: 0.00007 [2024-11-25 19:46:07,529 INFO misc.py line 119 2586773] Train: [47/50][41/376] Data 0.002 (0.002) Batch 0.462 (0.510) Remain 00:12:26 loss: 0.1658 Lr: 0.00007 [2024-11-25 19:46:08,025 INFO misc.py line 119 2586773] Train: [47/50][42/376] Data 0.003 (0.002) Batch 0.496 (0.510) Remain 00:12:25 loss: 0.1828 Lr: 0.00007 [2024-11-25 19:46:08,520 INFO misc.py line 119 2586773] Train: [47/50][43/376] Data 0.002 (0.002) Batch 0.494 (0.509) Remain 00:12:24 loss: 0.1458 Lr: 0.00007 [2024-11-25 19:46:09,030 INFO misc.py line 119 2586773] Train: [47/50][44/376] Data 0.003 (0.002) Batch 0.510 (0.509) Remain 00:12:23 loss: 0.2047 Lr: 0.00007 [2024-11-25 19:46:09,557 INFO misc.py line 119 2586773] Train: [47/50][45/376] Data 0.002 (0.002) Batch 0.527 (0.510) Remain 00:12:23 loss: 0.1730 Lr: 0.00007 [2024-11-25 19:46:10,021 INFO misc.py line 119 2586773] Train: [47/50][46/376] Data 0.003 (0.002) Batch 0.465 (0.509) Remain 00:12:21 loss: 0.2156 Lr: 0.00007 [2024-11-25 19:46:10,527 INFO misc.py line 119 2586773] Train: [47/50][47/376] Data 0.002 (0.002) Batch 0.505 (0.509) Remain 00:12:21 loss: 0.3448 Lr: 0.00007 [2024-11-25 19:46:11,027 INFO misc.py line 119 2586773] Train: [47/50][48/376] Data 0.003 (0.002) Batch 0.501 (0.508) Remain 00:12:20 loss: 0.1555 Lr: 0.00007 [2024-11-25 19:46:11,569 INFO misc.py line 119 2586773] Train: [47/50][49/376] Data 0.003 (0.002) Batch 0.541 (0.509) Remain 00:12:20 loss: 0.1809 Lr: 0.00007 [2024-11-25 19:46:12,106 INFO misc.py line 119 2586773] Train: [47/50][50/376] Data 0.003 (0.002) Batch 0.537 (0.510) Remain 00:12:21 loss: 0.1864 Lr: 0.00007 [2024-11-25 19:46:12,590 INFO misc.py line 119 2586773] Train: [47/50][51/376] Data 0.003 (0.002) Batch 0.485 (0.509) Remain 00:12:19 loss: 0.1610 Lr: 0.00007 [2024-11-25 19:46:13,091 INFO misc.py line 119 2586773] Train: [47/50][52/376] Data 0.002 (0.002) Batch 0.501 (0.509) Remain 00:12:19 loss: 0.2265 Lr: 0.00007 [2024-11-25 19:46:13,599 INFO misc.py line 119 2586773] Train: [47/50][53/376] Data 0.003 (0.002) Batch 0.508 (0.509) Remain 00:12:18 loss: 0.1427 Lr: 0.00007 [2024-11-25 19:46:14,155 INFO misc.py line 119 2586773] Train: [47/50][54/376] Data 0.003 (0.002) Batch 0.556 (0.510) Remain 00:12:19 loss: 0.1700 Lr: 0.00007 [2024-11-25 19:46:14,658 INFO misc.py line 119 2586773] Train: [47/50][55/376] Data 0.003 (0.002) Batch 0.503 (0.510) Remain 00:12:18 loss: 0.2443 Lr: 0.00007 [2024-11-25 19:46:15,181 INFO misc.py line 119 2586773] Train: [47/50][56/376] Data 0.003 (0.002) Batch 0.523 (0.510) Remain 00:12:18 loss: 0.1807 Lr: 0.00007 [2024-11-25 19:46:15,674 INFO misc.py line 119 2586773] Train: [47/50][57/376] Data 0.003 (0.002) Batch 0.493 (0.510) Remain 00:12:17 loss: 0.2054 Lr: 0.00007 [2024-11-25 19:46:16,182 INFO misc.py line 119 2586773] Train: [47/50][58/376] Data 0.002 (0.002) Batch 0.509 (0.510) Remain 00:12:17 loss: 0.1598 Lr: 0.00007 [2024-11-25 19:46:16,730 INFO misc.py line 119 2586773] Train: [47/50][59/376] Data 0.002 (0.002) Batch 0.548 (0.510) Remain 00:12:17 loss: 0.1903 Lr: 0.00007 [2024-11-25 19:46:17,265 INFO misc.py line 119 2586773] Train: [47/50][60/376] Data 0.003 (0.002) Batch 0.535 (0.511) Remain 00:12:17 loss: 0.2268 Lr: 0.00007 [2024-11-25 19:46:17,811 INFO misc.py line 119 2586773] Train: [47/50][61/376] Data 0.003 (0.002) Batch 0.546 (0.511) Remain 00:12:18 loss: 0.2170 Lr: 0.00007 [2024-11-25 19:46:18,352 INFO misc.py line 119 2586773] Train: [47/50][62/376] Data 0.002 (0.002) Batch 0.541 (0.512) Remain 00:12:18 loss: 0.1440 Lr: 0.00007 [2024-11-25 19:46:18,861 INFO misc.py line 119 2586773] Train: [47/50][63/376] Data 0.002 (0.002) Batch 0.509 (0.512) Remain 00:12:17 loss: 0.1766 Lr: 0.00007 [2024-11-25 19:46:19,361 INFO misc.py line 119 2586773] Train: [47/50][64/376] Data 0.002 (0.002) Batch 0.500 (0.512) Remain 00:12:16 loss: 0.1710 Lr: 0.00007 [2024-11-25 19:46:19,873 INFO misc.py line 119 2586773] Train: [47/50][65/376] Data 0.002 (0.002) Batch 0.513 (0.512) Remain 00:12:16 loss: 0.1626 Lr: 0.00007 [2024-11-25 19:46:20,392 INFO misc.py line 119 2586773] Train: [47/50][66/376] Data 0.002 (0.002) Batch 0.518 (0.512) Remain 00:12:16 loss: 0.2211 Lr: 0.00007 [2024-11-25 19:46:20,896 INFO misc.py line 119 2586773] Train: [47/50][67/376] Data 0.002 (0.002) Batch 0.503 (0.512) Remain 00:12:15 loss: 0.1497 Lr: 0.00007 [2024-11-25 19:46:21,396 INFO misc.py line 119 2586773] Train: [47/50][68/376] Data 0.004 (0.002) Batch 0.500 (0.512) Remain 00:12:14 loss: 0.2954 Lr: 0.00007 [2024-11-25 19:46:21,870 INFO misc.py line 119 2586773] Train: [47/50][69/376] Data 0.004 (0.002) Batch 0.475 (0.511) Remain 00:12:13 loss: 0.1637 Lr: 0.00007 [2024-11-25 19:46:22,370 INFO misc.py line 119 2586773] Train: [47/50][70/376] Data 0.004 (0.003) Batch 0.500 (0.511) Remain 00:12:12 loss: 0.1597 Lr: 0.00007 [2024-11-25 19:46:22,857 INFO misc.py line 119 2586773] Train: [47/50][71/376] Data 0.003 (0.003) Batch 0.487 (0.510) Remain 00:12:11 loss: 0.1839 Lr: 0.00007 [2024-11-25 19:46:23,359 INFO misc.py line 119 2586773] Train: [47/50][72/376] Data 0.004 (0.003) Batch 0.502 (0.510) Remain 00:12:10 loss: 0.1837 Lr: 0.00007 [2024-11-25 19:46:23,869 INFO misc.py line 119 2586773] Train: [47/50][73/376] Data 0.003 (0.003) Batch 0.510 (0.510) Remain 00:12:10 loss: 0.1712 Lr: 0.00007 [2024-11-25 19:46:24,357 INFO misc.py line 119 2586773] Train: [47/50][74/376] Data 0.004 (0.003) Batch 0.488 (0.510) Remain 00:12:09 loss: 0.1910 Lr: 0.00007 [2024-11-25 19:46:24,903 INFO misc.py line 119 2586773] Train: [47/50][75/376] Data 0.004 (0.003) Batch 0.546 (0.511) Remain 00:12:09 loss: 0.2566 Lr: 0.00007 [2024-11-25 19:46:25,384 INFO misc.py line 119 2586773] Train: [47/50][76/376] Data 0.004 (0.003) Batch 0.482 (0.510) Remain 00:12:08 loss: 0.2346 Lr: 0.00007 [2024-11-25 19:46:25,893 INFO misc.py line 119 2586773] Train: [47/50][77/376] Data 0.003 (0.003) Batch 0.508 (0.510) Remain 00:12:07 loss: 0.1542 Lr: 0.00007 [2024-11-25 19:46:26,394 INFO misc.py line 119 2586773] Train: [47/50][78/376] Data 0.003 (0.003) Batch 0.501 (0.510) Remain 00:12:07 loss: 0.1726 Lr: 0.00007 [2024-11-25 19:46:26,910 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Batch 0.482 (0.509) Remain 00:11:46 loss: 0.1625 Lr: 0.00006 [2024-11-25 19:46:46,134 INFO misc.py line 119 2586773] Train: [47/50][117/376] Data 0.002 (0.003) Batch 0.495 (0.509) Remain 00:11:45 loss: 0.2329 Lr: 0.00006 [2024-11-25 19:46:46,649 INFO misc.py line 119 2586773] Train: [47/50][118/376] Data 0.002 (0.003) Batch 0.515 (0.509) Remain 00:11:45 loss: 0.1882 Lr: 0.00006 [2024-11-25 19:46:47,133 INFO misc.py line 119 2586773] Train: [47/50][119/376] Data 0.003 (0.003) Batch 0.484 (0.509) Remain 00:11:44 loss: 0.2128 Lr: 0.00006 [2024-11-25 19:46:47,658 INFO misc.py line 119 2586773] Train: [47/50][120/376] Data 0.002 (0.003) Batch 0.525 (0.509) Remain 00:11:43 loss: 0.1836 Lr: 0.00006 [2024-11-25 19:46:48,149 INFO misc.py line 119 2586773] Train: [47/50][121/376] Data 0.002 (0.003) Batch 0.491 (0.509) Remain 00:11:43 loss: 0.2270 Lr: 0.00006 [2024-11-25 19:46:48,655 INFO misc.py line 119 2586773] Train: [47/50][122/376] Data 0.003 (0.003) Batch 0.505 (0.508) Remain 00:11:42 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line 119 2586773] Train: [47/50][160/376] Data 0.002 (0.003) Batch 0.515 (0.506) Remain 00:11:20 loss: 0.1945 Lr: 0.00006 [2024-11-25 19:47:08,175 INFO misc.py line 119 2586773] Train: [47/50][161/376] Data 0.003 (0.003) Batch 0.515 (0.507) Remain 00:11:20 loss: 0.1914 Lr: 0.00006 [2024-11-25 19:47:08,649 INFO misc.py line 119 2586773] Train: [47/50][162/376] Data 0.003 (0.003) Batch 0.473 (0.506) Remain 00:11:19 loss: 0.2052 Lr: 0.00006 [2024-11-25 19:47:09,183 INFO misc.py line 119 2586773] Train: [47/50][163/376] Data 0.003 (0.003) Batch 0.535 (0.506) Remain 00:11:19 loss: 0.2389 Lr: 0.00006 [2024-11-25 19:47:09,683 INFO misc.py line 119 2586773] Train: [47/50][164/376] Data 0.003 (0.003) Batch 0.500 (0.506) Remain 00:11:18 loss: 0.2250 Lr: 0.00006 [2024-11-25 19:47:10,247 INFO misc.py line 119 2586773] Train: [47/50][165/376] Data 0.003 (0.003) Batch 0.563 (0.507) Remain 00:11:18 loss: 0.2236 Lr: 0.00006 [2024-11-25 19:47:10,738 INFO misc.py line 119 2586773] Train: 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Batch 0.488 (0.507) Remain 00:11:14 loss: 0.2021 Lr: 0.00006 [2024-11-25 19:47:14,222 INFO misc.py line 119 2586773] Train: [47/50][173/376] Data 0.003 (0.003) Batch 0.474 (0.506) Remain 00:11:13 loss: 0.1816 Lr: 0.00006 [2024-11-25 19:47:14,729 INFO misc.py line 119 2586773] Train: [47/50][174/376] Data 0.002 (0.003) Batch 0.506 (0.506) Remain 00:11:13 loss: 0.2187 Lr: 0.00006 [2024-11-25 19:47:15,205 INFO misc.py line 119 2586773] Train: [47/50][175/376] Data 0.003 (0.003) Batch 0.477 (0.506) Remain 00:11:12 loss: 0.2031 Lr: 0.00006 [2024-11-25 19:47:15,734 INFO misc.py line 119 2586773] Train: [47/50][176/376] Data 0.003 (0.003) Batch 0.528 (0.506) Remain 00:11:12 loss: 0.1941 Lr: 0.00006 [2024-11-25 19:47:16,220 INFO misc.py line 119 2586773] Train: [47/50][177/376] Data 0.003 (0.003) Batch 0.486 (0.506) Remain 00:11:11 loss: 0.1684 Lr: 0.00006 [2024-11-25 19:47:16,704 INFO misc.py line 119 2586773] Train: [47/50][178/376] Data 0.003 (0.003) Batch 0.483 (0.506) Remain 00:11:11 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loss: 0.1562 Lr: 0.00005 [2024-11-25 19:48:42,450 INFO misc.py line 119 2586773] Train: [47/50][347/376] Data 0.002 (0.003) Batch 0.481 (0.507) Remain 00:09:46 loss: 0.1774 Lr: 0.00005 [2024-11-25 19:48:42,980 INFO misc.py line 119 2586773] Train: [47/50][348/376] Data 0.002 (0.003) Batch 0.530 (0.507) Remain 00:09:45 loss: 0.1723 Lr: 0.00004 [2024-11-25 19:48:43,478 INFO misc.py line 119 2586773] Train: [47/50][349/376] Data 0.002 (0.003) Batch 0.498 (0.507) Remain 00:09:45 loss: 0.2115 Lr: 0.00004 [2024-11-25 19:48:43,983 INFO misc.py line 119 2586773] Train: [47/50][350/376] Data 0.002 (0.003) Batch 0.505 (0.507) Remain 00:09:44 loss: 0.1992 Lr: 0.00004 [2024-11-25 19:48:44,505 INFO misc.py line 119 2586773] Train: [47/50][351/376] Data 0.002 (0.003) Batch 0.522 (0.507) Remain 00:09:44 loss: 0.1826 Lr: 0.00004 [2024-11-25 19:48:45,060 INFO misc.py line 119 2586773] Train: [47/50][352/376] Data 0.002 (0.003) Batch 0.555 (0.507) Remain 00:09:43 loss: 0.1898 Lr: 0.00004 [2024-11-25 19:48:45,561 INFO misc.py line 119 2586773] Train: [47/50][353/376] Data 0.002 (0.003) Batch 0.501 (0.507) Remain 00:09:43 loss: 0.1580 Lr: 0.00004 [2024-11-25 19:48:46,104 INFO misc.py line 119 2586773] Train: [47/50][354/376] Data 0.002 (0.003) Batch 0.543 (0.507) Remain 00:09:43 loss: 0.2172 Lr: 0.00004 [2024-11-25 19:48:46,596 INFO misc.py line 119 2586773] Train: [47/50][355/376] Data 0.002 (0.003) Batch 0.492 (0.507) Remain 00:09:42 loss: 0.1540 Lr: 0.00004 [2024-11-25 19:48:47,095 INFO misc.py line 119 2586773] Train: [47/50][356/376] Data 0.002 (0.003) Batch 0.499 (0.507) Remain 00:09:41 loss: 0.1997 Lr: 0.00004 [2024-11-25 19:48:47,630 INFO misc.py line 119 2586773] Train: [47/50][357/376] Data 0.002 (0.003) Batch 0.535 (0.507) Remain 00:09:41 loss: 0.1623 Lr: 0.00004 [2024-11-25 19:48:48,139 INFO misc.py line 119 2586773] Train: [47/50][358/376] Data 0.002 (0.003) Batch 0.508 (0.507) Remain 00:09:41 loss: 0.1726 Lr: 0.00004 [2024-11-25 19:48:48,633 INFO misc.py line 119 2586773] Train: [47/50][359/376] Data 0.002 (0.003) Batch 0.494 (0.507) Remain 00:09:40 loss: 0.1648 Lr: 0.00004 [2024-11-25 19:48:49,111 INFO misc.py line 119 2586773] Train: [47/50][360/376] Data 0.002 (0.003) Batch 0.478 (0.507) Remain 00:09:39 loss: 0.2281 Lr: 0.00004 [2024-11-25 19:48:49,605 INFO misc.py line 119 2586773] Train: [47/50][361/376] Data 0.002 (0.003) Batch 0.494 (0.507) Remain 00:09:39 loss: 0.2062 Lr: 0.00004 [2024-11-25 19:48:50,138 INFO misc.py line 119 2586773] Train: [47/50][362/376] Data 0.002 (0.003) Batch 0.533 (0.507) Remain 00:09:38 loss: 0.2295 Lr: 0.00004 [2024-11-25 19:48:50,609 INFO misc.py line 119 2586773] Train: [47/50][363/376] Data 0.002 (0.003) Batch 0.471 (0.507) Remain 00:09:38 loss: 0.1688 Lr: 0.00004 [2024-11-25 19:48:51,121 INFO misc.py line 119 2586773] Train: [47/50][364/376] Data 0.002 (0.003) Batch 0.512 (0.507) Remain 00:09:37 loss: 0.1798 Lr: 0.00004 [2024-11-25 19:48:51,629 INFO misc.py line 119 2586773] Train: [47/50][365/376] Data 0.002 (0.003) Batch 0.508 (0.507) Remain 00:09:37 loss: 0.1864 Lr: 0.00004 [2024-11-25 19:48:52,123 INFO misc.py line 119 2586773] Train: [47/50][366/376] Data 0.002 (0.003) Batch 0.494 (0.507) Remain 00:09:36 loss: 0.1442 Lr: 0.00004 [2024-11-25 19:48:52,600 INFO misc.py line 119 2586773] Train: [47/50][367/376] Data 0.002 (0.003) Batch 0.477 (0.507) Remain 00:09:36 loss: 0.1514 Lr: 0.00004 [2024-11-25 19:48:53,146 INFO misc.py line 119 2586773] Train: [47/50][368/376] Data 0.002 (0.003) Batch 0.547 (0.507) Remain 00:09:35 loss: 0.2502 Lr: 0.00004 [2024-11-25 19:48:53,651 INFO misc.py line 119 2586773] Train: [47/50][369/376] Data 0.002 (0.003) Batch 0.505 (0.507) Remain 00:09:35 loss: 0.1941 Lr: 0.00004 [2024-11-25 19:48:54,179 INFO misc.py line 119 2586773] Train: [47/50][370/376] Data 0.002 (0.003) Batch 0.528 (0.507) Remain 00:09:34 loss: 0.2178 Lr: 0.00004 [2024-11-25 19:48:54,646 INFO misc.py line 119 2586773] Train: [47/50][371/376] Data 0.002 (0.003) Batch 0.467 (0.507) Remain 00:09:34 loss: 0.1856 Lr: 0.00004 [2024-11-25 19:48:55,129 INFO misc.py line 119 2586773] Train: [47/50][372/376] Data 0.002 (0.003) Batch 0.483 (0.507) Remain 00:09:33 loss: 0.2691 Lr: 0.00004 [2024-11-25 19:48:55,611 INFO misc.py line 119 2586773] Train: [47/50][373/376] Data 0.002 (0.003) Batch 0.483 (0.507) Remain 00:09:33 loss: 0.2132 Lr: 0.00004 [2024-11-25 19:48:56,095 INFO misc.py line 119 2586773] Train: [47/50][374/376] Data 0.002 (0.003) Batch 0.484 (0.507) Remain 00:09:32 loss: 0.1849 Lr: 0.00004 [2024-11-25 19:48:56,609 INFO misc.py line 119 2586773] Train: [47/50][375/376] Data 0.002 (0.003) Batch 0.513 (0.507) Remain 00:09:31 loss: 0.3608 Lr: 0.00004 [2024-11-25 19:48:57,127 INFO misc.py line 119 2586773] Train: [47/50][376/376] Data 0.003 (0.003) Batch 0.519 (0.507) Remain 00:09:31 loss: 0.1554 Lr: 0.00004 [2024-11-25 19:48:57,132 INFO misc.py line 136 2586773] Train result: loss: 0.1959 [2024-11-25 19:48:57,132 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:49:08,115 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1945 [2024-11-25 19:49:08,367 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2314 [2024-11-25 19:49:08,629 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2588 [2024-11-25 19:49:08,854 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.2008 [2024-11-25 19:49:09,116 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2873 [2024-11-25 19:49:09,386 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1932 [2024-11-25 19:49:09,610 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2148 [2024-11-25 19:49:09,877 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2364 [2024-11-25 19:49:10,101 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2679 [2024-11-25 19:49:10,362 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2351 [2024-11-25 19:49:10,594 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2049 [2024-11-25 19:49:10,863 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2489 [2024-11-25 19:49:11,129 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2569 [2024-11-25 19:49:11,394 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2228 [2024-11-25 19:49:11,626 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2341 [2024-11-25 19:49:11,865 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3071 [2024-11-25 19:49:12,132 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2717 [2024-11-25 19:49:12,378 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2128 [2024-11-25 19:49:12,610 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2596 [2024-11-25 19:49:12,872 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2231 [2024-11-25 19:49:13,107 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2566 [2024-11-25 19:49:13,374 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2465 [2024-11-25 19:49:13,610 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2132 [2024-11-25 19:49:13,876 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2315 [2024-11-25 19:49:14,142 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2138 [2024-11-25 19:49:14,375 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2540 [2024-11-25 19:49:14,627 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2556 [2024-11-25 19:49:14,872 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2227 [2024-11-25 19:49:15,139 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2619 [2024-11-25 19:49:15,391 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2867 [2024-11-25 19:49:15,625 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2560 [2024-11-25 19:49:15,888 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.2005 [2024-11-25 19:49:16,109 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2712 [2024-11-25 19:49:16,348 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2410 [2024-11-25 19:49:16,610 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1958 [2024-11-25 19:49:16,854 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2729 [2024-11-25 19:49:17,080 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1948 [2024-11-25 19:49:17,348 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2354 [2024-11-25 19:49:17,579 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2657 [2024-11-25 19:49:17,813 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2306 [2024-11-25 19:49:18,085 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3124 [2024-11-25 19:49:18,335 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2740 [2024-11-25 19:49:18,572 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2560 [2024-11-25 19:49:18,802 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2185 [2024-11-25 19:49:19,045 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2412 [2024-11-25 19:49:19,297 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2229 [2024-11-25 19:49:19,554 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2157 [2024-11-25 19:49:19,807 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2835 [2024-11-25 19:49:20,030 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2093 [2024-11-25 19:49:20,264 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2179 [2024-11-25 19:49:20,487 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2627 [2024-11-25 19:49:20,740 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2234 [2024-11-25 19:49:21,004 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2247 [2024-11-25 19:49:21,264 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3200 [2024-11-25 19:49:21,496 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2279 [2024-11-25 19:49:21,735 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2290 [2024-11-25 19:49:21,992 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2593 [2024-11-25 19:49:22,266 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2792 [2024-11-25 19:49:22,525 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2366 [2024-11-25 19:49:22,787 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2351 [2024-11-25 19:49:23,040 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2123 [2024-11-25 19:49:23,308 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2309 [2024-11-25 19:49:23,537 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2439 [2024-11-25 19:49:23,798 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2460 [2024-11-25 19:49:24,066 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2501 [2024-11-25 19:49:24,334 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1869 [2024-11-25 19:49:24,578 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1963 [2024-11-25 19:49:24,836 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2477 [2024-11-25 19:49:25,105 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2547 [2024-11-25 19:49:25,372 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2677 [2024-11-25 19:49:25,617 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2055 [2024-11-25 19:49:25,850 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2919 [2024-11-25 19:49:26,106 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2634 [2024-11-25 19:49:26,350 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2506 [2024-11-25 19:49:26,566 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2666 [2024-11-25 19:49:26,788 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2131 [2024-11-25 19:49:27,054 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2445 [2024-11-25 19:49:27,292 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2134 [2024-11-25 19:49:27,549 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2435 [2024-11-25 19:49:27,802 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2889 [2024-11-25 19:49:28,042 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2362 [2024-11-25 19:49:28,305 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2644 [2024-11-25 19:49:28,553 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2130 [2024-11-25 19:49:28,799 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2385 [2024-11-25 19:49:29,071 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2647 [2024-11-25 19:49:29,319 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2683 [2024-11-25 19:49:29,582 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2390 [2024-11-25 19:49:29,842 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2390 [2024-11-25 19:49:30,090 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2760 [2024-11-25 19:49:30,336 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2533 [2024-11-25 19:49:30,572 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2439 [2024-11-25 19:49:30,825 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2614 [2024-11-25 19:49:31,094 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2430 [2024-11-25 19:49:31,360 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1896 [2024-11-25 19:49:31,626 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2284 [2024-11-25 19:49:31,874 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2140 [2024-11-25 19:49:32,141 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2282 [2024-11-25 19:49:32,361 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.3016 [2024-11-25 19:49:32,633 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2410 [2024-11-25 19:49:32,870 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2460 [2024-11-25 19:49:33,141 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.2019 [2024-11-25 19:49:33,400 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2550 [2024-11-25 19:49:33,659 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2364 [2024-11-25 19:49:33,910 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2642 [2024-11-25 19:49:34,132 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2476 [2024-11-25 19:49:34,367 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2233 [2024-11-25 19:49:34,623 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2237 [2024-11-25 19:49:34,894 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2366 [2024-11-25 19:49:35,126 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2510 [2024-11-25 19:49:35,395 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2017 [2024-11-25 19:49:35,659 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2517 [2024-11-25 19:49:35,881 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2261 [2024-11-25 19:49:36,117 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2042 [2024-11-25 19:49:36,335 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2058 [2024-11-25 19:49:36,558 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2158 [2024-11-25 19:49:36,829 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2851 [2024-11-25 19:49:37,092 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2467 [2024-11-25 19:49:37,360 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2389 [2024-11-25 19:49:37,627 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2224 [2024-11-25 19:49:37,888 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2816 [2024-11-25 19:49:38,148 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2842 [2024-11-25 19:49:38,411 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2330 [2024-11-25 19:49:38,666 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2424 [2024-11-25 19:49:38,927 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2420 [2024-11-25 19:49:39,191 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2498 [2024-11-25 19:49:39,442 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2498 [2024-11-25 19:49:39,672 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2017 [2024-11-25 19:49:39,930 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2454 [2024-11-25 19:49:40,164 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2559 [2024-11-25 19:49:40,391 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1920 [2024-11-25 19:49:40,603 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2244 [2024-11-25 19:49:40,824 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1862 [2024-11-25 19:49:41,418 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7781/0.8559/0.9963. [2024-11-25 19:49:41,419 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 19:49:41,419 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5599/0.7137 [2024-11-25 19:49:41,419 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:49:41,420 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 19:49:41,420 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:49:44,027 INFO misc.py line 119 2586773] Train: [48/50][1/376] Data 0.078 (0.078) Batch 0.542 (0.542) Remain 00:10:10 loss: 0.2247 Lr: 0.00004 [2024-11-25 19:49:44,523 INFO misc.py line 119 2586773] Train: [48/50][2/376] Data 0.002 (0.002) Batch 0.496 (0.496) Remain 00:09:18 loss: 0.1824 Lr: 0.00004 [2024-11-25 19:49:44,977 INFO misc.py line 119 2586773] Train: [48/50][3/376] Data 0.002 (0.002) Batch 0.454 (0.454) Remain 00:08:30 loss: 0.1927 Lr: 0.00004 [2024-11-25 19:49:45,503 INFO misc.py line 119 2586773] Train: [48/50][4/376] Data 0.002 (0.002) Batch 0.526 (0.526) Remain 00:09:51 loss: 0.2161 Lr: 0.00004 [2024-11-25 19:49:46,049 INFO misc.py line 119 2586773] Train: [48/50][5/376] Data 0.002 (0.002) Batch 0.546 (0.536) Remain 00:10:02 loss: 0.1636 Lr: 0.00004 [2024-11-25 19:49:46,535 INFO misc.py line 119 2586773] Train: [48/50][6/376] Data 0.002 (0.002) Batch 0.486 (0.520) Remain 00:09:42 loss: 0.2168 Lr: 0.00004 [2024-11-25 19:49:47,043 INFO misc.py line 119 2586773] Train: [48/50][7/376] Data 0.002 (0.002) Batch 0.508 (0.517) Remain 00:09:39 loss: 0.2002 Lr: 0.00004 [2024-11-25 19:49:47,555 INFO misc.py line 119 2586773] Train: [48/50][8/376] Data 0.002 (0.002) Batch 0.512 (0.516) Remain 00:09:37 loss: 0.1841 Lr: 0.00004 [2024-11-25 19:49:48,088 INFO misc.py line 119 2586773] Train: [48/50][9/376] Data 0.003 (0.002) Batch 0.533 (0.519) Remain 00:09:40 loss: 0.1768 Lr: 0.00004 [2024-11-25 19:49:48,599 INFO misc.py line 119 2586773] Train: [48/50][10/376] Data 0.002 (0.002) Batch 0.511 (0.517) Remain 00:09:38 loss: 0.3299 Lr: 0.00004 [2024-11-25 19:49:49,089 INFO misc.py line 119 2586773] Train: [48/50][11/376] Data 0.002 (0.002) Batch 0.491 (0.514) Remain 00:09:34 loss: 0.2087 Lr: 0.00004 [2024-11-25 19:49:49,620 INFO misc.py line 119 2586773] Train: [48/50][12/376] Data 0.002 (0.002) Batch 0.530 (0.516) Remain 00:09:35 loss: 0.1624 Lr: 0.00004 [2024-11-25 19:49:50,120 INFO misc.py line 119 2586773] Train: [48/50][13/376] Data 0.002 (0.002) Batch 0.500 (0.514) Remain 00:09:33 loss: 0.1325 Lr: 0.00004 [2024-11-25 19:49:50,608 INFO misc.py line 119 2586773] Train: [48/50][14/376] Data 0.002 (0.002) Batch 0.489 (0.512) Remain 00:09:30 loss: 0.2147 Lr: 0.00004 [2024-11-25 19:49:51,145 INFO misc.py line 119 2586773] Train: [48/50][15/376] Data 0.002 (0.002) Batch 0.537 (0.514) Remain 00:09:32 loss: 0.1675 Lr: 0.00004 [2024-11-25 19:49:51,597 INFO misc.py line 119 2586773] Train: [48/50][16/376] Data 0.002 (0.002) Batch 0.452 (0.509) Remain 00:09:26 loss: 0.1473 Lr: 0.00004 [2024-11-25 19:49:52,099 INFO misc.py line 119 2586773] Train: [48/50][17/376] Data 0.002 (0.002) Batch 0.502 (0.509) Remain 00:09:25 loss: 0.1664 Lr: 0.00004 [2024-11-25 19:49:52,619 INFO misc.py line 119 2586773] Train: [48/50][18/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 00:09:25 loss: 0.2172 Lr: 0.00004 [2024-11-25 19:49:53,062 INFO misc.py line 119 2586773] Train: [48/50][19/376] Data 0.002 (0.002) Batch 0.443 (0.505) Remain 00:09:20 loss: 0.1889 Lr: 0.00004 [2024-11-25 19:49:53,602 INFO misc.py line 119 2586773] Train: [48/50][20/376] Data 0.002 (0.002) Batch 0.540 (0.507) Remain 00:09:22 loss: 0.1704 Lr: 0.00004 [2024-11-25 19:49:54,108 INFO misc.py line 119 2586773] Train: [48/50][21/376] Data 0.002 (0.002) Batch 0.506 (0.507) Remain 00:09:21 loss: 0.1576 Lr: 0.00004 [2024-11-25 19:49:54,624 INFO misc.py line 119 2586773] Train: [48/50][22/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:09:21 loss: 0.2082 Lr: 0.00004 [2024-11-25 19:49:55,139 INFO misc.py line 119 2586773] Train: [48/50][23/376] Data 0.002 (0.002) Batch 0.515 (0.508) Remain 00:09:21 loss: 0.1791 Lr: 0.00004 [2024-11-25 19:49:55,683 INFO misc.py line 119 2586773] Train: [48/50][24/376] Data 0.002 (0.002) Batch 0.544 (0.510) Remain 00:09:22 loss: 0.2073 Lr: 0.00004 [2024-11-25 19:49:56,203 INFO misc.py line 119 2586773] Train: [48/50][25/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 00:09:22 loss: 0.2105 Lr: 0.00004 [2024-11-25 19:49:56,740 INFO misc.py line 119 2586773] Train: [48/50][26/376] Data 0.002 (0.002) Batch 0.536 (0.511) Remain 00:09:23 loss: 0.2120 Lr: 0.00004 [2024-11-25 19:49:57,288 INFO misc.py line 119 2586773] Train: [48/50][27/376] Data 0.002 (0.002) Batch 0.548 (0.513) Remain 00:09:24 loss: 0.1991 Lr: 0.00004 [2024-11-25 19:49:57,769 INFO misc.py line 119 2586773] Train: [48/50][28/376] Data 0.002 (0.002) Batch 0.481 (0.512) Remain 00:09:22 loss: 0.1565 Lr: 0.00004 [2024-11-25 19:49:58,334 INFO misc.py line 119 2586773] Train: [48/50][29/376] Data 0.002 (0.002) Batch 0.565 (0.514) Remain 00:09:24 loss: 0.2236 Lr: 0.00004 [2024-11-25 19:49:58,867 INFO misc.py line 119 2586773] Train: [48/50][30/376] Data 0.002 (0.002) Batch 0.534 (0.514) Remain 00:09:24 loss: 0.1937 Lr: 0.00004 [2024-11-25 19:49:59,354 INFO misc.py line 119 2586773] Train: [48/50][31/376] Data 0.002 (0.002) Batch 0.487 (0.513) Remain 00:09:23 loss: 0.2153 Lr: 0.00004 [2024-11-25 19:49:59,889 INFO misc.py line 119 2586773] Train: [48/50][32/376] Data 0.002 (0.002) Batch 0.535 (0.514) Remain 00:09:23 loss: 0.1725 Lr: 0.00004 [2024-11-25 19:50:00,383 INFO misc.py line 119 2586773] Train: [48/50][33/376] Data 0.002 (0.002) Batch 0.493 (0.514) Remain 00:09:22 loss: 0.1891 Lr: 0.00004 [2024-11-25 19:50:00,876 INFO misc.py line 119 2586773] Train: [48/50][34/376] Data 0.002 (0.002) Batch 0.494 (0.513) Remain 00:09:21 loss: 0.1926 Lr: 0.00004 [2024-11-25 19:50:01,367 INFO misc.py line 119 2586773] Train: [48/50][35/376] Data 0.002 (0.002) Batch 0.490 (0.512) Remain 00:09:19 loss: 0.2317 Lr: 0.00004 [2024-11-25 19:50:01,870 INFO misc.py line 119 2586773] Train: [48/50][36/376] Data 0.002 (0.002) Batch 0.504 (0.512) Remain 00:09:19 loss: 0.1522 Lr: 0.00004 [2024-11-25 19:50:02,374 INFO misc.py line 119 2586773] Train: [48/50][37/376] Data 0.003 (0.002) Batch 0.504 (0.512) Remain 00:09:18 loss: 0.1935 Lr: 0.00004 [2024-11-25 19:50:02,865 INFO misc.py line 119 2586773] Train: [48/50][38/376] Data 0.002 (0.002) Batch 0.491 (0.511) Remain 00:09:17 loss: 0.1987 Lr: 0.00004 [2024-11-25 19:50:03,346 INFO misc.py line 119 2586773] Train: [48/50][39/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 00:09:15 loss: 0.1989 Lr: 0.00004 [2024-11-25 19:50:03,883 INFO misc.py line 119 2586773] Train: [48/50][40/376] Data 0.002 (0.002) Batch 0.537 (0.511) Remain 00:09:15 loss: 0.1530 Lr: 0.00004 [2024-11-25 19:50:04,374 INFO misc.py line 119 2586773] Train: [48/50][41/376] Data 0.002 (0.002) Batch 0.491 (0.510) Remain 00:09:14 loss: 0.1862 Lr: 0.00004 [2024-11-25 19:50:04,873 INFO misc.py line 119 2586773] Train: [48/50][42/376] Data 0.002 (0.002) Batch 0.498 (0.510) Remain 00:09:14 loss: 0.1854 Lr: 0.00004 [2024-11-25 19:50:05,335 INFO misc.py line 119 2586773] Train: [48/50][43/376] Data 0.002 (0.002) Batch 0.463 (0.509) Remain 00:09:12 loss: 0.1952 Lr: 0.00004 [2024-11-25 19:50:05,846 INFO misc.py line 119 2586773] Train: [48/50][44/376] Data 0.002 (0.002) Batch 0.511 (0.509) Remain 00:09:11 loss: 0.1646 Lr: 0.00004 [2024-11-25 19:50:06,373 INFO misc.py line 119 2586773] Train: [48/50][45/376] Data 0.002 (0.002) Batch 0.527 (0.509) Remain 00:09:11 loss: 0.1917 Lr: 0.00004 [2024-11-25 19:50:06,894 INFO misc.py line 119 2586773] Train: [48/50][46/376] Data 0.002 (0.002) Batch 0.520 (0.510) Remain 00:09:11 loss: 0.1952 Lr: 0.00004 [2024-11-25 19:50:07,369 INFO misc.py line 119 2586773] Train: [48/50][47/376] Data 0.002 (0.002) Batch 0.475 (0.509) Remain 00:09:10 loss: 0.1991 Lr: 0.00004 [2024-11-25 19:50:07,880 INFO misc.py line 119 2586773] Train: [48/50][48/376] Data 0.002 (0.002) Batch 0.512 (0.509) Remain 00:09:09 loss: 0.1969 Lr: 0.00004 [2024-11-25 19:50:08,421 INFO misc.py line 119 2586773] Train: [48/50][49/376] Data 0.003 (0.002) Batch 0.540 (0.510) Remain 00:09:09 loss: 0.2076 Lr: 0.00004 [2024-11-25 19:50:09,023 INFO misc.py line 119 2586773] Train: [48/50][50/376] Data 0.002 (0.002) Batch 0.602 (0.512) Remain 00:09:11 loss: 0.2225 Lr: 0.00004 [2024-11-25 19:50:09,555 INFO misc.py line 119 2586773] Train: [48/50][51/376] Data 0.003 (0.002) Batch 0.532 (0.512) Remain 00:09:11 loss: 0.1847 Lr: 0.00004 [2024-11-25 19:50:10,027 INFO misc.py line 119 2586773] Train: [48/50][52/376] Data 0.002 (0.002) Batch 0.472 (0.511) Remain 00:09:10 loss: 0.1907 Lr: 0.00004 [2024-11-25 19:50:10,519 INFO misc.py line 119 2586773] Train: [48/50][53/376] Data 0.002 (0.002) Batch 0.492 (0.511) Remain 00:09:09 loss: 0.1891 Lr: 0.00004 [2024-11-25 19:50:11,041 INFO misc.py line 119 2586773] Train: [48/50][54/376] Data 0.002 (0.002) Batch 0.522 (0.511) Remain 00:09:08 loss: 0.1795 Lr: 0.00004 [2024-11-25 19:50:11,507 INFO misc.py line 119 2586773] Train: [48/50][55/376] Data 0.002 (0.002) Batch 0.466 (0.510) Remain 00:09:07 loss: 0.2789 Lr: 0.00004 [2024-11-25 19:50:12,057 INFO misc.py line 119 2586773] Train: [48/50][56/376] Data 0.002 (0.002) Batch 0.550 (0.511) Remain 00:09:07 loss: 0.1801 Lr: 0.00004 [2024-11-25 19:50:12,554 INFO misc.py line 119 2586773] Train: [48/50][57/376] Data 0.002 (0.002) Batch 0.497 (0.511) Remain 00:09:06 loss: 0.2539 Lr: 0.00004 [2024-11-25 19:50:13,096 INFO misc.py line 119 2586773] Train: [48/50][58/376] Data 0.002 (0.002) Batch 0.541 (0.511) Remain 00:09:07 loss: 0.1887 Lr: 0.00004 [2024-11-25 19:50:13,613 INFO misc.py line 119 2586773] Train: [48/50][59/376] Data 0.002 (0.002) Batch 0.517 (0.511) Remain 00:09:06 loss: 0.2003 Lr: 0.00004 [2024-11-25 19:50:14,107 INFO misc.py line 119 2586773] Train: [48/50][60/376] Data 0.002 (0.002) Batch 0.494 (0.511) Remain 00:09:05 loss: 0.1916 Lr: 0.00004 [2024-11-25 19:50:14,616 INFO misc.py line 119 2586773] Train: [48/50][61/376] Data 0.002 (0.002) Batch 0.510 (0.511) Remain 00:09:05 loss: 0.2417 Lr: 0.00004 [2024-11-25 19:50:15,107 INFO misc.py line 119 2586773] Train: [48/50][62/376] Data 0.002 (0.002) Batch 0.491 (0.511) Remain 00:09:04 loss: 0.1793 Lr: 0.00004 [2024-11-25 19:50:15,585 INFO misc.py line 119 2586773] Train: [48/50][63/376] Data 0.002 (0.002) Batch 0.478 (0.510) Remain 00:09:03 loss: 0.1648 Lr: 0.00004 [2024-11-25 19:50:16,070 INFO misc.py line 119 2586773] Train: [48/50][64/376] Data 0.002 (0.002) Batch 0.485 (0.510) Remain 00:09:02 loss: 0.1816 Lr: 0.00004 [2024-11-25 19:50:16,555 INFO misc.py line 119 2586773] Train: [48/50][65/376] Data 0.002 (0.002) Batch 0.485 (0.509) Remain 00:09:01 loss: 0.1757 Lr: 0.00004 [2024-11-25 19:50:17,075 INFO misc.py line 119 2586773] Train: [48/50][66/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 00:09:01 loss: 0.1701 Lr: 0.00004 [2024-11-25 19:50:17,587 INFO misc.py line 119 2586773] Train: [48/50][67/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 00:09:00 loss: 0.1618 Lr: 0.00004 [2024-11-25 19:50:18,092 INFO misc.py line 119 2586773] Train: [48/50][68/376] Data 0.002 (0.002) Batch 0.505 (0.509) Remain 00:09:00 loss: 0.2264 Lr: 0.00004 [2024-11-25 19:50:18,576 INFO misc.py line 119 2586773] Train: [48/50][69/376] Data 0.002 (0.002) Batch 0.484 (0.509) Remain 00:08:59 loss: 0.2629 Lr: 0.00004 [2024-11-25 19:50:19,046 INFO misc.py line 119 2586773] Train: [48/50][70/376] Data 0.002 (0.002) Batch 0.469 (0.508) Remain 00:08:57 loss: 0.1993 Lr: 0.00004 [2024-11-25 19:50:19,585 INFO misc.py line 119 2586773] Train: [48/50][71/376] Data 0.002 (0.002) Batch 0.539 (0.509) Remain 00:08:57 loss: 0.3030 Lr: 0.00004 [2024-11-25 19:50:20,103 INFO misc.py line 119 2586773] Train: [48/50][72/376] Data 0.002 (0.002) Batch 0.518 (0.509) Remain 00:08:57 loss: 0.2292 Lr: 0.00004 [2024-11-25 19:50:20,571 INFO misc.py line 119 2586773] Train: [48/50][73/376] Data 0.002 (0.002) Batch 0.468 (0.508) Remain 00:08:56 loss: 0.2179 Lr: 0.00004 [2024-11-25 19:50:21,075 INFO misc.py line 119 2586773] Train: [48/50][74/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 00:08:55 loss: 0.2986 Lr: 0.00004 [2024-11-25 19:50:21,589 INFO misc.py line 119 2586773] Train: [48/50][75/376] Data 0.002 (0.002) Batch 0.514 (0.509) Remain 00:08:55 loss: 0.2384 Lr: 0.00004 [2024-11-25 19:50:22,112 INFO misc.py line 119 2586773] Train: [48/50][76/376] Data 0.002 (0.002) Batch 0.523 (0.509) Remain 00:08:55 loss: 0.1709 Lr: 0.00004 [2024-11-25 19:50:22,612 INFO misc.py line 119 2586773] Train: [48/50][77/376] Data 0.002 (0.002) Batch 0.500 (0.509) Remain 00:08:54 loss: 0.2255 Lr: 0.00004 [2024-11-25 19:50:23,107 INFO misc.py line 119 2586773] Train: [48/50][78/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:08:53 loss: 0.3009 Lr: 0.00004 [2024-11-25 19:50:23,604 INFO misc.py line 119 2586773] Train: [48/50][79/376] Data 0.002 (0.002) Batch 0.498 (0.508) Remain 00:08:53 loss: 0.1523 Lr: 0.00004 [2024-11-25 19:50:24,113 INFO misc.py line 119 2586773] Train: [48/50][80/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:08:52 loss: 0.2028 Lr: 0.00004 [2024-11-25 19:50:24,639 INFO misc.py line 119 2586773] Train: [48/50][81/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 00:08:52 loss: 0.2551 Lr: 0.00004 [2024-11-25 19:50:25,126 INFO misc.py line 119 2586773] Train: [48/50][82/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 00:08:51 loss: 0.1631 Lr: 0.00004 [2024-11-25 19:50:25,642 INFO misc.py line 119 2586773] Train: [48/50][83/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:08:51 loss: 0.2032 Lr: 0.00004 [2024-11-25 19:50:26,130 INFO misc.py line 119 2586773] Train: [48/50][84/376] Data 0.002 (0.002) Batch 0.488 (0.508) Remain 00:08:50 loss: 0.1863 Lr: 0.00004 [2024-11-25 19:50:26,606 INFO misc.py line 119 2586773] Train: 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Batch 0.521 (0.510) Remain 00:07:38 loss: 0.1946 Lr: 0.00003 [2024-11-25 19:51:40,146 INFO misc.py line 119 2586773] Train: [48/50][229/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 00:07:38 loss: 0.1923 Lr: 0.00003 [2024-11-25 19:51:40,696 INFO misc.py line 119 2586773] Train: [48/50][230/376] Data 0.002 (0.002) Batch 0.549 (0.510) Remain 00:07:37 loss: 0.2114 Lr: 0.00003 [2024-11-25 19:51:41,207 INFO misc.py line 119 2586773] Train: [48/50][231/376] Data 0.002 (0.002) Batch 0.511 (0.510) Remain 00:07:37 loss: 0.3085 Lr: 0.00003 [2024-11-25 19:51:41,722 INFO misc.py line 119 2586773] Train: [48/50][232/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 00:07:36 loss: 0.1949 Lr: 0.00003 [2024-11-25 19:51:42,205 INFO misc.py line 119 2586773] Train: [48/50][233/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 00:07:36 loss: 0.2987 Lr: 0.00003 [2024-11-25 19:51:42,682 INFO misc.py line 119 2586773] Train: [48/50][234/376] Data 0.002 (0.002) Batch 0.478 (0.510) Remain 00:07:35 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Batch 0.543 (0.508) Remain 00:07:08 loss: 0.2889 Lr: 0.00003 [2024-11-25 19:52:08,315 INFO misc.py line 119 2586773] Train: [48/50][285/376] Data 0.002 (0.002) Batch 0.572 (0.508) Remain 00:07:08 loss: 0.1945 Lr: 0.00003 [2024-11-25 19:52:08,842 INFO misc.py line 119 2586773] Train: [48/50][286/376] Data 0.002 (0.002) Batch 0.527 (0.508) Remain 00:07:08 loss: 0.1588 Lr: 0.00003 [2024-11-25 19:52:09,396 INFO misc.py line 119 2586773] Train: [48/50][287/376] Data 0.002 (0.002) Batch 0.554 (0.509) Remain 00:07:07 loss: 0.1949 Lr: 0.00003 [2024-11-25 19:52:09,907 INFO misc.py line 119 2586773] Train: [48/50][288/376] Data 0.002 (0.002) Batch 0.510 (0.509) Remain 00:07:07 loss: 0.1427 Lr: 0.00003 [2024-11-25 19:52:10,341 INFO misc.py line 119 2586773] Train: [48/50][289/376] Data 0.002 (0.002) Batch 0.434 (0.508) Remain 00:07:06 loss: 0.2077 Lr: 0.00003 [2024-11-25 19:52:10,804 INFO misc.py line 119 2586773] Train: [48/50][290/376] Data 0.002 (0.002) Batch 0.463 (0.508) Remain 00:07:05 loss: 0.2126 Lr: 0.00003 [2024-11-25 19:52:11,305 INFO misc.py line 119 2586773] Train: [48/50][291/376] Data 0.002 (0.002) Batch 0.501 (0.508) Remain 00:07:05 loss: 0.2157 Lr: 0.00003 [2024-11-25 19:52:11,810 INFO misc.py line 119 2586773] Train: [48/50][292/376] Data 0.002 (0.002) Batch 0.505 (0.508) Remain 00:07:04 loss: 0.1804 Lr: 0.00003 [2024-11-25 19:52:12,294 INFO misc.py line 119 2586773] Train: [48/50][293/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:07:04 loss: 0.2198 Lr: 0.00003 [2024-11-25 19:52:12,789 INFO misc.py line 119 2586773] Train: [48/50][294/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:07:03 loss: 0.2981 Lr: 0.00003 [2024-11-25 19:52:13,315 INFO misc.py line 119 2586773] Train: [48/50][295/376] Data 0.002 (0.002) Batch 0.526 (0.508) Remain 00:07:03 loss: 0.3055 Lr: 0.00003 [2024-11-25 19:52:13,828 INFO misc.py line 119 2586773] Train: [48/50][296/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 00:07:02 loss: 0.1744 Lr: 0.00003 [2024-11-25 19:52:14,323 INFO misc.py line 119 2586773] Train: [48/50][297/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:07:02 loss: 0.1848 Lr: 0.00003 [2024-11-25 19:52:14,817 INFO misc.py line 119 2586773] Train: [48/50][298/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 00:07:01 loss: 0.2060 Lr: 0.00003 [2024-11-25 19:52:15,357 INFO misc.py line 119 2586773] Train: [48/50][299/376] Data 0.002 (0.002) Batch 0.540 (0.508) Remain 00:07:01 loss: 0.1849 Lr: 0.00003 [2024-11-25 19:52:15,868 INFO misc.py line 119 2586773] Train: [48/50][300/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:07:00 loss: 0.1821 Lr: 0.00003 [2024-11-25 19:52:16,424 INFO misc.py line 119 2586773] Train: [48/50][301/376] Data 0.002 (0.002) Batch 0.556 (0.508) Remain 00:07:00 loss: 0.1769 Lr: 0.00003 [2024-11-25 19:52:16,920 INFO misc.py line 119 2586773] Train: [48/50][302/376] Data 0.002 (0.002) Batch 0.496 (0.508) Remain 00:06:59 loss: 0.1857 Lr: 0.00003 [2024-11-25 19:52:17,456 INFO misc.py line 119 2586773] Train: [48/50][303/376] Data 0.002 (0.002) Batch 0.536 (0.508) Remain 00:06:59 loss: 0.2215 Lr: 0.00003 [2024-11-25 19:52:17,968 INFO misc.py line 119 2586773] Train: [48/50][304/376] Data 0.002 (0.002) Batch 0.512 (0.508) Remain 00:06:58 loss: 0.2067 Lr: 0.00002 [2024-11-25 19:52:18,465 INFO misc.py line 119 2586773] Train: [48/50][305/376] Data 0.003 (0.002) Batch 0.497 (0.508) Remain 00:06:58 loss: 0.2107 Lr: 0.00002 [2024-11-25 19:52:18,995 INFO misc.py line 119 2586773] Train: [48/50][306/376] Data 0.002 (0.002) Batch 0.530 (0.508) Remain 00:06:57 loss: 0.2223 Lr: 0.00002 [2024-11-25 19:52:19,540 INFO misc.py line 119 2586773] Train: [48/50][307/376] Data 0.002 (0.002) Batch 0.545 (0.508) Remain 00:06:57 loss: 0.2272 Lr: 0.00002 [2024-11-25 19:52:20,056 INFO misc.py line 119 2586773] Train: [48/50][308/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:06:56 loss: 0.2013 Lr: 0.00002 [2024-11-25 19:52:20,583 INFO misc.py line 119 2586773] Train: [48/50][309/376] Data 0.002 (0.002) Batch 0.526 (0.509) Remain 00:06:56 loss: 0.2403 Lr: 0.00002 [2024-11-25 19:52:21,100 INFO misc.py line 119 2586773] Train: [48/50][310/376] Data 0.003 (0.002) Batch 0.518 (0.509) Remain 00:06:55 loss: 0.2051 Lr: 0.00002 [2024-11-25 19:52:21,619 INFO misc.py line 119 2586773] Train: [48/50][311/376] Data 0.002 (0.002) Batch 0.519 (0.509) Remain 00:06:55 loss: 0.1552 Lr: 0.00002 [2024-11-25 19:52:22,108 INFO misc.py line 119 2586773] Train: [48/50][312/376] Data 0.002 (0.002) Batch 0.490 (0.509) Remain 00:06:54 loss: 0.1745 Lr: 0.00002 [2024-11-25 19:52:22,599 INFO misc.py line 119 2586773] Train: [48/50][313/376] Data 0.002 (0.002) Batch 0.490 (0.508) Remain 00:06:54 loss: 0.1866 Lr: 0.00002 [2024-11-25 19:52:23,087 INFO misc.py line 119 2586773] Train: [48/50][314/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:06:53 loss: 0.1736 Lr: 0.00002 [2024-11-25 19:52:23,573 INFO misc.py line 119 2586773] Train: [48/50][315/376] Data 0.002 (0.002) Batch 0.486 (0.508) Remain 00:06:53 loss: 0.1887 Lr: 0.00002 [2024-11-25 19:52:24,096 INFO misc.py line 119 2586773] Train: [48/50][316/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 00:06:52 loss: 0.2100 Lr: 0.00002 [2024-11-25 19:52:24,593 INFO misc.py line 119 2586773] Train: [48/50][317/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 00:06:52 loss: 0.3203 Lr: 0.00002 [2024-11-25 19:52:25,073 INFO misc.py line 119 2586773] Train: [48/50][318/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 00:06:51 loss: 0.1529 Lr: 0.00002 [2024-11-25 19:52:25,575 INFO misc.py line 119 2586773] Train: [48/50][319/376] Data 0.002 (0.002) Batch 0.502 (0.508) Remain 00:06:51 loss: 0.1673 Lr: 0.00002 [2024-11-25 19:52:26,038 INFO misc.py line 119 2586773] Train: [48/50][320/376] Data 0.002 (0.002) Batch 0.463 (0.508) Remain 00:06:50 loss: 0.1589 Lr: 0.00002 [2024-11-25 19:52:26,551 INFO misc.py line 119 2586773] Train: [48/50][321/376] Data 0.002 (0.002) Batch 0.513 (0.508) Remain 00:06:50 loss: 0.1527 Lr: 0.00002 [2024-11-25 19:52:27,034 INFO misc.py line 119 2586773] Train: [48/50][322/376] Data 0.002 (0.002) Batch 0.483 (0.508) Remain 00:06:49 loss: 0.2184 Lr: 0.00002 [2024-11-25 19:52:27,556 INFO misc.py line 119 2586773] Train: [48/50][323/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 00:06:48 loss: 0.1645 Lr: 0.00002 [2024-11-25 19:52:28,060 INFO misc.py line 119 2586773] Train: [48/50][324/376] Data 0.002 (0.002) Batch 0.504 (0.508) Remain 00:06:48 loss: 0.1755 Lr: 0.00002 [2024-11-25 19:52:28,548 INFO misc.py line 119 2586773] Train: [48/50][325/376] Data 0.002 (0.002) Batch 0.487 (0.508) Remain 00:06:47 loss: 0.1604 Lr: 0.00002 [2024-11-25 19:52:29,100 INFO misc.py line 119 2586773] Train: [48/50][326/376] Data 0.002 (0.002) Batch 0.552 (0.508) Remain 00:06:47 loss: 0.2176 Lr: 0.00002 [2024-11-25 19:52:29,599 INFO misc.py line 119 2586773] Train: [48/50][327/376] Data 0.002 (0.002) Batch 0.499 (0.508) Remain 00:06:46 loss: 0.2634 Lr: 0.00002 [2024-11-25 19:52:30,083 INFO misc.py line 119 2586773] Train: [48/50][328/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:06:46 loss: 0.2078 Lr: 0.00002 [2024-11-25 19:52:30,561 INFO misc.py line 119 2586773] Train: [48/50][329/376] Data 0.002 (0.002) Batch 0.478 (0.508) Remain 00:06:45 loss: 0.1913 Lr: 0.00002 [2024-11-25 19:52:31,053 INFO misc.py line 119 2586773] Train: [48/50][330/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 00:06:45 loss: 0.1630 Lr: 0.00002 [2024-11-25 19:52:31,567 INFO misc.py line 119 2586773] Train: [48/50][331/376] Data 0.002 (0.002) Batch 0.514 (0.508) Remain 00:06:44 loss: 0.2355 Lr: 0.00002 [2024-11-25 19:52:32,098 INFO misc.py line 119 2586773] Train: [48/50][332/376] Data 0.002 (0.002) Batch 0.531 (0.508) Remain 00:06:44 loss: 0.1734 Lr: 0.00002 [2024-11-25 19:52:32,615 INFO misc.py line 119 2586773] Train: [48/50][333/376] Data 0.003 (0.002) Batch 0.517 (0.508) Remain 00:06:43 loss: 0.2285 Lr: 0.00002 [2024-11-25 19:52:33,113 INFO misc.py line 119 2586773] Train: [48/50][334/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 00:06:43 loss: 0.1661 Lr: 0.00002 [2024-11-25 19:52:33,632 INFO misc.py line 119 2586773] Train: [48/50][335/376] Data 0.003 (0.002) Batch 0.520 (0.508) Remain 00:06:42 loss: 0.2738 Lr: 0.00002 [2024-11-25 19:52:34,048 INFO misc.py line 119 2586773] Train: [48/50][336/376] Data 0.002 (0.002) Batch 0.415 (0.508) Remain 00:06:42 loss: 0.1799 Lr: 0.00002 [2024-11-25 19:52:34,555 INFO misc.py line 119 2586773] Train: [48/50][337/376] Data 0.002 (0.002) Batch 0.508 (0.508) Remain 00:06:41 loss: 0.1590 Lr: 0.00002 [2024-11-25 19:52:35,084 INFO misc.py line 119 2586773] Train: [48/50][338/376] Data 0.002 (0.002) Batch 0.529 (0.508) Remain 00:06:41 loss: 0.1642 Lr: 0.00002 [2024-11-25 19:52:35,607 INFO misc.py line 119 2586773] Train: [48/50][339/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 00:06:40 loss: 0.1897 Lr: 0.00002 [2024-11-25 19:52:36,081 INFO misc.py line 119 2586773] Train: [48/50][340/376] Data 0.002 (0.002) Batch 0.474 (0.508) Remain 00:06:40 loss: 0.1741 Lr: 0.00002 [2024-11-25 19:52:36,575 INFO misc.py line 119 2586773] Train: [48/50][341/376] Data 0.003 (0.002) Batch 0.494 (0.508) Remain 00:06:39 loss: 0.2037 Lr: 0.00002 [2024-11-25 19:52:37,106 INFO misc.py line 119 2586773] Train: [48/50][342/376] Data 0.002 (0.002) Batch 0.532 (0.508) Remain 00:06:39 loss: 0.1559 Lr: 0.00002 [2024-11-25 19:52:37,617 INFO misc.py line 119 2586773] Train: [48/50][343/376] Data 0.002 (0.002) Batch 0.511 (0.508) Remain 00:06:38 loss: 0.1939 Lr: 0.00002 [2024-11-25 19:52:38,139 INFO misc.py line 119 2586773] Train: [48/50][344/376] Data 0.002 (0.002) Batch 0.522 (0.508) Remain 00:06:38 loss: 0.2329 Lr: 0.00002 [2024-11-25 19:52:38,619 INFO misc.py line 119 2586773] Train: [48/50][345/376] Data 0.002 (0.002) Batch 0.480 (0.508) Remain 00:06:37 loss: 0.1622 Lr: 0.00002 [2024-11-25 19:52:39,110 INFO misc.py line 119 2586773] Train: [48/50][346/376] Data 0.002 (0.002) Batch 0.491 (0.508) Remain 00:06:37 loss: 0.1838 Lr: 0.00002 [2024-11-25 19:52:39,595 INFO misc.py line 119 2586773] Train: [48/50][347/376] Data 0.002 (0.002) Batch 0.485 (0.508) Remain 00:06:36 loss: 0.1955 Lr: 0.00002 [2024-11-25 19:52:40,084 INFO misc.py line 119 2586773] Train: [48/50][348/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:06:35 loss: 0.1552 Lr: 0.00002 [2024-11-25 19:52:40,564 INFO misc.py line 119 2586773] Train: [48/50][349/376] Data 0.002 (0.002) Batch 0.480 (0.507) Remain 00:06:35 loss: 0.1668 Lr: 0.00002 [2024-11-25 19:52:41,052 INFO misc.py line 119 2586773] Train: [48/50][350/376] Data 0.003 (0.002) Batch 0.488 (0.507) Remain 00:06:34 loss: 0.1918 Lr: 0.00002 [2024-11-25 19:52:41,585 INFO misc.py line 119 2586773] Train: [48/50][351/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:06:34 loss: 0.1742 Lr: 0.00002 [2024-11-25 19:52:42,137 INFO misc.py line 119 2586773] Train: [48/50][352/376] Data 0.002 (0.002) Batch 0.552 (0.508) Remain 00:06:33 loss: 0.1983 Lr: 0.00002 [2024-11-25 19:52:42,615 INFO misc.py line 119 2586773] Train: [48/50][353/376] Data 0.003 (0.002) Batch 0.478 (0.508) Remain 00:06:33 loss: 0.2037 Lr: 0.00002 [2024-11-25 19:52:43,131 INFO misc.py line 119 2586773] Train: [48/50][354/376] Data 0.002 (0.002) Batch 0.516 (0.508) Remain 00:06:32 loss: 0.2432 Lr: 0.00002 [2024-11-25 19:52:43,640 INFO misc.py line 119 2586773] Train: [48/50][355/376] Data 0.003 (0.002) Batch 0.509 (0.508) Remain 00:06:32 loss: 0.1866 Lr: 0.00002 [2024-11-25 19:52:44,157 INFO misc.py line 119 2586773] Train: [48/50][356/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 00:06:31 loss: 0.1649 Lr: 0.00002 [2024-11-25 19:52:44,630 INFO misc.py line 119 2586773] Train: [48/50][357/376] Data 0.002 (0.002) Batch 0.473 (0.507) Remain 00:06:31 loss: 0.1927 Lr: 0.00002 [2024-11-25 19:52:45,124 INFO misc.py line 119 2586773] Train: [48/50][358/376] Data 0.002 (0.002) Batch 0.494 (0.507) Remain 00:06:30 loss: 0.2018 Lr: 0.00002 [2024-11-25 19:52:45,658 INFO misc.py line 119 2586773] Train: [48/50][359/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 00:06:30 loss: 0.1937 Lr: 0.00002 [2024-11-25 19:52:46,182 INFO misc.py line 119 2586773] Train: [48/50][360/376] Data 0.003 (0.002) Batch 0.524 (0.508) Remain 00:06:29 loss: 0.1750 Lr: 0.00002 [2024-11-25 19:52:46,661 INFO misc.py line 119 2586773] Train: [48/50][361/376] Data 0.002 (0.002) Batch 0.479 (0.507) Remain 00:06:29 loss: 0.1691 Lr: 0.00002 [2024-11-25 19:52:47,205 INFO misc.py line 119 2586773] Train: [48/50][362/376] Data 0.002 (0.002) Batch 0.544 (0.508) Remain 00:06:28 loss: 0.2022 Lr: 0.00002 [2024-11-25 19:52:47,729 INFO misc.py line 119 2586773] Train: [48/50][363/376] Data 0.002 (0.002) Batch 0.524 (0.508) Remain 00:06:28 loss: 0.1851 Lr: 0.00002 [2024-11-25 19:52:48,247 INFO misc.py line 119 2586773] Train: [48/50][364/376] Data 0.002 (0.002) Batch 0.517 (0.508) Remain 00:06:27 loss: 0.2185 Lr: 0.00002 [2024-11-25 19:52:48,739 INFO misc.py line 119 2586773] Train: [48/50][365/376] Data 0.002 (0.002) Batch 0.492 (0.508) Remain 00:06:27 loss: 0.1747 Lr: 0.00002 [2024-11-25 19:52:49,233 INFO misc.py line 119 2586773] Train: [48/50][366/376] Data 0.002 (0.002) Batch 0.494 (0.508) Remain 00:06:26 loss: 0.1876 Lr: 0.00002 [2024-11-25 19:52:49,721 INFO misc.py line 119 2586773] Train: [48/50][367/376] Data 0.002 (0.002) Batch 0.489 (0.508) Remain 00:06:26 loss: 0.2389 Lr: 0.00002 [2024-11-25 19:52:50,255 INFO misc.py line 119 2586773] Train: [48/50][368/376] Data 0.002 (0.002) Batch 0.534 (0.508) Remain 00:06:25 loss: 0.1692 Lr: 0.00002 [2024-11-25 19:52:50,770 INFO misc.py line 119 2586773] Train: [48/50][369/376] Data 0.002 (0.002) Batch 0.515 (0.508) Remain 00:06:25 loss: 0.2269 Lr: 0.00002 [2024-11-25 19:52:51,249 INFO misc.py line 119 2586773] Train: [48/50][370/376] Data 0.002 (0.002) Batch 0.479 (0.508) Remain 00:06:24 loss: 0.2026 Lr: 0.00002 [2024-11-25 19:52:51,759 INFO misc.py line 119 2586773] Train: [48/50][371/376] Data 0.002 (0.002) Batch 0.509 (0.508) Remain 00:06:24 loss: 0.1823 Lr: 0.00002 [2024-11-25 19:52:52,266 INFO misc.py line 119 2586773] Train: [48/50][372/376] Data 0.002 (0.002) Batch 0.507 (0.508) Remain 00:06:23 loss: 0.1735 Lr: 0.00002 [2024-11-25 19:52:52,782 INFO misc.py line 119 2586773] Train: [48/50][373/376] Data 0.003 (0.002) Batch 0.516 (0.508) Remain 00:06:23 loss: 0.1730 Lr: 0.00002 [2024-11-25 19:52:53,276 INFO misc.py line 119 2586773] Train: [48/50][374/376] Data 0.002 (0.002) Batch 0.495 (0.508) Remain 00:06:22 loss: 0.1976 Lr: 0.00002 [2024-11-25 19:52:53,773 INFO misc.py line 119 2586773] Train: [48/50][375/376] Data 0.002 (0.002) Batch 0.497 (0.508) Remain 00:06:22 loss: 0.1945 Lr: 0.00002 [2024-11-25 19:52:54,236 INFO misc.py line 119 2586773] Train: [48/50][376/376] Data 0.002 (0.002) Batch 0.463 (0.507) Remain 00:06:21 loss: 0.1872 Lr: 0.00002 [2024-11-25 19:52:54,236 INFO misc.py line 136 2586773] Train result: loss: 0.1976 [2024-11-25 19:52:54,237 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:53:04,933 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1801 [2024-11-25 19:53:05,577 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2112 [2024-11-25 19:53:05,840 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2495 [2024-11-25 19:53:06,062 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1937 [2024-11-25 19:53:06,327 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2874 [2024-11-25 19:53:06,593 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1890 [2024-11-25 19:53:06,825 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2113 [2024-11-25 19:53:07,095 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2458 [2024-11-25 19:53:07,319 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2643 [2024-11-25 19:53:07,578 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2337 [2024-11-25 19:53:07,810 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2055 [2024-11-25 19:53:08,082 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2510 [2024-11-25 19:53:08,348 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2565 [2024-11-25 19:53:08,612 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2254 [2024-11-25 19:53:08,845 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2303 [2024-11-25 19:53:09,083 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3082 [2024-11-25 19:53:09,348 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2643 [2024-11-25 19:53:09,594 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2048 [2024-11-25 19:53:09,825 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2310 [2024-11-25 19:53:10,085 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2120 [2024-11-25 19:53:10,321 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2424 [2024-11-25 19:53:10,587 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2419 [2024-11-25 19:53:10,824 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2088 [2024-11-25 19:53:11,090 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2339 [2024-11-25 19:53:11,352 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2216 [2024-11-25 19:53:11,586 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2500 [2024-11-25 19:53:11,839 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2497 [2024-11-25 19:53:12,084 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2182 [2024-11-25 19:53:12,349 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2584 [2024-11-25 19:53:12,600 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2765 [2024-11-25 19:53:12,834 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2629 [2024-11-25 19:53:13,100 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1938 [2024-11-25 19:53:13,321 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2592 [2024-11-25 19:53:13,559 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2403 [2024-11-25 19:53:13,822 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1939 [2024-11-25 19:53:14,065 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2678 [2024-11-25 19:53:14,290 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1938 [2024-11-25 19:53:14,561 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2318 [2024-11-25 19:53:14,793 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2642 [2024-11-25 19:53:15,035 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2291 [2024-11-25 19:53:15,302 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3050 [2024-11-25 19:53:15,561 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2650 [2024-11-25 19:53:15,798 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2527 [2024-11-25 19:53:16,030 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2250 [2024-11-25 19:53:16,269 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2395 [2024-11-25 19:53:16,514 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2236 [2024-11-25 19:53:16,780 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2229 [2024-11-25 19:53:17,034 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2840 [2024-11-25 19:53:17,266 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2131 [2024-11-25 19:53:17,499 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2124 [2024-11-25 19:53:17,724 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2400 [2024-11-25 19:53:17,984 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2244 [2024-11-25 19:53:18,252 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2189 [2024-11-25 19:53:18,519 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3152 [2024-11-25 19:53:18,760 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2314 [2024-11-25 19:53:18,999 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2215 [2024-11-25 19:53:19,264 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2472 [2024-11-25 19:53:19,536 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2663 [2024-11-25 19:53:19,793 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2345 [2024-11-25 19:53:20,055 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2438 [2024-11-25 19:53:20,315 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2123 [2024-11-25 19:53:20,585 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2293 [2024-11-25 19:53:20,813 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2386 [2024-11-25 19:53:21,073 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2464 [2024-11-25 19:53:21,343 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2518 [2024-11-25 19:53:21,606 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1947 [2024-11-25 19:53:21,853 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1918 [2024-11-25 19:53:22,108 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2543 [2024-11-25 19:53:22,378 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2388 [2024-11-25 19:53:22,639 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2654 [2024-11-25 19:53:22,885 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2049 [2024-11-25 19:53:23,119 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2859 [2024-11-25 19:53:23,376 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2541 [2024-11-25 19:53:23,620 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2456 [2024-11-25 19:53:23,836 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2527 [2024-11-25 19:53:24,057 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2132 [2024-11-25 19:53:24,326 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2499 [2024-11-25 19:53:24,563 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.1993 [2024-11-25 19:53:24,821 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2344 [2024-11-25 19:53:25,072 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2781 [2024-11-25 19:53:25,315 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2352 [2024-11-25 19:53:25,575 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2543 [2024-11-25 19:53:25,824 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.2005 [2024-11-25 19:53:26,072 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2403 [2024-11-25 19:53:26,348 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2596 [2024-11-25 19:53:26,583 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2663 [2024-11-25 19:53:26,847 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2427 [2024-11-25 19:53:27,108 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2269 [2024-11-25 19:53:27,359 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2654 [2024-11-25 19:53:27,607 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2593 [2024-11-25 19:53:27,840 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2479 [2024-11-25 19:53:28,097 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2641 [2024-11-25 19:53:28,365 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2324 [2024-11-25 19:53:28,631 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1905 [2024-11-25 19:53:28,895 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2286 [2024-11-25 19:53:29,142 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2016 [2024-11-25 19:53:29,411 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2250 [2024-11-25 19:53:29,632 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2979 [2024-11-25 19:53:29,903 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2473 [2024-11-25 19:53:30,142 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2473 [2024-11-25 19:53:30,412 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1998 [2024-11-25 19:53:30,673 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2518 [2024-11-25 19:53:30,933 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2332 [2024-11-25 19:53:31,186 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2666 [2024-11-25 19:53:31,407 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2448 [2024-11-25 19:53:31,641 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2294 [2024-11-25 19:53:31,900 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2269 [2024-11-25 19:53:32,170 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2322 [2024-11-25 19:53:32,403 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2462 [2024-11-25 19:53:32,665 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2039 [2024-11-25 19:53:32,926 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2334 [2024-11-25 19:53:33,148 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2196 [2024-11-25 19:53:33,384 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2029 [2024-11-25 19:53:33,603 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2118 [2024-11-25 19:53:33,831 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2157 [2024-11-25 19:53:34,103 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2848 [2024-11-25 19:53:34,363 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2511 [2024-11-25 19:53:34,631 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2362 [2024-11-25 19:53:34,895 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2096 [2024-11-25 19:53:35,159 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2759 [2024-11-25 19:53:35,420 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2760 [2024-11-25 19:53:35,685 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2125 [2024-11-25 19:53:35,941 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2472 [2024-11-25 19:53:36,203 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2359 [2024-11-25 19:53:36,465 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2415 [2024-11-25 19:53:36,715 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2437 [2024-11-25 19:53:36,947 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.2057 [2024-11-25 19:53:37,205 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2487 [2024-11-25 19:53:37,440 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2507 [2024-11-25 19:53:37,666 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1892 [2024-11-25 19:53:37,879 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2206 [2024-11-25 19:53:38,099 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1853 [2024-11-25 19:53:38,828 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7789/0.8542/0.9964. [2024-11-25 19:53:38,828 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9983 [2024-11-25 19:53:38,828 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5615/0.7101 [2024-11-25 19:53:38,829 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:53:38,830 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 19:53:38,830 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:53:41,587 INFO misc.py line 119 2586773] Train: [49/50][1/376] Data 0.117 (0.117) Batch 0.591 (0.591) Remain 00:07:23 loss: 0.2793 Lr: 0.00002 [2024-11-25 19:53:42,091 INFO misc.py line 119 2586773] Train: [49/50][2/376] Data 0.002 (0.002) Batch 0.504 (0.504) Remain 00:06:18 loss: 0.1569 Lr: 0.00002 [2024-11-25 19:53:42,616 INFO misc.py line 119 2586773] Train: [49/50][3/376] Data 0.002 (0.002) Batch 0.525 (0.525) Remain 00:06:33 loss: 0.1671 Lr: 0.00002 [2024-11-25 19:53:43,105 INFO misc.py line 119 2586773] Train: [49/50][4/376] Data 0.002 (0.002) Batch 0.489 (0.489) Remain 00:06:05 loss: 0.1743 Lr: 0.00002 [2024-11-25 19:53:43,638 INFO misc.py line 119 2586773] Train: [49/50][5/376] Data 0.002 (0.002) Batch 0.533 (0.511) Remain 00:06:21 loss: 0.1822 Lr: 0.00002 [2024-11-25 19:53:44,151 INFO misc.py line 119 2586773] Train: [49/50][6/376] Data 0.002 (0.002) Batch 0.513 (0.512) Remain 00:06:21 loss: 0.2301 Lr: 0.00002 [2024-11-25 19:53:44,677 INFO misc.py line 119 2586773] Train: [49/50][7/376] Data 0.002 (0.002) Batch 0.526 (0.515) Remain 00:06:23 loss: 0.1830 Lr: 0.00002 [2024-11-25 19:53:45,165 INFO misc.py line 119 2586773] Train: [49/50][8/376] Data 0.002 (0.002) Batch 0.488 (0.510) Remain 00:06:19 loss: 0.1591 Lr: 0.00002 [2024-11-25 19:53:45,688 INFO misc.py line 119 2586773] Train: [49/50][9/376] Data 0.002 (0.002) Batch 0.523 (0.512) Remain 00:06:20 loss: 0.1949 Lr: 0.00002 [2024-11-25 19:53:46,163 INFO misc.py line 119 2586773] Train: [49/50][10/376] Data 0.002 (0.002) Batch 0.475 (0.507) Remain 00:06:15 loss: 0.2223 Lr: 0.00002 [2024-11-25 19:53:46,660 INFO misc.py line 119 2586773] Train: [49/50][11/376] Data 0.002 (0.002) Batch 0.497 (0.505) Remain 00:06:14 loss: 0.1608 Lr: 0.00002 [2024-11-25 19:53:47,188 INFO misc.py line 119 2586773] Train: [49/50][12/376] Data 0.002 (0.002) Batch 0.528 (0.508) Remain 00:06:15 loss: 0.2276 Lr: 0.00002 [2024-11-25 19:53:47,726 INFO misc.py line 119 2586773] Train: [49/50][13/376] Data 0.002 (0.002) Batch 0.538 (0.511) Remain 00:06:17 loss: 0.1928 Lr: 0.00002 [2024-11-25 19:53:48,195 INFO misc.py line 119 2586773] Train: [49/50][14/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:06:14 loss: 0.2092 Lr: 0.00002 [2024-11-25 19:53:48,743 INFO misc.py line 119 2586773] Train: [49/50][15/376] Data 0.002 (0.002) Batch 0.548 (0.511) Remain 00:06:16 loss: 0.2046 Lr: 0.00002 [2024-11-25 19:53:49,244 INFO misc.py line 119 2586773] Train: [49/50][16/376] Data 0.002 (0.002) Batch 0.501 (0.510) Remain 00:06:15 loss: 0.1666 Lr: 0.00002 [2024-11-25 19:53:49,759 INFO misc.py line 119 2586773] Train: [49/50][17/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 00:06:14 loss: 0.2523 Lr: 0.00002 [2024-11-25 19:53:50,291 INFO misc.py line 119 2586773] Train: [49/50][18/376] Data 0.002 (0.002) Batch 0.531 (0.512) Remain 00:06:15 loss: 0.1638 Lr: 0.00002 [2024-11-25 19:53:50,795 INFO misc.py line 119 2586773] Train: [49/50][19/376] Data 0.002 (0.002) Batch 0.505 (0.511) Remain 00:06:14 loss: 0.1698 Lr: 0.00002 [2024-11-25 19:53:51,277 INFO misc.py line 119 2586773] Train: [49/50][20/376] Data 0.002 (0.002) Batch 0.482 (0.509) Remain 00:06:12 loss: 0.1988 Lr: 0.00002 [2024-11-25 19:53:51,769 INFO misc.py line 119 2586773] Train: [49/50][21/376] Data 0.002 (0.002) Batch 0.492 (0.509) Remain 00:06:11 loss: 0.1676 Lr: 0.00002 [2024-11-25 19:53:52,295 INFO misc.py line 119 2586773] Train: [49/50][22/376] Data 0.002 (0.002) Batch 0.526 (0.509) Remain 00:06:11 loss: 0.2177 Lr: 0.00002 [2024-11-25 19:53:52,817 INFO misc.py line 119 2586773] Train: [49/50][23/376] Data 0.002 (0.002) Batch 0.521 (0.510) Remain 00:06:11 loss: 0.2536 Lr: 0.00002 [2024-11-25 19:53:53,312 INFO misc.py line 119 2586773] Train: [49/50][24/376] Data 0.002 (0.002) Batch 0.495 (0.509) Remain 00:06:10 loss: 0.1969 Lr: 0.00002 [2024-11-25 19:53:53,866 INFO misc.py line 119 2586773] Train: [49/50][25/376] Data 0.002 (0.002) Batch 0.554 (0.511) Remain 00:06:11 loss: 0.2151 Lr: 0.00002 [2024-11-25 19:53:54,360 INFO misc.py line 119 2586773] Train: [49/50][26/376] Data 0.003 (0.002) Batch 0.494 (0.511) Remain 00:06:10 loss: 0.1773 Lr: 0.00002 [2024-11-25 19:53:54,900 INFO misc.py line 119 2586773] Train: [49/50][27/376] Data 0.002 (0.002) Batch 0.540 (0.512) Remain 00:06:11 loss: 0.2079 Lr: 0.00002 [2024-11-25 19:53:55,417 INFO misc.py line 119 2586773] Train: [49/50][28/376] Data 0.002 (0.002) Batch 0.517 (0.512) Remain 00:06:10 loss: 0.2266 Lr: 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19:56:12,602 INFO misc.py line 119 2586773] Train: [49/50][297/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 00:03:52 loss: 0.1503 Lr: 0.00001 [2024-11-25 19:56:13,123 INFO misc.py line 119 2586773] Train: [49/50][298/376] Data 0.002 (0.002) Batch 0.521 (0.510) Remain 00:03:51 loss: 0.1861 Lr: 0.00001 [2024-11-25 19:56:13,647 INFO misc.py line 119 2586773] Train: [49/50][299/376] Data 0.002 (0.002) Batch 0.525 (0.510) Remain 00:03:51 loss: 0.1611 Lr: 0.00001 [2024-11-25 19:56:14,128 INFO misc.py line 119 2586773] Train: [49/50][300/376] Data 0.002 (0.002) Batch 0.481 (0.510) Remain 00:03:50 loss: 0.2295 Lr: 0.00001 [2024-11-25 19:56:14,632 INFO misc.py line 119 2586773] Train: [49/50][301/376] Data 0.002 (0.002) Batch 0.504 (0.510) Remain 00:03:50 loss: 0.1437 Lr: 0.00001 [2024-11-25 19:56:15,103 INFO misc.py line 119 2586773] Train: [49/50][302/376] Data 0.002 (0.002) Batch 0.471 (0.510) Remain 00:03:49 loss: 0.1921 Lr: 0.00001 [2024-11-25 19:56:15,613 INFO misc.py line 119 2586773] Train: [49/50][303/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:03:48 loss: 0.1905 Lr: 0.00001 [2024-11-25 19:56:16,156 INFO misc.py line 119 2586773] Train: [49/50][304/376] Data 0.002 (0.002) Batch 0.543 (0.510) Remain 00:03:48 loss: 0.1432 Lr: 0.00001 [2024-11-25 19:56:16,704 INFO misc.py line 119 2586773] Train: [49/50][305/376] Data 0.002 (0.002) Batch 0.549 (0.510) Remain 00:03:48 loss: 0.1884 Lr: 0.00001 [2024-11-25 19:56:17,225 INFO misc.py line 119 2586773] Train: [49/50][306/376] Data 0.002 (0.002) Batch 0.521 (0.510) Remain 00:03:47 loss: 0.1514 Lr: 0.00001 [2024-11-25 19:56:17,769 INFO misc.py line 119 2586773] Train: [49/50][307/376] Data 0.002 (0.002) Batch 0.544 (0.510) Remain 00:03:47 loss: 0.1571 Lr: 0.00001 [2024-11-25 19:56:18,306 INFO misc.py line 119 2586773] Train: [49/50][308/376] Data 0.002 (0.002) Batch 0.537 (0.510) Remain 00:03:46 loss: 0.2230 Lr: 0.00001 [2024-11-25 19:56:18,788 INFO misc.py line 119 2586773] Train: [49/50][309/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 00:03:46 loss: 0.1967 Lr: 0.00001 [2024-11-25 19:56:19,277 INFO misc.py line 119 2586773] Train: [49/50][310/376] Data 0.003 (0.002) Batch 0.489 (0.510) Remain 00:03:45 loss: 0.1947 Lr: 0.00001 [2024-11-25 19:56:19,777 INFO misc.py line 119 2586773] Train: [49/50][311/376] Data 0.002 (0.002) Batch 0.501 (0.510) Remain 00:03:45 loss: 0.1937 Lr: 0.00001 [2024-11-25 19:56:20,264 INFO misc.py line 119 2586773] Train: [49/50][312/376] Data 0.002 (0.002) Batch 0.487 (0.510) Remain 00:03:44 loss: 0.1845 Lr: 0.00001 [2024-11-25 19:56:20,771 INFO misc.py line 119 2586773] Train: [49/50][313/376] Data 0.002 (0.002) Batch 0.506 (0.510) Remain 00:03:43 loss: 0.1938 Lr: 0.00001 [2024-11-25 19:56:21,286 INFO misc.py line 119 2586773] Train: [49/50][314/376] Data 0.002 (0.002) Batch 0.515 (0.510) Remain 00:03:43 loss: 0.1981 Lr: 0.00001 [2024-11-25 19:56:21,815 INFO misc.py line 119 2586773] Train: [49/50][315/376] Data 0.002 (0.002) Batch 0.529 (0.510) Remain 00:03:42 loss: 0.2507 Lr: 0.00001 [2024-11-25 19:56:22,325 INFO misc.py line 119 2586773] Train: [49/50][316/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:03:42 loss: 0.3019 Lr: 0.00001 [2024-11-25 19:56:22,838 INFO misc.py line 119 2586773] Train: [49/50][317/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 00:03:41 loss: 0.2098 Lr: 0.00001 [2024-11-25 19:56:23,314 INFO misc.py line 119 2586773] Train: [49/50][318/376] Data 0.002 (0.002) Batch 0.477 (0.510) Remain 00:03:41 loss: 0.1790 Lr: 0.00001 [2024-11-25 19:56:23,836 INFO misc.py line 119 2586773] Train: [49/50][319/376] Data 0.002 (0.002) Batch 0.522 (0.510) Remain 00:03:40 loss: 0.1661 Lr: 0.00001 [2024-11-25 19:56:24,342 INFO misc.py line 119 2586773] Train: [49/50][320/376] Data 0.003 (0.002) Batch 0.506 (0.510) Remain 00:03:40 loss: 0.1704 Lr: 0.00001 [2024-11-25 19:56:24,801 INFO misc.py line 119 2586773] Train: [49/50][321/376] Data 0.002 (0.002) Batch 0.459 (0.510) Remain 00:03:39 loss: 0.1700 Lr: 0.00001 [2024-11-25 19:56:25,351 INFO misc.py line 119 2586773] Train: [49/50][322/376] Data 0.002 (0.002) Batch 0.550 (0.510) Remain 00:03:39 loss: 0.1980 Lr: 0.00001 [2024-11-25 19:56:25,823 INFO misc.py line 119 2586773] Train: [49/50][323/376] Data 0.002 (0.002) Batch 0.472 (0.510) Remain 00:03:38 loss: 0.2209 Lr: 0.00001 [2024-11-25 19:56:26,325 INFO misc.py line 119 2586773] Train: [49/50][324/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 00:03:38 loss: 0.1433 Lr: 0.00001 [2024-11-25 19:56:26,889 INFO misc.py line 119 2586773] Train: [49/50][325/376] Data 0.003 (0.002) Batch 0.564 (0.510) Remain 00:03:37 loss: 0.1998 Lr: 0.00001 [2024-11-25 19:56:27,358 INFO misc.py line 119 2586773] Train: [49/50][326/376] Data 0.003 (0.002) Batch 0.469 (0.510) Remain 00:03:37 loss: 0.1609 Lr: 0.00001 [2024-11-25 19:56:27,895 INFO misc.py line 119 2586773] Train: [49/50][327/376] Data 0.002 (0.002) Batch 0.537 (0.510) Remain 00:03:36 loss: 0.1649 Lr: 0.00001 [2024-11-25 19:56:28,413 INFO misc.py line 119 2586773] Train: [49/50][328/376] Data 0.002 (0.002) Batch 0.518 (0.510) Remain 00:03:36 loss: 0.2153 Lr: 0.00001 [2024-11-25 19:56:28,921 INFO misc.py line 119 2586773] Train: [49/50][329/376] Data 0.002 (0.002) Batch 0.508 (0.510) Remain 00:03:35 loss: 0.1690 Lr: 0.00001 [2024-11-25 19:56:29,416 INFO misc.py line 119 2586773] Train: [49/50][330/376] Data 0.002 (0.002) Batch 0.495 (0.510) Remain 00:03:35 loss: 0.1707 Lr: 0.00001 [2024-11-25 19:56:29,928 INFO misc.py line 119 2586773] Train: [49/50][331/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 00:03:34 loss: 0.1492 Lr: 0.00001 [2024-11-25 19:56:30,464 INFO misc.py line 119 2586773] Train: [49/50][332/376] Data 0.002 (0.002) Batch 0.536 (0.510) Remain 00:03:34 loss: 0.2043 Lr: 0.00001 [2024-11-25 19:56:30,965 INFO misc.py line 119 2586773] Train: [49/50][333/376] Data 0.002 (0.002) Batch 0.502 (0.510) Remain 00:03:33 loss: 0.2271 Lr: 0.00001 [2024-11-25 19:56:31,418 INFO misc.py line 119 2586773] Train: [49/50][334/376] Data 0.002 (0.002) Batch 0.453 (0.510) Remain 00:03:33 loss: 0.1823 Lr: 0.00001 [2024-11-25 19:56:31,941 INFO misc.py line 119 2586773] Train: [49/50][335/376] Data 0.002 (0.002) Batch 0.523 (0.510) Remain 00:03:32 loss: 0.1716 Lr: 0.00001 [2024-11-25 19:56:32,451 INFO misc.py line 119 2586773] Train: [49/50][336/376] Data 0.002 (0.002) Batch 0.510 (0.510) Remain 00:03:32 loss: 0.2321 Lr: 0.00001 [2024-11-25 19:56:32,936 INFO misc.py line 119 2586773] Train: [49/50][337/376] Data 0.002 (0.002) Batch 0.485 (0.510) Remain 00:03:31 loss: 0.1748 Lr: 0.00001 [2024-11-25 19:56:33,418 INFO misc.py line 119 2586773] Train: [49/50][338/376] Data 0.002 (0.002) Batch 0.482 (0.510) Remain 00:03:31 loss: 0.1737 Lr: 0.00001 [2024-11-25 19:56:33,951 INFO misc.py line 119 2586773] Train: [49/50][339/376] Data 0.002 (0.002) Batch 0.533 (0.510) Remain 00:03:30 loss: 0.1468 Lr: 0.00001 [2024-11-25 19:56:34,443 INFO misc.py line 119 2586773] Train: [49/50][340/376] Data 0.002 (0.002) Batch 0.491 (0.510) Remain 00:03:30 loss: 0.1602 Lr: 0.00001 [2024-11-25 19:56:34,929 INFO misc.py line 119 2586773] Train: [49/50][341/376] Data 0.002 (0.002) Batch 0.486 (0.510) Remain 00:03:29 loss: 0.2019 Lr: 0.00001 [2024-11-25 19:56:35,439 INFO misc.py line 119 2586773] Train: [49/50][342/376] Data 0.003 (0.002) Batch 0.510 (0.510) Remain 00:03:29 loss: 0.2042 Lr: 0.00001 [2024-11-25 19:56:35,918 INFO misc.py line 119 2586773] Train: [49/50][343/376] Data 0.002 (0.002) Batch 0.479 (0.510) Remain 00:03:28 loss: 0.2252 Lr: 0.00001 [2024-11-25 19:56:36,407 INFO misc.py line 119 2586773] Train: [49/50][344/376] Data 0.003 (0.002) Batch 0.490 (0.510) Remain 00:03:27 loss: 0.1459 Lr: 0.00001 [2024-11-25 19:56:36,932 INFO misc.py line 119 2586773] Train: [49/50][345/376] Data 0.002 (0.002) Batch 0.524 (0.510) Remain 00:03:27 loss: 0.2085 Lr: 0.00001 [2024-11-25 19:56:37,426 INFO misc.py line 119 2586773] Train: [49/50][346/376] Data 0.002 (0.002) Batch 0.494 (0.510) Remain 00:03:26 loss: 0.1629 Lr: 0.00001 [2024-11-25 19:56:37,910 INFO misc.py line 119 2586773] Train: [49/50][347/376] Data 0.002 (0.002) Batch 0.484 (0.510) Remain 00:03:26 loss: 0.1993 Lr: 0.00001 [2024-11-25 19:56:38,452 INFO misc.py line 119 2586773] Train: [49/50][348/376] Data 0.002 (0.002) Batch 0.543 (0.510) Remain 00:03:25 loss: 0.1988 Lr: 0.00001 [2024-11-25 19:56:38,943 INFO misc.py line 119 2586773] Train: [49/50][349/376] Data 0.002 (0.002) Batch 0.490 (0.510) Remain 00:03:25 loss: 0.1685 Lr: 0.00001 [2024-11-25 19:56:39,454 INFO misc.py line 119 2586773] Train: [49/50][350/376] Data 0.002 (0.002) Batch 0.512 (0.510) Remain 00:03:24 loss: 0.1604 Lr: 0.00001 [2024-11-25 19:56:39,991 INFO misc.py line 119 2586773] Train: [49/50][351/376] Data 0.003 (0.002) Batch 0.537 (0.510) Remain 00:03:24 loss: 0.2810 Lr: 0.00001 [2024-11-25 19:56:40,485 INFO misc.py line 119 2586773] Train: [49/50][352/376] Data 0.003 (0.002) Batch 0.494 (0.510) Remain 00:03:23 loss: 0.1586 Lr: 0.00001 [2024-11-25 19:56:40,955 INFO misc.py line 119 2586773] Train: [49/50][353/376] Data 0.003 (0.002) Batch 0.469 (0.510) Remain 00:03:23 loss: 0.1720 Lr: 0.00001 [2024-11-25 19:56:41,443 INFO misc.py line 119 2586773] Train: [49/50][354/376] Data 0.003 (0.002) Batch 0.488 (0.509) Remain 00:03:22 loss: 0.2322 Lr: 0.00001 [2024-11-25 19:56:41,913 INFO misc.py line 119 2586773] Train: [49/50][355/376] Data 0.002 (0.002) Batch 0.471 (0.509) Remain 00:03:22 loss: 0.2064 Lr: 0.00001 [2024-11-25 19:56:42,402 INFO misc.py line 119 2586773] Train: [49/50][356/376] Data 0.002 (0.002) Batch 0.489 (0.509) Remain 00:03:21 loss: 0.1949 Lr: 0.00001 [2024-11-25 19:56:42,950 INFO misc.py line 119 2586773] Train: [49/50][357/376] Data 0.002 (0.002) Batch 0.549 (0.509) Remain 00:03:21 loss: 0.2456 Lr: 0.00001 [2024-11-25 19:56:43,455 INFO misc.py line 119 2586773] Train: [49/50][358/376] Data 0.002 (0.002) Batch 0.505 (0.509) Remain 00:03:20 loss: 0.2685 Lr: 0.00001 [2024-11-25 19:56:43,946 INFO misc.py line 119 2586773] Train: [49/50][359/376] Data 0.002 (0.002) Batch 0.491 (0.509) Remain 00:03:20 loss: 0.1803 Lr: 0.00001 [2024-11-25 19:56:44,401 INFO misc.py line 119 2586773] Train: [49/50][360/376] Data 0.002 (0.002) Batch 0.455 (0.509) Remain 00:03:19 loss: 0.2039 Lr: 0.00001 [2024-11-25 19:56:44,927 INFO misc.py line 119 2586773] Train: [49/50][361/376] Data 0.002 (0.002) Batch 0.525 (0.509) Remain 00:03:19 loss: 0.1666 Lr: 0.00001 [2024-11-25 19:56:45,442 INFO misc.py line 119 2586773] Train: [49/50][362/376] Data 0.002 (0.002) Batch 0.516 (0.509) Remain 00:03:18 loss: 0.1662 Lr: 0.00001 [2024-11-25 19:56:45,970 INFO misc.py line 119 2586773] Train: [49/50][363/376] Data 0.002 (0.002) Batch 0.527 (0.509) Remain 00:03:18 loss: 0.1657 Lr: 0.00001 [2024-11-25 19:56:46,482 INFO misc.py line 119 2586773] Train: [49/50][364/376] Data 0.002 (0.002) Batch 0.512 (0.509) Remain 00:03:17 loss: 0.1435 Lr: 0.00001 [2024-11-25 19:56:46,959 INFO misc.py line 119 2586773] Train: [49/50][365/376] Data 0.002 (0.002) Batch 0.477 (0.509) Remain 00:03:17 loss: 0.1593 Lr: 0.00001 [2024-11-25 19:56:47,504 INFO misc.py line 119 2586773] Train: [49/50][366/376] Data 0.002 (0.002) Batch 0.545 (0.509) Remain 00:03:16 loss: 0.2201 Lr: 0.00001 [2024-11-25 19:56:48,000 INFO misc.py line 119 2586773] Train: [49/50][367/376] Data 0.002 (0.002) Batch 0.496 (0.509) Remain 00:03:16 loss: 0.1859 Lr: 0.00001 [2024-11-25 19:56:48,537 INFO misc.py line 119 2586773] Train: [49/50][368/376] Data 0.002 (0.002) Batch 0.537 (0.509) Remain 00:03:15 loss: 0.2203 Lr: 0.00001 [2024-11-25 19:56:49,070 INFO misc.py line 119 2586773] Train: [49/50][369/376] Data 0.002 (0.002) Batch 0.533 (0.509) Remain 00:03:15 loss: 0.1720 Lr: 0.00001 [2024-11-25 19:56:49,547 INFO misc.py line 119 2586773] Train: [49/50][370/376] Data 0.002 (0.002) Batch 0.476 (0.509) Remain 00:03:14 loss: 0.1788 Lr: 0.00001 [2024-11-25 19:56:50,023 INFO misc.py line 119 2586773] Train: [49/50][371/376] Data 0.002 (0.002) Batch 0.477 (0.509) Remain 00:03:14 loss: 0.1744 Lr: 0.00001 [2024-11-25 19:56:50,523 INFO misc.py line 119 2586773] Train: [49/50][372/376] Data 0.002 (0.002) Batch 0.499 (0.509) Remain 00:03:13 loss: 0.1751 Lr: 0.00001 [2024-11-25 19:56:51,010 INFO misc.py line 119 2586773] Train: [49/50][373/376] Data 0.002 (0.002) Batch 0.487 (0.509) Remain 00:03:12 loss: 0.2504 Lr: 0.00001 [2024-11-25 19:56:51,494 INFO misc.py line 119 2586773] Train: [49/50][374/376] Data 0.002 (0.002) Batch 0.484 (0.509) Remain 00:03:12 loss: 0.2133 Lr: 0.00001 [2024-11-25 19:56:51,966 INFO misc.py line 119 2586773] Train: [49/50][375/376] Data 0.002 (0.002) Batch 0.472 (0.509) Remain 00:03:11 loss: 0.2250 Lr: 0.00001 [2024-11-25 19:56:52,435 INFO misc.py line 119 2586773] Train: [49/50][376/376] Data 0.002 (0.002) Batch 0.470 (0.509) Remain 00:03:11 loss: 0.3226 Lr: 0.00001 [2024-11-25 19:56:52,436 INFO misc.py line 136 2586773] Train result: loss: 0.1950 [2024-11-25 19:56:52,437 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 19:57:03,088 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1805 [2024-11-25 19:57:03,349 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2265 [2024-11-25 19:57:03,628 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2540 [2024-11-25 19:57:03,852 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1950 [2024-11-25 19:57:04,117 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2797 [2024-11-25 19:57:04,389 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1821 [2024-11-25 19:57:04,610 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.1911 [2024-11-25 19:57:04,880 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2155 [2024-11-25 19:57:05,104 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2581 [2024-11-25 19:57:05,363 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2272 [2024-11-25 19:57:05,595 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2053 [2024-11-25 19:57:05,867 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2459 [2024-11-25 19:57:06,131 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2510 [2024-11-25 19:57:06,393 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2176 [2024-11-25 19:57:06,626 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2241 [2024-11-25 19:57:06,864 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3009 [2024-11-25 19:57:07,131 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2666 [2024-11-25 19:57:07,376 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.1922 [2024-11-25 19:57:07,607 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2420 [2024-11-25 19:57:07,871 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2125 [2024-11-25 19:57:08,104 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2445 [2024-11-25 19:57:08,369 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2526 [2024-11-25 19:57:08,604 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2083 [2024-11-25 19:57:08,871 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2228 [2024-11-25 19:57:09,132 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2146 [2024-11-25 19:57:09,370 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2458 [2024-11-25 19:57:09,623 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2454 [2024-11-25 19:57:09,867 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2193 [2024-11-25 19:57:10,133 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2650 [2024-11-25 19:57:10,386 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2785 [2024-11-25 19:57:10,620 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2584 [2024-11-25 19:57:10,882 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1963 [2024-11-25 19:57:11,101 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2648 [2024-11-25 19:57:11,341 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2284 [2024-11-25 19:57:11,603 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.1990 [2024-11-25 19:57:11,847 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2453 [2024-11-25 19:57:12,073 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1880 [2024-11-25 19:57:12,341 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2260 [2024-11-25 19:57:12,570 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2643 [2024-11-25 19:57:12,805 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2211 [2024-11-25 19:57:13,076 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3199 [2024-11-25 19:57:13,326 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2625 [2024-11-25 19:57:13,565 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2406 [2024-11-25 19:57:13,798 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2091 [2024-11-25 19:57:14,034 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2308 [2024-11-25 19:57:14,283 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2178 [2024-11-25 19:57:14,543 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2143 [2024-11-25 19:57:14,792 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2839 [2024-11-25 19:57:15,017 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2117 [2024-11-25 19:57:15,252 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2145 [2024-11-25 19:57:15,474 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2415 [2024-11-25 19:57:15,727 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2176 [2024-11-25 19:57:15,993 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2149 [2024-11-25 19:57:16,254 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3111 [2024-11-25 19:57:16,485 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2288 [2024-11-25 19:57:16,724 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2299 [2024-11-25 19:57:16,981 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2449 [2024-11-25 19:57:17,247 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2714 [2024-11-25 19:57:17,502 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2351 [2024-11-25 19:57:17,764 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2266 [2024-11-25 19:57:18,015 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2058 [2024-11-25 19:57:18,288 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2264 [2024-11-25 19:57:18,518 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2312 [2024-11-25 19:57:18,777 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2499 [2024-11-25 19:57:19,043 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2636 [2024-11-25 19:57:19,310 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1937 [2024-11-25 19:57:19,556 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1830 [2024-11-25 19:57:19,812 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2552 [2024-11-25 19:57:20,080 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2435 [2024-11-25 19:57:20,345 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2728 [2024-11-25 19:57:20,588 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.2023 [2024-11-25 19:57:20,822 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2725 [2024-11-25 19:57:21,077 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2453 [2024-11-25 19:57:21,323 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2498 [2024-11-25 19:57:21,540 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2557 [2024-11-25 19:57:21,762 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2100 [2024-11-25 19:57:22,031 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2558 [2024-11-25 19:57:22,268 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2113 [2024-11-25 19:57:22,526 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2327 [2024-11-25 19:57:22,777 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2778 [2024-11-25 19:57:23,021 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2320 [2024-11-25 19:57:23,286 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2614 [2024-11-25 19:57:23,538 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1828 [2024-11-25 19:57:23,784 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2393 [2024-11-25 19:57:24,054 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2426 [2024-11-25 19:57:24,295 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2654 [2024-11-25 19:57:24,559 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2382 [2024-11-25 19:57:24,820 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2256 [2024-11-25 19:57:25,067 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2731 [2024-11-25 19:57:25,315 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2533 [2024-11-25 19:57:25,548 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2475 [2024-11-25 19:57:25,800 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2625 [2024-11-25 19:57:26,070 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2460 [2024-11-25 19:57:26,334 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1910 [2024-11-25 19:57:26,600 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2197 [2024-11-25 19:57:26,848 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2019 [2024-11-25 19:57:27,117 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2099 [2024-11-25 19:57:27,339 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2999 [2024-11-25 19:57:27,611 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2469 [2024-11-25 19:57:27,847 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2472 [2024-11-25 19:57:28,117 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1991 [2024-11-25 19:57:28,378 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2580 [2024-11-25 19:57:28,638 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2316 [2024-11-25 19:57:28,891 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2621 [2024-11-25 19:57:29,112 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2376 [2024-11-25 19:57:29,353 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2167 [2024-11-25 19:57:29,612 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2241 [2024-11-25 19:57:29,880 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2365 [2024-11-25 19:57:30,114 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2483 [2024-11-25 19:57:30,379 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.1962 [2024-11-25 19:57:30,647 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2395 [2024-11-25 19:57:30,869 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2218 [2024-11-25 19:57:31,105 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.2002 [2024-11-25 19:57:31,324 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2106 [2024-11-25 19:57:31,550 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2113 [2024-11-25 19:57:31,820 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2867 [2024-11-25 19:57:32,078 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2563 [2024-11-25 19:57:32,345 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2300 [2024-11-25 19:57:32,610 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2140 [2024-11-25 19:57:32,872 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.3016 [2024-11-25 19:57:33,129 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2813 [2024-11-25 19:57:33,396 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.1998 [2024-11-25 19:57:33,655 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2446 [2024-11-25 19:57:33,917 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2237 [2024-11-25 19:57:34,180 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2407 [2024-11-25 19:57:34,431 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2378 [2024-11-25 19:57:34,662 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1973 [2024-11-25 19:57:34,920 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2411 [2024-11-25 19:57:35,154 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2500 [2024-11-25 19:57:35,381 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1859 [2024-11-25 19:57:35,595 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2178 [2024-11-25 19:57:35,812 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1815 [2024-11-25 19:57:36,446 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7777/0.8533/0.9963. [2024-11-25 19:57:36,446 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9982 [2024-11-25 19:57:36,446 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5590/0.7084 [2024-11-25 19:57:36,447 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 19:57:36,447 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 19:57:36,447 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 19:57:39,081 INFO misc.py line 119 2586773] Train: [50/50][1/376] Data 0.099 (0.099) Batch 0.604 (0.604) Remain 00:03:46 loss: 0.1882 Lr: 0.00001 [2024-11-25 19:57:39,577 INFO misc.py line 119 2586773] Train: [50/50][2/376] Data 0.003 (0.003) Batch 0.496 (0.496) Remain 00:03:05 loss: 0.1964 Lr: 0.00001 [2024-11-25 19:57:40,096 INFO misc.py line 119 2586773] Train: [50/50][3/376] Data 0.002 (0.002) Batch 0.519 (0.519) Remain 00:03:13 loss: 0.1887 Lr: 0.00001 [2024-11-25 19:57:40,631 INFO misc.py line 119 2586773] Train: [50/50][4/376] Data 0.002 (0.002) Batch 0.535 (0.535) Remain 00:03:19 loss: 0.1879 Lr: 0.00001 [2024-11-25 19:57:41,102 INFO misc.py line 119 2586773] Train: [50/50][5/376] Data 0.002 (0.002) Batch 0.471 (0.503) Remain 00:03:06 loss: 0.1433 Lr: 0.00001 [2024-11-25 19:57:41,672 INFO misc.py line 119 2586773] Train: [50/50][6/376] Data 0.002 (0.002) Batch 0.570 (0.525) Remain 00:03:14 loss: 0.1943 Lr: 0.00001 [2024-11-25 19:57:42,166 INFO misc.py line 119 2586773] Train: [50/50][7/376] Data 0.003 (0.002) Batch 0.494 (0.518) Remain 00:03:10 loss: 0.1669 Lr: 0.00001 [2024-11-25 19:57:42,692 INFO misc.py line 119 2586773] Train: [50/50][8/376] Data 0.002 (0.002) Batch 0.525 (0.519) Remain 00:03:11 loss: 0.1985 Lr: 0.00001 [2024-11-25 19:57:43,194 INFO misc.py line 119 2586773] Train: [50/50][9/376] Data 0.003 (0.002) Batch 0.502 (0.516) Remain 00:03:09 loss: 0.2005 Lr: 0.00001 [2024-11-25 19:57:43,678 INFO misc.py line 119 2586773] Train: [50/50][10/376] Data 0.002 (0.002) Batch 0.484 (0.512) Remain 00:03:07 loss: 0.1934 Lr: 0.00001 [2024-11-25 19:57:44,196 INFO misc.py line 119 2586773] Train: [50/50][11/376] Data 0.003 (0.002) Batch 0.519 (0.513) Remain 00:03:07 loss: 0.1523 Lr: 0.00001 [2024-11-25 19:57:44,719 INFO misc.py line 119 2586773] Train: [50/50][12/376] Data 0.002 (0.002) Batch 0.522 (0.514) Remain 00:03:06 loss: 0.2179 Lr: 0.00001 [2024-11-25 19:57:45,226 INFO misc.py line 119 2586773] Train: [50/50][13/376] Data 0.002 (0.002) Batch 0.508 (0.513) Remain 00:03:06 loss: 0.1951 Lr: 0.00001 [2024-11-25 19:57:45,773 INFO misc.py line 119 2586773] Train: [50/50][14/376] Data 0.002 (0.002) Batch 0.547 (0.516) Remain 00:03:06 loss: 0.1881 Lr: 0.00001 [2024-11-25 19:57:46,255 INFO misc.py line 119 2586773] Train: [50/50][15/376] Data 0.002 (0.002) Batch 0.482 (0.513) Remain 00:03:05 loss: 0.2611 Lr: 0.00001 [2024-11-25 19:57:46,802 INFO misc.py line 119 2586773] Train: [50/50][16/376] Data 0.002 (0.002) Batch 0.547 (0.516) Remain 00:03:05 loss: 0.1915 Lr: 0.00001 [2024-11-25 19:57:47,307 INFO misc.py line 119 2586773] Train: [50/50][17/376] Data 0.003 (0.002) Batch 0.505 (0.515) Remain 00:03:04 loss: 0.1844 Lr: 0.00001 [2024-11-25 19:57:47,796 INFO misc.py line 119 2586773] Train: [50/50][18/376] Data 0.003 (0.002) Batch 0.489 (0.513) Remain 00:03:03 loss: 0.2102 Lr: 0.00001 [2024-11-25 19:57:48,327 INFO misc.py line 119 2586773] Train: [50/50][19/376] Data 0.002 (0.002) Batch 0.531 (0.514) Remain 00:03:03 loss: 0.2082 Lr: 0.00001 [2024-11-25 19:57:48,804 INFO misc.py line 119 2586773] Train: [50/50][20/376] Data 0.003 (0.002) Batch 0.477 (0.512) Remain 00:03:02 loss: 0.1623 Lr: 0.00001 [2024-11-25 19:57:49,290 INFO misc.py line 119 2586773] Train: [50/50][21/376] Data 0.002 (0.002) Batch 0.486 (0.511) Remain 00:03:01 loss: 0.1923 Lr: 0.00001 [2024-11-25 19:57:49,779 INFO misc.py line 119 2586773] Train: [50/50][22/376] Data 0.002 (0.002) Batch 0.489 (0.510) Remain 00:03:00 loss: 0.1700 Lr: 0.00001 [2024-11-25 19:57:50,285 INFO misc.py line 119 2586773] Train: [50/50][23/376] Data 0.002 (0.002) Batch 0.506 (0.509) Remain 00:02:59 loss: 0.1852 Lr: 0.00001 [2024-11-25 19:57:50,769 INFO misc.py line 119 2586773] Train: [50/50][24/376] Data 0.002 (0.002) Batch 0.484 (0.508) Remain 00:02:58 loss: 0.1792 Lr: 0.00001 [2024-11-25 19:57:51,254 INFO misc.py line 119 2586773] Train: [50/50][25/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:02:58 loss: 0.1775 Lr: 0.00001 [2024-11-25 19:57:51,777 INFO misc.py line 119 2586773] Train: [50/50][26/376] Data 0.002 (0.002) Batch 0.523 (0.508) Remain 00:02:57 loss: 0.1627 Lr: 0.00001 [2024-11-25 19:57:52,274 INFO misc.py line 119 2586773] Train: [50/50][27/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:02:57 loss: 0.1727 Lr: 0.00001 [2024-11-25 19:57:52,747 INFO misc.py line 119 2586773] Train: [50/50][28/376] Data 0.002 (0.002) Batch 0.473 (0.506) Remain 00:02:56 loss: 0.2356 Lr: 0.00001 [2024-11-25 19:57:53,255 INFO misc.py line 119 2586773] Train: [50/50][29/376] Data 0.002 (0.002) Batch 0.507 (0.506) Remain 00:02:55 loss: 0.1878 Lr: 0.00001 [2024-11-25 19:57:53,771 INFO misc.py line 119 2586773] Train: [50/50][30/376] Data 0.002 (0.002) Batch 0.516 (0.506) Remain 00:02:55 loss: 0.2108 Lr: 0.00001 [2024-11-25 19:57:54,264 INFO misc.py line 119 2586773] Train: [50/50][31/376] Data 0.002 (0.002) Batch 0.493 (0.506) Remain 00:02:54 loss: 0.1719 Lr: 0.00001 [2024-11-25 19:57:54,746 INFO misc.py line 119 2586773] Train: [50/50][32/376] Data 0.002 (0.002) Batch 0.482 (0.505) Remain 00:02:53 loss: 0.2002 Lr: 0.00001 [2024-11-25 19:57:55,245 INFO misc.py line 119 2586773] Train: [50/50][33/376] Data 0.002 (0.002) Batch 0.499 (0.505) Remain 00:02:53 loss: 0.1434 Lr: 0.00001 [2024-11-25 19:57:55,762 INFO misc.py line 119 2586773] Train: [50/50][34/376] Data 0.002 (0.002) Batch 0.518 (0.505) Remain 00:02:52 loss: 0.1860 Lr: 0.00001 [2024-11-25 19:57:56,269 INFO misc.py line 119 2586773] Train: [50/50][35/376] Data 0.002 (0.002) Batch 0.507 (0.505) Remain 00:02:52 loss: 0.1732 Lr: 0.00001 [2024-11-25 19:57:56,785 INFO misc.py line 119 2586773] Train: [50/50][36/376] Data 0.003 (0.002) Batch 0.516 (0.506) Remain 00:02:51 loss: 0.1423 Lr: 0.00001 [2024-11-25 19:57:57,275 INFO misc.py line 119 2586773] Train: [50/50][37/376] Data 0.002 (0.002) Batch 0.490 (0.505) Remain 00:02:51 loss: 0.2542 Lr: 0.00001 [2024-11-25 19:57:57,783 INFO misc.py line 119 2586773] Train: [50/50][38/376] Data 0.002 (0.002) Batch 0.508 (0.505) Remain 00:02:50 loss: 0.1682 Lr: 0.00001 [2024-11-25 19:57:58,258 INFO misc.py line 119 2586773] Train: [50/50][39/376] Data 0.002 (0.002) Batch 0.475 (0.504) Remain 00:02:50 loss: 0.1697 Lr: 0.00001 [2024-11-25 19:57:58,761 INFO misc.py line 119 2586773] Train: [50/50][40/376] Data 0.006 (0.002) Batch 0.503 (0.504) Remain 00:02:49 loss: 0.1654 Lr: 0.00001 [2024-11-25 19:57:59,272 INFO misc.py line 119 2586773] Train: [50/50][41/376] Data 0.002 (0.002) Batch 0.511 (0.505) Remain 00:02:49 loss: 0.1941 Lr: 0.00001 [2024-11-25 19:57:59,755 INFO misc.py line 119 2586773] Train: [50/50][42/376] Data 0.002 (0.002) Batch 0.482 (0.504) Remain 00:02:48 loss: 0.1846 Lr: 0.00001 [2024-11-25 19:58:00,303 INFO misc.py line 119 2586773] Train: [50/50][43/376] Data 0.002 (0.002) Batch 0.549 (0.505) Remain 00:02:48 loss: 0.1967 Lr: 0.00001 [2024-11-25 19:58:00,807 INFO misc.py line 119 2586773] Train: [50/50][44/376] Data 0.003 (0.002) Batch 0.504 (0.505) Remain 00:02:47 loss: 0.2327 Lr: 0.00001 [2024-11-25 19:58:01,266 INFO misc.py line 119 2586773] Train: [50/50][45/376] Data 0.002 (0.002) Batch 0.459 (0.504) Remain 00:02:46 loss: 0.3449 Lr: 0.00001 [2024-11-25 19:58:01,749 INFO misc.py line 119 2586773] Train: [50/50][46/376] Data 0.002 (0.002) Batch 0.483 (0.504) Remain 00:02:46 loss: 0.2730 Lr: 0.00001 [2024-11-25 19:58:02,274 INFO misc.py line 119 2586773] Train: [50/50][47/376] Data 0.002 (0.002) Batch 0.525 (0.504) Remain 00:02:45 loss: 0.1464 Lr: 0.00001 [2024-11-25 19:58:02,766 INFO misc.py line 119 2586773] Train: [50/50][48/376] Data 0.002 (0.002) Batch 0.492 (0.504) Remain 00:02:45 loss: 0.1752 Lr: 0.00001 [2024-11-25 19:58:03,274 INFO misc.py line 119 2586773] Train: [50/50][49/376] Data 0.003 (0.002) Batch 0.508 (0.504) Remain 00:02:44 loss: 0.1675 Lr: 0.00001 [2024-11-25 19:58:03,758 INFO misc.py line 119 2586773] Train: [50/50][50/376] Data 0.002 (0.002) Batch 0.484 (0.503) Remain 00:02:44 loss: 0.1664 Lr: 0.00001 [2024-11-25 19:58:04,282 INFO misc.py line 119 2586773] Train: [50/50][51/376] Data 0.002 (0.002) Batch 0.524 (0.504) Remain 00:02:43 loss: 0.2136 Lr: 0.00001 [2024-11-25 19:58:04,817 INFO misc.py line 119 2586773] Train: [50/50][52/376] Data 0.002 (0.002) Batch 0.535 (0.505) Remain 00:02:43 loss: 0.2108 Lr: 0.00001 [2024-11-25 19:58:05,338 INFO misc.py line 119 2586773] Train: [50/50][53/376] Data 0.002 (0.002) Batch 0.521 (0.505) Remain 00:02:43 loss: 0.3094 Lr: 0.00001 [2024-11-25 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[2024-11-25 20:00:21,803 INFO misc.py line 119 2586773] Train: [50/50][322/376] Data 0.002 (0.002) Batch 0.463 (0.507) Remain 00:00:27 loss: 0.2229 Lr: 0.00000 [2024-11-25 20:00:22,292 INFO misc.py line 119 2586773] Train: [50/50][323/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:00:26 loss: 0.2141 Lr: 0.00000 [2024-11-25 20:00:22,770 INFO misc.py line 119 2586773] Train: [50/50][324/376] Data 0.002 (0.002) Batch 0.477 (0.507) Remain 00:00:26 loss: 0.1776 Lr: 0.00000 [2024-11-25 20:00:23,257 INFO misc.py line 119 2586773] Train: [50/50][325/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:00:25 loss: 0.1942 Lr: 0.00000 [2024-11-25 20:00:23,750 INFO misc.py line 119 2586773] Train: [50/50][326/376] Data 0.002 (0.002) Batch 0.493 (0.507) Remain 00:00:25 loss: 0.1600 Lr: 0.00000 [2024-11-25 20:00:24,239 INFO misc.py line 119 2586773] Train: [50/50][327/376] Data 0.002 (0.002) Batch 0.489 (0.507) Remain 00:00:24 loss: 0.1609 Lr: 0.00000 [2024-11-25 20:00:24,782 INFO misc.py line 119 2586773] Train: [50/50][328/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 00:00:24 loss: 0.1976 Lr: 0.00000 [2024-11-25 20:00:25,321 INFO misc.py line 119 2586773] Train: [50/50][329/376] Data 0.002 (0.002) Batch 0.539 (0.507) Remain 00:00:23 loss: 0.1953 Lr: 0.00000 [2024-11-25 20:00:25,862 INFO misc.py line 119 2586773] Train: [50/50][330/376] Data 0.002 (0.002) Batch 0.541 (0.507) Remain 00:00:23 loss: 0.2306 Lr: 0.00000 [2024-11-25 20:00:26,365 INFO misc.py line 119 2586773] Train: [50/50][331/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:00:22 loss: 0.1808 Lr: 0.00000 [2024-11-25 20:00:26,889 INFO misc.py line 119 2586773] Train: [50/50][332/376] Data 0.002 (0.002) Batch 0.524 (0.507) Remain 00:00:22 loss: 0.1677 Lr: 0.00000 [2024-11-25 20:00:27,419 INFO misc.py line 119 2586773] Train: [50/50][333/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 00:00:21 loss: 0.2571 Lr: 0.00000 [2024-11-25 20:00:27,949 INFO misc.py line 119 2586773] Train: [50/50][334/376] Data 0.002 (0.002) Batch 0.530 (0.507) Remain 00:00:21 loss: 0.1718 Lr: 0.00000 [2024-11-25 20:00:28,439 INFO misc.py line 119 2586773] Train: [50/50][335/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:00:20 loss: 0.1962 Lr: 0.00000 [2024-11-25 20:00:28,982 INFO misc.py line 119 2586773] Train: [50/50][336/376] Data 0.002 (0.002) Batch 0.543 (0.507) Remain 00:00:20 loss: 0.2907 Lr: 0.00000 [2024-11-25 20:00:29,469 INFO misc.py line 119 2586773] Train: [50/50][337/376] Data 0.002 (0.002) Batch 0.487 (0.507) Remain 00:00:19 loss: 0.2200 Lr: 0.00000 [2024-11-25 20:00:29,916 INFO misc.py line 119 2586773] Train: [50/50][338/376] Data 0.002 (0.002) Batch 0.447 (0.507) Remain 00:00:19 loss: 0.2084 Lr: 0.00000 [2024-11-25 20:00:30,421 INFO misc.py line 119 2586773] Train: [50/50][339/376] Data 0.002 (0.002) Batch 0.505 (0.507) Remain 00:00:18 loss: 0.2159 Lr: 0.00000 [2024-11-25 20:00:31,012 INFO misc.py line 119 2586773] Train: [50/50][340/376] Data 0.002 (0.002) Batch 0.592 (0.507) Remain 00:00:18 loss: 0.1780 Lr: 0.00000 [2024-11-25 20:00:31,497 INFO misc.py line 119 2586773] Train: [50/50][341/376] Data 0.002 (0.002) Batch 0.485 (0.507) Remain 00:00:17 loss: 0.2027 Lr: 0.00000 [2024-11-25 20:00:32,025 INFO misc.py line 119 2586773] Train: [50/50][342/376] Data 0.002 (0.002) Batch 0.528 (0.507) Remain 00:00:17 loss: 0.2572 Lr: 0.00000 [2024-11-25 20:00:32,558 INFO misc.py line 119 2586773] Train: [50/50][343/376] Data 0.002 (0.002) Batch 0.533 (0.507) Remain 00:00:16 loss: 0.1878 Lr: 0.00000 [2024-11-25 20:00:33,072 INFO misc.py line 119 2586773] Train: [50/50][344/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 00:00:16 loss: 0.2692 Lr: 0.00000 [2024-11-25 20:00:33,597 INFO misc.py line 119 2586773] Train: [50/50][345/376] Data 0.002 (0.002) Batch 0.525 (0.507) Remain 00:00:15 loss: 0.1825 Lr: 0.00000 [2024-11-25 20:00:34,121 INFO misc.py line 119 2586773] Train: [50/50][346/376] Data 0.002 (0.002) Batch 0.523 (0.507) Remain 00:00:15 loss: 0.2106 Lr: 0.00000 [2024-11-25 20:00:34,619 INFO misc.py line 119 2586773] Train: [50/50][347/376] Data 0.002 (0.002) Batch 0.498 (0.507) Remain 00:00:14 loss: 0.1929 Lr: 0.00000 [2024-11-25 20:00:35,107 INFO misc.py line 119 2586773] Train: [50/50][348/376] Data 0.003 (0.002) Batch 0.488 (0.507) Remain 00:00:14 loss: 0.2086 Lr: 0.00000 [2024-11-25 20:00:35,628 INFO misc.py line 119 2586773] Train: [50/50][349/376] Data 0.003 (0.002) Batch 0.521 (0.507) Remain 00:00:13 loss: 0.1661 Lr: 0.00000 [2024-11-25 20:00:36,172 INFO misc.py line 119 2586773] Train: [50/50][350/376] Data 0.003 (0.002) Batch 0.544 (0.507) Remain 00:00:13 loss: 0.1659 Lr: 0.00000 [2024-11-25 20:00:36,678 INFO misc.py line 119 2586773] Train: [50/50][351/376] Data 0.003 (0.002) Batch 0.507 (0.507) Remain 00:00:12 loss: 0.3193 Lr: 0.00000 [2024-11-25 20:00:37,165 INFO misc.py line 119 2586773] Train: [50/50][352/376] Data 0.003 (0.002) Batch 0.487 (0.507) Remain 00:00:12 loss: 0.2257 Lr: 0.00000 [2024-11-25 20:00:37,680 INFO misc.py line 119 2586773] Train: [50/50][353/376] Data 0.002 (0.002) Batch 0.515 (0.507) Remain 00:00:11 loss: 0.1445 Lr: 0.00000 [2024-11-25 20:00:38,161 INFO misc.py line 119 2586773] Train: [50/50][354/376] Data 0.002 (0.002) Batch 0.481 (0.507) Remain 00:00:11 loss: 0.1330 Lr: 0.00000 [2024-11-25 20:00:38,658 INFO misc.py line 119 2586773] Train: [50/50][355/376] Data 0.002 (0.002) Batch 0.497 (0.507) Remain 00:00:10 loss: 0.1823 Lr: 0.00000 [2024-11-25 20:00:39,149 INFO misc.py line 119 2586773] Train: [50/50][356/376] Data 0.002 (0.002) Batch 0.491 (0.507) Remain 00:00:10 loss: 0.1722 Lr: 0.00000 [2024-11-25 20:00:39,632 INFO misc.py line 119 2586773] Train: [50/50][357/376] Data 0.003 (0.002) Batch 0.482 (0.507) Remain 00:00:09 loss: 0.1940 Lr: 0.00000 [2024-11-25 20:00:40,125 INFO misc.py line 119 2586773] Train: [50/50][358/376] Data 0.003 (0.002) Batch 0.494 (0.507) Remain 00:00:09 loss: 0.1585 Lr: 0.00000 [2024-11-25 20:00:40,638 INFO misc.py line 119 2586773] Train: [50/50][359/376] Data 0.003 (0.002) Batch 0.512 (0.507) Remain 00:00:08 loss: 0.1466 Lr: 0.00000 [2024-11-25 20:00:41,124 INFO misc.py line 119 2586773] Train: [50/50][360/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 00:00:08 loss: 0.2052 Lr: 0.00000 [2024-11-25 20:00:41,610 INFO misc.py line 119 2586773] Train: [50/50][361/376] Data 0.002 (0.002) Batch 0.486 (0.507) Remain 00:00:07 loss: 0.1983 Lr: 0.00000 [2024-11-25 20:00:42,079 INFO misc.py line 119 2586773] Train: [50/50][362/376] Data 0.002 (0.002) Batch 0.469 (0.507) Remain 00:00:07 loss: 0.2510 Lr: 0.00000 [2024-11-25 20:00:42,616 INFO misc.py line 119 2586773] Train: [50/50][363/376] Data 0.002 (0.002) Batch 0.537 (0.507) Remain 00:00:06 loss: 0.2164 Lr: 0.00000 [2024-11-25 20:00:43,165 INFO misc.py line 119 2586773] Train: [50/50][364/376] Data 0.002 (0.002) Batch 0.549 (0.507) Remain 00:00:06 loss: 0.1942 Lr: 0.00000 [2024-11-25 20:00:43,694 INFO misc.py line 119 2586773] Train: [50/50][365/376] Data 0.002 (0.002) Batch 0.529 (0.507) Remain 00:00:05 loss: 0.1802 Lr: 0.00000 [2024-11-25 20:00:44,168 INFO misc.py line 119 2586773] Train: [50/50][366/376] Data 0.002 (0.002) Batch 0.475 (0.507) Remain 00:00:05 loss: 0.1905 Lr: 0.00000 [2024-11-25 20:00:44,687 INFO misc.py line 119 2586773] Train: [50/50][367/376] Data 0.002 (0.002) Batch 0.518 (0.507) Remain 00:00:04 loss: 0.2321 Lr: 0.00000 [2024-11-25 20:00:45,177 INFO misc.py line 119 2586773] Train: [50/50][368/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:00:04 loss: 0.1545 Lr: 0.00000 [2024-11-25 20:00:45,634 INFO misc.py line 119 2586773] Train: [50/50][369/376] Data 0.002 (0.002) Batch 0.457 (0.507) Remain 00:00:03 loss: 0.2525 Lr: 0.00000 [2024-11-25 20:00:46,124 INFO misc.py line 119 2586773] Train: [50/50][370/376] Data 0.002 (0.002) Batch 0.490 (0.507) Remain 00:00:03 loss: 0.2716 Lr: 0.00000 [2024-11-25 20:00:46,623 INFO misc.py line 119 2586773] Train: [50/50][371/376] Data 0.002 (0.002) Batch 0.500 (0.507) Remain 00:00:02 loss: 0.1947 Lr: 0.00000 [2024-11-25 20:00:47,190 INFO misc.py line 119 2586773] Train: [50/50][372/376] Data 0.002 (0.002) Batch 0.567 (0.507) Remain 00:00:02 loss: 0.1964 Lr: 0.00000 [2024-11-25 20:00:47,703 INFO misc.py line 119 2586773] Train: [50/50][373/376] Data 0.002 (0.002) Batch 0.514 (0.507) Remain 00:00:01 loss: 0.1560 Lr: 0.00000 [2024-11-25 20:00:48,206 INFO misc.py line 119 2586773] Train: [50/50][374/376] Data 0.002 (0.002) Batch 0.502 (0.507) Remain 00:00:01 loss: 0.1882 Lr: 0.00000 [2024-11-25 20:00:48,709 INFO misc.py line 119 2586773] Train: [50/50][375/376] Data 0.002 (0.002) Batch 0.503 (0.507) Remain 00:00:00 loss: 0.2281 Lr: 0.00000 [2024-11-25 20:00:49,259 INFO misc.py line 119 2586773] Train: [50/50][376/376] Data 0.002 (0.002) Batch 0.550 (0.507) Remain 00:00:00 loss: 0.1788 Lr: 0.00000 [2024-11-25 20:00:49,259 INFO misc.py line 136 2586773] Train result: loss: 0.1967 [2024-11-25 20:00:49,260 INFO evaluator.py line 112 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 20:01:00,118 INFO evaluator.py line 159 2586773] Test: [1/132] Loss 0.1730 [2024-11-25 20:01:00,373 INFO evaluator.py line 159 2586773] Test: [2/132] Loss 0.2100 [2024-11-25 20:01:00,637 INFO evaluator.py line 159 2586773] Test: [3/132] Loss 0.2488 [2024-11-25 20:01:00,869 INFO evaluator.py line 159 2586773] Test: [4/132] Loss 0.1897 [2024-11-25 20:01:01,131 INFO evaluator.py line 159 2586773] Test: [5/132] Loss 0.2835 [2024-11-25 20:01:01,397 INFO evaluator.py line 159 2586773] Test: [6/132] Loss 0.1850 [2024-11-25 20:01:01,632 INFO evaluator.py line 159 2586773] Test: [7/132] Loss 0.2056 [2024-11-25 20:01:01,899 INFO evaluator.py line 159 2586773] Test: [8/132] Loss 0.2109 [2024-11-25 20:01:02,128 INFO evaluator.py line 159 2586773] Test: [9/132] Loss 0.2578 [2024-11-25 20:01:02,388 INFO evaluator.py line 159 2586773] Test: [10/132] Loss 0.2263 [2024-11-25 20:01:02,629 INFO evaluator.py line 159 2586773] Test: [11/132] Loss 0.2013 [2024-11-25 20:01:02,898 INFO evaluator.py line 159 2586773] Test: [12/132] Loss 0.2432 [2024-11-25 20:01:03,165 INFO evaluator.py line 159 2586773] Test: [13/132] Loss 0.2373 [2024-11-25 20:01:03,427 INFO evaluator.py line 159 2586773] Test: [14/132] Loss 0.2229 [2024-11-25 20:01:03,660 INFO evaluator.py line 159 2586773] Test: [15/132] Loss 0.2234 [2024-11-25 20:01:03,900 INFO evaluator.py line 159 2586773] Test: [16/132] Loss 0.3099 [2024-11-25 20:01:04,169 INFO evaluator.py line 159 2586773] Test: [17/132] Loss 0.2613 [2024-11-25 20:01:04,424 INFO evaluator.py line 159 2586773] Test: [18/132] Loss 0.2008 [2024-11-25 20:01:04,655 INFO evaluator.py line 159 2586773] Test: [19/132] Loss 0.2413 [2024-11-25 20:01:04,915 INFO evaluator.py line 159 2586773] Test: [20/132] Loss 0.2091 [2024-11-25 20:01:05,149 INFO evaluator.py line 159 2586773] Test: [21/132] Loss 0.2536 [2024-11-25 20:01:05,416 INFO evaluator.py line 159 2586773] Test: [22/132] Loss 0.2524 [2024-11-25 20:01:05,660 INFO evaluator.py line 159 2586773] Test: [23/132] Loss 0.2049 [2024-11-25 20:01:05,926 INFO evaluator.py line 159 2586773] Test: [24/132] Loss 0.2274 [2024-11-25 20:01:06,198 INFO evaluator.py line 159 2586773] Test: [25/132] Loss 0.2186 [2024-11-25 20:01:06,431 INFO evaluator.py line 159 2586773] Test: [26/132] Loss 0.2511 [2024-11-25 20:01:06,695 INFO evaluator.py line 159 2586773] Test: [27/132] Loss 0.2475 [2024-11-25 20:01:06,939 INFO evaluator.py line 159 2586773] Test: [28/132] Loss 0.2100 [2024-11-25 20:01:07,205 INFO evaluator.py line 159 2586773] Test: [29/132] Loss 0.2586 [2024-11-25 20:01:07,457 INFO evaluator.py line 159 2586773] Test: [30/132] Loss 0.2705 [2024-11-25 20:01:07,700 INFO evaluator.py line 159 2586773] Test: [31/132] Loss 0.2543 [2024-11-25 20:01:07,973 INFO evaluator.py line 159 2586773] Test: [32/132] Loss 0.1933 [2024-11-25 20:01:08,204 INFO evaluator.py line 159 2586773] Test: [33/132] Loss 0.2640 [2024-11-25 20:01:08,445 INFO evaluator.py line 159 2586773] Test: [34/132] Loss 0.2163 [2024-11-25 20:01:08,707 INFO evaluator.py line 159 2586773] Test: [35/132] Loss 0.2047 [2024-11-25 20:01:08,951 INFO evaluator.py line 159 2586773] Test: [36/132] Loss 0.2620 [2024-11-25 20:01:09,177 INFO evaluator.py line 159 2586773] Test: [37/132] Loss 0.1862 [2024-11-25 20:01:09,451 INFO evaluator.py line 159 2586773] Test: [38/132] Loss 0.2197 [2024-11-25 20:01:09,682 INFO evaluator.py line 159 2586773] Test: [39/132] Loss 0.2583 [2024-11-25 20:01:09,926 INFO evaluator.py line 159 2586773] Test: [40/132] Loss 0.2267 [2024-11-25 20:01:10,201 INFO evaluator.py line 159 2586773] Test: [41/132] Loss 0.3015 [2024-11-25 20:01:10,458 INFO evaluator.py line 159 2586773] Test: [42/132] Loss 0.2619 [2024-11-25 20:01:10,694 INFO evaluator.py line 159 2586773] Test: [43/132] Loss 0.2397 [2024-11-25 20:01:10,925 INFO evaluator.py line 159 2586773] Test: [44/132] Loss 0.2260 [2024-11-25 20:01:11,171 INFO evaluator.py line 159 2586773] Test: [45/132] Loss 0.2379 [2024-11-25 20:01:11,418 INFO evaluator.py line 159 2586773] Test: [46/132] Loss 0.2253 [2024-11-25 20:01:11,683 INFO evaluator.py line 159 2586773] Test: [47/132] Loss 0.2183 [2024-11-25 20:01:11,932 INFO evaluator.py line 159 2586773] Test: [48/132] Loss 0.2839 [2024-11-25 20:01:12,165 INFO evaluator.py line 159 2586773] Test: [49/132] Loss 0.2050 [2024-11-25 20:01:12,400 INFO evaluator.py line 159 2586773] Test: [50/132] Loss 0.2153 [2024-11-25 20:01:12,628 INFO evaluator.py line 159 2586773] Test: [51/132] Loss 0.2406 [2024-11-25 20:01:12,890 INFO evaluator.py line 159 2586773] Test: [52/132] Loss 0.2153 [2024-11-25 20:01:13,157 INFO evaluator.py line 159 2586773] Test: [53/132] Loss 0.2127 [2024-11-25 20:01:13,418 INFO evaluator.py line 159 2586773] Test: [54/132] Loss 0.3092 [2024-11-25 20:01:13,659 INFO evaluator.py line 159 2586773] Test: [55/132] Loss 0.2280 [2024-11-25 20:01:13,896 INFO evaluator.py line 159 2586773] Test: [56/132] Loss 0.2269 [2024-11-25 20:01:14,161 INFO evaluator.py line 159 2586773] Test: [57/132] Loss 0.2391 [2024-11-25 20:01:14,436 INFO evaluator.py line 159 2586773] Test: [58/132] Loss 0.2698 [2024-11-25 20:01:14,691 INFO evaluator.py line 159 2586773] Test: [59/132] Loss 0.2329 [2024-11-25 20:01:14,951 INFO evaluator.py line 159 2586773] Test: [60/132] Loss 0.2228 [2024-11-25 20:01:15,216 INFO evaluator.py line 159 2586773] Test: [61/132] Loss 0.2139 [2024-11-25 20:01:15,492 INFO evaluator.py line 159 2586773] Test: [62/132] Loss 0.2285 [2024-11-25 20:01:15,724 INFO evaluator.py line 159 2586773] Test: [63/132] Loss 0.2265 [2024-11-25 20:01:15,991 INFO evaluator.py line 159 2586773] Test: [64/132] Loss 0.2396 [2024-11-25 20:01:16,258 INFO evaluator.py line 159 2586773] Test: [65/132] Loss 0.2363 [2024-11-25 20:01:16,532 INFO evaluator.py line 159 2586773] Test: [66/132] Loss 0.1845 [2024-11-25 20:01:16,789 INFO evaluator.py line 159 2586773] Test: [67/132] Loss 0.1844 [2024-11-25 20:01:17,044 INFO evaluator.py line 159 2586773] Test: [68/132] Loss 0.2489 [2024-11-25 20:01:17,313 INFO evaluator.py line 159 2586773] Test: [69/132] Loss 0.2497 [2024-11-25 20:01:17,578 INFO evaluator.py line 159 2586773] Test: [70/132] Loss 0.2689 [2024-11-25 20:01:17,820 INFO evaluator.py line 159 2586773] Test: [71/132] Loss 0.1962 [2024-11-25 20:01:18,057 INFO evaluator.py line 159 2586773] Test: [72/132] Loss 0.2853 [2024-11-25 20:01:18,316 INFO evaluator.py line 159 2586773] Test: [73/132] Loss 0.2476 [2024-11-25 20:01:18,560 INFO evaluator.py line 159 2586773] Test: [74/132] Loss 0.2508 [2024-11-25 20:01:18,786 INFO evaluator.py line 159 2586773] Test: [75/132] Loss 0.2531 [2024-11-25 20:01:19,013 INFO evaluator.py line 159 2586773] Test: [76/132] Loss 0.2110 [2024-11-25 20:01:19,284 INFO evaluator.py line 159 2586773] Test: [77/132] Loss 0.2459 [2024-11-25 20:01:19,526 INFO evaluator.py line 159 2586773] Test: [78/132] Loss 0.2121 [2024-11-25 20:01:19,792 INFO evaluator.py line 159 2586773] Test: [79/132] Loss 0.2259 [2024-11-25 20:01:20,043 INFO evaluator.py line 159 2586773] Test: [80/132] Loss 0.2809 [2024-11-25 20:01:20,291 INFO evaluator.py line 159 2586773] Test: [81/132] Loss 0.2167 [2024-11-25 20:01:20,554 INFO evaluator.py line 159 2586773] Test: [82/132] Loss 0.2529 [2024-11-25 20:01:20,803 INFO evaluator.py line 159 2586773] Test: [83/132] Loss 0.1894 [2024-11-25 20:01:21,054 INFO evaluator.py line 159 2586773] Test: [84/132] Loss 0.2323 [2024-11-25 20:01:21,337 INFO evaluator.py line 159 2586773] Test: [85/132] Loss 0.2463 [2024-11-25 20:01:21,573 INFO evaluator.py line 159 2586773] Test: [86/132] Loss 0.2518 [2024-11-25 20:01:21,839 INFO evaluator.py line 159 2586773] Test: [87/132] Loss 0.2441 [2024-11-25 20:01:22,103 INFO evaluator.py line 159 2586773] Test: [88/132] Loss 0.2280 [2024-11-25 20:01:22,358 INFO evaluator.py line 159 2586773] Test: [89/132] Loss 0.2694 [2024-11-25 20:01:22,612 INFO evaluator.py line 159 2586773] Test: [90/132] Loss 0.2546 [2024-11-25 20:01:22,854 INFO evaluator.py line 159 2586773] Test: [91/132] Loss 0.2490 [2024-11-25 20:01:23,108 INFO evaluator.py line 159 2586773] Test: [92/132] Loss 0.2613 [2024-11-25 20:01:23,375 INFO evaluator.py line 159 2586773] Test: [93/132] Loss 0.2280 [2024-11-25 20:01:23,650 INFO evaluator.py line 159 2586773] Test: [94/132] Loss 0.1814 [2024-11-25 20:01:23,916 INFO evaluator.py line 159 2586773] Test: [95/132] Loss 0.2225 [2024-11-25 20:01:24,172 INFO evaluator.py line 159 2586773] Test: [96/132] Loss 0.2055 [2024-11-25 20:01:24,441 INFO evaluator.py line 159 2586773] Test: [97/132] Loss 0.2183 [2024-11-25 20:01:24,664 INFO evaluator.py line 159 2586773] Test: [98/132] Loss 0.2967 [2024-11-25 20:01:24,937 INFO evaluator.py line 159 2586773] Test: [99/132] Loss 0.2396 [2024-11-25 20:01:25,184 INFO evaluator.py line 159 2586773] Test: [100/132] Loss 0.2460 [2024-11-25 20:01:25,462 INFO evaluator.py line 159 2586773] Test: [101/132] Loss 0.1966 [2024-11-25 20:01:25,728 INFO evaluator.py line 159 2586773] Test: [102/132] Loss 0.2493 [2024-11-25 20:01:25,987 INFO evaluator.py line 159 2586773] Test: [103/132] Loss 0.2239 [2024-11-25 20:01:26,243 INFO evaluator.py line 159 2586773] Test: [104/132] Loss 0.2613 [2024-11-25 20:01:26,472 INFO evaluator.py line 159 2586773] Test: [105/132] Loss 0.2260 [2024-11-25 20:01:26,716 INFO evaluator.py line 159 2586773] Test: [106/132] Loss 0.2158 [2024-11-25 20:01:26,972 INFO evaluator.py line 159 2586773] Test: [107/132] Loss 0.2149 [2024-11-25 20:01:27,250 INFO evaluator.py line 159 2586773] Test: [108/132] Loss 0.2263 [2024-11-25 20:01:27,485 INFO evaluator.py line 159 2586773] Test: [109/132] Loss 0.2401 [2024-11-25 20:01:27,744 INFO evaluator.py line 159 2586773] Test: [110/132] Loss 0.2004 [2024-11-25 20:01:28,015 INFO evaluator.py line 159 2586773] Test: [111/132] Loss 0.2275 [2024-11-25 20:01:28,245 INFO evaluator.py line 159 2586773] Test: [112/132] Loss 0.2253 [2024-11-25 20:01:28,487 INFO evaluator.py line 159 2586773] Test: [113/132] Loss 0.1999 [2024-11-25 20:01:28,711 INFO evaluator.py line 159 2586773] Test: [114/132] Loss 0.2081 [2024-11-25 20:01:28,936 INFO evaluator.py line 159 2586773] Test: [115/132] Loss 0.2110 [2024-11-25 20:01:29,209 INFO evaluator.py line 159 2586773] Test: [116/132] Loss 0.2867 [2024-11-25 20:01:29,470 INFO evaluator.py line 159 2586773] Test: [117/132] Loss 0.2490 [2024-11-25 20:01:29,737 INFO evaluator.py line 159 2586773] Test: [118/132] Loss 0.2382 [2024-11-25 20:01:30,009 INFO evaluator.py line 159 2586773] Test: [119/132] Loss 0.2073 [2024-11-25 20:01:30,272 INFO evaluator.py line 159 2586773] Test: [120/132] Loss 0.2684 [2024-11-25 20:01:30,531 INFO evaluator.py line 159 2586773] Test: [121/132] Loss 0.2830 [2024-11-25 20:01:30,797 INFO evaluator.py line 159 2586773] Test: [122/132] Loss 0.2144 [2024-11-25 20:01:31,053 INFO evaluator.py line 159 2586773] Test: [123/132] Loss 0.2400 [2024-11-25 20:01:31,332 INFO evaluator.py line 159 2586773] Test: [124/132] Loss 0.2266 [2024-11-25 20:01:31,596 INFO evaluator.py line 159 2586773] Test: [125/132] Loss 0.2506 [2024-11-25 20:01:31,846 INFO evaluator.py line 159 2586773] Test: [126/132] Loss 0.2281 [2024-11-25 20:01:32,076 INFO evaluator.py line 159 2586773] Test: [127/132] Loss 0.1932 [2024-11-25 20:01:32,340 INFO evaluator.py line 159 2586773] Test: [128/132] Loss 0.2342 [2024-11-25 20:01:32,575 INFO evaluator.py line 159 2586773] Test: [129/132] Loss 0.2556 [2024-11-25 20:01:32,811 INFO evaluator.py line 159 2586773] Test: [130/132] Loss 0.1866 [2024-11-25 20:01:33,027 INFO evaluator.py line 159 2586773] Test: [131/132] Loss 0.2161 [2024-11-25 20:01:33,254 INFO evaluator.py line 159 2586773] Test: [132/132] Loss 0.1800 [2024-11-25 20:01:33,984 INFO evaluator.py line 174 2586773] Val result: mIoU/mAcc/allAcc 0.7788/0.8538/0.9964. [2024-11-25 20:01:33,984 INFO evaluator.py line 180 2586773] Class_0-background Result: iou/accuracy 0.9963/0.9983 [2024-11-25 20:01:33,985 INFO evaluator.py line 180 2586773] Class_1-lane Result: iou/accuracy 0.5613/0.7094 [2024-11-25 20:01:33,985 INFO evaluator.py line 194 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<< [2024-11-25 20:01:33,986 INFO misc.py line 165 2586773] Currently Best mIoU: 0.7829 [2024-11-25 20:01:33,986 INFO misc.py line 174 2586773] Saving checkpoint to: exp/nuscenes/train_highbay_07/model/model_last.pth [2024-11-25 20:01:36,093 INFO evaluator.py line 199 2586773] Best mIoU: 0.7829 [2024-11-25 20:01:36,093 INFO misc.py line 261 2586773] >>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>> [2024-11-25 20:01:36,160 INFO test.py line 41 2586773] => Loading config ... [2024-11-25 20:01:36,160 INFO test.py line 53 2586773] => Building test dataset & dataloader ... [2024-11-25 20:01:36,162 INFO defaults.py line 60 2586773] Totally 527 x 1 samples in test set. [2024-11-25 20:01:44,376 INFO misc.py line 272 2586773] => Testing on model_best ... [2024-11-25 20:01:45,031 INFO test.py line 163 2586773] >>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>> [2024-11-25 20:02:00,547 INFO test.py line 250 2586773] Test: 1/132-08_000483, Batch: 0/1 [2024-11-25 20:02:00,556 INFO test.py line 292 2586773] Test: 08_000483 [1/132]-131072 Batch 0.265 (0.265) Accuracy 0.9982 (0.9171) mIoU 0.8293 (0.8293) [2024-11-25 20:02:00,781 INFO test.py line 250 2586773] Test: 2/132-08_000013, Batch: 0/1 [2024-11-25 20:02:00,788 INFO test.py line 292 2586773] Test: 08_000013 [2/132]-131072 Batch 0.222 (0.244) Accuracy 0.9977 (0.8983) mIoU 0.7963 (0.8125) [2024-11-25 20:02:01,061 INFO test.py line 250 2586773] Test: 3/132-08_000126, Batch: 0/1 [2024-11-25 20:02:01,067 INFO test.py line 292 2586773] Test: 08_000126 [3/132]-131072 Batch 0.269 (0.252) Accuracy 0.9981 (0.8759) mIoU 0.7565 (0.7980) [2024-11-25 20:02:01,287 INFO test.py line 250 2586773] Test: 4/132-08_000308, Batch: 0/1 [2024-11-25 20:02:01,294 INFO test.py line 292 2586773] Test: 08_000308 [4/132]-131072 Batch 0.216 (0.243) Accuracy 0.9977 (0.8766) mIoU 0.8244 (0.8062) [2024-11-25 20:02:01,532 INFO test.py line 250 2586773] Test: 5/132-08_000384, Batch: 0/1 [2024-11-25 20:02:01,539 INFO test.py line 292 2586773] Test: 08_000384 [5/132]-131072 Batch 0.234 (0.241) Accuracy 0.9969 (0.8557) mIoU 0.7369 (0.7912) [2024-11-25 20:02:01,782 INFO test.py line 250 2586773] Test: 6/132-08_000370, Batch: 0/1 [2024-11-25 20:02:01,788 INFO test.py line 292 2586773] Test: 08_000370 [6/132]-131072 Batch 0.239 (0.241) Accuracy 0.9986 (0.8590) mIoU 0.8277 (0.7959) [2024-11-25 20:02:02,015 INFO test.py line 250 2586773] Test: 7/132-08_000259, Batch: 0/1 [2024-11-25 20:02:02,022 INFO test.py line 292 2586773] Test: 08_000259 [7/132]-131072 Batch 0.223 (0.239) Accuracy 0.9980 (0.8651) mIoU 0.8118 (0.7982) [2024-11-25 20:02:02,243 INFO test.py line 250 2586773] Test: 8/132-08_000494, Batch: 0/1 [2024-11-25 20:02:02,250 INFO test.py line 292 2586773] Test: 08_000494 [8/132]-131072 Batch 0.217 (0.236) Accuracy 0.9979 (0.8685) mIoU 0.8030 (0.7988) [2024-11-25 20:02:02,474 INFO test.py line 250 2586773] Test: 9/132-08_000040, Batch: 0/1 [2024-11-25 20:02:02,481 INFO test.py line 292 2586773] Test: 08_000040 [9/132]-131072 Batch 0.220 (0.234) Accuracy 0.9971 (0.8687) mIoU 0.7555 (0.7934) [2024-11-25 20:02:02,722 INFO test.py line 250 2586773] Test: 10/132-08_000400, Batch: 0/1 [2024-11-25 20:02:02,729 INFO test.py line 292 2586773] Test: 08_000400 [10/132]-131072 Batch 0.238 (0.234) Accuracy 0.9973 (0.8639) mIoU 0.7779 (0.7917) [2024-11-25 20:02:02,952 INFO test.py line 250 2586773] Test: 11/132-08_000450, Batch: 0/1 [2024-11-25 20:02:02,959 INFO test.py line 292 2586773] Test: 08_000450 [11/132]-131072 Batch 0.220 (0.233) Accuracy 0.9972 (0.8647) mIoU 0.8084 (0.7937) [2024-11-25 20:02:03,185 INFO test.py line 250 2586773] Test: 12/132-08_000498, Batch: 0/1 [2024-11-25 20:02:03,191 INFO test.py line 292 2586773] Test: 08_000498 [12/132]-131072 Batch 0.221 (0.232) Accuracy 0.9973 (0.8673) mIoU 0.7736 (0.7919) [2024-11-25 20:02:03,419 INFO test.py line 250 2586773] Test: 13/132-08_000061, Batch: 0/1 [2024-11-25 20:02:03,426 INFO test.py line 292 2586773] Test: 08_000061 [13/132]-131072 Batch 0.224 (0.231) Accuracy 0.9977 (0.8671) mIoU 0.7853 (0.7914) [2024-11-25 20:02:03,661 INFO test.py line 250 2586773] Test: 14/132-08_000350, Batch: 0/1 [2024-11-25 20:02:03,667 INFO test.py line 292 2586773] Test: 08_000350 [14/132]-131072 Batch 0.230 (0.231) Accuracy 0.9982 (0.8673) mIoU 0.7928 (0.7915) [2024-11-25 20:02:03,907 INFO test.py line 250 2586773] Test: 15/132-08_000235, Batch: 0/1 [2024-11-25 20:02:03,914 INFO test.py line 292 2586773] Test: 08_000235 [15/132]-131072 Batch 0.237 (0.232) Accuracy 0.9975 (0.8660) mIoU 0.7748 (0.7904) [2024-11-25 20:02:04,142 INFO test.py line 250 2586773] Test: 16/132-08_000526, Batch: 0/1 [2024-11-25 20:02:04,149 INFO test.py line 292 2586773] Test: 08_000526 [16/132]-131072 Batch 0.224 (0.231) Accuracy 0.9965 (0.8645) mIoU 0.7193 (0.7854) [2024-11-25 20:02:04,424 INFO test.py line 250 2586773] Test: 17/132-08_000138, Batch: 0/1 [2024-11-25 20:02:04,431 INFO test.py line 292 2586773] Test: 08_000138 [17/132]-131072 Batch 0.271 (0.234) Accuracy 0.9982 (0.8619) mIoU 0.7502 (0.7840) [2024-11-25 20:02:04,659 INFO test.py line 250 2586773] Test: 18/132-08_000256, Batch: 0/1 [2024-11-25 20:02:04,666 INFO test.py line 292 2586773] Test: 08_000256 [18/132]-131072 Batch 0.225 (0.233) Accuracy 0.9981 (0.8637) mIoU 0.8084 (0.7853) [2024-11-25 20:02:04,894 INFO test.py line 250 2586773] Test: 19/132-08_000035, Batch: 0/1 [2024-11-25 20:02:04,901 INFO test.py line 292 2586773] Test: 08_000035 [19/132]-131072 Batch 0.224 (0.233) Accuracy 0.9973 (0.8645) mIoU 0.7728 (0.7846) [2024-11-25 20:02:05,176 INFO test.py line 250 2586773] Test: 20/132-08_000185, Batch: 0/1 [2024-11-25 20:02:05,183 INFO test.py line 292 2586773] Test: 08_000185 [20/132]-131072 Batch 0.270 (0.234) Accuracy 0.9981 (0.8651) mIoU 0.8047 (0.7855) [2024-11-25 20:02:05,407 INFO test.py line 250 2586773] Test: 21/132-08_000033, Batch: 0/1 [2024-11-25 20:02:05,415 INFO test.py line 292 2586773] Test: 08_000033 [21/132]-131072 Batch 0.222 (0.234) Accuracy 0.9971 (0.8664) mIoU 0.7675 (0.7846) [2024-11-25 20:02:05,671 INFO test.py line 250 2586773] Test: 22/132-08_000101, Batch: 0/1 [2024-11-25 20:02:05,677 INFO test.py line 292 2586773] Test: 08_000101 [22/132]-131072 Batch 0.250 (0.235) Accuracy 0.9973 (0.8667) mIoU 0.7754 (0.7841) [2024-11-25 20:02:05,898 INFO test.py line 250 2586773] Test: 23/132-08_000315, Batch: 0/1 [2024-11-25 20:02:05,906 INFO test.py line 292 2586773] Test: 08_000315 [23/132]-131072 Batch 0.219 (0.234) Accuracy 0.9973 (0.8666) mIoU 0.8077 (0.7854) [2024-11-25 20:02:06,151 INFO test.py line 250 2586773] Test: 24/132-08_000399, Batch: 0/1 [2024-11-25 20:02:06,158 INFO test.py line 292 2586773] Test: 08_000399 [24/132]-131072 Batch 0.241 (0.234) Accuracy 0.9974 (0.8649) mIoU 0.7845 (0.7853) [2024-11-25 20:02:06,392 INFO test.py line 250 2586773] Test: 25/132-08_000431, Batch: 0/1 [2024-11-25 20:02:06,399 INFO test.py line 292 2586773] Test: 08_000431 [25/132]-131072 Batch 0.231 (0.234) Accuracy 0.9968 (0.8645) mIoU 0.7993 (0.7861) [2024-11-25 20:02:06,626 INFO test.py line 250 2586773] Test: 26/132-08_000516, Batch: 0/1 [2024-11-25 20:02:06,633 INFO test.py line 292 2586773] Test: 08_000516 [26/132]-131072 Batch 0.222 (0.234) Accuracy 0.9974 (0.8651) mIoU 0.7781 (0.7858) [2024-11-25 20:02:06,873 INFO test.py line 250 2586773] Test: 27/132-08_000391, Batch: 0/1 [2024-11-25 20:02:06,881 INFO test.py line 292 2586773] Test: 08_000391 [27/132]-131072 Batch 0.237 (0.234) Accuracy 0.9974 (0.8638) mIoU 0.7746 (0.7854) [2024-11-25 20:02:07,126 INFO test.py line 250 2586773] Test: 28/132-08_000086, Batch: 0/1 [2024-11-25 20:02:07,133 INFO test.py line 292 2586773] Test: 08_000086 [28/132]-131072 Batch 0.241 (0.234) Accuracy 0.9978 (0.8642) mIoU 0.7896 (0.7855) [2024-11-25 20:02:07,395 INFO test.py line 250 2586773] Test: 29/132-08_000206, Batch: 0/1 [2024-11-25 20:02:07,401 INFO test.py line 292 2586773] Test: 08_000206 [29/132]-131072 Batch 0.258 (0.235) Accuracy 0.9973 (0.8636) mIoU 0.7681 (0.7849) [2024-11-25 20:02:07,663 INFO test.py line 250 2586773] Test: 30/132-08_000198, Batch: 0/1 [2024-11-25 20:02:07,670 INFO test.py line 292 2586773] Test: 08_000198 [30/132]-131072 Batch 0.258 (0.236) Accuracy 0.9967 (0.8617) mIoU 0.7379 (0.7832) [2024-11-25 20:02:07,894 INFO test.py line 250 2586773] Test: 31/132-08_000340, Batch: 0/1 [2024-11-25 20:02:07,901 INFO test.py line 292 2586773] Test: 08_000340 [31/132]-131072 Batch 0.220 (0.235) Accuracy 0.9970 (0.8592) mIoU 0.7560 (0.7822) [2024-11-25 20:02:08,135 INFO test.py line 250 2586773] Test: 32/132-08_000076, Batch: 0/1 [2024-11-25 20:02:08,142 INFO test.py line 292 2586773] Test: 08_000076 [32/132]-131072 Batch 0.231 (0.235) Accuracy 0.9981 (0.8607) mIoU 0.8207 (0.7833) [2024-11-25 20:02:08,369 INFO test.py line 250 2586773] Test: 33/132-08_000037, Batch: 0/1 [2024-11-25 20:02:08,375 INFO test.py line 292 2586773] Test: 08_000037 [33/132]-131072 Batch 0.223 (0.235) Accuracy 0.9974 (0.8612) mIoU 0.7673 (0.7829) [2024-11-25 20:02:08,602 INFO test.py line 250 2586773] Test: 34/132-08_000279, Batch: 0/1 [2024-11-25 20:02:08,609 INFO test.py line 292 2586773] Test: 08_000279 [34/132]-131072 Batch 0.224 (0.234) Accuracy 0.9978 (0.8620) mIoU 0.8041 (0.7835) [2024-11-25 20:02:08,831 INFO test.py line 250 2586773] Test: 35/132-08_000291, Batch: 0/1 [2024-11-25 20:02:08,838 INFO test.py line 292 2586773] Test: 08_000291 [35/132]-131072 Batch 0.219 (0.234) Accuracy 0.9978 (0.8625) mIoU 0.8135 (0.7844) [2024-11-25 20:02:09,069 INFO test.py line 250 2586773] Test: 36/132-08_000005, Batch: 0/1 [2024-11-25 20:02:09,076 INFO test.py line 292 2586773] Test: 08_000005 [36/132]-131072 Batch 0.227 (0.234) Accuracy 0.9975 (0.8625) mIoU 0.7752 (0.7841) [2024-11-25 20:02:09,298 INFO test.py line 250 2586773] Test: 37/132-08_000289, Batch: 0/1 [2024-11-25 20:02:09,305 INFO test.py line 292 2586773] Test: 08_000289 [37/132]-131072 Batch 0.219 (0.233) Accuracy 0.9980 (0.8639) mIoU 0.8266 (0.7853) [2024-11-25 20:02:09,550 INFO test.py line 250 2586773] Test: 38/132-08_000379, Batch: 0/1 [2024-11-25 20:02:09,556 INFO test.py line 292 2586773] Test: 08_000379 [38/132]-131072 Batch 0.241 (0.233) Accuracy 0.9978 (0.8633) mIoU 0.7907 (0.7854) [2024-11-25 20:02:09,797 INFO test.py line 250 2586773] Test: 39/132-08_000419, Batch: 0/1 [2024-11-25 20:02:09,804 INFO test.py line 292 2586773] Test: 08_000419 [39/132]-131072 Batch 0.237 (0.234) Accuracy 0.9965 (0.8622) mIoU 0.7596 (0.7846) [2024-11-25 20:02:10,030 INFO test.py line 250 2586773] Test: 40/132-08_000342, Batch: 0/1 [2024-11-25 20:02:10,037 INFO test.py line 292 2586773] Test: 08_000342 [40/132]-131072 Batch 0.223 (0.233) Accuracy 0.9977 (0.8611) mIoU 0.7916 (0.7847) [2024-11-25 20:02:10,262 INFO test.py line 250 2586773] Test: 41/132-08_000017, Batch: 0/1 [2024-11-25 20:02:10,270 INFO test.py line 292 2586773] Test: 08_000017 [41/132]-131072 Batch 0.223 (0.233) Accuracy 0.9966 (0.8602) mIoU 0.7259 (0.7832) [2024-11-25 20:02:10,528 INFO test.py line 250 2586773] Test: 42/132-08_000104, Batch: 0/1 [2024-11-25 20:02:10,536 INFO test.py line 292 2586773] Test: 08_000104 [42/132]-131072 Batch 0.255 (0.234) Accuracy 0.9972 (0.8594) mIoU 0.7577 (0.7826) [2024-11-25 20:02:10,785 INFO test.py line 250 2586773] Test: 43/132-08_000227, Batch: 0/1 [2024-11-25 20:02:10,792 INFO test.py line 292 2586773] Test: 08_000227 [43/132]-131072 Batch 0.246 (0.234) Accuracy 0.9971 (0.8593) mIoU 0.7686 (0.7822) [2024-11-25 20:02:11,024 INFO test.py line 250 2586773] Test: 44/132-08_000069, Batch: 0/1 [2024-11-25 20:02:11,031 INFO test.py line 292 2586773] Test: 08_000069 [44/132]-131072 Batch 0.229 (0.234) Accuracy 0.9979 (0.8597) mIoU 0.7982 (0.7825) [2024-11-25 20:02:11,261 INFO test.py line 250 2586773] Test: 45/132-08_000065, Batch: 0/1 [2024-11-25 20:02:11,268 INFO test.py line 292 2586773] Test: 08_000065 [45/132]-131072 Batch 0.226 (0.234) Accuracy 0.9976 (0.8596) mIoU 0.7768 (0.7824) [2024-11-25 20:02:11,523 INFO test.py line 250 2586773] Test: 46/132-08_000215, Batch: 0/1 [2024-11-25 20:02:11,530 INFO test.py line 292 2586773] Test: 08_000215 [46/132]-131072 Batch 0.252 (0.234) Accuracy 0.9975 (0.8603) mIoU 0.7998 (0.7828) [2024-11-25 20:02:11,798 INFO test.py line 250 2586773] Test: 47/132-08_000171, Batch: 0/1 [2024-11-25 20:02:11,805 INFO test.py line 292 2586773] Test: 08_000171 [47/132]-131072 Batch 0.263 (0.235) Accuracy 0.9983 (0.8603) mIoU 0.7893 (0.7829) [2024-11-25 20:02:12,067 INFO test.py line 250 2586773] Test: 48/132-08_000108, Batch: 0/1 [2024-11-25 20:02:12,076 INFO test.py line 292 2586773] Test: 08_000108 [48/132]-131072 Batch 0.261 (0.235) Accuracy 0.9971 (0.8591) mIoU 0.7361 (0.7820) [2024-11-25 20:02:12,298 INFO test.py line 250 2586773] Test: 49/132-08_000453, Batch: 0/1 [2024-11-25 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Batch 0.217 (0.234) Accuracy 0.9972 (0.8586) mIoU 0.7679 (0.7823) [2024-11-25 20:02:17,447 INFO test.py line 250 2586773] Test: 70/132-08_000107, Batch: 0/1 [2024-11-25 20:02:17,454 INFO test.py line 292 2586773] Test: 08_000107 [70/132]-131072 Batch 0.252 (0.235) Accuracy 0.9971 (0.8582) mIoU 0.7378 (0.7817) [2024-11-25 20:02:17,538 INFO test.py line 250 2586773] Test: 71/132-08_000000, Batch: 0/1 [2024-11-25 20:02:17,543 INFO test.py line 292 2586773] Test: 08_000000 [71/132]-131072 Batch 0.079 (0.233) Accuracy 0.9994 (0.8584) mIoU 0.8228 (0.7819) [2024-11-25 20:02:17,769 INFO test.py line 250 2586773] Test: 72/132-08_000001, Batch: 0/1 [2024-11-25 20:02:17,777 INFO test.py line 292 2586773] Test: 08_000001 [72/132]-131072 Batch 0.223 (0.232) Accuracy 0.9972 (0.8581) mIoU 0.7512 (0.7815) [2024-11-25 20:02:17,995 INFO test.py line 250 2586773] Test: 73/132-08_000326, Batch: 0/1 [2024-11-25 20:02:18,002 INFO test.py line 292 2586773] Test: 08_000326 [73/132]-131072 Batch 0.215 (0.232) Accuracy 0.9969 (0.8576) mIoU 0.7677 (0.7812) [2024-11-25 20:02:18,251 INFO test.py line 250 2586773] Test: 74/132-08_000097, Batch: 0/1 [2024-11-25 20:02:18,257 INFO test.py line 292 2586773] Test: 08_000097 [74/132]-131072 Batch 0.246 (0.232) Accuracy 0.9971 (0.8579) mIoU 0.7667 (0.7810) [2024-11-25 20:02:18,477 INFO test.py line 250 2586773] Test: 75/132-08_000505, Batch: 0/1 [2024-11-25 20:02:18,484 INFO test.py line 292 2586773] Test: 08_000505 [75/132]-131072 Batch 0.216 (0.232) Accuracy 0.9970 (0.8582) mIoU 0.7560 (0.7807) [2024-11-25 20:02:18,701 INFO test.py line 250 2586773] Test: 76/132-08_000466, Batch: 0/1 [2024-11-25 20:02:18,708 INFO test.py line 292 2586773] Test: 08_000466 [76/132]-131072 Batch 0.213 (0.232) Accuracy 0.9975 (0.8584) mIoU 0.8015 (0.7810) [2024-11-25 20:02:18,935 INFO test.py line 250 2586773] Test: 77/132-08_000438, Batch: 0/1 [2024-11-25 20:02:18,941 INFO test.py line 292 2586773] Test: 08_000438 [77/132]-131072 Batch 0.224 (0.232) Accuracy 0.9960 (0.8577) mIoU 0.7739 (0.7808) [2024-11-25 20:02:19,162 INFO test.py line 250 2586773] Test: 78/132-08_000290, Batch: 0/1 [2024-11-25 20:02:19,169 INFO test.py line 292 2586773] Test: 08_000290 [78/132]-131072 Batch 0.217 (0.232) Accuracy 0.9979 (0.8582) mIoU 0.8177 (0.7813) [2024-11-25 20:02:19,393 INFO test.py line 250 2586773] Test: 79/132-08_000014, Batch: 0/1 [2024-11-25 20:02:19,399 INFO test.py line 292 2586773] Test: 08_000014 [79/132]-131072 Batch 0.220 (0.231) Accuracy 0.9975 (0.8585) mIoU 0.7822 (0.7813) [2024-11-25 20:02:19,658 INFO test.py line 250 2586773] Test: 80/132-08_000204, Batch: 0/1 [2024-11-25 20:02:19,664 INFO test.py line 292 2586773] Test: 08_000204 [80/132]-131072 Batch 0.255 (0.232) Accuracy 0.9964 (0.8576) mIoU 0.7277 (0.7805) [2024-11-25 20:02:19,888 INFO test.py line 250 2586773] Test: 81/132-08_000263, Batch: 0/1 [2024-11-25 20:02:19,894 INFO test.py line 292 2586773] Test: 08_000263 [81/132]-131072 Batch 0.220 (0.232) Accuracy 0.9979 (0.8582) mIoU 0.8059 (0.7808) [2024-11-25 20:02:20,115 INFO test.py line 250 2586773] Test: 82/132-08_000518, Batch: 0/1 [2024-11-25 20:02:20,122 INFO test.py line 292 2586773] Test: 08_000518 [82/132]-131072 Batch 0.218 (0.231) Accuracy 0.9973 (0.8585) mIoU 0.7723 (0.7807) [2024-11-25 20:02:20,351 INFO test.py line 250 2586773] Test: 83/132-08_000008, Batch: 0/1 [2024-11-25 20:02:20,358 INFO test.py line 292 2586773] Test: 08_000008 [83/132]-131072 Batch 0.224 (0.231) Accuracy 0.9982 (0.8589) mIoU 0.8248 (0.7812) [2024-11-25 20:02:20,600 INFO test.py line 250 2586773] Test: 84/132-08_000369, Batch: 0/1 [2024-11-25 20:02:20,607 INFO test.py line 292 2586773] Test: 08_000369 [84/132]-131072 Batch 0.239 (0.231) Accuracy 0.9981 (0.8589) mIoU 0.7839 (0.7812) [2024-11-25 20:02:20,827 INFO test.py line 250 2586773] Test: 85/132-08_000488, Batch: 0/1 [2024-11-25 20:02:20,833 INFO test.py line 292 2586773] Test: 08_000488 [85/132]-131072 Batch 0.216 (0.231) Accuracy 0.9974 (0.8588) mIoU 0.7730 (0.7811) [2024-11-25 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0.8110 (0.7801) [2024-11-25 20:02:25,238 INFO test.py line 250 2586773] Test: 102/132-08_000191, Batch: 0/1 [2024-11-25 20:02:25,245 INFO test.py line 292 2586773] Test: 08_000191 [102/132]-131072 Batch 0.262 (0.234) Accuracy 0.9973 (0.8585) mIoU 0.7559 (0.7799) [2024-11-25 20:02:25,512 INFO test.py line 250 2586773] Test: 103/132-08_000187, Batch: 0/1 [2024-11-25 20:02:25,519 INFO test.py line 292 2586773] Test: 08_000187 [103/132]-131072 Batch 0.264 (0.235) Accuracy 0.9977 (0.8584) mIoU 0.7856 (0.7800) [2024-11-25 20:02:25,781 INFO test.py line 250 2586773] Test: 104/132-08_000112, Batch: 0/1 [2024-11-25 20:02:25,787 INFO test.py line 292 2586773] Test: 08_000112 [104/132]-131072 Batch 0.258 (0.235) Accuracy 0.9976 (0.8581) mIoU 0.7499 (0.7797) [2024-11-25 20:02:26,017 INFO test.py line 250 2586773] Test: 105/132-08_000258, Batch: 0/1 [2024-11-25 20:02:26,023 INFO test.py line 292 2586773] Test: 08_000258 [105/132]-131072 Batch 0.226 (0.235) Accuracy 0.9980 (0.8583) mIoU 0.8033 (0.7799) [2024-11-25 20:02:26,256 INFO test.py line 250 2586773] Test: 106/132-08_000246, Batch: 0/1 [2024-11-25 20:02:26,263 INFO test.py line 292 2586773] Test: 08_000246 [106/132]-131072 Batch 0.229 (0.235) Accuracy 0.9978 (0.8584) mIoU 0.7932 (0.7800) [2024-11-25 20:02:26,493 INFO test.py line 250 2586773] Test: 107/132-08_000067, Batch: 0/1 [2024-11-25 20:02:26,500 INFO test.py line 292 2586773] Test: 08_000067 [107/132]-131072 Batch 0.227 (0.235) Accuracy 0.9979 (0.8586) mIoU 0.7978 (0.7802) [2024-11-25 20:02:26,774 INFO test.py line 250 2586773] Test: 108/132-08_000153, Batch: 0/1 [2024-11-25 20:02:26,780 INFO test.py line 292 2586773] Test: 08_000153 [108/132]-131072 Batch 0.270 (0.235) Accuracy 0.9986 (0.8586) mIoU 0.7830 (0.7802) [2024-11-25 20:02:26,998 INFO test.py line 250 2586773] Test: 109/132-08_000334, Batch: 0/1 [2024-11-25 20:02:27,005 INFO test.py line 292 2586773] Test: 08_000334 [109/132]-131072 Batch 0.214 (0.235) Accuracy 0.9971 (0.8582) mIoU 0.7821 (0.7802) [2024-11-25 20:02:27,239 INFO test.py line 250 2586773] Test: 110/132-08_000357, Batch: 0/1 [2024-11-25 20:02:27,246 INFO test.py line 292 2586773] Test: 08_000357 [110/132]-131072 Batch 0.231 (0.235) Accuracy 0.9986 (0.8583) mIoU 0.8149 (0.7804) [2024-11-25 20:02:27,469 INFO test.py line 250 2586773] Test: 111/132-08_000510, Batch: 0/1 [2024-11-25 20:02:27,476 INFO test.py line 292 2586773] Test: 08_000510 [111/132]-131072 Batch 0.220 (0.235) Accuracy 0.9977 (0.8585) mIoU 0.7853 (0.7805) [2024-11-25 20:02:27,697 INFO test.py line 250 2586773] Test: 112/132-08_000318, Batch: 0/1 [2024-11-25 20:02:27,704 INFO test.py line 292 2586773] Test: 08_000318 [112/132]-131072 Batch 0.218 (0.234) Accuracy 0.9971 (0.8584) mIoU 0.7953 (0.7807) [2024-11-25 20:02:27,927 INFO test.py line 250 2586773] Test: 113/132-08_000312, Batch: 0/1 [2024-11-25 20:02:27,935 INFO test.py line 292 2586773] Test: 08_000312 [113/132]-131072 Batch 0.219 (0.234) Accuracy 0.9975 (0.8586) mIoU 0.8130 (0.7810) [2024-11-25 20:02:28,162 INFO test.py line 250 2586773] Test: 114/132-08_000449, Batch: 0/1 [2024-11-25 20:02:28,169 INFO test.py line 292 2586773] Test: 08_000449 [114/132]-131072 Batch 0.224 (0.234) Accuracy 0.9971 (0.8586) mIoU 0.7999 (0.7812) [2024-11-25 20:02:28,396 INFO test.py line 250 2586773] Test: 115/132-08_000452, Batch: 0/1 [2024-11-25 20:02:28,403 INFO test.py line 292 2586773] Test: 08_000452 [115/132]-131072 Batch 0.222 (0.234) Accuracy 0.9973 (0.8587) mIoU 0.8071 (0.7815) [2024-11-25 20:02:28,676 INFO test.py line 250 2586773] Test: 116/132-08_000131, Batch: 0/1 [2024-11-25 20:02:28,682 INFO test.py line 292 2586773] Test: 08_000131 [116/132]-131072 Batch 0.269 (0.234) Accuracy 0.9980 (0.8582) mIoU 0.7213 (0.7812) [2024-11-25 20:02:28,949 INFO test.py line 250 2586773] Test: 117/132-08_000115, Batch: 0/1 [2024-11-25 20:02:28,957 INFO test.py line 292 2586773] Test: 08_000115 [117/132]-131072 Batch 0.264 (0.235) Accuracy 0.9978 (0.8581) mIoU 0.7608 (0.7810) [2024-11-25 20:02:29,234 INFO test.py line 250 2586773] Test: 118/132-08_000158, Batch: 0/1 [2024-11-25 20:02:29,241 INFO test.py line 292 2586773] Test: 08_000158 [118/132]-131072 Batch 0.273 (0.235) Accuracy 0.9985 (0.8582) mIoU 0.7826 (0.7810) [2024-11-25 20:02:29,479 INFO test.py line 250 2586773] Test: 119/132-08_000077, Batch: 0/1 [2024-11-25 20:02:29,486 INFO test.py line 292 2586773] Test: 08_000077 [119/132]-131072 Batch 0.235 (0.235) Accuracy 0.9978 (0.8583) mIoU 0.7902 (0.7811) [2024-11-25 20:02:29,759 INFO test.py line 250 2586773] Test: 120/132-08_000123, Batch: 0/1 [2024-11-25 20:02:29,765 INFO test.py line 292 2586773] Test: 08_000123 [120/132]-131072 Batch 0.269 (0.235) Accuracy 0.9982 (0.8580) mIoU 0.7475 (0.7809) [2024-11-25 20:02:29,984 INFO test.py line 250 2586773] Test: 121/132-08_000471, Batch: 0/1 [2024-11-25 20:02:29,991 INFO test.py line 292 2586773] Test: 08_000471 [121/132]-131072 Batch 0.216 (0.235) Accuracy 0.9965 (0.8576) mIoU 0.7338 (0.7805) [2024-11-25 20:02:30,220 INFO test.py line 250 2586773] Test: 122/132-08_000032, Batch: 0/1 [2024-11-25 20:02:30,227 INFO test.py line 292 2586773] Test: 08_000032 [122/132]-131072 Batch 0.225 (0.235) Accuracy 0.9977 (0.8579) mIoU 0.7957 (0.7806) [2024-11-25 20:02:30,480 INFO test.py line 250 2586773] Test: 123/132-08_000103, Batch: 0/1 [2024-11-25 20:02:30,487 INFO test.py line 292 2586773] Test: 08_000103 [123/132]-131072 Batch 0.250 (0.235) Accuracy 0.9974 (0.8580) mIoU 0.7777 (0.7806) [2024-11-25 20:02:30,704 INFO test.py line 250 2586773] Test: 124/132-08_000328, Batch: 0/1 [2024-11-25 20:02:30,710 INFO test.py line 292 2586773] Test: 08_000328 [124/132]-131072 Batch 0.213 (0.235) Accuracy 0.9973 (0.8577) mIoU 0.7900 (0.7807) [2024-11-25 20:02:30,938 INFO test.py line 250 2586773] Test: 125/132-08_000285, Batch: 0/1 [2024-11-25 20:02:30,944 INFO test.py line 292 2586773] Test: 08_000285 [125/132]-131072 Batch 0.223 (0.235) Accuracy 0.9975 (0.8579) mIoU 0.7807 (0.7807) [2024-11-25 20:02:31,182 INFO test.py line 250 2586773] Test: 126/132-08_000394, Batch: 0/1 [2024-11-25 20:02:31,189 INFO test.py line 292 2586773] Test: 08_000394 [126/132]-131072 Batch 0.235 (0.235) Accuracy 0.9977 (0.8577) mIoU 0.7838 (0.7807) [2024-11-25 20:02:31,432 INFO test.py line 250 2586773] Test: 127/132-08_000366, Batch: 0/1 [2024-11-25 20:02:31,439 INFO test.py line 292 2586773] Test: 08_000366 [127/132]-131072 Batch 0.238 (0.235) Accuracy 0.9986 (0.8578) mIoU 0.8164 (0.7809) [2024-11-25 20:02:31,677 INFO test.py line 250 2586773] Test: 128/132-08_000389, Batch: 0/1 [2024-11-25 20:02:31,683 INFO test.py line 292 2586773] Test: 08_000389 [128/132]-131072 Batch 0.235 (0.235) Accuracy 0.9975 (0.8575) mIoU 0.7810 (0.7809) [2024-11-25 20:02:31,902 INFO test.py line 250 2586773] Test: 129/132-08_000459, Batch: 0/1 [2024-11-25 20:02:31,909 INFO test.py line 292 2586773] Test: 08_000459 [129/132]-131072 Batch 0.215 (0.235) Accuracy 0.9967 (0.8572) mIoU 0.7663 (0.7807) [2024-11-25 20:02:32,128 INFO test.py line 250 2586773] Test: 130/132-08_000309, Batch: 0/1 [2024-11-25 20:02:32,135 INFO test.py line 292 2586773] Test: 08_000309 [130/132]-131072 Batch 0.216 (0.235) Accuracy 0.9976 (0.8574) mIoU 0.8195 (0.7811) [2024-11-25 20:02:32,351 INFO test.py line 250 2586773] Test: 131/132-08_000301, Batch: 0/1 [2024-11-25 20:02:32,358 INFO test.py line 292 2586773] Test: 08_000301 [131/132]-131072 Batch 0.213 (0.234) Accuracy 0.9972 (0.8574) mIoU 0.7976 (0.7813) [2024-11-25 20:02:32,574 INFO test.py line 250 2586773] Test: 132/132-08_000474, Batch: 0/1 [2024-11-25 20:02:32,581 INFO test.py line 292 2586773] Test: 08_000474 [132/132]-131072 Batch 0.212 (0.234) Accuracy 0.9981 (0.8577) mIoU 0.8248 (0.7816) [2024-11-25 20:02:32,983 INFO test.py line 361 2586773] Syncing ... [2024-11-25 20:02:33,929 INFO test.py line 389 2586773] Val result: mIoU/mAcc/allAcc 0.7805/0.8577/0.9975 [2024-11-25 20:02:33,929 INFO test.py line 395 2586773] Class_0 - background Result: iou/accuracy 0.9975/0.9988 [2024-11-25 20:02:33,929 INFO test.py line 395 2586773] Class_1 - lane Result: iou/accuracy 0.5635/0.7167 [2024-11-25 20:02:33,929 INFO test.py line 403 2586773] <<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<