| 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)) |
|
|
