{"env_info": "sys.platform: linux\nPython: 3.8.20 (default, Oct  3 2024, 15:24:27) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1: NVIDIA RTX A6000\nCUDA_HOME: /usr/local/cuda-11.8\nNVCC: Cuda compilation tools, release 11.8, V11.8.89\nGCC: gcc (Ubuntu 9.5.0-1ubuntu1~22.04) 9.5.0\nPyTorch: 1.13.1+cu117\nPyTorch compiling details: PyTorch built with:\n  - GCC 9.3\n  - C++ Version: 201402\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n  - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\n  - LAPACK is enabled (usually provided by MKL)\n  - NNPACK is enabled\n  - CPU capability usage: AVX2\n  - CUDA Runtime 11.7\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n  - CuDNN 8.5\n  - Magma 2.6.1\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.13.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, \n\nTorchVision: 0.14.1+cu117\nOpenCV: 4.13.0\nMMCV: 1.7.0\nMMCV Compiler: GCC 9.3\nMMCV CUDA Compiler: 11.7\nMMDetection: 2.28.2\nMMSegmentation: 0.30.0\nMMDetection3D: 1.0.0rc6+2f2b98c\nspconv2.0: False", "config": "checkpoint_config = dict(interval=9322)\nlog_config = dict(\n    interval=50,\n    hooks=[\n        dict(type='TextLoggerHook'),\n        dict(type='TensorboardLoggerHook'),\n        dict(\n            type='WandbLoggerHook',\n            init_kwargs=dict(\n                entity='IRCV_Mapping',\n                project='FullOld-ResilientMap-Sumin',\n                name='Full-ResilientMap-oldsplit/stage1_pixelgate'))\n    ])\ndist_params = dict(backend='nccl')\nlog_level = 'INFO'\nwork_dir = './work_dirs/Full-ResilientMap-Sumin-oldsplit/stage1_pixelgate'\nload_from = None\nresume_from = None\nworkflow = [('train', 1)]\ntype = 'Mapper'\nplugin = True\nplugin_dir = 'plugin/'\nimg_norm_cfg = dict(\n    mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\nimg_h = 480\nimg_w = 800\nimg_size = (480, 800)\nnum_cams = 6\nnum_gpus = 2\nbatch_size = 3\nnum_iters_per_epoch = 4661\nnum_epochs = 12\nnum_epochs_interval = 2\ntotal_iters = 55932\nnum_queries = 100\ncat2id = dict(ped_crossing=0, divider=1, boundary=2)\nnum_class = 3\nroi_size = (60, 30)\nbev_h = 50\nbev_w = 100\npc_range = [-30.0, -15.0, -3, 30.0, 15.0, 5]\ncoords_dim = 2\nsample_dist = -1\nsample_num = -1\nsimplify = True\ncanvas_size = (200, 100)\nthickness = 3\nmeta = dict(\n    use_lidar=False,\n    use_camera=True,\n    use_radar=False,\n    use_map=False,\n    use_external=False,\n    output_format='vector')\nbev_embed_dims = 256\nembed_dims = 512\nnum_feat_levels = 3\nnorm_cfg = dict(type='BN2d')\nnum_points = 20\npermute = True\naid4ad_root = './datasets/nuscenes/AID4AD_frames'\nmodel = dict(\n    type='SatMapTracker',\n    roi_size=(60, 30),\n    bev_h=50,\n    bev_w=100,\n    history_steps=4,\n    test_time_history_steps=20,\n    mem_select_dist_ranges=[1, 5, 10, 15],\n    skip_vector_head=True,\n    freeze_bev=False,\n    track_fp_aug=False,\n    use_memory=False,\n    mem_len=4,\n    mem_warmup_iters=500,\n    history_mode='cam',\n    freeze_sat_encoder=False,\n    sat_encoder_cfg=dict(\n        type='SatelliteEncoder',\n        backbone_cfg=dict(\n            type='ResNet',\n            depth=50,\n            num_stages=4,\n            out_indices=(0, 1, 2, 3),\n            frozen_stages=-1,\n            norm_cfg=dict(type='BN2d'),\n            norm_eval=True,\n            style='pytorch',\n            init_cfg=dict(\n                type='Pretrained', checkpoint='torchvision://resnet50')),\n        out_channels=256,\n        bev_size=(50, 100)),\n    sat_fusion_cfg=dict(type='ReliabilityAwareFusion', in_channels=256),\n    backbone_cfg=dict(\n        type='BEVFormerBackbone',\n        roi_size=(60, 30),\n        bev_h=50,\n        bev_w=100,\n        use_grid_mask=True,\n        history_steps=4,\n        img_backbone=dict(\n            type='ResNet',\n            with_cp=False,\n            pretrained='open-mmlab://detectron2/resnet50_caffe',\n            depth=50,\n            num_stages=4,\n            out_indices=(1, 2, 3),\n            frozen_stages=-1,\n            norm_cfg=dict(type='BN2d'),\n            norm_eval=True,\n            style='caffe',\n            dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),\n            stage_with_dcn=(False, False, True, True)),\n        img_neck=dict(\n            type='FPN',\n            in_channels=[512, 1024, 2048],\n            out_channels=256,\n            start_level=0,\n            add_extra_convs=True,\n            num_outs=3,\n            norm_cfg=dict(type='BN2d'),\n            relu_before_extra_convs=True),\n        transformer=dict(\n            type='PerceptionTransformer',\n            embed_dims=256,\n            encoder=dict(\n                type='BEVFormerEncoder',\n                num_layers=2,\n                pc_range=[-30.0, -15.0, -3, 30.0, 15.0, 5],\n                num_points_in_pillar=4,\n                return_intermediate=False,\n                transformerlayers=dict(\n                    type='BEVFormerLayer',\n                    attn_cfgs=[\n                        dict(\n                            type='TemporalSelfAttention',\n                            embed_dims=256,\n                            num_levels=1),\n                        dict(\n                            type='SpatialCrossAttention',\n                            deformable_attention=dict(\n                                type='MSDeformableAttention3D',\n                                embed_dims=256,\n                                num_points=8,\n                                num_levels=3),\n                            embed_dims=256)\n                    ],\n                    feedforward_channels=512,\n                    ffn_dropout=0.1,\n                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                     'ffn', 'norm')))),\n        positional_encoding=dict(\n            type='LearnedPositionalEncoding',\n            num_feats=128,\n            row_num_embed=50,\n            col_num_embed=100)),\n    head_cfg=dict(\n        type='MapDetectorHead',\n        num_queries=100,\n        embed_dims=512,\n        num_classes=3,\n        in_channels=256,\n        num_points=20,\n        roi_size=(60, 30),\n        coord_dim=2,\n        different_heads=False,\n        predict_refine=False,\n        sync_cls_avg_factor=True,\n        trans_loss_weight=0.1,\n        transformer=dict(\n            type='MapTransformer',\n            num_feature_levels=1,\n            num_points=20,\n            coord_dim=2,\n            encoder=dict(type='PlaceHolderEncoder', embed_dims=512),\n            decoder=dict(\n                type='MapTransformerDecoder_new',\n                num_layers=6,\n                prop_add_stage=1,\n                return_intermediate=True,\n                transformerlayers=dict(\n                    type='MapTransformerLayer',\n                    attn_cfgs=[\n                        dict(\n                            type='MultiheadAttention',\n                            embed_dims=512,\n                            num_heads=8,\n                            attn_drop=0.1,\n                            proj_drop=0.1),\n                        dict(\n                            type='CustomMSDeformableAttention',\n                            embed_dims=512,\n                            num_heads=8,\n                            num_levels=1,\n                            num_points=20,\n                            dropout=0.1),\n                        dict(\n                            type='MultiheadAttention',\n                            embed_dims=512,\n                            num_heads=8,\n                            attn_drop=0.1,\n                            proj_drop=0.1)\n                    ],\n                    ffn_cfgs=dict(\n                        type='FFN',\n                        embed_dims=512,\n                        feedforward_channels=1024,\n                        num_fcs=2,\n                        ffn_drop=0.1,\n                        act_cfg=dict(type='ReLU', inplace=True)),\n                    feedforward_channels=1024,\n                    ffn_dropout=0.1,\n                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',\n                                     'cross_attn', 'norm', 'ffn', 'norm')))),\n        loss_cls=dict(\n            type='FocalLoss',\n            use_sigmoid=True,\n            gamma=2.0,\n            alpha=0.25,\n            loss_weight=5.0),\n        loss_reg=dict(type='LinesL1Loss', loss_weight=50.0, beta=0.01),\n        assigner=dict(\n            type='HungarianLinesAssigner',\n            cost=dict(\n                type='MapQueriesCost',\n                cls_cost=dict(type='FocalLossCost', weight=5.0),\n                reg_cost=dict(\n                    type='LinesL1Cost', weight=50.0, beta=0.01,\n                    permute=True)))),\n    seg_cfg=dict(\n        type='MapSegHead',\n        num_classes=3,\n        in_channels=256,\n        embed_dims=256,\n        bev_size=(100, 50),\n        canvas_size=(200, 100),\n        loss_seg=dict(\n            type='MaskFocalLoss', use_sigmoid=True, loss_weight=10.0),\n        loss_dice=dict(type='MaskDiceLoss', loss_weight=1.0)),\n    model_name='SatMapTracker_Stage1',\n    use_reliability_fusion=True,\n    reliability_mode='max_sigmoid',\n    use_prediction_moe=False,\n    use_modality_masking=True,\n    mask_cam_zero_prob=0.1,\n    mask_cam_noise_prob=0.1,\n    mask_cam_block_prob=0.05,\n    mask_sat_zero_prob=0.05,\n    mask_noise_scale=1.0,\n    use_norm_dampening=False)\ntrain_pipeline = [\n    dict(\n        type='VectorizeMap',\n        coords_dim=2,\n        roi_size=(60, 30),\n        sample_num=20,\n        normalize=True,\n        permute=True),\n    dict(\n        type='RasterizeMap',\n        roi_size=(60, 30),\n        coords_dim=2,\n        canvas_size=(200, 100),\n        thickness=3,\n        semantic_mask=True),\n    dict(type='LoadMultiViewImagesFromFiles', to_float32=True),\n    dict(\n        type='LoadAID4ADSatelliteImage',\n        data_root='./datasets/nuscenes/AID4AD_frames',\n        canvas_size=None,\n        normalize=True),\n    dict(type='PhotoMetricDistortionMultiViewImage'),\n    dict(\n        type='ResizeMultiViewImages', size=(480, 800), change_intrinsics=True),\n    dict(\n        type='Normalize3D',\n        mean=[103.53, 116.28, 123.675],\n        std=[1.0, 1.0, 1.0],\n        to_rgb=False),\n    dict(type='PadMultiViewImages', size_divisor=32),\n    dict(type='FormatBundleMap'),\n    dict(\n        type='Collect3D',\n        keys=['img', 'vectors', 'semantic_mask', 'sat_img'],\n        meta_keys=('token', 'ego2img', 'sample_idx', 'ego2global_translation',\n                   'ego2global_rotation', 'img_shape', 'scene_name'))\n]\ntest_pipeline = [\n    dict(type='LoadMultiViewImagesFromFiles', to_float32=True),\n    dict(\n        type='LoadAID4ADSatelliteImage',\n        data_root='./datasets/nuscenes/AID4AD_frames',\n        canvas_size=None,\n        normalize=True),\n    dict(\n        type='ResizeMultiViewImages', size=(480, 800), change_intrinsics=True),\n    dict(\n        type='Normalize3D',\n        mean=[103.53, 116.28, 123.675],\n        std=[1.0, 1.0, 1.0],\n        to_rgb=False),\n    dict(type='PadMultiViewImages', size_divisor=32),\n    dict(\n        type='ApplySensorCorruption',\n        enabled=False,\n        camera_mask=0,\n        camera_names=[\n            'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT', 'CAM_BACK',\n            'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'\n        ],\n        camera_mode='zero',\n        camera_noise_std=1.0,\n        satellite_corrupted=False,\n        satellite_mode='zero',\n        satellite_noise_std=1.0),\n    dict(type='FormatBundleMap'),\n    dict(\n        type='Collect3D',\n        keys=['img', 'sat_img'],\n        meta_keys=('token', 'ego2img', 'sample_idx', 'ego2global_translation',\n                   'ego2global_rotation', 'img_shape', 'scene_name',\n                   'sensor_corruption'))\n]\neval_config = dict(\n    type='NuscDataset',\n    data_root='./datasets/nuscenes',\n    ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n    meta=dict(\n        use_lidar=False,\n        use_camera=True,\n        use_radar=False,\n        use_map=False,\n        use_external=False,\n        output_format='vector'),\n    roi_size=(60, 30),\n    cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n    pipeline=[\n        dict(\n            type='VectorizeMap',\n            coords_dim=2,\n            simplify=True,\n            normalize=False,\n            roi_size=(60, 30)),\n        dict(\n            type='RasterizeMap',\n            roi_size=(60, 30),\n            coords_dim=2,\n            canvas_size=(200, 100),\n            thickness=3,\n            semantic_mask=True),\n        dict(type='FormatBundleMap'),\n        dict(\n            type='Collect3D',\n            keys=['vectors', 'semantic_mask'],\n            meta_keys=[\n                'token', 'ego2img', 'sample_idx', 'ego2global_translation',\n                'ego2global_rotation', 'img_shape', 'scene_name'\n            ])\n    ],\n    interval=1)\nmatch_config = dict(\n    type='NuscDataset',\n    data_root='./datasets/nuscenes',\n    ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n    meta=dict(\n        use_lidar=False,\n        use_camera=True,\n        use_radar=False,\n        use_map=False,\n        use_external=False,\n        output_format='vector'),\n    roi_size=(60, 30),\n    cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n    pipeline=[\n        dict(\n            type='VectorizeMap',\n            coords_dim=2,\n            simplify=False,\n            normalize=True,\n            roi_size=(60, 30),\n            sample_num=20),\n        dict(\n            type='RasterizeMap',\n            roi_size=(60, 30),\n            coords_dim=2,\n            canvas_size=(200, 100),\n            thickness=3),\n        dict(type='FormatBundleMap'),\n        dict(\n            type='Collect3D',\n            keys=['vectors', 'semantic_mask'],\n            meta_keys=[\n                'token', 'ego2img', 'ego2cam', 'sample_idx',\n                'ego2global_translation', 'ego2global_rotation', 'img_shape',\n                'scene_name', 'img_filenames', 'cam_intrinsics',\n                'cam_extrinsics', 'lidar2ego_translation', 'lidar2ego_rotation'\n            ])\n    ],\n    interval=1)\ndata = dict(\n    samples_per_gpu=3,\n    workers_per_gpu=8,\n    train=dict(\n        type='NuscDataset',\n        data_root='./datasets/nuscenes',\n        ann_file='./datasets/nuscenes/nuscenes_map_infos_train.pkl',\n        meta=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False,\n            output_format='vector'),\n        roi_size=(60, 30),\n        cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n        pipeline=[\n            dict(\n                type='VectorizeMap',\n                coords_dim=2,\n                roi_size=(60, 30),\n                sample_num=20,\n                normalize=True,\n                permute=True),\n            dict(\n                type='RasterizeMap',\n                roi_size=(60, 30),\n                coords_dim=2,\n                canvas_size=(200, 100),\n                thickness=3,\n                semantic_mask=True),\n            dict(type='LoadMultiViewImagesFromFiles', to_float32=True),\n            dict(\n                type='LoadAID4ADSatelliteImage',\n                data_root='./datasets/nuscenes/AID4AD_frames',\n                canvas_size=None,\n                normalize=True),\n            dict(type='PhotoMetricDistortionMultiViewImage'),\n            dict(\n                type='ResizeMultiViewImages',\n                size=(480, 800),\n                change_intrinsics=True),\n            dict(\n                type='Normalize3D',\n                mean=[103.53, 116.28, 123.675],\n                std=[1.0, 1.0, 1.0],\n                to_rgb=False),\n            dict(type='PadMultiViewImages', size_divisor=32),\n            dict(type='FormatBundleMap'),\n            dict(\n                type='Collect3D',\n                keys=['img', 'vectors', 'semantic_mask', 'sat_img'],\n                meta_keys=('token', 'ego2img', 'sample_idx',\n                           'ego2global_translation', 'ego2global_rotation',\n                           'img_shape', 'scene_name'))\n        ],\n        seq_split_num=-2,\n        matching=True,\n        multi_frame=5,\n        sampling_span=10),\n    val=dict(\n        type='NuscDataset',\n        data_root='./datasets/nuscenes',\n        ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n        meta=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False,\n            output_format='vector'),\n        roi_size=(60, 30),\n        cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n        pipeline=[\n            dict(type='LoadMultiViewImagesFromFiles', to_float32=True),\n            dict(\n                type='LoadAID4ADSatelliteImage',\n                data_root='./datasets/nuscenes/AID4AD_frames',\n                canvas_size=None,\n                normalize=True),\n            dict(\n                type='ResizeMultiViewImages',\n                size=(480, 800),\n                change_intrinsics=True),\n            dict(\n                type='Normalize3D',\n                mean=[103.53, 116.28, 123.675],\n                std=[1.0, 1.0, 1.0],\n                to_rgb=False),\n            dict(type='PadMultiViewImages', size_divisor=32),\n            dict(\n                type='ApplySensorCorruption',\n                enabled=False,\n                camera_mask=0,\n                camera_names=[\n                    'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT',\n                    'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'\n                ],\n                camera_mode='zero',\n                camera_noise_std=1.0,\n                satellite_corrupted=False,\n                satellite_mode='zero',\n                satellite_noise_std=1.0),\n            dict(type='FormatBundleMap'),\n            dict(\n                type='Collect3D',\n                keys=['img', 'sat_img'],\n                meta_keys=('token', 'ego2img', 'sample_idx',\n                           'ego2global_translation', 'ego2global_rotation',\n                           'img_shape', 'scene_name', 'sensor_corruption'))\n        ],\n        eval_config=dict(\n            type='NuscDataset',\n            data_root='./datasets/nuscenes',\n            ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n            meta=dict(\n                use_lidar=False,\n                use_camera=True,\n                use_radar=False,\n                use_map=False,\n                use_external=False,\n                output_format='vector'),\n            roi_size=(60, 30),\n            cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n            pipeline=[\n                dict(\n                    type='VectorizeMap',\n                    coords_dim=2,\n                    simplify=True,\n                    normalize=False,\n                    roi_size=(60, 30)),\n                dict(\n                    type='RasterizeMap',\n                    roi_size=(60, 30),\n                    coords_dim=2,\n                    canvas_size=(200, 100),\n                    thickness=3,\n                    semantic_mask=True),\n                dict(type='FormatBundleMap'),\n                dict(\n                    type='Collect3D',\n                    keys=['vectors', 'semantic_mask'],\n                    meta_keys=[\n                        'token', 'ego2img', 'sample_idx',\n                        'ego2global_translation', 'ego2global_rotation',\n                        'img_shape', 'scene_name'\n                    ])\n            ],\n            interval=1),\n        test_mode=True,\n        seq_split_num=1,\n        eval_semantic=True),\n    test=dict(\n        type='NuscDataset',\n        data_root='./datasets/nuscenes',\n        ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n        meta=dict(\n            use_lidar=False,\n            use_camera=True,\n            use_radar=False,\n            use_map=False,\n            use_external=False,\n            output_format='vector'),\n        roi_size=(60, 30),\n        cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n        pipeline=[\n            dict(type='LoadMultiViewImagesFromFiles', to_float32=True),\n            dict(\n                type='LoadAID4ADSatelliteImage',\n                data_root='./datasets/nuscenes/AID4AD_frames',\n                canvas_size=None,\n                normalize=True),\n            dict(\n                type='ResizeMultiViewImages',\n                size=(480, 800),\n                change_intrinsics=True),\n            dict(\n                type='Normalize3D',\n                mean=[103.53, 116.28, 123.675],\n                std=[1.0, 1.0, 1.0],\n                to_rgb=False),\n            dict(type='PadMultiViewImages', size_divisor=32),\n            dict(\n                type='ApplySensorCorruption',\n                enabled=False,\n                camera_mask=0,\n                camera_names=[\n                    'CAM_FRONT', 'CAM_FRONT_RIGHT', 'CAM_FRONT_LEFT',\n                    'CAM_BACK', 'CAM_BACK_LEFT', 'CAM_BACK_RIGHT'\n                ],\n                camera_mode='zero',\n                camera_noise_std=1.0,\n                satellite_corrupted=False,\n                satellite_mode='zero',\n                satellite_noise_std=1.0),\n            dict(type='FormatBundleMap'),\n            dict(\n                type='Collect3D',\n                keys=['img', 'sat_img'],\n                meta_keys=('token', 'ego2img', 'sample_idx',\n                           'ego2global_translation', 'ego2global_rotation',\n                           'img_shape', 'scene_name', 'sensor_corruption'))\n        ],\n        eval_config=dict(\n            type='NuscDataset',\n            data_root='./datasets/nuscenes',\n            ann_file='./datasets/nuscenes/nuscenes_map_infos_val.pkl',\n            meta=dict(\n                use_lidar=False,\n                use_camera=True,\n                use_radar=False,\n                use_map=False,\n                use_external=False,\n                output_format='vector'),\n            roi_size=(60, 30),\n            cat2id=dict(ped_crossing=0, divider=1, boundary=2),\n            pipeline=[\n                dict(\n                    type='VectorizeMap',\n                    coords_dim=2,\n                    simplify=True,\n                    normalize=False,\n                    roi_size=(60, 30)),\n                dict(\n                    type='RasterizeMap',\n                    roi_size=(60, 30),\n                    coords_dim=2,\n                    canvas_size=(200, 100),\n                    thickness=3,\n                    semantic_mask=True),\n                dict(type='FormatBundleMap'),\n                dict(\n                    type='Collect3D',\n                    keys=['vectors', 'semantic_mask'],\n                    meta_keys=[\n                        'token', 'ego2img', 'sample_idx',\n                        'ego2global_translation', 'ego2global_rotation',\n                        'img_shape', 'scene_name'\n                    ])\n            ],\n            interval=1),\n        test_mode=True,\n        seq_split_num=1,\n        eval_semantic=True),\n    shuffler_sampler=dict(type='DistributedGroupSampler'),\n    nonshuffler_sampler=dict(type='DistributedSampler'))\noptimizer = dict(\n    type='AdamW',\n    lr=0.0005,\n    paramwise_cfg=dict(custom_keys=dict(img_backbone=dict(lr_mult=0.1))),\n    weight_decay=0.01)\noptimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))\nlr_config = dict(\n    policy='CosineAnnealing',\n    warmup='linear',\n    warmup_iters=500,\n    warmup_ratio=0.3333333333333333,\n    min_lr_ratio=0.05)\nevaluation = dict(interval=9322)\nfind_unused_parameters = True\nrunner = dict(type='MyRunnerWrapper', max_iters=55932)\nSyncBN = True\ngpu_ids = range(0, 2)\n", "seed": 0, "exp_name": "satmaptracker_pixelgate_stage1_full.py"}
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{"mode": "train", "epoch": 1, "iter": 850, "lr": 5e-05, "memory": 34607, "data_time": 0.07288, "seg": 1.65833, "seg_dice": 0.54927, "seg_t0": 1.88581, "seg_dice_t0": 0.59779, "seg_t1": 1.7299, "seg_dice_t1": 0.56893, "seg_t2": 1.68578, "seg_dice_t2": 0.55465, "seg_t3": 1.66204, "seg_dice_t3": 0.55135, "total_t0": 2.4836, "total_t1": 2.29883, "total_t2": 2.24043, "total_t3": 2.21339, "total_t4": 2.2076, "total": 16.54281, "cam_bev_norm": 20.41263, "sat_bev_norm": 10.35733, "seg_sat": 2.16841, "seg_dice_sat": 0.65301, "mask_cam_zeroed": 0.16, "mask_cam_noised": 0.12, "seg_fused": 1.72036, "seg_dice_fused": 0.55717, "cam_rel_mean": 0.20994, "sat_rel_mean": 0.23426, "cam_rel_min": 0.03258, "sat_rel_min": 0.05563, "grad_norm": 67.72036, "time": 2.05787}
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