| auto_scale_lr = dict(base_batch_size=512) |
| backend_args = dict(backend='local') |
| codec = dict( |
| heatmap_size=( |
| 64, |
| 64, |
| ), |
| input_size=( |
| 256, |
| 256, |
| ), |
| sigma=2, |
| type='MSRAHeatmap') |
| custom_hooks = [ |
| dict(type='SyncBuffersHook'), |
| ] |
| data_mode = 'topdown' |
| data_root = '/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/' |
| dataset_type = 'CocoDataset' |
| default_hooks = dict( |
| badcase=dict( |
| badcase_thr=5, |
| enable=False, |
| metric_type='loss', |
| out_dir='badcase', |
| type='BadCaseAnalysisHook'), |
| checkpoint=dict( |
| interval=10, rule='greater', save_best='PCK', type='CheckpointHook'), |
| logger=dict(interval=50, type='LoggerHook'), |
| param_scheduler=dict(type='ParamSchedulerHook'), |
| sampler_seed=dict(type='DistSamplerSeedHook'), |
| timer=dict(type='IterTimerHook'), |
| visualization=dict(enable=False, type='PoseVisualizationHook')) |
| default_scope = 'mmpose' |
| env_cfg = dict( |
| cudnn_benchmark=False, |
| dist_cfg=dict(backend='nccl'), |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) |
| load_from = None |
| log_level = 'INFO' |
| log_processor = dict( |
| by_epoch=True, num_digits=6, type='LogProcessor', window_size=50) |
| model = dict( |
| backbone=dict( |
| extra=dict( |
| stage1=dict( |
| block='BOTTLENECK', |
| num_blocks=(4, ), |
| num_branches=1, |
| num_channels=(64, ), |
| num_modules=1), |
| stage2=dict( |
| block='BASIC', |
| num_blocks=( |
| 4, |
| 4, |
| ), |
| num_branches=2, |
| num_channels=( |
| 32, |
| 64, |
| ), |
| num_modules=1), |
| stage3=dict( |
| block='BASIC', |
| num_blocks=( |
| 4, |
| 4, |
| 4, |
| ), |
| num_branches=3, |
| num_channels=( |
| 32, |
| 64, |
| 128, |
| ), |
| num_modules=4), |
| stage4=dict( |
| block='BASIC', |
| num_blocks=( |
| 4, |
| 4, |
| 4, |
| 4, |
| ), |
| num_branches=4, |
| num_channels=( |
| 32, |
| 64, |
| 128, |
| 256, |
| ), |
| num_modules=3)), |
| in_channels=3, |
| init_cfg=dict( |
| checkpoint= |
| 'https://download.openmmlab.com/mmpose/pretrain_models/hrnet_w32-36af842e.pth', |
| type='Pretrained'), |
| type='HRNet'), |
| data_preprocessor=dict( |
| bgr_to_rgb=True, |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| type='PoseDataPreprocessor'), |
| head=dict( |
| decoder=dict( |
| heatmap_size=( |
| 64, |
| 64, |
| ), |
| input_size=( |
| 256, |
| 256, |
| ), |
| sigma=2, |
| type='MSRAHeatmap'), |
| deconv_out_channels=None, |
| in_channels=32, |
| loss=dict(type='KeypointMSELoss', use_target_weight=True), |
| out_channels=8, |
| type='HeatmapHead'), |
| test_cfg=dict(flip_mode='heatmap', flip_test=True, shift_heatmap=True), |
| type='TopdownPoseEstimator') |
| optim_wrapper = dict(optimizer=dict(lr=0.0005, type='AdamW')) |
| param_scheduler = [ |
| dict( |
| begin=0, by_epoch=False, end=500, start_factor=0.001, type='LinearLR'), |
| dict( |
| begin=0, |
| by_epoch=True, |
| end=210, |
| gamma=0.1, |
| milestones=[ |
| 170, |
| 200, |
| ], |
| type='MultiStepLR'), |
| ] |
| resume = False |
| test_cfg = dict() |
| test_dataloader = dict( |
| batch_size=24, |
| dataset=dict( |
| ann_file= |
| '/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/CrowOnlyKP_Test_COCO.json', |
| bbox_file=None, |
| data_mode='topdown', |
| data_prefix=dict(img=''), |
| data_root='/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/', |
| metainfo=dict( |
| dataset_name='coco', |
| joint_weights=[ |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| ], |
| keypoint_info=dict({ |
| 0: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=0, name='hd_bill_tip', swap=''), |
| 1: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=1, name='hd_bill_base', swap=''), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=2, |
| name='hd_eye_left', |
| swap='hd_eye_right'), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=3, |
| name='hd_eye_right', |
| swap='hd_eye_left'), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=4, |
| name='bd_shoulder_left', |
| swap='bd_shoulder_right'), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=5, |
| name='bd_shoulder_right', |
| swap='bd_shoulder_left'), |
| 6: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=6, name='bd_tail_base', swap=''), |
| 7: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=7, name='bd_tail_tip', swap='') |
| }), |
| sigmas=[ |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| ], |
| skeleton_info=dict({ |
| 0: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_shoulder_right', |
| )), |
| 1: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_tail_base', |
| )), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_right', |
| 'bd_tail_base', |
| )), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_tail_base', |
| 'bd_tail_tip', |
| )), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_bill_base', |
| 'hd_bill_tip', |
| )), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_eye_right', |
| )), |
| 6: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_bill_base', |
| )), |
| 7: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_right', |
| 'hd_bill_base', |
| )) |
| })), |
| pipeline=[ |
| dict(type='LoadImage'), |
| dict(type='GetBBoxCenterScale'), |
| dict(input_size=( |
| 256, |
| 256, |
| ), type='TopdownAffine'), |
| dict(type='PackPoseInputs'), |
| ], |
| test_mode=True, |
| type='CocoDataset'), |
| drop_last=False, |
| num_workers=2, |
| persistent_workers=True, |
| sampler=dict(round_up=False, shuffle=False, type='DefaultSampler')) |
| test_evaluator = [ |
| dict(thr=0.05, type='PCKAccuracy'), |
| dict(type='AUC'), |
| ] |
| train_cfg = dict(by_epoch=True, max_epochs=50, val_interval=10) |
| train_dataloader = dict( |
| batch_size=32, |
| dataset=dict( |
| ann_file= |
| '/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/CrowOnlyKP_Train_COCO.json', |
| data_mode='topdown', |
| data_prefix=dict(img=''), |
| data_root='/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/', |
| metainfo=dict( |
| dataset_name='coco', |
| joint_weights=[ |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| ], |
| keypoint_info=dict({ |
| 0: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=0, name='hd_bill_tip', swap=''), |
| 1: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=1, name='hd_bill_base', swap=''), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=2, |
| name='hd_eye_left', |
| swap='hd_eye_right'), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=3, |
| name='hd_eye_right', |
| swap='hd_eye_left'), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=4, |
| name='bd_shoulder_left', |
| swap='bd_shoulder_right'), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=5, |
| name='bd_shoulder_right', |
| swap='bd_shoulder_left'), |
| 6: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=6, name='bd_tail_base', swap=''), |
| 7: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=7, name='bd_tail_tip', swap='') |
| }), |
| sigmas=[ |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| ], |
| skeleton_info=dict({ |
| 0: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_shoulder_right', |
| )), |
| 1: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_tail_base', |
| )), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_right', |
| 'bd_tail_base', |
| )), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_tail_base', |
| 'bd_tail_tip', |
| )), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_bill_base', |
| 'hd_bill_tip', |
| )), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_eye_right', |
| )), |
| 6: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_bill_base', |
| )), |
| 7: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_right', |
| 'hd_bill_base', |
| )) |
| })), |
| pipeline=[ |
| dict(type='LoadImage'), |
| dict(type='GetBBoxCenterScale'), |
| dict(direction='horizontal', type='RandomFlip'), |
| dict(type='RandomHalfBody'), |
| dict(type='RandomBBoxTransform'), |
| dict(input_size=( |
| 256, |
| 256, |
| ), type='TopdownAffine'), |
| dict( |
| encoder=dict( |
| heatmap_size=( |
| 64, |
| 64, |
| ), |
| input_size=( |
| 256, |
| 256, |
| ), |
| sigma=2, |
| type='MSRAHeatmap'), |
| type='GenerateTarget'), |
| dict(type='PackPoseInputs'), |
| ], |
| type='CocoDataset'), |
| num_workers=2, |
| persistent_workers=True, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| train_pipeline = [ |
| dict(type='LoadImage'), |
| dict(type='GetBBoxCenterScale'), |
| dict(direction='horizontal', type='RandomFlip'), |
| dict(type='RandomHalfBody'), |
| dict(type='RandomBBoxTransform'), |
| dict(input_size=( |
| 256, |
| 256, |
| ), type='TopdownAffine'), |
| dict( |
| encoder=dict( |
| heatmap_size=( |
| 64, |
| 64, |
| ), |
| input_size=( |
| 256, |
| 256, |
| ), |
| sigma=2, |
| type='MSRAHeatmap'), |
| type='GenerateTarget'), |
| dict(type='PackPoseInputs'), |
| ] |
| val_cfg = dict() |
| val_dataloader = dict( |
| batch_size=24, |
| dataset=dict( |
| ann_file= |
| '/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/CrowOnlyKP_Test_COCO.json', |
| bbox_file=None, |
| data_mode='topdown', |
| data_prefix=dict(img=''), |
| data_root='/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/', |
| metainfo=dict( |
| dataset_name='coco', |
| joint_weights=[ |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| 1.0, |
| ], |
| keypoint_info=dict({ |
| 0: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=0, name='hd_bill_tip', swap=''), |
| 1: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=1, name='hd_bill_base', swap=''), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=2, |
| name='hd_eye_left', |
| swap='hd_eye_right'), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=3, |
| name='hd_eye_right', |
| swap='hd_eye_left'), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=4, |
| name='bd_shoulder_left', |
| swap='bd_shoulder_right'), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| id=5, |
| name='bd_shoulder_right', |
| swap='bd_shoulder_left'), |
| 6: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=6, name='bd_tail_base', swap=''), |
| 7: |
| dict(color=[ |
| 255, |
| 0, |
| 0, |
| ], id=7, name='bd_tail_tip', swap='') |
| }), |
| sigmas=[ |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| 0.025, |
| ], |
| skeleton_info=dict({ |
| 0: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_shoulder_right', |
| )), |
| 1: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_left', |
| 'bd_tail_base', |
| )), |
| 2: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_shoulder_right', |
| 'bd_tail_base', |
| )), |
| 3: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'bd_tail_base', |
| 'bd_tail_tip', |
| )), |
| 4: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_bill_base', |
| 'hd_bill_tip', |
| )), |
| 5: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_eye_right', |
| )), |
| 6: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_left', |
| 'hd_bill_base', |
| )), |
| 7: |
| dict( |
| color=[ |
| 255, |
| 0, |
| 0, |
| ], |
| link=( |
| 'hd_eye_right', |
| 'hd_bill_base', |
| )) |
| })), |
| pipeline=[ |
| dict(type='LoadImage'), |
| dict(type='GetBBoxCenterScale'), |
| dict(input_size=( |
| 256, |
| 256, |
| ), type='TopdownAffine'), |
| dict(type='PackPoseInputs'), |
| ], |
| test_mode=True, |
| type='CocoDataset'), |
| drop_last=False, |
| num_workers=2, |
| persistent_workers=True, |
| sampler=dict(round_up=False, shuffle=False, type='DefaultSampler')) |
| val_evaluator = [ |
| dict(thr=0.05, type='PCKAccuracy'), |
| dict(type='AUC'), |
| ] |
| val_pipeline = [ |
| dict(type='LoadImage'), |
| dict(type='GetBBoxCenterScale'), |
| dict(input_size=( |
| 256, |
| 256, |
| ), type='TopdownAffine'), |
| dict(type='PackPoseInputs'), |
| ] |
| vis_backends = [ |
| dict(type='LocalVisBackend'), |
| ] |
| visualizer = dict( |
| name='visualizer', |
| type='PoseLocalVisualizer', |
| vis_backends=[ |
| dict(type='LocalVisBackend'), |
| ]) |
| work_dir = '/media/alexchan/Extreme SSD/SampleDatasets/Vienna/Dataset2/CrowOnly_HrNet' |
|
|
