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 = '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/' 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=5, dataset=dict( ann_file= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/COCO_Val.json', bbox_file=None, data_mode='topdown', data_prefix=dict(img=''), data_root= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/', 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='bd_shoulder_left', swap='bd_shoulder_right'), 1: dict( color=[ 255, 0, 0, ], id=1, name='bd_shoulder_right', swap='bd_shoulder_left'), 2: dict(color=[ 255, 0, 0, ], id=2, name='bd_tail_base', swap=''), 3: dict(color=[ 255, 0, 0, ], id=3, name='bd_tail_tip', swap=''), 4: dict(color=[ 255, 0, 0, ], id=4, name='hd_bill_base', swap=''), 5: dict(color=[ 255, 0, 0, ], id=5, name='hd_bill_tip', swap=''), 6: dict( color=[ 255, 0, 0, ], id=6, name='hd_eye_left', swap='hd_eye_right'), 7: dict( color=[ 255, 0, 0, ], id=7, name='hd_eye_right', swap='hd_eye_left') }), sigmas=[ 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, ], skeleton_info=dict()), 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=5, dataset=dict( ann_file= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/COCO_Train.json', data_mode='topdown', data_prefix=dict(img=''), data_root= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/', 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='bd_shoulder_left', swap='bd_shoulder_right'), 1: dict( color=[ 255, 0, 0, ], id=1, name='bd_shoulder_right', swap='bd_shoulder_left'), 2: dict(color=[ 255, 0, 0, ], id=2, name='bd_tail_base', swap=''), 3: dict(color=[ 255, 0, 0, ], id=3, name='bd_tail_tip', swap=''), 4: dict(color=[ 255, 0, 0, ], id=4, name='hd_bill_base', swap=''), 5: dict(color=[ 255, 0, 0, ], id=5, name='hd_bill_tip', swap=''), 6: dict( color=[ 255, 0, 0, ], id=6, name='hd_eye_left', swap='hd_eye_right'), 7: dict( color=[ 255, 0, 0, ], id=7, name='hd_eye_right', swap='hd_eye_left') }), sigmas=[ 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, ], skeleton_info=dict()), 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=5, dataset=dict( ann_file= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/COCO_Val.json', bbox_file=None, data_mode='topdown', data_prefix=dict(img=''), data_root= '/home/alexchan/Documents/MAAP3D_Toolbox/data/SampleAnnotations/', 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='bd_shoulder_left', swap='bd_shoulder_right'), 1: dict( color=[ 255, 0, 0, ], id=1, name='bd_shoulder_right', swap='bd_shoulder_left'), 2: dict(color=[ 255, 0, 0, ], id=2, name='bd_tail_base', swap=''), 3: dict(color=[ 255, 0, 0, ], id=3, name='bd_tail_tip', swap=''), 4: dict(color=[ 255, 0, 0, ], id=4, name='hd_bill_base', swap=''), 5: dict(color=[ 255, 0, 0, ], id=5, name='hd_bill_tip', swap=''), 6: dict( color=[ 255, 0, 0, ], id=6, name='hd_eye_left', swap='hd_eye_right'), 7: dict( color=[ 255, 0, 0, ], id=7, name='hd_eye_right', swap='hd_eye_left') }), sigmas=[ 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, 0.025, ], skeleton_info=dict()), 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 = './data/Weights/hrnet'