diff --git "a/pose-detection-dwpose-estimation.py" "b/pose-detection-dwpose-estimation.py" new file mode 100644--- /dev/null +++ "b/pose-detection-dwpose-estimation.py" @@ -0,0 +1,4860 @@ +return { + "meta": { + "epoch": 20, + "iter": 19141, + "cfg": "default_scope = 'mmpose'\ndefault_hooks = dict(\n timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=50),\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict(\n type='CheckpointHook',\n interval=10,\n save_best='coco-wholebody/AP',\n rule='greater',\n max_keep_ckpts=1),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n visualization=dict(type='PoseVisualizationHook', enable=False))\ncustom_hooks = [\n dict(\n type='EMAHook',\n ema_type='ExpMomentumEMA',\n momentum=0.0002,\n update_buffers=True,\n priority=49),\n dict(\n type='mmdet.PipelineSwitchHook',\n switch_epoch=240,\n switch_pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n shift_factor=0.0,\n scale_factor=[0.75, 1.25],\n rotate_factor=60),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=0.5)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n ])\n]\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'))\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='PoseLocalVisualizer',\n vis_backends=[dict(type='LocalVisBackend')],\n name='visualizer')\nlog_processor = dict(\n type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)\nlog_level = 'INFO'\nload_from = None\nresume = False\nbackend_args = dict(backend='local')\ntrain_cfg = dict(by_epoch=True, max_epochs=60, val_interval=10)\nval_cfg = dict()\ntest_cfg = dict()\nmax_epochs = 270\nstage2_num_epochs = 30\nbase_lr = 0.004\nrandomness = dict(seed=21)\noptim_wrapper = dict(\n type='OptimWrapper',\n optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),\n paramwise_cfg=dict(\n norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),\n clip_grad=dict(max_norm=1.0, norm_type=2))\nparam_scheduler = [\n dict(\n type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,\n end=1000),\n dict(\n type='CosineAnnealingLR',\n eta_min=0.0002,\n begin=135,\n end=270,\n T_max=135,\n by_epoch=True,\n convert_to_iter_based=True)\n]\nauto_scale_lr = dict(base_batch_size=512)\ncodec = dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)\nmodel = dict(\n type='PoseEstimatorDistiller',\n two_dis=True,\n teacher_pretrained=\n '/home/yangzhendong/Projects/mmpose/work_dirs/rtmpose_x_dis_l__coco-ubody-384x288/rtm-l_66.4.pth',\n teacher_cfg=\n 'configs/wholebody_2d_keypoint/rtmpose/ubody/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py',\n student_cfg=\n 'configs/wholebody_2d_keypoint/rtmpose/ubody/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py',\n distill_cfg=[\n dict(methods=[\n dict(\n type='MGD_2Loss',\n name='loss_mgd',\n use_this=True,\n student_channels=1024,\n teacher_channels=1024,\n alpha_mgd=7e-05,\n lambda_mgd=0.15)\n ]),\n dict(methods=[\n dict(type='NKDLoss', name='loss_nkd', use_this=True, weight=1)\n ])\n ],\n data_preprocessor=dict(\n type='PoseDataPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n bgr_to_rgb=True),\n train_cfg=dict(max_epochs=60, val_interval=10))\ndataset_type = 'CocoWholeBodyDataset'\ndata_mode = 'topdown'\ndata_root = '/data/'\ntrain_pipeline = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=1.0)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n]\nval_pipeline = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n]\ntrain_pipeline_stage2 = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n shift_factor=0.0,\n scale_factor=[0.75, 1.25],\n rotate_factor=60),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=0.5)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n]\ndatasets = [\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Magic_show/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Magic_show/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Entertainment/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Entertainment/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/ConductMusic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/ConductMusic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Online_class/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Online_class/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TalkShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TalkShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Speech/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Speech/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Fitness/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Fitness/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Interview/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Interview/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Olympic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Olympic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TVShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TVShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Singing/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Singing/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/SignLanguage/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/SignLanguage/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Movie/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Movie/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/LiveVlog/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/LiveVlog/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/VideoConference/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/VideoConference/'),\n pipeline=[])\n]\ndataset_coco = dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[])\nscene = [\n 'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',\n 'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',\n 'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'\n]\ni = 14\ntrain_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n sampler=dict(type='DefaultSampler', shuffle=True),\n dataset=dict(\n type='CombinedDataset',\n metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),\n datasets=[\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Magic_show/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Magic_show/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Entertainment/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Entertainment/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/ConductMusic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/ConductMusic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Online_class/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Online_class/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TalkShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TalkShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Speech/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Speech/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Fitness/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Fitness/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Interview/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Interview/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Olympic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Olympic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TVShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TVShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Singing/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Singing/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/SignLanguage/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/SignLanguage/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Movie/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Movie/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/LiveVlog/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/LiveVlog/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/VideoConference/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/VideoConference/'),\n pipeline=[])\n ],\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n scale_factor=[0.6, 1.4],\n rotate_factor=80),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=1.0)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n ],\n test_mode=False))\nval_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_val_v1.0.json',\n bbox_file=\n '/data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',\n data_prefix=dict(img='coco/val2017/'),\n test_mode=True,\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n ]))\ntest_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_val_v1.0.json',\n bbox_file=\n '/data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',\n data_prefix=dict(img='coco/val2017/'),\n test_mode=True,\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n ]))\nval_evaluator = dict(\n type='CocoWholeBodyMetric',\n ann_file='/data/coco/annotations/coco_wholebody_val_v1.0.json')\ntest_evaluator = dict(\n type='CocoWholeBodyMetric',\n ann_file='/data/coco/annotations/coco_wholebody_val_v1.0.json')\nfind_unused_parameters = True\nmgd = True\nnkd = True\nlauncher = 'pytorch'\nwork_dir = './work_dirs/rtmpose_l-ll__coco-ubody-384x288'\n", + "seed": 21, + "experiment_name": "rtmpose_l-ll__coco-ubody-384x288_20230706_095637", + "time": "20230706_131003", + "mmengine_version": "0.7.2", + "dataset_meta": { + "dataset_name": "coco_wholebody", + "num_keypoints": 133, + "keypoint_colors": { + "type": "ndarray", + "repr": "array([[ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n 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timer=dict(type='IterTimerHook'),\n logger=dict(type='LoggerHook', interval=50),\n param_scheduler=dict(type='ParamSchedulerHook'),\n checkpoint=dict(\n type='CheckpointHook',\n interval=10,\n save_best='coco-wholebody/AP',\n rule='greater',\n max_keep_ckpts=1),\n sampler_seed=dict(type='DistSamplerSeedHook'),\n visualization=dict(type='PoseVisualizationHook', enable=False))\ncustom_hooks = [\n dict(\n type='EMAHook',\n ema_type='ExpMomentumEMA',\n momentum=0.0002,\n update_buffers=True,\n priority=49),\n dict(\n type='mmdet.PipelineSwitchHook',\n switch_epoch=240,\n switch_pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n shift_factor=0.0,\n scale_factor=[0.75, 1.25],\n rotate_factor=60),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=0.5)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n ])\n]\nenv_cfg = dict(\n cudnn_benchmark=False,\n mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),\n dist_cfg=dict(backend='nccl'))\nvis_backends = [dict(type='LocalVisBackend')]\nvisualizer = dict(\n type='PoseLocalVisualizer',\n vis_backends=[dict(type='LocalVisBackend')],\n name='visualizer')\nlog_processor = dict(\n type='LogProcessor', window_size=50, by_epoch=True, num_digits=6)\nlog_level = 'INFO'\nload_from = None\nresume = False\nbackend_args = dict(backend='local')\ntrain_cfg = dict(by_epoch=True, max_epochs=60, val_interval=10)\nval_cfg = dict()\ntest_cfg = dict()\nmax_epochs = 270\nstage2_num_epochs = 30\nbase_lr = 0.004\nrandomness = dict(seed=21)\noptim_wrapper = dict(\n type='OptimWrapper',\n optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05),\n paramwise_cfg=dict(\n norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),\n clip_grad=dict(max_norm=1.0, norm_type=2))\nparam_scheduler = [\n dict(\n type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0,\n end=1000),\n dict(\n type='CosineAnnealingLR',\n eta_min=0.0002,\n begin=135,\n end=270,\n T_max=135,\n by_epoch=True,\n convert_to_iter_based=True)\n]\nauto_scale_lr = dict(base_batch_size=512)\ncodec = dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)\nmodel = dict(\n type='PoseEstimatorDistiller',\n two_dis=True,\n teacher_pretrained=\n '/home/yangzhendong/Projects/mmpose/work_dirs/rtmpose_x_dis_l__coco-ubody-384x288/rtm-l_66.4.pth',\n teacher_cfg=\n 'configs/wholebody_2d_keypoint/rtmpose/ubody/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py',\n student_cfg=\n 'configs/wholebody_2d_keypoint/rtmpose/ubody/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py',\n distill_cfg=[\n dict(methods=[\n dict(\n type='MGD_2Loss',\n name='loss_mgd',\n use_this=True,\n student_channels=1024,\n teacher_channels=1024,\n alpha_mgd=7e-05,\n lambda_mgd=0.15)\n ]),\n dict(methods=[\n dict(type='NKDLoss', name='loss_nkd', use_this=True, weight=1)\n ])\n ],\n data_preprocessor=dict(\n type='PoseDataPreprocessor',\n mean=[123.675, 116.28, 103.53],\n std=[58.395, 57.12, 57.375],\n bgr_to_rgb=True),\n train_cfg=dict(max_epochs=60, val_interval=10))\ndataset_type = 'CocoWholeBodyDataset'\ndata_mode = 'topdown'\ndata_root = '/data/'\ntrain_pipeline = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=1.0)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n]\nval_pipeline = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n]\ntrain_pipeline_stage2 = [\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n shift_factor=0.0,\n scale_factor=[0.75, 1.25],\n rotate_factor=60),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=0.5)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n]\ndatasets = [\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Magic_show/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Magic_show/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Entertainment/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Entertainment/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/ConductMusic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/ConductMusic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Online_class/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Online_class/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TalkShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TalkShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Speech/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Speech/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Fitness/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Fitness/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Interview/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Interview/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Olympic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Olympic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TVShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TVShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Singing/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Singing/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/SignLanguage/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/SignLanguage/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Movie/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Movie/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/LiveVlog/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/LiveVlog/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/VideoConference/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/VideoConference/'),\n pipeline=[])\n]\ndataset_coco = dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[])\nscene = [\n 'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',\n 'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',\n 'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'\n]\ni = 14\ntrain_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n sampler=dict(type='DefaultSampler', shuffle=True),\n dataset=dict(\n type='CombinedDataset',\n metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),\n datasets=[\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_train_v1.0.json',\n data_prefix=dict(img='coco/train2017/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Magic_show/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Magic_show/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Entertainment/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Entertainment/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/ConductMusic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/ConductMusic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/Online_class/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Online_class/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TalkShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TalkShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Speech/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Speech/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Fitness/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Fitness/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Interview/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Interview/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Olympic/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Olympic/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/TVShow/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/TVShow/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Singing/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Singing/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/SignLanguage/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/SignLanguage/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/Movie/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/Movie/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='UBody/annotations/LiveVlog/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/LiveVlog/'),\n pipeline=[]),\n dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file=\n 'UBody/annotations/VideoConference/keypoint_annotation.json',\n data_prefix=dict(img='UBody/images/VideoConference/'),\n pipeline=[])\n ],\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='RandomFlip', direction='horizontal'),\n dict(type='RandomHalfBody'),\n dict(\n type='RandomBBoxTransform',\n scale_factor=[0.6, 1.4],\n rotate_factor=80),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='mmdet.YOLOXHSVRandomAug'),\n dict(\n type='Albumentation',\n transforms=[\n dict(type='Blur', p=0.1),\n dict(type='MedianBlur', p=0.1),\n dict(\n type='CoarseDropout',\n max_holes=1,\n max_height=0.4,\n max_width=0.4,\n min_holes=1,\n min_height=0.2,\n min_width=0.2,\n p=1.0)\n ]),\n dict(\n type='GenerateTarget',\n encoder=dict(\n type='SimCCLabel',\n input_size=(288, 384),\n sigma=(6.0, 6.93),\n simcc_split_ratio=2.0,\n normalize=False,\n use_dark=False)),\n dict(type='PackPoseInputs')\n ],\n test_mode=False))\nval_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_val_v1.0.json',\n bbox_file=\n '/data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',\n data_prefix=dict(img='coco/val2017/'),\n test_mode=True,\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n ]))\ntest_dataloader = dict(\n batch_size=32,\n num_workers=10,\n persistent_workers=True,\n drop_last=False,\n sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),\n dataset=dict(\n type='CocoWholeBodyDataset',\n data_root='/data/',\n data_mode='topdown',\n ann_file='coco/annotations/coco_wholebody_val_v1.0.json',\n bbox_file=\n '/data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json',\n data_prefix=dict(img='coco/val2017/'),\n test_mode=True,\n pipeline=[\n dict(type='LoadImage', backend_args=dict(backend='local')),\n dict(type='GetBBoxCenterScale'),\n dict(type='TopdownAffine', input_size=(288, 384)),\n dict(type='PackPoseInputs')\n ]))\nval_evaluator = dict(\n type='CocoWholeBodyMetric',\n ann_file='/data/coco/annotations/coco_wholebody_val_v1.0.json')\ntest_evaluator = dict(\n type='CocoWholeBodyMetric',\n ann_file='/data/coco/annotations/coco_wholebody_val_v1.0.json')\nfind_unused_parameters = True\nmgd = True\nnkd = True\nlauncher = 'pytorch'\nwork_dir = './work_dirs/rtmpose_l-ll__coco-ubody-384x288'\n", + "seed": 21, + "experiment_name": "rtmpose_l-ll__coco-ubody-384x288_20230706_095637", + "mmengine_version": "0.7.2", + "epoch": 19, + "iter": 19139, + "max_epochs": 60, + "max_iters": 57420, + "eta": {"type": "float64", "repr": "0.0", "dict": {}}, + "last_ckpt": "/home/yangzhendong/Projects/mmpose/work_dirs/rtmpose_l-ll__coco-ubody-384x288/epoch_20.pth", + "best_score": {"type": "float64", "repr": "0.665450746403108", "dict": {}}, + "best_ckpt": "/home/yangzhendong/Projects/mmpose/work_dirs/rtmpose_l-ll__coco-ubody-384x288/best_coco-wholebody_AP_epoch_20.pth", + "dataset_meta": { + "dataset_name": "coco_wholebody", + "num_keypoints": 133, + "keypoint_colors": { + "type": "ndarray", + "repr": "array([[ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 255, 255],\n [255, 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178, 255],\n [102, 178, 255],\n [102, 178, 255],\n [255, 51, 51],\n [255, 51, 51],\n [255, 51, 51],\n [255, 51, 51],\n [ 0, 255, 0],\n [ 0, 255, 0],\n [ 0, 255, 0],\n [ 0, 255, 0],\n [255, 255, 255],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 128, 0],\n [255, 153, 255],\n [255, 153, 255],\n [255, 153, 255],\n [255, 153, 255],\n [102, 178, 255],\n [102, 178, 255],\n [102, 178, 255],\n [102, 178, 255],\n [255, 51, 51],\n [255, 51, 51],\n [255, 51, 51],\n [255, 51, 51],\n [ 0, 255, 0],\n [ 0, 255, 0],\n [ 0, 255, 0],\n [ 0, 255, 0]], dtype=uint8)", + "dict": {}, + }, + "num_skeleton_links": 65, + "skeleton_link_colors": { + "type": "ndarray", + "repr": "array([[ 0, 255, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [255, 128, 0],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 0, 255, 0],\n [255, 128, 0],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 255],\n [ 51, 153, 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