mmaction2
/
projects
/gesture_recognition
/configs
/rtmpose-m_8xb32-210e_coco-wholebody-hand-256x256.py
| default_scope = 'mmpose' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=50), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict( | |
| type='CheckpointHook', | |
| interval=10, | |
| save_best='AUC', | |
| rule='greater', | |
| max_keep_ckpts=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='PoseVisualizationHook', enable=False)) | |
| custom_hooks = [ | |
| dict( | |
| type='EMAHook', | |
| ema_type='ExpMomentumEMA', | |
| momentum=0.0002, | |
| update_buffers=True, | |
| priority=49), | |
| dict( | |
| type='mmdet.PipelineSwitchHook', | |
| switch_epoch=180, | |
| switch_pipeline=[ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict( | |
| type='RandomBBoxTransform', | |
| shift_factor=0.0, | |
| scale_factor=[0.75, 1.25], | |
| rotate_factor=180), | |
| dict(type='RandomFlip', direction='horizontal'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='mmdet.YOLOXHSVRandomAug'), | |
| dict( | |
| type='Albumentation', | |
| transforms=[ | |
| dict(type='Blur', p=0.1), | |
| dict(type='MedianBlur', p=0.1), | |
| dict( | |
| type='CoarseDropout', | |
| max_holes=1, | |
| max_height=0.4, | |
| max_width=0.4, | |
| min_holes=1, | |
| min_height=0.2, | |
| min_width=0.2, | |
| p=0.5) | |
| ]), | |
| dict( | |
| type='GenerateTarget', | |
| encoder=dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False)), | |
| dict(type='PackPoseInputs') | |
| ]) | |
| ] | |
| env_cfg = dict( | |
| cudnn_benchmark=False, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='PoseLocalVisualizer', | |
| vis_backends=[dict(type='LocalVisBackend')], | |
| name='visualizer') | |
| log_processor = dict( | |
| type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |
| file_client_args = dict(backend='disk') | |
| train_cfg = dict(by_epoch=True, max_epochs=210, val_interval=10) | |
| val_cfg = dict() | |
| test_cfg = dict() | |
| max_epochs = 210 | |
| stage2_num_epochs = 30 | |
| base_lr = 0.004 | |
| randomness = dict(seed=21) | |
| optim_wrapper = dict( | |
| type='OptimWrapper', | |
| optimizer=dict(type='AdamW', lr=0.004, weight_decay=0.05), | |
| paramwise_cfg=dict( | |
| norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) | |
| param_scheduler = [ | |
| dict( | |
| type='LinearLR', start_factor=1e-05, by_epoch=False, begin=0, | |
| end=1000), | |
| dict( | |
| type='CosineAnnealingLR', | |
| eta_min=0.0002, | |
| begin=105, | |
| end=210, | |
| T_max=105, | |
| by_epoch=True, | |
| convert_to_iter_based=True) | |
| ] | |
| auto_scale_lr = dict(base_batch_size=256) | |
| codec = dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False) | |
| model = dict( | |
| type='TopdownPoseEstimator', | |
| data_preprocessor=dict( | |
| type='PoseDataPreprocessor', | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True), | |
| backbone=dict( | |
| _scope_='mmdet', | |
| type='CSPNeXt', | |
| arch='P5', | |
| expand_ratio=0.5, | |
| deepen_factor=0.67, | |
| widen_factor=0.75, | |
| out_indices=(4, ), | |
| channel_attention=True, | |
| norm_cfg=dict(type='SyncBN'), | |
| act_cfg=dict(type='SiLU'), | |
| init_cfg=dict( | |
| type='Pretrained', | |
| prefix='backbone.', | |
| checkpoint=('https://download.openmmlab.com/mmpose/v1/projects/' | |
| 'rtmpose/cspnext-m_udp-aic-coco_210e-256x192-' | |
| 'f2f7d6f6_20230130.pth'))), | |
| head=dict( | |
| type='RTMCCHead', | |
| in_channels=768, | |
| out_channels=21, | |
| input_size=(256, 256), | |
| in_featuremap_size=(8, 8), | |
| simcc_split_ratio=2.0, | |
| final_layer_kernel_size=7, | |
| gau_cfg=dict( | |
| hidden_dims=256, | |
| s=128, | |
| expansion_factor=2, | |
| dropout_rate=0.0, | |
| drop_path=0.0, | |
| act_fn='SiLU', | |
| use_rel_bias=False, | |
| pos_enc=False), | |
| loss=dict( | |
| type='KLDiscretLoss', | |
| use_target_weight=True, | |
| beta=10.0, | |
| label_softmax=True), | |
| decoder=dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False)), | |
| test_cfg=dict(flip_test=True)) | |
| dataset_type = 'CocoWholeBodyHandDataset' | |
| data_mode = 'topdown' | |
| data_root = 'data/coco/' | |
| train_pipeline = [ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict( | |
| type='RandomBBoxTransform', scale_factor=[0.5, 1.5], | |
| rotate_factor=180), | |
| dict(type='RandomFlip', direction='horizontal'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='mmdet.YOLOXHSVRandomAug'), | |
| dict( | |
| type='Albumentation', | |
| transforms=[ | |
| dict(type='Blur', p=0.1), | |
| dict(type='MedianBlur', p=0.1), | |
| dict( | |
| type='CoarseDropout', | |
| max_holes=1, | |
| max_height=0.4, | |
| max_width=0.4, | |
| min_holes=1, | |
| min_height=0.2, | |
| min_width=0.2, | |
| p=1.0) | |
| ]), | |
| dict( | |
| type='GenerateTarget', | |
| encoder=dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False)), | |
| dict(type='PackPoseInputs') | |
| ] | |
| val_pipeline = [ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='PackPoseInputs') | |
| ] | |
| train_pipeline_stage2 = [ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict( | |
| type='RandomBBoxTransform', | |
| shift_factor=0.0, | |
| scale_factor=[0.75, 1.25], | |
| rotate_factor=180), | |
| dict(type='RandomFlip', direction='horizontal'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='mmdet.YOLOXHSVRandomAug'), | |
| dict( | |
| type='Albumentation', | |
| transforms=[ | |
| dict(type='Blur', p=0.1), | |
| dict(type='MedianBlur', p=0.1), | |
| dict( | |
| type='CoarseDropout', | |
| max_holes=1, | |
| max_height=0.4, | |
| max_width=0.4, | |
| min_holes=1, | |
| min_height=0.2, | |
| min_width=0.2, | |
| p=0.5) | |
| ]), | |
| dict( | |
| type='GenerateTarget', | |
| encoder=dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False)), | |
| dict(type='PackPoseInputs') | |
| ] | |
| train_dataloader = dict( | |
| batch_size=32, | |
| num_workers=10, | |
| persistent_workers=True, | |
| sampler=dict(type='DefaultSampler', shuffle=True), | |
| dataset=dict( | |
| type='CocoWholeBodyHandDataset', | |
| data_root='data/coco/', | |
| data_mode='topdown', | |
| ann_file='annotations/coco_wholebody_train_v1.0.json', | |
| data_prefix=dict(img='train2017/'), | |
| pipeline=[ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict( | |
| type='RandomBBoxTransform', | |
| scale_factor=[0.5, 1.5], | |
| rotate_factor=180), | |
| dict(type='RandomFlip', direction='horizontal'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='mmdet.YOLOXHSVRandomAug'), | |
| dict( | |
| type='Albumentation', | |
| transforms=[ | |
| dict(type='Blur', p=0.1), | |
| dict(type='MedianBlur', p=0.1), | |
| dict( | |
| type='CoarseDropout', | |
| max_holes=1, | |
| max_height=0.4, | |
| max_width=0.4, | |
| min_holes=1, | |
| min_height=0.2, | |
| min_width=0.2, | |
| p=1.0) | |
| ]), | |
| dict( | |
| type='GenerateTarget', | |
| encoder=dict( | |
| type='SimCCLabel', | |
| input_size=(256, 256), | |
| sigma=(5.66, 5.66), | |
| simcc_split_ratio=2.0, | |
| normalize=False, | |
| use_dark=False)), | |
| dict(type='PackPoseInputs') | |
| ])) | |
| val_dataloader = dict( | |
| batch_size=32, | |
| num_workers=10, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), | |
| dataset=dict( | |
| type='CocoWholeBodyHandDataset', | |
| data_root='data/coco/', | |
| data_mode='topdown', | |
| ann_file='annotations/coco_wholebody_val_v1.0.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='PackPoseInputs') | |
| ])) | |
| test_dataloader = dict( | |
| batch_size=32, | |
| num_workers=10, | |
| persistent_workers=True, | |
| drop_last=False, | |
| sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), | |
| dataset=dict( | |
| type='CocoWholeBodyHandDataset', | |
| data_root='data/coco/', | |
| data_mode='topdown', | |
| ann_file='annotations/coco_wholebody_val_v1.0.json', | |
| data_prefix=dict(img='val2017/'), | |
| test_mode=True, | |
| pipeline=[ | |
| dict(type='LoadImage', file_client_args=dict(backend='disk')), | |
| dict(type='GetBBoxCenterScale'), | |
| dict(type='TopdownAffine', input_size=(256, 256)), | |
| dict(type='PackPoseInputs') | |
| ])) | |
| val_evaluator = [ | |
| dict(type='PCKAccuracy', thr=0.2), | |
| dict(type='AUC'), | |
| dict(type='EPE') | |
| ] | |
| test_evaluator = [ | |
| dict(type='PCKAccuracy', thr=0.2), | |
| dict(type='AUC'), | |
| dict(type='EPE') | |
| ] | |