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add humanart: hrenet-w32/48 vitpose-h/l rtmpose-t
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configs/body_2d_keypoint/rtmpose/humanart/rtmpose-t_8xb256-420e_humanart-256x192.py
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| _base_ = ['../../../_base_/default_runtime.py'] | ||
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|
||
| # runtime | ||
| max_epochs = 420 | ||
| stage2_num_epochs = 30 | ||
| base_lr = 4e-3 | ||
|
|
||
| train_cfg = dict(max_epochs=max_epochs, val_interval=10) | ||
| randomness = dict(seed=21) | ||
|
|
||
| # optimizer | ||
| optim_wrapper = dict( | ||
| type='OptimWrapper', | ||
| optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.), | ||
| paramwise_cfg=dict( | ||
| norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) | ||
|
|
||
| # learning rate | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', | ||
| start_factor=1.0e-5, | ||
| by_epoch=False, | ||
| begin=0, | ||
| end=1000), | ||
| dict( | ||
| # use cosine lr from 210 to 420 epoch | ||
| type='CosineAnnealingLR', | ||
| eta_min=base_lr * 0.05, | ||
| begin=max_epochs // 2, | ||
| end=max_epochs, | ||
| T_max=max_epochs // 2, | ||
| by_epoch=True, | ||
| convert_to_iter_based=True), | ||
| ] | ||
|
|
||
| # automatically scaling LR based on the actual training batch size | ||
| auto_scale_lr = dict(base_batch_size=1024) | ||
|
|
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| # codec settings | ||
| codec = dict( | ||
| type='SimCCLabel', | ||
| input_size=(192, 256), | ||
| sigma=(4.9, 5.66), | ||
| simcc_split_ratio=2.0, | ||
| normalize=False, | ||
| use_dark=False) | ||
|
|
||
| # model settings | ||
| 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=0.167, | ||
| widen_factor=0.375, | ||
| 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-tiny_udp-aic-coco_210e-256x192-cbed682d_20230130.pth' # noqa | ||
| )), | ||
| head=dict( | ||
| type='RTMCCHead', | ||
| in_channels=384, | ||
| out_channels=17, | ||
| input_size=codec['input_size'], | ||
| in_featuremap_size=(6, 8), | ||
| simcc_split_ratio=codec['simcc_split_ratio'], | ||
| final_layer_kernel_size=7, | ||
| gau_cfg=dict( | ||
| hidden_dims=256, | ||
| s=128, | ||
| expansion_factor=2, | ||
| dropout_rate=0., | ||
| drop_path=0., | ||
| act_fn='SiLU', | ||
| use_rel_bias=False, | ||
| pos_enc=False), | ||
| loss=dict( | ||
| type='KLDiscretLoss', | ||
| use_target_weight=True, | ||
| beta=10., | ||
| label_softmax=True), | ||
| decoder=codec), | ||
| test_cfg=dict(flip_test=True)) | ||
|
|
||
| # base dataset settings | ||
| dataset_type = 'HumanArtDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/' | ||
|
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| backend_args = dict(backend='local') | ||
| # backend_args = dict( | ||
| # backend='petrel', | ||
| # path_mapping=dict({ | ||
| # f'{data_root}': 's3://openmmlab/datasets/detection/coco/', | ||
| # f'{data_root}': 's3://openmmlab/datasets/detection/coco/' | ||
| # })) | ||
|
|
||
| # pipelines | ||
| train_pipeline = [ | ||
| dict(type='LoadImage', backend_args=backend_args), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='RandomFlip', direction='horizontal'), | ||
| dict(type='RandomHalfBody'), | ||
| dict( | ||
| type='RandomBBoxTransform', scale_factor=[0.6, 1.4], rotate_factor=80), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| 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.), | ||
| ]), | ||
| dict(type='GenerateTarget', encoder=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='LoadImage', backend_args=backend_args), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
|
|
||
| train_pipeline_stage2 = [ | ||
| dict(type='LoadImage', backend_args=backend_args), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='RandomFlip', direction='horizontal'), | ||
| dict(type='RandomHalfBody'), | ||
| dict( | ||
| type='RandomBBoxTransform', | ||
| shift_factor=0., | ||
| scale_factor=[0.75, 1.25], | ||
| rotate_factor=60), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| 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=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
|
|
||
| # data loaders | ||
| train_dataloader = dict( | ||
| batch_size=256, | ||
| num_workers=10, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='HumanArt/annotations/training_humanart_coco.json', | ||
| data_prefix=dict(img=''), | ||
| pipeline=train_pipeline, | ||
| )) | ||
| val_dataloader = dict( | ||
| batch_size=64, | ||
| num_workers=10, | ||
| persistent_workers=True, | ||
| drop_last=False, | ||
| sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), | ||
| dataset=dict( | ||
| type=dataset_type, | ||
| data_root=data_root, | ||
| data_mode=data_mode, | ||
| ann_file='HumanArt/annotations/validation_humanart.json', | ||
| # bbox_file=f'{data_root}HumanArt/person_detection_results/' | ||
| # 'HumanArt_validation_detections_AP_H_56_person.json', | ||
| data_prefix=dict(img=''), | ||
| test_mode=True, | ||
| pipeline=val_pipeline, | ||
| )) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| # hooks | ||
| default_hooks = dict( | ||
| checkpoint=dict(save_best='coco/AP', rule='greater', max_keep_ckpts=1)) | ||
|
|
||
| custom_hooks = [ | ||
| # Turn off EMA while training the tiny model | ||
| # dict( | ||
| # type='EMAHook', | ||
| # ema_type='ExpMomentumEMA', | ||
| # momentum=0.0002, | ||
| # update_buffers=True, | ||
| # priority=49), | ||
| dict( | ||
| type='mmdet.PipelineSwitchHook', | ||
| switch_epoch=max_epochs - stage2_num_epochs, | ||
| switch_pipeline=train_pipeline_stage2) | ||
| ] | ||
|
|
||
| # evaluators | ||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| ann_file=data_root + 'HumanArt/annotations/validation_humanart.json') | ||
| test_evaluator = val_evaluator |
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