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[Feature] Update RTMPose-x wholebody and body models (#2498)
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projects/rtmpose/rtmpose/body_2d_keypoint/rtmpose-x_8xb256-700e_coco-384x288.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,238 @@ | ||
| _base_ = ['mmpose::_base_/default_runtime.py'] | ||
|
|
||
| # common setting | ||
| num_keypoints = 17 | ||
| input_size = (288, 384) | ||
|
|
||
| # runtime | ||
| max_epochs = 700 | ||
| stage2_num_epochs = 20 | ||
| base_lr = 4e-3 | ||
| train_batch_size = 256 | ||
| val_batch_size = 64 | ||
|
|
||
| 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=0.05), | ||
| clip_grad=dict(max_norm=35, norm_type=2), | ||
| 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( | ||
| 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) | ||
|
|
||
| # codec settings | ||
| codec = dict( | ||
| type='SimCCLabel', | ||
| input_size=input_size, | ||
| sigma=(6., 6.93), | ||
| 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=1.33, | ||
| widen_factor=1.28, | ||
| 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/' | ||
| 'rtmposev1/cspnext-x_udp-body7_210e-384x288-d28b58e6_20230529.pth' # noqa | ||
| )), | ||
| head=dict( | ||
| type='RTMCCHead', | ||
| in_channels=1280, | ||
| out_channels=num_keypoints, | ||
| input_size=codec['input_size'], | ||
| in_featuremap_size=tuple([s // 32 for s in codec['input_size']]), | ||
| 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 = 'CocoDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/coco/' | ||
|
|
||
| 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=[0.5, 1.5], rotate_factor=90), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='PhotometricDistortion'), | ||
| 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=[0.5, 1.5], | ||
| rotate_factor=90), | ||
| 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=train_batch_size, | ||
| 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='annotations/person_keypoints_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| pipeline=train_pipeline, | ||
| )) | ||
| val_dataloader = dict( | ||
| batch_size=val_batch_size, | ||
| 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='annotations/person_keypoints_val2017.json', | ||
| bbox_file=f'{data_root}person_detection_results/' | ||
| 'COCO_val2017_detections_AP_H_56_person.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| 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 = [ | ||
| 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 + 'annotations/person_keypoints_val2017.json') | ||
| test_evaluator = val_evaluator |
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