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...ody_2d_keypoint/topdown_heatmap/coco/td-hm-vis_res50_8xb64-210e_coco-aic-256x192-merge.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,167 @@ | ||
| _base_ = ['../../../_base_/default_runtime.py'] | ||
|
|
||
| # runtime | ||
| train_cfg = dict(max_epochs=210, val_interval=10) | ||
|
|
||
| # optimizer | ||
| optim_wrapper = dict(optimizer=dict( | ||
| type='Adam', | ||
| lr=5e-4, | ||
| )) | ||
|
|
||
| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='LinearLR', begin=0, end=500, start_factor=0.001, | ||
| by_epoch=False), # warm-up | ||
| dict( | ||
| type='MultiStepLR', | ||
| begin=0, | ||
| end=210, | ||
| milestones=[170, 200], | ||
| gamma=0.1, | ||
| by_epoch=True) | ||
| ] | ||
|
|
||
| # automatically scaling LR based on the actual training batch size | ||
| auto_scale_lr = dict(base_batch_size=512) | ||
|
|
||
| # hooks | ||
| default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater')) | ||
|
|
||
| # codec settings | ||
| codec = dict( | ||
| type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2) | ||
|
|
||
| # 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( | ||
| type='ResNet', | ||
| depth=50, | ||
| init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'), | ||
| ), | ||
| head=dict( | ||
| type='VisPredictHead', | ||
| loss=dict( | ||
| type='BCELoss', | ||
| use_target_weight=True, | ||
| use_sigmoid=True, | ||
| loss_weight=1e-3, | ||
| ), | ||
| pose_cfg=dict( | ||
| type='HeatmapHead', | ||
| in_channels=2048, | ||
| out_channels=17, | ||
| loss=dict(type='KeypointMSELoss', use_target_weight=True), | ||
| decoder=codec)), | ||
| test_cfg=dict( | ||
| flip_test=True, | ||
| flip_mode='heatmap', | ||
| shift_heatmap=True, | ||
| )) | ||
|
|
||
| # base dataset settings | ||
| dataset_type = 'CocoDataset' | ||
| data_mode = 'topdown' | ||
| data_root = 'data/coco/' | ||
|
|
||
| # pipelines | ||
| train_pipeline = [ | ||
| dict(type='LoadImage'), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='RandomFlip', direction='horizontal'), | ||
| dict(type='RandomHalfBody'), | ||
| dict(type='RandomBBoxTransform'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='GenerateTarget', encoder=codec), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
| val_pipeline = [ | ||
| dict(type='LoadImage'), | ||
| dict(type='GetBBoxCenterScale'), | ||
| dict(type='TopdownAffine', input_size=codec['input_size']), | ||
| dict(type='PackPoseInputs') | ||
| ] | ||
|
|
||
| # train datasets | ||
| dataset_coco = 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=[], | ||
| ) | ||
|
|
||
| dataset_aic = dict( | ||
| type='AicDataset', | ||
| data_root='data/aic/', | ||
| data_mode=data_mode, | ||
| ann_file='annotations/aic_train.json', | ||
| data_prefix=dict(img='ai_challenger_keypoint_train_20170902/' | ||
| 'keypoint_train_images_20170902/'), | ||
| pipeline=[ | ||
| dict( | ||
| type='KeypointConverter', | ||
| num_keypoints=17, | ||
| mapping=[ | ||
| (0, 6), | ||
| (1, 8), | ||
| (2, 10), | ||
| (3, 5), | ||
| (4, 7), | ||
| (5, 9), | ||
| (6, 12), | ||
| (7, 14), | ||
| (8, 16), | ||
| (9, 11), | ||
| (10, 13), | ||
| (11, 15), | ||
| ]) | ||
| ], | ||
| ) | ||
|
|
||
| # data loaders | ||
| train_dataloader = dict( | ||
| batch_size=64, | ||
| num_workers=2, | ||
| persistent_workers=True, | ||
| sampler=dict(type='DefaultSampler', shuffle=True), | ||
| dataset=dict( | ||
| type='CombinedDataset', | ||
| metainfo=dict(from_file='configs/_base_/datasets/coco.py'), | ||
| datasets=[dataset_coco, dataset_aic], | ||
| pipeline=train_pipeline, | ||
| test_mode=False, | ||
| )) | ||
| val_dataloader = dict( | ||
| batch_size=32, | ||
| num_workers=2, | ||
| 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='data/coco/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 | ||
|
|
||
| # evaluators | ||
| val_evaluator = dict( | ||
| type='CocoMetric', | ||
| # score_mode='bbox', | ||
| ann_file=data_root + 'annotations/person_keypoints_val2017.json') | ||
| test_evaluator = val_evaluator |
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