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| # _base_ = [ | |
| # '../../../../_base_/default_runtime.py', | |
| # '../../../../_base_/datasets/coco.py' | |
| # ] | |
| evaluation = dict(interval=10, metric='mAP', save_best='AP') | |
| optimizer = dict( | |
| type='Adam', | |
| lr=5e-4, | |
| ) | |
| optimizer_config = dict(grad_clip=None) | |
| # learning policy | |
| lr_config = dict( | |
| policy='step', | |
| warmup='linear', | |
| warmup_iters=500, | |
| warmup_ratio=0.001, | |
| step=[170, 200]) | |
| total_epochs = 210 | |
| channel_cfg = dict( | |
| num_output_channels=17, | |
| dataset_joints=17, | |
| dataset_channel=[ | |
| [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], | |
| ], | |
| inference_channel=[ | |
| 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 | |
| ]) | |
| # model settings | |
| model = dict( | |
| type='TopDown', | |
| pretrained='https://download.openmmlab.com/mmpose/' | |
| 'pretrain_models/hrnet_w48-8ef0771d.pth', | |
| backbone=dict( | |
| type='HRNet', | |
| in_channels=3, | |
| extra=dict( | |
| stage1=dict( | |
| num_modules=1, | |
| num_branches=1, | |
| block='BOTTLENECK', | |
| num_blocks=(4, ), | |
| num_channels=(64, )), | |
| stage2=dict( | |
| num_modules=1, | |
| num_branches=2, | |
| block='BASIC', | |
| num_blocks=(4, 4), | |
| num_channels=(48, 96)), | |
| stage3=dict( | |
| num_modules=4, | |
| num_branches=3, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4), | |
| num_channels=(48, 96, 192)), | |
| stage4=dict( | |
| num_modules=3, | |
| num_branches=4, | |
| block='BASIC', | |
| num_blocks=(4, 4, 4, 4), | |
| num_channels=(48, 96, 192, 384))), | |
| ), | |
| keypoint_head=dict( | |
| type='TopdownHeatmapSimpleHead', | |
| in_channels=48, | |
| out_channels=channel_cfg['num_output_channels'], | |
| num_deconv_layers=0, | |
| extra=dict(final_conv_kernel=1, ), | |
| loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), | |
| train_cfg=dict(), | |
| test_cfg=dict( | |
| flip_test=True, | |
| post_process='default', | |
| shift_heatmap=True, | |
| modulate_kernel=11)) | |
| data_cfg = dict( | |
| image_size=[192, 256], | |
| heatmap_size=[48, 64], | |
| num_output_channels=channel_cfg['num_output_channels'], | |
| num_joints=channel_cfg['dataset_joints'], | |
| dataset_channel=channel_cfg['dataset_channel'], | |
| inference_channel=channel_cfg['inference_channel'], | |
| soft_nms=False, | |
| nms_thr=1.0, | |
| oks_thr=0.9, | |
| vis_thr=0.2, | |
| use_gt_bbox=False, | |
| det_bbox_thr=0.0, | |
| bbox_file='data/coco/person_detection_results/' | |
| 'COCO_val2017_detections_AP_H_56_person.json', | |
| ) | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='TopDownGetBboxCenterScale', padding=1.25), | |
| dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3), | |
| dict(type='TopDownRandomFlip', flip_prob=0.5), | |
| dict( | |
| type='TopDownHalfBodyTransform', | |
| num_joints_half_body=8, | |
| prob_half_body=0.3), | |
| dict( | |
| type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), | |
| dict(type='TopDownAffine'), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='NormalizeTensor', | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]), | |
| dict(type='TopDownGenerateTarget', sigma=2), | |
| dict( | |
| type='Collect', | |
| keys=['img', 'target', 'target_weight'], | |
| meta_keys=[ | |
| 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', | |
| 'rotation', 'bbox_score', 'flip_pairs' | |
| ]), | |
| ] | |
| val_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict(type='TopDownGetBboxCenterScale', padding=1.25), | |
| dict(type='TopDownAffine'), | |
| dict(type='ToTensor'), | |
| dict( | |
| type='NormalizeTensor', | |
| mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]), | |
| dict( | |
| type='Collect', | |
| keys=['img'], | |
| meta_keys=[ | |
| 'image_file', 'center', 'scale', 'rotation', 'bbox_score', | |
| 'flip_pairs' | |
| ]), | |
| ] | |
| test_pipeline = val_pipeline | |
| data_root = 'data/coco' | |
| data = dict( | |
| samples_per_gpu=32, | |
| workers_per_gpu=2, | |
| val_dataloader=dict(samples_per_gpu=32), | |
| test_dataloader=dict(samples_per_gpu=32), | |
| train=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', | |
| img_prefix=f'{data_root}/train2017/', | |
| data_cfg=data_cfg, | |
| pipeline=train_pipeline, | |
| dataset_info={{_base_.dataset_info}}), | |
| val=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | |
| img_prefix=f'{data_root}/val2017/', | |
| data_cfg=data_cfg, | |
| pipeline=val_pipeline, | |
| dataset_info={{_base_.dataset_info}}), | |
| test=dict( | |
| type='TopDownCocoDataset', | |
| ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', | |
| img_prefix=f'{data_root}/val2017/', | |
| data_cfg=data_cfg, | |
| pipeline=test_pipeline, | |
| dataset_info={{_base_.dataset_info}}), | |
| ) | |