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_base_ = [ |
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'../../../../_base_/default_runtime.py', |
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'../../../../_base_/datasets/coco.py' |
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] |
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evaluation = dict(interval=10, metric='mAP', save_best='AP') |
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|
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optimizer = dict(type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1, |
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constructor='LayerDecayOptimizerConstructor', |
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paramwise_cfg=dict( |
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num_layers=12, |
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layer_decay_rate=0.75, |
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custom_keys={ |
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'bias': dict(decay_multi=0.), |
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'pos_embed': dict(decay_mult=0.), |
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'relative_position_bias_table': dict(decay_mult=0.), |
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'norm': dict(decay_mult=0.) |
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} |
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) |
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) |
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|
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optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2)) |
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|
|
|
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lr_config = dict( |
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policy='step', |
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warmup='linear', |
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warmup_iters=500, |
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warmup_ratio=0.001, |
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step=[170, 200]) |
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total_epochs = 210 |
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target_type = 'GaussianHeatmap' |
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channel_cfg = dict( |
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num_output_channels=17, |
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dataset_joints=17, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
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], |
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inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
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]) |
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|
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model = dict( |
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type='TopDown', |
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pretrained=None, |
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backbone=dict( |
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type='ViT', |
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img_size=(256, 192), |
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patch_size=16, |
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embed_dim=768, |
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depth=12, |
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num_heads=12, |
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ratio=1, |
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use_checkpoint=False, |
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mlp_ratio=4, |
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qkv_bias=True, |
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drop_path_rate=0.3, |
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), |
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keypoint_head=dict( |
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type='TopdownHeatmapSimpleHead', |
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in_channels=768, |
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num_deconv_layers=2, |
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num_deconv_filters=(256, 256), |
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num_deconv_kernels=(4, 4), |
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extra=dict(final_conv_kernel=1, ), |
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out_channels=channel_cfg['num_output_channels'], |
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loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
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train_cfg=dict(), |
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test_cfg=dict( |
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flip_test=True, |
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post_process='default', |
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shift_heatmap=False, |
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target_type=target_type, |
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modulate_kernel=11, |
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use_udp=True)) |
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|
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data_cfg = dict( |
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image_size=[192, 256], |
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heatmap_size=[48, 64], |
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num_output_channels=channel_cfg['num_output_channels'], |
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num_joints=channel_cfg['dataset_joints'], |
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dataset_channel=channel_cfg['dataset_channel'], |
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inference_channel=channel_cfg['inference_channel'], |
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soft_nms=False, |
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nms_thr=1.0, |
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oks_thr=0.9, |
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vis_thr=0.2, |
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use_gt_bbox=False, |
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det_bbox_thr=0.0, |
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bbox_file='data/coco/person_detection_results/' |
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'COCO_val2017_detections_AP_H_56_person.json', |
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) |
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|
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train_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='TopDownRandomFlip', flip_prob=0.5), |
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dict( |
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type='TopDownHalfBodyTransform', |
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num_joints_half_body=8, |
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prob_half_body=0.3), |
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dict( |
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type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
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dict(type='TopDownAffine', use_udp=True), |
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dict(type='ToTensor'), |
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dict( |
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type='NormalizeTensor', |
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mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]), |
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dict( |
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type='TopDownGenerateTarget', |
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sigma=2, |
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encoding='UDP', |
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target_type=target_type), |
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dict( |
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type='Collect', |
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keys=['img', 'target', 'target_weight'], |
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meta_keys=[ |
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'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
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'rotation', 'bbox_score', 'flip_pairs' |
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]), |
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] |
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|
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val_pipeline = [ |
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dict(type='LoadImageFromFile'), |
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dict(type='TopDownAffine', use_udp=True), |
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dict(type='ToTensor'), |
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dict( |
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type='NormalizeTensor', |
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mean=[0.485, 0.456, 0.406], |
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std=[0.229, 0.224, 0.225]), |
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dict( |
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type='Collect', |
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keys=['img'], |
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meta_keys=[ |
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'image_file', 'center', 'scale', 'rotation', 'bbox_score', |
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'flip_pairs' |
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]), |
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] |
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|
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test_pipeline = val_pipeline |
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|
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data_root = 'data/coco' |
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data = dict( |
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samples_per_gpu=64, |
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workers_per_gpu=4, |
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val_dataloader=dict(samples_per_gpu=32), |
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test_dataloader=dict(samples_per_gpu=32), |
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train=dict( |
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type='TopDownCocoDataset', |
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ann_file=f'{data_root}/annotations/person_keypoints_train2017.json', |
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img_prefix=f'{data_root}/train2017/', |
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data_cfg=data_cfg, |
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pipeline=train_pipeline, |
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dataset_info={{_base_.dataset_info}}), |
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val=dict( |
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type='TopDownCocoDataset', |
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ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', |
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img_prefix=f'{data_root}/val2017/', |
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data_cfg=data_cfg, |
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pipeline=val_pipeline, |
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dataset_info={{_base_.dataset_info}}), |
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test=dict( |
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type='TopDownCocoDataset', |
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ann_file=f'{data_root}/annotations/person_keypoints_val2017.json', |
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img_prefix=f'{data_root}/val2017/', |
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data_cfg=data_cfg, |
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pipeline=test_pipeline, |
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dataset_info={{_base_.dataset_info}}), |
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) |
