Yufei
3 years ago
29 changed files with 3992 additions and 62 deletions
@ -0,0 +1,157 @@ |
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_base_ = [ |
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'../../../../_base_/default_runtime.py', |
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'../../../../_base_/datasets/ap10k.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( |
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type='Adam', |
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lr=5e-4, |
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) |
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optimizer_config = dict(grad_clip=None) |
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# learning policy |
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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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log_config = dict( |
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interval=1, |
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hooks=[ |
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dict(type='TextLoggerHook'), |
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# dict(type='TensorboardLoggerHook') |
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]) |
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|
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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 settings |
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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=True, |
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modulate_kernel=11)) |
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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=True, |
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det_bbox_thr=0.0, |
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bbox_file='', |
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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'), |
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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(type='TopDownGenerateTarget', sigma=2), |
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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'), |
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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/apt36k' |
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data = dict( |
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samples_per_gpu=32, |
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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='AnimalAP10KDataset', |
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ann_file=f'{data_root}/annotations/train_annotations_1.json', |
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img_prefix=f'{data_root}/', |
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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='AnimalAP10KDataset', |
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ann_file=f'{data_root}/annotations/val_annotations_1.json', |
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img_prefix=f'{data_root}/', |
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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='AnimalAP10KDataset', |
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ann_file=f'{data_root}/annotations/val_annotations_1.json', |
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img_prefix=f'{data_root}/', |
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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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) |
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@ -0,0 +1,157 @@ |
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|
_base_ = [ |
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|
'../../../../_base_/default_runtime.py', |
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|
'../../../../_base_/datasets/ap10k.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( |
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|
type='Adam', |
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|
lr=5e-4, |
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|
) |
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|
optimizer_config = dict(grad_clip=None) |
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|
# learning policy |
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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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|
log_config = dict( |
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|
interval=1, |
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|
hooks=[ |
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|
dict(type='TextLoggerHook'), |
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|
# dict(type='TensorboardLoggerHook') |
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|
]) |
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|
|
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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 settings |
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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=1280, |
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depth=32, |
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num_heads=16, |
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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=1280, |
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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=True, |
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modulate_kernel=11)) |
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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=True, |
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|
det_bbox_thr=0.0, |
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|
bbox_file='', |
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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'), |
||||
|
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(type='TopDownGenerateTarget', sigma=2), |
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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'), |
||||
|
dict(type='TopDownAffine'), |
||||
|
dict(type='ToTensor'), |
||||
|
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/ap10k' |
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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='AnimalAP10KDataset', |
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|
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
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|
img_prefix=f'{data_root}/data/', |
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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='AnimalAP10KDataset', |
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|
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
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|
img_prefix=f'{data_root}/data/', |
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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='AnimalAP10KDataset', |
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|
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
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|
img_prefix=f'{data_root}/data/', |
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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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|
) |
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@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1024, |
||||
|
depth=24, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1024, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/ap10k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/ap10k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=768, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=768, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/ap10k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-train-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-val-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/ap10k-test-split1.json', |
||||
|
img_prefix=f'{data_root}/data/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1280, |
||||
|
depth=32, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1280, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/apt36k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=32, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/train_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1024, |
||||
|
depth=24, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1024, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/apt36k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=32, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/train_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,157 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ap10k.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 |
||||
|
log_config = dict( |
||||
|
interval=1, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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='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/apt36k' |
||||
|
data = dict( |
||||
|
samples_per_gpu=32, |
||||
|
workers_per_gpu=4, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/train_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{data_root}/annotations/val_annotations_1.json', |
||||
|
img_prefix=f'{data_root}/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,151 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/aic.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=14, |
||||
|
dataset_joints=14, |
||||
|
dataset_channel=[ |
||||
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], |
||||
|
], |
||||
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
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='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='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/aic' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownAicDataset', |
||||
|
ann_file=f'{data_root}/annotations/aic_train.json', |
||||
|
img_prefix=f'{data_root}/ai_challenger_keypoint_train_20170902/' |
||||
|
'keypoint_train_images_20170902/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownAicDataset', |
||||
|
ann_file=f'{data_root}/annotations/aic_val.json', |
||||
|
img_prefix=f'{data_root}/ai_challenger_keypoint_validation_20170911/' |
||||
|
'keypoint_validation_images_20170911/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownAicDataset', |
||||
|
ann_file=f'{data_root}/annotations/aic_val.json', |
||||
|
img_prefix=f'{data_root}/ai_challenger_keypoint_validation_20170911/' |
||||
|
'keypoint_validation_images_20170911/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}})) |
||||
@ -0,0 +1,170 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco.py' |
||||
|
] |
||||
|
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
||||
|
|
||||
|
optimizer = dict(type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1, |
||||
|
constructor='LayerDecayOptimizerConstructor', |
||||
|
paramwise_cfg=dict( |
||||
|
num_layers=12, |
||||
|
layer_decay_rate=0.9, |
||||
|
custom_keys={ |
||||
|
'bias': dict(decay_multi=0.), |
||||
|
'pos_embed': dict(decay_mult=0.), |
||||
|
'relative_position_bias_table': dict(decay_mult=0.), |
||||
|
'norm': dict(decay_mult=0.) |
||||
|
} |
||||
|
) |
||||
|
) |
||||
|
|
||||
|
optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2)) |
||||
|
|
||||
|
# learning policy |
||||
|
lr_config = dict( |
||||
|
policy='step', |
||||
|
warmup='linear', |
||||
|
warmup_iters=500, |
||||
|
warmup_ratio=0.001, |
||||
|
step=[170, 200]) |
||||
|
total_epochs = 210 |
||||
|
target_type = 'GaussianHeatmap' |
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
train_cfg=dict(), |
||||
|
test_cfg=dict( |
||||
|
flip_test=True, |
||||
|
post_process='default', |
||||
|
shift_heatmap=False, |
||||
|
target_type=target_type, |
||||
|
modulate_kernel=11, |
||||
|
use_udp=True)) |
||||
|
|
||||
|
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='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', use_udp=True), |
||||
|
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, |
||||
|
encoding='UDP', |
||||
|
target_type=target_type), |
||||
|
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='TopDownAffine', use_udp=True), |
||||
|
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=64, |
||||
|
workers_per_gpu=4, |
||||
|
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}}), |
||||
|
) |
||||
|
|
||||
@ -0,0 +1,170 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco.py' |
||||
|
] |
||||
|
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
||||
|
|
||||
|
optimizer = dict(type='AdamW', lr=5e-4, betas=(0.9, 0.999), weight_decay=0.1, |
||||
|
constructor='LayerDecayOptimizerConstructor', |
||||
|
paramwise_cfg=dict( |
||||
|
num_layers=12, |
||||
|
layer_decay_rate=0.9, |
||||
|
custom_keys={ |
||||
|
'bias': dict(decay_multi=0.), |
||||
|
'pos_embed': dict(decay_mult=0.), |
||||
|
'relative_position_bias_table': dict(decay_mult=0.), |
||||
|
'norm': dict(decay_mult=0.) |
||||
|
} |
||||
|
) |
||||
|
) |
||||
|
|
||||
|
optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2)) |
||||
|
|
||||
|
# learning policy |
||||
|
lr_config = dict( |
||||
|
policy='step', |
||||
|
warmup='linear', |
||||
|
warmup_iters=500, |
||||
|
warmup_ratio=0.001, |
||||
|
step=[170, 200]) |
||||
|
total_epochs = 210 |
||||
|
target_type = 'GaussianHeatmap' |
||||
|
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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
train_cfg=dict(), |
||||
|
test_cfg=dict( |
||||
|
flip_test=True, |
||||
|
post_process='default', |
||||
|
shift_heatmap=False, |
||||
|
target_type=target_type, |
||||
|
modulate_kernel=11, |
||||
|
use_udp=True)) |
||||
|
|
||||
|
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='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', use_udp=True), |
||||
|
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, |
||||
|
encoding='UDP', |
||||
|
target_type=target_type), |
||||
|
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='TopDownAffine', use_udp=True), |
||||
|
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=64, |
||||
|
workers_per_gpu=4, |
||||
|
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}}), |
||||
|
) |
||||
|
|
||||
@ -0,0 +1,491 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco.py', |
||||
|
'../../../../_base_/datasets/aic_info.py', |
||||
|
'../../../../_base_/datasets/mpii_info.py', |
||||
|
'../../../../_base_/datasets/ap10k_info.py', |
||||
|
'../../../../_base_/datasets/coco_wholebody_info.py' |
||||
|
] |
||||
|
evaluation = dict(interval=10, metric='mAP', save_best='AP') |
||||
|
|
||||
|
optimizer = dict(type='AdamW', lr=1e-3, betas=(0.9, 0.999), weight_decay=0.1, |
||||
|
constructor='LayerDecayOptimizerConstructor', |
||||
|
paramwise_cfg=dict( |
||||
|
num_layers=12, |
||||
|
layer_decay_rate=0.9, |
||||
|
custom_keys={ |
||||
|
'bias': dict(decay_multi=0.), |
||||
|
'pos_embed': dict(decay_mult=0.), |
||||
|
'relative_position_bias_table': dict(decay_mult=0.), |
||||
|
'norm': dict(decay_mult=0.) |
||||
|
} |
||||
|
) |
||||
|
) |
||||
|
|
||||
|
optimizer_config = dict(grad_clip=dict(max_norm=1., norm_type=2)) |
||||
|
|
||||
|
# learning policy |
||||
|
lr_config = dict( |
||||
|
policy='step', |
||||
|
warmup='linear', |
||||
|
warmup_iters=500, |
||||
|
warmup_ratio=0.001, |
||||
|
step=[170, 200]) |
||||
|
total_epochs = 210 |
||||
|
target_type = 'GaussianHeatmap' |
||||
|
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 |
||||
|
]) |
||||
|
aic_channel_cfg = dict( |
||||
|
num_output_channels=14, |
||||
|
dataset_joints=14, |
||||
|
dataset_channel=[ |
||||
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], |
||||
|
], |
||||
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) |
||||
|
mpii_channel_cfg = dict( |
||||
|
num_output_channels=16, |
||||
|
dataset_joints=16, |
||||
|
dataset_channel=list(range(16)), |
||||
|
inference_channel=list(range(16))) |
||||
|
crowdpose_channel_cfg = dict( |
||||
|
num_output_channels=14, |
||||
|
dataset_joints=14, |
||||
|
dataset_channel=[ |
||||
|
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], |
||||
|
], |
||||
|
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) |
||||
|
ap10k_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 |
||||
|
]) |
||||
|
cocowholebody_channel_cfg = dict( |
||||
|
num_output_channels=133, |
||||
|
dataset_joints=133, |
||||
|
dataset_channel=[ |
||||
|
list(range(133)), |
||||
|
], |
||||
|
inference_channel=list(range(133))) |
||||
|
|
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDownMoE', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViTMoE', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
num_expert=6, |
||||
|
part_features=192 |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
associate_keypoint_head=[ |
||||
|
dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=aic_channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=mpii_channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=ap10k_channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=ap10k_channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=cocowholebody_channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
], |
||||
|
train_cfg=dict(), |
||||
|
test_cfg=dict( |
||||
|
flip_test=True, |
||||
|
post_process='default', |
||||
|
shift_heatmap=False, |
||||
|
target_type=target_type, |
||||
|
modulate_kernel=11, |
||||
|
use_udp=True)) |
||||
|
|
||||
|
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', |
||||
|
max_num_joints=133, |
||||
|
dataset_idx=0, |
||||
|
) |
||||
|
|
||||
|
aic_data_cfg = dict( |
||||
|
image_size=[192, 256], |
||||
|
heatmap_size=[48, 64], |
||||
|
num_output_channels=aic_channel_cfg['num_output_channels'], |
||||
|
num_joints=aic_channel_cfg['dataset_joints'], |
||||
|
dataset_channel=aic_channel_cfg['dataset_channel'], |
||||
|
inference_channel=aic_channel_cfg['inference_channel'], |
||||
|
soft_nms=False, |
||||
|
nms_thr=1.0, |
||||
|
oks_thr=0.9, |
||||
|
vis_thr=0.2, |
||||
|
use_gt_bbox=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='data/coco/person_detection_results/' |
||||
|
'COCO_val2017_detections_AP_H_56_person.json', |
||||
|
max_num_joints=133, |
||||
|
dataset_idx=1, |
||||
|
) |
||||
|
|
||||
|
mpii_data_cfg = dict( |
||||
|
image_size=[192, 256], |
||||
|
heatmap_size=[48, 64], |
||||
|
num_output_channels=mpii_channel_cfg['num_output_channels'], |
||||
|
num_joints=mpii_channel_cfg['dataset_joints'], |
||||
|
dataset_channel=mpii_channel_cfg['dataset_channel'], |
||||
|
inference_channel=mpii_channel_cfg['inference_channel'], |
||||
|
max_num_joints=133, |
||||
|
dataset_idx=2, |
||||
|
use_gt_bbox=True, |
||||
|
bbox_file=None, |
||||
|
) |
||||
|
|
||||
|
ap10k_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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
max_num_joints=133, |
||||
|
dataset_idx=3, |
||||
|
) |
||||
|
|
||||
|
ap36k_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=True, |
||||
|
det_bbox_thr=0.0, |
||||
|
bbox_file='', |
||||
|
max_num_joints=133, |
||||
|
dataset_idx=4, |
||||
|
) |
||||
|
|
||||
|
cocowholebody_data_cfg = dict( |
||||
|
image_size=[192, 256], |
||||
|
heatmap_size=[48, 64], |
||||
|
num_output_channels=cocowholebody_channel_cfg['num_output_channels'], |
||||
|
num_joints=cocowholebody_channel_cfg['dataset_joints'], |
||||
|
dataset_channel=cocowholebody_channel_cfg['dataset_channel'], |
||||
|
inference_channel=cocowholebody_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', |
||||
|
dataset_idx=5, |
||||
|
max_num_joints=133, |
||||
|
) |
||||
|
|
||||
|
cocowholebody_train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
ap10k_train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
aic_train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
mpii_train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
||||
|
dict(type='TopDownAffine', use_udp=True), |
||||
|
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, |
||||
|
encoding='UDP', |
||||
|
target_type=target_type), |
||||
|
dict( |
||||
|
type='Collect', |
||||
|
keys=['img', 'target', 'target_weight'], |
||||
|
meta_keys=[ |
||||
|
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
||||
|
'rotation', 'flip_pairs', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', use_udp=True), |
||||
|
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, |
||||
|
encoding='UDP', |
||||
|
target_type=target_type), |
||||
|
dict( |
||||
|
type='Collect', |
||||
|
keys=['img', 'target', 'target_weight'], |
||||
|
meta_keys=[ |
||||
|
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
||||
|
'rotation', 'bbox_score', 'flip_pairs', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownAffine', use_udp=True), |
||||
|
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', 'dataset_idx' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/coco' |
||||
|
aic_data_root = 'data/aic' |
||||
|
mpii_data_root = 'data/mpii' |
||||
|
ap10k_data_root = 'data/ap10k' |
||||
|
ap36k_data_root = 'data/ap36k' |
||||
|
|
||||
|
data = dict( |
||||
|
samples_per_gpu=128, |
||||
|
workers_per_gpu=8, |
||||
|
val_dataloader=dict(samples_per_gpu=64), |
||||
|
test_dataloader=dict(samples_per_gpu=64), |
||||
|
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}}), |
||||
|
dict( |
||||
|
type='TopDownAicDataset', |
||||
|
ann_file=f'{aic_data_root}/annotations/person_keypoints_train2017.json', |
||||
|
img_prefix=f'{aic_data_root}/ai_challenger_keypoint_train_20170909/' |
||||
|
'keypoint_train_images_20170902/', |
||||
|
data_cfg=aic_data_cfg, |
||||
|
pipeline=aic_train_pipeline, |
||||
|
dataset_info={{_base_.aic_info}}), |
||||
|
dict( |
||||
|
type='TopDownMpiiDataset', |
||||
|
ann_file=f'{mpii_data_root}/annotations/mpii_train.json', |
||||
|
img_prefix=f'{mpii_data_root}/images/', |
||||
|
data_cfg=mpii_data_cfg, |
||||
|
pipeline=mpii_train_pipeline, |
||||
|
dataset_info={{_base_.mpii_info}}), |
||||
|
dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{ap10k_data_root}/annotations/ap10k-train-split1.json', |
||||
|
img_prefix=f'{ap10k_data_root}/data/', |
||||
|
data_cfg=ap10k_data_cfg, |
||||
|
pipeline=ap10k_train_pipeline, |
||||
|
dataset_info={{_base_.ap10k_info}}), |
||||
|
dict( |
||||
|
type='AnimalAP10KDataset', |
||||
|
ann_file=f'{ap36k_data_root}/annotations/train_annotations_1.json', |
||||
|
img_prefix=f'{ap36k_data_root}/', |
||||
|
data_cfg=ap36k_data_cfg, |
||||
|
pipeline=ap10k_train_pipeline, |
||||
|
dataset_info={{_base_.ap10k_info}}), |
||||
|
dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', |
||||
|
img_prefix=f'{data_root}/train2017/', |
||||
|
data_cfg=cocowholebody_data_cfg, |
||||
|
pipeline=cocowholebody_train_pipeline, |
||||
|
dataset_info={{_base_.cocowholebody_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}}), |
||||
|
) |
||||
|
|
||||
@ -0,0 +1,146 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/mpii.py' |
||||
|
] |
||||
|
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh') |
||||
|
|
||||
|
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 |
||||
|
log_config = dict( |
||||
|
interval=50, hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
]) |
||||
|
|
||||
|
target_type = 'GaussianHeatmap' |
||||
|
channel_cfg = dict( |
||||
|
num_output_channels=16, |
||||
|
dataset_joints=16, |
||||
|
dataset_channel=list(range(16)), |
||||
|
inference_channel=list(range(16))) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)), |
||||
|
train_cfg=dict(), |
||||
|
test_cfg=dict( |
||||
|
flip_test=True, |
||||
|
post_process='default', |
||||
|
shift_heatmap=False, |
||||
|
target_type=target_type, |
||||
|
modulate_kernel=11, |
||||
|
use_udp=True)) |
||||
|
|
||||
|
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'], |
||||
|
use_gt_bbox=True, |
||||
|
bbox_file=None, |
||||
|
) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5), |
||||
|
dict(type='TopDownAffine', use_udp=True), |
||||
|
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, |
||||
|
encoding='UDP', |
||||
|
target_type=target_type), |
||||
|
dict( |
||||
|
type='Collect', |
||||
|
keys=['img', 'target', 'target_weight'], |
||||
|
meta_keys=[ |
||||
|
'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale', |
||||
|
'rotation', 'flip_pairs' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownAffine', use_udp=True), |
||||
|
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', 'flip_pairs']), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/mpii' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownMpiiDataset', |
||||
|
ann_file=f'{data_root}/annotations/mpii_train.json', |
||||
|
img_prefix=f'{data_root}/images/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownMpiiDataset', |
||||
|
ann_file=f'{data_root}/annotations/mpii_val.json', |
||||
|
img_prefix=f'{data_root}/images/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownMpiiDataset', |
||||
|
ann_file=f'{data_root}/annotations/mpii_val.json', |
||||
|
img_prefix=f'{data_root}/images/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,153 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/ochuman.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=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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=True, |
||||
|
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='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='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/ochuman' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownCocoDataset', |
||||
|
ann_file='data/coco/annotations/person_keypoints_train2017.json', |
||||
|
img_prefix='data/coco//train2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownOCHumanDataset', |
||||
|
ann_file=f'{data_root}/annotations/' |
||||
|
'ochuman_coco_format_val_range_0.00_1.00.json', |
||||
|
img_prefix=f'{data_root}/images/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownOCHumanDataset', |
||||
|
ann_file=f'{data_root}/annotations/' |
||||
|
'ochuman_coco_format_test_range_0.00_1.00.json', |
||||
|
img_prefix=f'{data_root}/images/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,162 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/interhand2d.py' |
||||
|
] |
||||
|
checkpoint_config = dict(interval=5) |
||||
|
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
||||
|
|
||||
|
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=[40, 50]) |
||||
|
total_epochs = 60 |
||||
|
log_config = dict( |
||||
|
interval=20, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
channel_cfg = dict( |
||||
|
num_output_channels=21, |
||||
|
dataset_joints=21, |
||||
|
dataset_channel=[ |
||||
|
[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
||||
|
19, 20 |
||||
|
], |
||||
|
], |
||||
|
inference_channel=[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
||||
|
20 |
||||
|
]) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=768, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=768, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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']) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
||||
|
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', 'flip_pairs' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'flip_pairs']), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/interhand2.6m' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/train/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_data.json', |
||||
|
camera_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/val/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/test/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,162 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/interhand2d.py' |
||||
|
] |
||||
|
checkpoint_config = dict(interval=5) |
||||
|
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
||||
|
|
||||
|
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=[40, 50]) |
||||
|
total_epochs = 60 |
||||
|
log_config = dict( |
||||
|
interval=20, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
channel_cfg = dict( |
||||
|
num_output_channels=21, |
||||
|
dataset_joints=21, |
||||
|
dataset_channel=[ |
||||
|
[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
||||
|
19, 20 |
||||
|
], |
||||
|
], |
||||
|
inference_channel=[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
||||
|
20 |
||||
|
]) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1280, |
||||
|
depth=32, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1280, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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']) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
||||
|
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', 'flip_pairs' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'flip_pairs']), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/interhand2.6m' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/train/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_data.json', |
||||
|
camera_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/val/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/test/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,162 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/interhand2d.py' |
||||
|
] |
||||
|
checkpoint_config = dict(interval=5) |
||||
|
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
||||
|
|
||||
|
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=[40, 50]) |
||||
|
total_epochs = 60 |
||||
|
log_config = dict( |
||||
|
interval=20, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
channel_cfg = dict( |
||||
|
num_output_channels=21, |
||||
|
dataset_joints=21, |
||||
|
dataset_channel=[ |
||||
|
[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
||||
|
19, 20 |
||||
|
], |
||||
|
], |
||||
|
inference_channel=[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
||||
|
20 |
||||
|
]) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1024, |
||||
|
depth=24, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1024, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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']) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
||||
|
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', 'flip_pairs' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'flip_pairs']), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/interhand2.6m' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/train/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_data.json', |
||||
|
camera_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/val/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/test/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,162 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/interhand2d.py' |
||||
|
] |
||||
|
checkpoint_config = dict(interval=5) |
||||
|
evaluation = dict(interval=5, metric=['PCK', 'AUC', 'EPE'], save_best='AUC') |
||||
|
|
||||
|
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=[40, 50]) |
||||
|
total_epochs = 60 |
||||
|
log_config = dict( |
||||
|
interval=20, |
||||
|
hooks=[ |
||||
|
dict(type='TextLoggerHook'), |
||||
|
# dict(type='TensorboardLoggerHook') |
||||
|
]) |
||||
|
|
||||
|
channel_cfg = dict( |
||||
|
num_output_channels=21, |
||||
|
dataset_joints=21, |
||||
|
dataset_channel=[ |
||||
|
[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, |
||||
|
19, 20 |
||||
|
], |
||||
|
], |
||||
|
inference_channel=[ |
||||
|
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
||||
|
20 |
||||
|
]) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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']) |
||||
|
|
||||
|
train_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
dict(type='TopDownRandomFlip', flip_prob=0.5), |
||||
|
dict( |
||||
|
type='TopDownGetRandomScaleRotation', rot_factor=90, scale_factor=0.3), |
||||
|
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', 'flip_pairs' |
||||
|
]), |
||||
|
] |
||||
|
|
||||
|
val_pipeline = [ |
||||
|
dict(type='LoadImageFromFile'), |
||||
|
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', 'flip_pairs']), |
||||
|
] |
||||
|
|
||||
|
test_pipeline = val_pipeline |
||||
|
|
||||
|
data_root = 'data/interhand2.6m' |
||||
|
data = dict( |
||||
|
samples_per_gpu=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_train_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/train/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_data.json', |
||||
|
camera_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/machine_annot/' |
||||
|
'InterHand2.6M_val_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/val/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='InterHand2DDataset', |
||||
|
ann_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_data.json', |
||||
|
camera_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_camera.json', |
||||
|
joint_file=f'{data_root}/annotations/all/' |
||||
|
'InterHand2.6M_test_joint_3d.json', |
||||
|
img_prefix=f'{data_root}/images/test/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,149 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco_wholebody.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=133, |
||||
|
dataset_joints=133, |
||||
|
dataset_channel=[ |
||||
|
list(range(133)), |
||||
|
], |
||||
|
inference_channel=list(range(133))) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=768, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=768, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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='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='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=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', |
||||
|
img_prefix=f'{data_root}/train2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,149 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco_wholebody.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=133, |
||||
|
dataset_joints=133, |
||||
|
dataset_channel=[ |
||||
|
list(range(133)), |
||||
|
], |
||||
|
inference_channel=list(range(133))) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1280, |
||||
|
depth=32, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1280, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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='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='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=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', |
||||
|
img_prefix=f'{data_root}/train2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,149 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco_wholebody.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=133, |
||||
|
dataset_joints=133, |
||||
|
dataset_channel=[ |
||||
|
list(range(133)), |
||||
|
], |
||||
|
inference_channel=list(range(133))) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=1024, |
||||
|
depth=24, |
||||
|
num_heads=16, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=1024, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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='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='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=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', |
||||
|
img_prefix=f'{data_root}/train2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
@ -0,0 +1,149 @@ |
|||||
|
_base_ = [ |
||||
|
'../../../../_base_/default_runtime.py', |
||||
|
'../../../../_base_/datasets/coco_wholebody.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=133, |
||||
|
dataset_joints=133, |
||||
|
dataset_channel=[ |
||||
|
list(range(133)), |
||||
|
], |
||||
|
inference_channel=list(range(133))) |
||||
|
|
||||
|
# model settings |
||||
|
model = dict( |
||||
|
type='TopDown', |
||||
|
pretrained=None, |
||||
|
backbone=dict( |
||||
|
type='ViT', |
||||
|
img_size=(256, 192), |
||||
|
patch_size=16, |
||||
|
embed_dim=384, |
||||
|
depth=12, |
||||
|
num_heads=12, |
||||
|
ratio=1, |
||||
|
use_checkpoint=False, |
||||
|
mlp_ratio=4, |
||||
|
qkv_bias=True, |
||||
|
drop_path_rate=0.3, |
||||
|
), |
||||
|
keypoint_head=dict( |
||||
|
type='TopdownHeatmapSimpleHead', |
||||
|
in_channels=384, |
||||
|
num_deconv_layers=2, |
||||
|
num_deconv_filters=(256, 256), |
||||
|
num_deconv_kernels=(4, 4), |
||||
|
extra=dict(final_conv_kernel=1, ), |
||||
|
out_channels=channel_cfg['num_output_channels'], |
||||
|
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='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='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=64, |
||||
|
workers_per_gpu=2, |
||||
|
val_dataloader=dict(samples_per_gpu=32), |
||||
|
test_dataloader=dict(samples_per_gpu=32), |
||||
|
train=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json', |
||||
|
img_prefix=f'{data_root}/train2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=train_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
val=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=val_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
test=dict( |
||||
|
type='TopDownCocoWholeBodyDataset', |
||||
|
ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json', |
||||
|
img_prefix=f'{data_root}/val2017/', |
||||
|
data_cfg=data_cfg, |
||||
|
pipeline=test_pipeline, |
||||
|
dataset_info={{_base_.dataset_info}}), |
||||
|
) |
||||
|
Before Width: | Height: | Size: 116 KiB After Width: | Height: | Size: 103 KiB |
@ -0,0 +1,93 @@ |
|||||
|
import torch |
||||
|
import os |
||||
|
import argparse |
||||
|
import copy |
||||
|
|
||||
|
def parse_args(): |
||||
|
parser = argparse.ArgumentParser() |
||||
|
parser.add_argument('--source', type=str) |
||||
|
parser.add_argument('--target', type=str, default=None) |
||||
|
args = parser.parse_args() |
||||
|
return args |
||||
|
|
||||
|
def main(): |
||||
|
|
||||
|
args = parse_args() |
||||
|
|
||||
|
if args.target is None: |
||||
|
args.target = '/'.join(args.source.split('/')[:-1]) |
||||
|
|
||||
|
ckpt = torch.load(args.source, map_location='cpu') |
||||
|
|
||||
|
experts = dict() |
||||
|
|
||||
|
new_ckpt = copy.deepcopy(ckpt) |
||||
|
|
||||
|
state_dict = new_ckpt['state_dict'] |
||||
|
|
||||
|
for key, value in state_dict.items(): |
||||
|
if 'mlp.experts' in key: |
||||
|
experts[key] = value |
||||
|
|
||||
|
keys = ckpt['state_dict'].keys() |
||||
|
|
||||
|
target_expert = 0 |
||||
|
new_ckpt = copy.deepcopy(ckpt) |
||||
|
|
||||
|
for key in keys: |
||||
|
if 'mlp.fc2' in key: |
||||
|
value = new_ckpt['state_dict'][key] |
||||
|
value = torch.cat([value, experts[key.replace('fc2.', f'experts.{target_expert}.')]], dim=0) |
||||
|
new_ckpt['state_dict'][key] = value |
||||
|
|
||||
|
torch.save(new_ckpt, os.path.join(args.targetPath, 'coco.pth')) |
||||
|
|
||||
|
names = ['aic', 'mpii', 'ap10k', 'apt36k','wholebody'] |
||||
|
num_keypoints = [14, 16, 17, 17, 133] |
||||
|
weight_names = ['keypoint_head.deconv_layers.0.weight', |
||||
|
'keypoint_head.deconv_layers.1.weight', |
||||
|
'keypoint_head.deconv_layers.1.bias', |
||||
|
'keypoint_head.deconv_layers.1.running_mean', |
||||
|
'keypoint_head.deconv_layers.1.running_var', |
||||
|
'keypoint_head.deconv_layers.1.num_batches_tracked', |
||||
|
'keypoint_head.deconv_layers.3.weight', |
||||
|
'keypoint_head.deconv_layers.4.weight', |
||||
|
'keypoint_head.deconv_layers.4.bias', |
||||
|
'keypoint_head.deconv_layers.4.running_mean', |
||||
|
'keypoint_head.deconv_layers.4.running_var', |
||||
|
'keypoint_head.deconv_layers.4.num_batches_tracked', |
||||
|
'keypoint_head.final_layer.weight', |
||||
|
'keypoint_head.final_layer.bias'] |
||||
|
|
||||
|
exist_range = True |
||||
|
|
||||
|
for i in range(5): |
||||
|
|
||||
|
new_ckpt = copy.deepcopy(ckpt) |
||||
|
|
||||
|
target_expert = i + 1 |
||||
|
|
||||
|
for key in keys: |
||||
|
if 'mlp.fc2' in key: |
||||
|
expert_key = key.replace('fc2.', f'experts.{target_expert}.') |
||||
|
if expert_key in experts: |
||||
|
value = new_ckpt['state_dict'][key] |
||||
|
value = torch.cat([value, experts[expert_key]], dim=0) |
||||
|
else: |
||||
|
exist_range = False |
||||
|
|
||||
|
new_ckpt['state_dict'][key] = value |
||||
|
|
||||
|
if not exist_range: |
||||
|
break |
||||
|
|
||||
|
for tensor_name in weight_names: |
||||
|
new_ckpt['state_dict'][tensor_name] = new_ckpt['state_dict'][tensor_name.replace('keypoint_head', f'associate_keypoint_heads.{i}')] |
||||
|
|
||||
|
for tensor_name in ['keypoint_head.final_layer.weight', 'keypoint_head.final_layer.bias']: |
||||
|
new_ckpt['state_dict'][tensor_name] = new_ckpt['state_dict'][tensor_name][:num_keypoints[i]] |
||||
|
|
||||
|
torch.save(new_ckpt, os.path.join(args.target, f'{names[i]}.pth')) |
||||
|
|
||||
|
if __name__ == '__main__': |
||||
|
main() |
||||
Loading…
Reference in new issue