170 lines
5.0 KiB
Python
170 lines
5.0 KiB
Python
# _base_ = [
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# '../../../../_base_/default_runtime.py',
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# '../../../../_base_/datasets/coco.py'
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# ]
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evaluation = dict(interval=10, metric='mAP', save_best='AP')
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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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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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# model settings
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model = dict(
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type='TopDown',
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pretrained='https://download.openmmlab.com/mmpose/'
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'pretrain_models/hrnet_w48-8ef0771d.pth',
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backbone=dict(
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type='HRNet',
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in_channels=3,
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extra=dict(
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stage1=dict(
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num_modules=1,
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num_branches=1,
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block='BOTTLENECK',
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num_blocks=(4, ),
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num_channels=(64, )),
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stage2=dict(
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num_modules=1,
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num_branches=2,
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block='BASIC',
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num_blocks=(4, 4),
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num_channels=(48, 96)),
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stage3=dict(
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num_modules=4,
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num_branches=3,
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block='BASIC',
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num_blocks=(4, 4, 4),
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num_channels=(48, 96, 192)),
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stage4=dict(
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num_modules=3,
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num_branches=4,
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block='BASIC',
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num_blocks=(4, 4, 4, 4),
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num_channels=(48, 96, 192, 384))),
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),
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keypoint_head=dict(
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type='TopdownHeatmapSimpleHead',
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in_channels=48,
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out_channels=channel_cfg['num_output_channels'],
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num_deconv_layers=0,
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extra=dict(final_conv_kernel=1, ),
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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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data_cfg = dict(
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image_size=[192, 256],
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heatmap_size=[48, 64],
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num_output_channels=channel_cfg['num_output_channels'],
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num_joints=channel_cfg['dataset_joints'],
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dataset_channel=channel_cfg['dataset_channel'],
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inference_channel=channel_cfg['inference_channel'],
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soft_nms=False,
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nms_thr=1.0,
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oks_thr=0.9,
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vis_thr=0.2,
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use_gt_bbox=False,
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det_bbox_thr=0.0,
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bbox_file='data/coco/person_detection_results/'
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'COCO_val2017_detections_AP_H_56_person.json',
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)
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train_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='TopDownGetBboxCenterScale', padding=1.25),
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dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
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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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val_pipeline = [
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dict(type='LoadImageFromFile'),
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dict(type='TopDownGetBboxCenterScale', padding=1.25),
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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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test_pipeline = val_pipeline
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data_root = 'data/coco'
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data = dict(
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samples_per_gpu=32,
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workers_per_gpu=2,
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val_dataloader=dict(samples_per_gpu=32),
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test_dataloader=dict(samples_per_gpu=32),
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train=dict(
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type='TopDownCocoDataset',
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ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
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img_prefix=f'{data_root}/train2017/',
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data_cfg=data_cfg,
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pipeline=train_pipeline,
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dataset_info={{_base_.dataset_info}}),
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val=dict(
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type='TopDownCocoDataset',
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ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
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img_prefix=f'{data_root}/val2017/',
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data_cfg=data_cfg,
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pipeline=val_pipeline,
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dataset_info={{_base_.dataset_info}}),
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test=dict(
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type='TopDownCocoDataset',
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ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
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img_prefix=f'{data_root}/val2017/',
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data_cfg=data_cfg,
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pipeline=test_pipeline,
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dataset_info={{_base_.dataset_info}}),
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)
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