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