mirror of
https://github.com/open-mmlab/mmdeploy.git
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787ebc2392
* add mmpose code * update * update * add rewrites * test trt * test litehrnet with trt * revert unused change * add docs about mmpose * add docstring and staticmethod * update * update * update docs * fix config name and docs * add pose_detection ut * add pose data * fix lint of model.py * add pose_detection_model ut * fix docs and docstrinf * add test_mmpose_models.py * fix yapf * fix lint * fix create input * support ort ut * fix yapf * fix docs * fix createinput * test ci bug * rm test1.py * fix yapf * fix flake8 * fix yapf * add config and update benchmark * fix table format * update mmpose benchmark * update benchmark for mmpose * run mmpose tests seperately in ci * fix lint * resolve comments * add trt ut config * fix test * fix tests * resolve comments * resolve comments * update tests Co-authored-by: VVsssssk <shenkun@pjlab.org.cn> Co-authored-by: hanrui1sensetime <hanrui1@sensetime.com>
251 lines
6.9 KiB
Python
251 lines
6.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# model settings
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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 = dict(
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type='TopDown',
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pretrained=None,
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backbone=dict(type='ResNet', depth=18),
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keypoint_head=dict(
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type='TopdownHeatmapSimpleHead',
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in_channels=512,
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out_channels=17,
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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=False,
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post_process='default',
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shift_heatmap=False,
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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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# here use_gt_bbox must be true in ut, or should use predicted
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# bboxes.
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use_gt_bbox=True,
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det_bbox_thr=0.0,
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bbox_file='tests/test_codebase/test_mmpose/data/coco/' +
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'person_detection_results' +
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'/COCO_val2017_detections_AP_H_56_person.json',
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)
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test_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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dataset_info = dict(
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dataset_name='coco',
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paper_info=dict(),
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keypoint_info={
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0:
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dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
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1:
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dict(
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name='left_eye',
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id=1,
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color=[51, 153, 255],
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type='upper',
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swap='right_eye'),
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2:
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dict(
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name='right_eye',
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id=2,
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color=[51, 153, 255],
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type='upper',
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swap='left_eye'),
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3:
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dict(
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name='left_ear',
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id=3,
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color=[51, 153, 255],
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type='upper',
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swap='right_ear'),
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4:
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dict(
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name='right_ear',
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id=4,
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color=[51, 153, 255],
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type='upper',
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swap='left_ear'),
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5:
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dict(
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name='left_shoulder',
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id=5,
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color=[0, 255, 0],
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type='upper',
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swap='right_shoulder'),
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6:
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dict(
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name='right_shoulder',
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id=6,
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color=[255, 128, 0],
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type='upper',
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swap='left_shoulder'),
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7:
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dict(
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name='left_elbow',
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id=7,
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color=[0, 255, 0],
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type='upper',
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swap='right_elbow'),
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8:
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dict(
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name='right_elbow',
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id=8,
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color=[255, 128, 0],
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type='upper',
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swap='left_elbow'),
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9:
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dict(
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name='left_wrist',
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id=9,
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color=[0, 255, 0],
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type='upper',
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swap='right_wrist'),
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10:
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dict(
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name='right_wrist',
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id=10,
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color=[255, 128, 0],
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type='upper',
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swap='left_wrist'),
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11:
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dict(
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name='left_hip',
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id=11,
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color=[0, 255, 0],
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type='lower',
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swap='right_hip'),
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12:
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dict(
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name='right_hip',
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id=12,
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color=[255, 128, 0],
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type='lower',
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swap='left_hip'),
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13:
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dict(
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name='left_knee',
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id=13,
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color=[0, 255, 0],
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type='lower',
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swap='right_knee'),
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14:
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dict(
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name='right_knee',
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id=14,
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color=[255, 128, 0],
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type='lower',
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swap='left_knee'),
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15:
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dict(
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name='left_ankle',
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id=15,
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color=[0, 255, 0],
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type='lower',
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swap='right_ankle'),
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16:
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dict(
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name='right_ankle',
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id=16,
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color=[255, 128, 0],
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type='lower',
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swap='left_ankle')
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},
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skeleton_info={
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0:
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dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
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1:
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dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
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2:
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dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
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3:
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dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
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4:
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dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
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5:
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dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
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6:
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dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
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7:
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dict(
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link=('left_shoulder', 'right_shoulder'),
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id=7,
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color=[51, 153, 255]),
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8:
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dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
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9:
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dict(
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link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
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10:
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dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
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11:
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dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
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12:
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dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
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13:
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dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
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14:
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dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
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15:
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dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
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16:
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dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
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17:
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dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
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18:
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dict(
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link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
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},
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joint_weights=[
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1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
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1.5
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],
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sigmas=[
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0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
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0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
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])
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data = dict(
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samples_per_gpu=64,
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workers_per_gpu=2,
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test_dataloader=dict(samples_per_gpu=32),
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test=dict(
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type='TopDownCocoDataset',
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ann_file='tests/test_codebase/test_mmpose/data/annotations/' +
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'person_keypoints_val2017.json',
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img_prefix='tests/test_codebase/test_mmpose/data/val2017/',
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data_cfg=data_cfg,
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pipeline=test_pipeline,
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dataset_info=dataset_info),
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)
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