mmdeploy/tests/test_codebase/test_mmpose/data/model.py

# Copyright (c) OpenMMLab. All rights reserved.
# model settings
import mmpose
from packaging import version

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 = dict(
    type='TopDown',
    pretrained=None,
    backbone=dict(type='ResNet', depth=18),
    keypoint_head=dict(
        type='TopdownHeatmapSimpleHead',
        in_channels=512,
        out_channels=17,
        loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
    train_cfg=dict(),
    test_cfg=dict(
        flip_test=False,
        post_process='default',
        shift_heatmap=False,
        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,
    # here use_gt_bbox must be true in ut, or should use predicted
    # bboxes.
    use_gt_bbox=True,
    det_bbox_thr=0.0,
    bbox_file='tests/test_codebase/test_mmpose/data/coco/' +
    'person_detection_results' +
    '/COCO_val2017_detections_AP_H_56_person.json',
)

test_pipeline = [
    dict(type='LoadImageFromFile'),
    # dict(type='TopDownGetBboxCenterScale'),
    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'
        ]),
]
# compatible with mmpose >=v0.26.0
if version.parse(mmpose.__version__) >= version.parse('0.26.0'):
    test_pipeline.insert(1, dict(type='TopDownGetBboxCenterScale'))

dataset_info = dict(
    dataset_name='coco',
    paper_info=dict(),
    keypoint_info={
        0:
        dict(name='nose', id=0, color=[51, 153, 255], type='upper', swap=''),
        1:
        dict(
            name='left_eye',
            id=1,
            color=[51, 153, 255],
            type='upper',
            swap='right_eye'),
        2:
        dict(
            name='right_eye',
            id=2,
            color=[51, 153, 255],
            type='upper',
            swap='left_eye'),
        3:
        dict(
            name='left_ear',
            id=3,
            color=[51, 153, 255],
            type='upper',
            swap='right_ear'),
        4:
        dict(
            name='right_ear',
            id=4,
            color=[51, 153, 255],
            type='upper',
            swap='left_ear'),
        5:
        dict(
            name='left_shoulder',
            id=5,
            color=[0, 255, 0],
            type='upper',
            swap='right_shoulder'),
        6:
        dict(
            name='right_shoulder',
            id=6,
            color=[255, 128, 0],
            type='upper',
            swap='left_shoulder'),
        7:
        dict(
            name='left_elbow',
            id=7,
            color=[0, 255, 0],
            type='upper',
            swap='right_elbow'),
        8:
        dict(
            name='right_elbow',
            id=8,
            color=[255, 128, 0],
            type='upper',
            swap='left_elbow'),
        9:
        dict(
            name='left_wrist',
            id=9,
            color=[0, 255, 0],
            type='upper',
            swap='right_wrist'),
        10:
        dict(
            name='right_wrist',
            id=10,
            color=[255, 128, 0],
            type='upper',
            swap='left_wrist'),
        11:
        dict(
            name='left_hip',
            id=11,
            color=[0, 255, 0],
            type='lower',
            swap='right_hip'),
        12:
        dict(
            name='right_hip',
            id=12,
            color=[255, 128, 0],
            type='lower',
            swap='left_hip'),
        13:
        dict(
            name='left_knee',
            id=13,
            color=[0, 255, 0],
            type='lower',
            swap='right_knee'),
        14:
        dict(
            name='right_knee',
            id=14,
            color=[255, 128, 0],
            type='lower',
            swap='left_knee'),
        15:
        dict(
            name='left_ankle',
            id=15,
            color=[0, 255, 0],
            type='lower',
            swap='right_ankle'),
        16:
        dict(
            name='right_ankle',
            id=16,
            color=[255, 128, 0],
            type='lower',
            swap='left_ankle')
    },
    skeleton_info={
        0:
        dict(link=('left_ankle', 'left_knee'), id=0, color=[0, 255, 0]),
        1:
        dict(link=('left_knee', 'left_hip'), id=1, color=[0, 255, 0]),
        2:
        dict(link=('right_ankle', 'right_knee'), id=2, color=[255, 128, 0]),
        3:
        dict(link=('right_knee', 'right_hip'), id=3, color=[255, 128, 0]),
        4:
        dict(link=('left_hip', 'right_hip'), id=4, color=[51, 153, 255]),
        5:
        dict(link=('left_shoulder', 'left_hip'), id=5, color=[51, 153, 255]),
        6:
        dict(link=('right_shoulder', 'right_hip'), id=6, color=[51, 153, 255]),
        7:
        dict(
            link=('left_shoulder', 'right_shoulder'),
            id=7,
            color=[51, 153, 255]),
        8:
        dict(link=('left_shoulder', 'left_elbow'), id=8, color=[0, 255, 0]),
        9:
        dict(
            link=('right_shoulder', 'right_elbow'), id=9, color=[255, 128, 0]),
        10:
        dict(link=('left_elbow', 'left_wrist'), id=10, color=[0, 255, 0]),
        11:
        dict(link=('right_elbow', 'right_wrist'), id=11, color=[255, 128, 0]),
        12:
        dict(link=('left_eye', 'right_eye'), id=12, color=[51, 153, 255]),
        13:
        dict(link=('nose', 'left_eye'), id=13, color=[51, 153, 255]),
        14:
        dict(link=('nose', 'right_eye'), id=14, color=[51, 153, 255]),
        15:
        dict(link=('left_eye', 'left_ear'), id=15, color=[51, 153, 255]),
        16:
        dict(link=('right_eye', 'right_ear'), id=16, color=[51, 153, 255]),
        17:
        dict(link=('left_ear', 'left_shoulder'), id=17, color=[51, 153, 255]),
        18:
        dict(
            link=('right_ear', 'right_shoulder'), id=18, color=[51, 153, 255])
    },
    joint_weights=[
        1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
        1.5
    ],
    sigmas=[
        0.026, 0.025, 0.025, 0.035, 0.035, 0.079, 0.079, 0.072, 0.072, 0.062,
        0.062, 0.107, 0.107, 0.087, 0.087, 0.089, 0.089
    ])

data = dict(
    samples_per_gpu=64,
    workers_per_gpu=2,
    test_dataloader=dict(samples_per_gpu=32),
    test=dict(
        type='TopDownCocoDataset',
        ann_file='tests/test_codebase/test_mmpose/data/annotations/' +
        'person_keypoints_val2017.json',
        img_prefix='tests/test_codebase/test_mmpose/data/val2017/',
        data_cfg=data_cfg,
        pipeline=test_pipeline,
        dataset_info=dataset_info),
)