mmyolo/configs/yolox/pose/yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py

_base_ = '../yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py'

load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth'  # noqa

num_keypoints = 17
scaling_ratio_range = (0.75, 1.0)
mixup_ratio_range = (0.8, 1.6)
num_last_epochs = 20

# model settings
model = dict(
    bbox_head=dict(
        type='YOLOXPoseHead',
        head_module=dict(
            type='YOLOXPoseHeadModule',
            num_classes=1,
            num_keypoints=num_keypoints,
        ),
        loss_pose=dict(
            type='OksLoss',
            metainfo='configs/_base_/pose/coco.py',
            loss_weight=30.0)),
    train_cfg=dict(
        assigner=dict(
            type='PoseSimOTAAssigner',
            center_radius=2.5,
            oks_weight=3.0,
            iou_calculator=dict(type='mmdet.BboxOverlaps2D'),
            oks_calculator=dict(
                type='OksLoss', metainfo='configs/_base_/pose/coco.py'))),
    test_cfg=dict(score_thr=0.01))

# pipelines
pre_transform = [
    dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
    dict(type='LoadAnnotations', with_keypoints=True)
]

img_scale = _base_.img_scale

train_pipeline_stage1 = [
    *pre_transform,
    dict(
        type='Mosaic',
        img_scale=img_scale,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(
        type='RandomAffine',
        scaling_ratio_range=scaling_ratio_range,
        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
    dict(
        type='YOLOXMixUp',
        img_scale=img_scale,
        ratio_range=mixup_ratio_range,
        pad_val=114.0,
        pre_transform=pre_transform),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='FilterAnnotations', by_keypoints=True, keep_empty=False),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
]

train_pipeline_stage2 = [
    *pre_transform,
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='mmdet.Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(type='mmdet.YOLOXHSVRandomAug'),
    dict(type='RandomFlip', prob=0.5),
    dict(type='FilterAnnotations', by_keypoints=True, keep_empty=False),
    dict(type='PackDetInputs')
]

test_pipeline = [
    *pre_transform,
    dict(type='Resize', scale=img_scale, keep_ratio=True),
    dict(
        type='mmdet.Pad',
        pad_to_square=True,
        pad_val=dict(img=(114.0, 114.0, 114.0))),
    dict(
        type='PackDetInputs',
        meta_keys=('id', 'img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'flip_indices'))
]

# dataset settings
dataset_type = 'PoseCocoDataset'

train_dataloader = dict(
    dataset=dict(
        type=dataset_type,
        data_mode='bottomup',
        ann_file='annotations/person_keypoints_train2017.json',
        pipeline=train_pipeline_stage1))

val_dataloader = dict(
    dataset=dict(
        type=dataset_type,
        data_mode='bottomup',
        ann_file='annotations/person_keypoints_val2017.json',
        pipeline=test_pipeline))
test_dataloader = val_dataloader

# evaluators
val_evaluator = dict(
    _delete_=True,
    type='mmpose.CocoMetric',
    ann_file=_base_.data_root + 'annotations/person_keypoints_val2017.json',
    score_mode='bbox')
test_evaluator = val_evaluator

default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))

visualizer = dict(type='mmpose.PoseLocalVisualizer')

custom_hooks = [
    dict(
        type='YOLOXModeSwitchHook',
        num_last_epochs=num_last_epochs,
        new_train_pipeline=train_pipeline_stage2,
        priority=48),
    dict(type='mmdet.SyncNormHook', priority=48),
    dict(
        type='EMAHook',
        ema_type='ExpMomentumEMA',
        momentum=0.0002,
        update_buffers=True,
        strict_load=False,
        priority=49)
]