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_base_ = './yolox-pose_s_8xb32-300e-rtmdet-hyp_coco.py'
load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco/yolox_tiny_fast_8xb32-300e-rtmdet-hyp_coco_20230210_143637-4c338102.pth' # noqa
deepen_factor = 0.33
widen_factor = 0.375
scaling_ratio_range = (0.75, 1.0)
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='YOLOXBatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=1)
]),
backbone=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
neck=dict(
deepen_factor=deepen_factor,
widen_factor=widen_factor,
),
bbox_head=dict(head_module=dict(widen_factor=widen_factor)))
# data settings
img_scale = _base_.img_scale
pre_transform = _base_.pre_transform
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='mmdet.YOLOXHSVRandomAug'),
dict(type='RandomFlip', prob=0.5),
dict(
type='FilterAnnotations',
by_keypoints=True,
min_gt_bbox_wh=(1, 1),
keep_empty=False),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
]
test_pipeline = [
*pre_transform,
dict(type='Resize', scale=(416, 416), 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'))
]
train_dataloader = dict(dataset=dict(pipeline=train_pipeline_stage1))
val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
test_dataloader = val_dataloader
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