mirror of
https://github.com/deepinsight/insightface.git
synced 2026-05-19 15:41:33 +00:00
86 lines
3.0 KiB
Python
Executable File
86 lines
3.0 KiB
Python
Executable File
dataset_type = 'RetinaFaceDataset'
|
|
data_root = 'data/retinaface/'
|
|
train_root = data_root+'train/'
|
|
val_root = data_root+'val/'
|
|
#img_norm_cfg = dict(
|
|
# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
|
|
img_norm_cfg = dict(
|
|
mean=[127.5, 127.5, 127.5], std=[128.0, 128.0, 128.0], to_rgb=True)
|
|
train_pipeline = [
|
|
#dict(type='LoadImageFromFile'),
|
|
#dict(type='LoadAnnotations', with_bbox=True),
|
|
#dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
|
|
#dict(type='RandomFlip', flip_ratio=0.5),
|
|
#dict(type='Normalize', **img_norm_cfg),
|
|
#dict(type='Pad', size_divisor=32),
|
|
#dict(type='DefaultFormatBundle'),
|
|
#dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_keypoints', 'gt_labels']),
|
|
|
|
dict(type='LoadImageFromFile', to_float32=True),
|
|
dict(type='LoadAnnotations', with_bbox=True, with_keypoints=True),
|
|
dict(type='RandomSquareCrop',
|
|
crop_choice=[0.3, 0.45, 0.6, 0.8, 1.0]),
|
|
dict(
|
|
type='PhotoMetricDistortion',
|
|
brightness_delta=32,
|
|
contrast_range=(0.5, 1.5),
|
|
saturation_range=(0.5, 1.5),
|
|
hue_delta=18),
|
|
dict(type='RandomFlip', flip_ratio=0.5),
|
|
dict(type='Resize', img_scale=(640, 640), keep_ratio=False),
|
|
dict(type='Normalize', **img_norm_cfg),
|
|
dict(type='DefaultFormatBundle'),
|
|
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_ignore', 'gt_keypointss']),
|
|
#dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_bboxes_ignore']),
|
|
]
|
|
test_pipeline = [
|
|
#dict(type='LoadImageFromFile'),
|
|
#dict(
|
|
# type='MultiScaleFlipAug',
|
|
# img_scale=(1333, 800),
|
|
# flip=False,
|
|
# transforms=[
|
|
# dict(type='Resize', keep_ratio=True),
|
|
# dict(type='RandomFlip'),
|
|
# dict(type='Normalize', **img_norm_cfg),
|
|
# dict(type='Pad', size_divisor=32),
|
|
# dict(type='ImageToTensor', keys=['img']),
|
|
# dict(type='Collect', keys=['img']),
|
|
# ])
|
|
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='MultiScaleFlipAug',
|
|
img_scale=(1100, 1650),
|
|
flip=False,
|
|
transforms=[
|
|
dict(type='Resize', keep_ratio=True),
|
|
dict(type='RandomFlip', flip_ratio=0.0),
|
|
dict(type='Normalize', **img_norm_cfg),
|
|
dict(type='Pad', size_divisor=32, pad_val=0),
|
|
dict(type='ImageToTensor', keys=['img']),
|
|
dict(type='Collect', keys=['img'])
|
|
])
|
|
]
|
|
data = dict(
|
|
samples_per_gpu=8,
|
|
workers_per_gpu=2,
|
|
train=dict(
|
|
type=dataset_type,
|
|
ann_file=train_root + 'labelv2.txt',
|
|
img_prefix=train_root+ 'images/',
|
|
pipeline=train_pipeline),
|
|
val=dict(
|
|
type=dataset_type,
|
|
ann_file=val_root + 'labelv2.txt',
|
|
img_prefix=val_root+ 'images/',
|
|
pipeline=test_pipeline),
|
|
test=dict(
|
|
type=dataset_type,
|
|
ann_file=val_root + 'labelv2.txt',
|
|
img_prefix=val_root+ 'images/',
|
|
pipeline=test_pipeline),
|
|
)
|
|
#evaluation = dict(interval=1, metric='bbox')
|
|
evaluation = dict(interval=10, metric='mAP')
|