MAERec-Gradio / configs /textdet /psenet /_base_psenet_resnet50_fpnf.py
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model = dict(
type='PSENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='SyncBN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPNF',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
fusion_type='concat'),
det_head=dict(
type='PSEHead',
in_channels=[256],
hidden_dim=256,
out_channel=7,
module_loss=dict(type='PSEModuleLoss'),
postprocessor=dict(type='PSEPostprocessor', text_repr_type='poly')),
data_preprocessor=dict(
type='TextDetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32))
train_pipeline = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=True,
with_label=True),
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=0.5),
dict(type='FixInvalidPolygon'),
dict(type='ShortScaleAspectJitter', short_size=736, scale_divisor=32),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='RandomRotate', max_angle=10),
dict(type='TextDetRandomCrop', target_size=(736, 736)),
dict(type='Pad', size=(736, 736)),
dict(
type='PackTextDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
]
test_pipeline = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(type='Resize', scale=(2240, 2240), keep_ratio=True),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=True,
with_label=True),
dict(
type='PackTextDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
]