MAERec-Gradio / configs /textdet /drrg /_base_drrg_resnet50_fpn-unet.py
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model = dict(
type='DRRG',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=True,
style='caffe'),
neck=dict(
type='FPN_UNet', in_channels=[256, 512, 1024, 2048], out_channels=32),
det_head=dict(
type='DRRGHead',
in_channels=32,
text_region_thr=0.3,
center_region_thr=0.4,
module_loss=dict(type='DRRGModuleLoss'),
postprocessor=dict(type='DRRGPostprocessor', link_thr=0.80)),
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_bbox=True,
with_polygon=True,
with_label=True),
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=0.5),
dict(
type='RandomResize',
scale=(800, 800),
ratio_range=(0.75, 2.5),
keep_ratio=True),
dict(
type='TextDetRandomCropFlip',
crop_ratio=0.5,
iter_num=1,
min_area_ratio=0.2),
dict(
type='RandomApply',
transforms=[dict(type='RandomCrop', min_side_ratio=0.3)],
prob=0.8),
dict(
type='RandomApply',
transforms=[
dict(
type='RandomRotate',
max_angle=60,
use_canvas=True,
pad_with_fixed_color=False)
],
prob=0.5),
dict(
type='RandomChoice',
transforms=[[
dict(type='Resize', scale=800, keep_ratio=True),
dict(type='SourceImagePad', target_scale=800)
],
dict(type='Resize', scale=800, keep_ratio=False)],
prob=[0.4, 0.6]),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='PackTextDetInputs',
meta_keys=('img_path', 'ori_shape', 'img_shape'))
]
test_pipeline = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(type='Resize', scale=(1024, 640), keep_ratio=True),
# add loading annotation after ``Resize`` because ground truth
# does not need to do resize data transform
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'))
]