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model = dict(
type='FCENet',
backbone=dict(
type='mmdet.ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
frozen_stages=-1,
norm_cfg=dict(type='BN', requires_grad=True),
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
norm_eval=False,
style='pytorch'),
neck=dict(
type='mmdet.FPN',
in_channels=[512, 1024, 2048],
out_channels=256,
add_extra_convs='on_output',
num_outs=3,
relu_before_extra_convs=True,
act_cfg=None),
det_head=dict(
type='FCEHead',
in_channels=256,
fourier_degree=5,
module_loss=dict(type='FCEModuleLoss', num_sample=50),
postprocessor=dict(
type='FCEPostprocessor',
scales=(8, 16, 32),
text_repr_type='quad',
num_reconstr_points=50,
alpha=1.2,
beta=1.0,
score_thr=0.3)),
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='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=30,
pad_with_fixed_color=False,
use_canvas=True)
],
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=[0.6, 0.4]),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=0.5,
contrast=0.5),
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=(2260, 2260), 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'))
]
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