MAERec-Gradio / configs /textdet /panet /_base_panet_resnet18_fpem-ffm.py
Mountchicken's picture
Upload 704 files
9bf4bd7
raw
history blame
2.57 kB
# BasicBlock has a little difference from official PANet
# BasicBlock in mmdet lacks RELU in the last convolution.
model = dict(
type='PANet',
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),
backbone=dict(
type='mmdet.ResNet',
depth=18,
num_stages=4,
stem_channels=128,
deep_stem=True,
out_indices=(0, 1, 2, 3),
frozen_stages=-1,
norm_eval=False,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18'),
style='pytorch'),
neck=dict(type='FPEM_FFM', in_channels=[64, 128, 256, 512]),
det_head=dict(
type='PANHead',
in_channels=[128, 128, 128, 128],
hidden_dim=128,
out_channel=6,
module_loss=dict(
type='PANModuleLoss',
loss_text=dict(type='MaskedSquareDiceLoss'),
loss_kernel=dict(type='MaskedSquareDiceLoss'),
),
postprocessor=dict(type='PANPostprocessor', text_repr_type='quad')))
train_pipeline = [
dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
dict(
type='LoadOCRAnnotations',
with_polygon=True,
with_bbox=True,
with_label=True,
),
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='TorchVisionWrapper',
op='ColorJitter',
brightness=32.0 / 255,
saturation=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'),
# TODO Replace with mmcv.RescaleToShort when it's ready
dict(
type='ShortScaleAspectJitter',
short_size=736,
scale_divisor=1,
ratio_range=(1.0, 1.0),
aspect_ratio_range=(1.0, 1.0)),
# 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'))
]