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model = dict( | |
type='DBNet', | |
backbone=dict( | |
type='mmdet.ResNet', | |
depth=18, | |
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://resnet18'), | |
norm_eval=False, | |
style='caffe'), | |
neck=dict( | |
type='FPNC', in_channels=[64, 128, 256, 512], lateral_channels=256), | |
det_head=dict( | |
type='DBHead', | |
in_channels=256, | |
module_loss=dict(type='DBModuleLoss'), | |
postprocessor=dict(type='DBPostprocessor', text_repr_type='quad')), | |
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='ImgAugWrapper', | |
args=[['Fliplr', 0.5], | |
dict(cls='Affine', rotate=[-10, 10]), ['Resize', [0.5, 3.0]]]), | |
dict(type='RandomCrop', min_side_ratio=0.1), | |
dict(type='Resize', scale=(640, 640), keep_ratio=True), | |
dict(type='Pad', size=(640, 640)), | |
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=(1333, 736), 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')) | |
] | |