mirror of https://github.com/open-mmlab/mmocr.git
107 lines
3.1 KiB
Python
107 lines
3.1 KiB
Python
model = dict(
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type='FCENet',
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backbone=dict(
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type='mmdet.ResNet',
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depth=50,
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num_stages=4,
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out_indices=(1, 2, 3),
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frozen_stages=-1,
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norm_cfg=dict(type='BN', requires_grad=True),
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init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
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norm_eval=False,
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style='pytorch'),
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neck=dict(
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type='mmdet.FPN',
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in_channels=[512, 1024, 2048],
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out_channels=256,
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add_extra_convs='on_output',
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num_outs=3,
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relu_before_extra_convs=True,
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act_cfg=None),
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det_head=dict(
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type='FCEHead',
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in_channels=256,
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fourier_degree=5,
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module_loss=dict(type='FCEModuleLoss', num_sample=50),
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postprocessor=dict(
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type='FCEPostprocessor',
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scales=(8, 16, 32),
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text_repr_type='quad',
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num_reconstr_points=50,
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alpha=1.2,
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beta=1.0,
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score_thr=0.3)),
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data_preprocessor=dict(
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type='TextDetDataPreprocessor',
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mean=[123.675, 116.28, 103.53],
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std=[58.395, 57.12, 57.375],
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bgr_to_rgb=True,
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pad_size_divisor=32))
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train_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(
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type='LoadOCRAnnotations',
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with_polygon=True,
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with_bbox=True,
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with_label=True,
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),
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dict(
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type='RandomResize',
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scale=(800, 800),
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ratio_range=(0.75, 2.5),
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keep_ratio=True),
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dict(
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type='TextDetRandomCropFlip',
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crop_ratio=0.5,
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iter_num=1,
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min_area_ratio=0.2),
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dict(
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type='RandomApply',
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transforms=[dict(type='RandomCrop', min_side_ratio=0.3)],
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prob=0.8),
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dict(
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type='RandomApply',
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transforms=[
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dict(
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type='RandomRotate',
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max_angle=30,
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pad_with_fixed_color=False,
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use_canvas=True)
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],
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prob=0.5),
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dict(
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type='RandomChoice',
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transforms=[[
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dict(type='Resize', scale=800, keep_ratio=True),
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dict(type='SourceImagePad', target_scale=800)
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],
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dict(type='Resize', scale=800, keep_ratio=False)],
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prob=[0.6, 0.4]),
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dict(type='RandomFlip', prob=0.5, direction='horizontal'),
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dict(
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type='TorchVisionWrapper',
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op='ColorJitter',
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brightness=32.0 / 255,
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saturation=0.5,
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contrast=0.5),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
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]
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test_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(type='Resize', scale=(2260, 2260), keep_ratio=True),
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# add loading annotation after ``Resize`` because ground truth
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# does not need to do resize data transform
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dict(
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type='LoadOCRAnnotations',
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with_polygon=True,
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with_bbox=True,
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with_label=True),
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dict(
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type='PackTextDetInputs',
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meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
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]
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