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MMOCR
File size: 4,021 Bytes 
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img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

# for icdar2015
leval_prop_range_icdar2015 = ((0, 0.4), (0.3, 0.7), (0.6, 1.0))
train_pipeline_icdar2015 = [
    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
    dict(
        type='LoadTextAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(
        type='ColorJitter',
        brightness=32.0 / 255,
        saturation=0.5,
        contrast=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomScaling', size=800, scale=(3. / 4, 5. / 2)),
    dict(
        type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
    dict(
        type='RandomCropPolyInstances',
        instance_key='gt_masks',
        crop_ratio=0.8,
        min_side_ratio=0.3),
    dict(
        type='RandomRotatePolyInstances',
        rotate_ratio=0.5,
        max_angle=30,
        pad_with_fixed_color=False),
    dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
    dict(type='Pad', size_divisor=32),
    dict(
        type='FCENetTargets',
        fourier_degree=5,
        level_proportion_range=leval_prop_range_icdar2015),
    dict(
        type='CustomFormatBundle',
        keys=['p3_maps', 'p4_maps', 'p5_maps'],
        visualize=dict(flag=False, boundary_key=None)),
    dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
]

img_scale_icdar2015 = (2260, 2260)
test_pipeline_icdar2015 = [
    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=img_scale_icdar2015,
        flip=False,
        transforms=[
            dict(type='Resize', img_scale=(1280, 800), keep_ratio=True),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]

# for ctw1500
leval_prop_range_ctw1500 = ((0, 0.25), (0.2, 0.65), (0.55, 1.0))
train_pipeline_ctw1500 = [
    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
    dict(
        type='LoadTextAnnotations',
        with_bbox=True,
        with_mask=True,
        poly2mask=False),
    dict(
        type='ColorJitter',
        brightness=32.0 / 255,
        saturation=0.5,
        contrast=0.5),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='RandomScaling', size=800, scale=(3. / 4, 5. / 2)),
    dict(
        type='RandomCropFlip', crop_ratio=0.5, iter_num=1, min_area_ratio=0.2),
    dict(
        type='RandomCropPolyInstances',
        instance_key='gt_masks',
        crop_ratio=0.8,
        min_side_ratio=0.3),
    dict(
        type='RandomRotatePolyInstances',
        rotate_ratio=0.5,
        max_angle=30,
        pad_with_fixed_color=False),
    dict(type='SquareResizePad', target_size=800, pad_ratio=0.6),
    dict(type='RandomFlip', flip_ratio=0.5, direction='horizontal'),
    dict(type='Pad', size_divisor=32),
    dict(
        type='FCENetTargets',
        fourier_degree=5,
        level_proportion_range=leval_prop_range_ctw1500),
    dict(
        type='CustomFormatBundle',
        keys=['p3_maps', 'p4_maps', 'p5_maps'],
        visualize=dict(flag=False, boundary_key=None)),
    dict(type='Collect', keys=['img', 'p3_maps', 'p4_maps', 'p5_maps'])
]

img_scale_ctw1500 = (1080, 736)
test_pipeline_ctw1500 = [
    dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=img_scale_ctw1500,
        flip=False,
        transforms=[
            dict(type='Resize', img_scale=(1280, 800), keep_ratio=True),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]