dbnet.py

# Copyright (c) OpenMMLab. All rights reserved.
model = dict(
    type='mmocr.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='mmocr.TextDetDataPreprocessor',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True,
        pad_size_divisor=32))

dataset_type = 'IcdarDataset'
data_root = 'tests/test_codebase/test_mmocr/data'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)

test_pipeline = [
    dict(
        type='LoadImageFromFile',
        file_client_args=dict(backend='disk'),
        color_type='color_ignore_orientation'),
    dict(
        type='PackTextDetInputs',
        meta_keys=('img_path', 'ori_shape', 'img_shape', 'scale_factor'))
]

test_dataloader = dict(
    batch_size=16,
    num_workers=8,
    persistent_workers=True,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type='ConcatDataset',
        datasets=[
            dict(
                type='OCRDataset',
                data_root='data/det/icdar2015',
                ann_file='instances_test.json',
                data_prefix=dict(img_path='imgs/'),
                test_mode=True,
                pipeline=None)
        ],
        pipeline=[
            dict(
                type='LoadImageFromFile',
                file_client_args=dict(backend='disk'),
                color_type='color_ignore_orientation'),
            dict(type='Resize', scale=(1333, 736), keep_ratio=True),
            dict(
                type='mmocr.PackTextDetInputs',
                meta_keys=('ori_shape', 'img_shape', 'scale_factor',
                           'instances'))
        ]))

visualizer = dict(type='TextDetLocalVisualizer', name='visualizer')
default_scope = 'mmocr'