# 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='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='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'