51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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model = dict(
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type='DBNet',
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backbone=dict(
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type='mmdet.ResNet',
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depth=18,
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num_stages=4,
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out_indices=(0, 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://resnet18'),
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norm_eval=False,
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style='caffe'),
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neck=dict(type='FPNC', in_channels=[2, 4, 8, 16], lateral_channels=8),
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bbox_head=dict(
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type='DBHead',
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text_repr_type='quad',
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in_channels=8,
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loss=dict(type='DBLoss', alpha=5.0, beta=10.0, bbce_loss=True)),
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train_cfg=None,
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test_cfg=None)
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dataset_type = 'IcdarDataset'
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data_root = 'tests/test_codebase/test_mmocr/data'
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img_norm_cfg = dict(
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mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
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test_pipeline = [
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dict(type='LoadImageFromFile', color_type='color_ignore_orientation'),
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dict(
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type='MultiScaleFlipAug',
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img_scale=(128, 64),
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flip=False,
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transforms=[
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dict(type='Resize', img_scale=(256, 128), keep_ratio=True),
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dict(type='Normalize', **img_norm_cfg),
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dict(type='Pad', size_divisor=32),
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dict(type='ImageToTensor', keys=['img']),
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dict(type='Collect', keys=['img']),
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])
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]
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data = dict(
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samples_per_gpu=16,
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test_dataloader=dict(samples_per_gpu=1),
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test=dict(
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type=dataset_type,
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ann_file=data_root + '/text_detection.json',
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img_prefix=data_root,
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pipeline=test_pipeline))
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evaluation = dict(interval=100, metric='hmean-iou')
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