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9e227b228b
* WIP test_mmocr 8 out of 20 * test_mmocr_export * test mmocr apis * add test data * add mmocr model unittest 5 passed 1 failed * finish mmocr unittest * fix lint * fix yapf * fix isort * fix flake8 * fix docformatter * fix docformatter * try to fix unittest after merge master * Change test.py for backend.DEFAULT * fix flake8 * fix ut * fix yapf * fix ut build * fix yapf * fix mmocr_export ut * fix mmocr_apis ort not cuda * remove explicit .forward * remove backendwrapper * simplify the crnn and dbnet config * simplify instance_test.json * add another case of decoder * increase coverage of test_mmocr_models base_recognizer * improve coverage * improve encode_decoder coverage * reply for grimoire codereview * what if not check cuda? * remove image data * reply to runningleon code review * fix fpnc * fix lint * try to fix CI UT error * fix fpnc with and wo custom ops * fix yapf * skip fpnc when cuda is not ready in ci * reply for code review * reply for code review * fix yapf * reply for code review * fix yapf * fix conflict * remove unmatched data path * remove unnecessary comments
50 lines
1.6 KiB
Python
Executable File
50 lines
1.6 KiB
Python
Executable File
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 = 'data/icdar2015'
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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 + '/instances_test.json',
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img_prefix=data_root + '/imgs',
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pipeline=test_pipeline))
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evaluation = dict(interval=100, metric='hmean-iou')
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