mirror of
https://github.com/open-mmlab/mmocr.git
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0a1787d6bc
* base refactor * update config * modify implementation of nrtr * add config file * add mask * add operation order * fix contiguous bug * update config * fix pytest * fix pytest * update readme * update readme and metafile * update docstring * fix norm cfg and dict size * rm useless * use mmocr builder instead * update pytest * update * remove useless * fix ckpt name * fix path * include all config file into pytest * update inference * Update test_recog_config.py
150 lines
4.9 KiB
Python
150 lines
4.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import copy
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import os
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import pytest
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from mmcv import Config
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from mmcv.image import imread
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from mmocr.apis.inference import (disable_text_recog_aug_test, init_detector,
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model_inference)
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from mmocr.datasets import build_dataset # noqa: F401
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from mmocr.models import build_detector # noqa: F401
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from mmocr.utils import revert_sync_batchnorm
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def build_model(config_file):
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device = 'cpu'
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model = init_detector(config_file, checkpoint=None, device=device)
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model = revert_sync_batchnorm(model)
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if model.cfg.data.test['type'] == 'ConcatDataset':
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model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
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0].pipeline
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return model
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@pytest.mark.parametrize('cfg_file', [
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'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
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'../configs/textrecog/crnn/crnn_academic_dataset.py',
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'../configs/textrecog/seg/seg_r31_1by16_fpnocr_academic.py',
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'../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'
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])
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def test_model_inference(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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model = build_model(config_file)
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with pytest.raises(AssertionError):
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model_inference(model, 1)
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sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
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model_inference(model, sample_img_path)
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# numpy inference
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img = imread(sample_img_path)
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model_inference(model, img)
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@pytest.mark.parametrize(
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'cfg_file',
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['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'])
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def test_model_batch_inference_det(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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model = build_model(config_file)
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sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
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results = model_inference(model, [sample_img_path], batch_mode=True)
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assert len(results) == 1
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# numpy inference
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img = imread(sample_img_path)
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results = model_inference(model, [img], batch_mode=True)
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assert len(results) == 1
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@pytest.mark.parametrize('cfg_file', [
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'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
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])
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def test_model_batch_inference_raises_exception_error_aug_test_recog(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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model = build_model(config_file)
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with pytest.raises(
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Exception,
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match='aug test does not support inference with batch size'):
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sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
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model_inference(model, [sample_img_path, sample_img_path])
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with pytest.raises(
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Exception,
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match='aug test does not support inference with batch size'):
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img = imread(sample_img_path)
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model_inference(model, [img, img])
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@pytest.mark.parametrize('cfg_file', [
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'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
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])
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def test_model_batch_inference_recog(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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model = build_model(config_file)
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sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_recog.jpg')
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results = model_inference(
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model, [sample_img_path, sample_img_path], batch_mode=True)
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assert len(results) == 2
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# numpy inference
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img = imread(sample_img_path)
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results = model_inference(model, [img, img], batch_mode=True)
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assert len(results) == 2
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@pytest.mark.parametrize(
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'cfg_file',
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['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'])
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def test_model_batch_inference_empty_detection(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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model = build_model(config_file)
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empty_detection = []
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with pytest.raises(
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Exception,
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match='empty imgs provided, please check and try again'):
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model_inference(model, empty_detection, batch_mode=True)
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@pytest.mark.parametrize('cfg_file', [
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'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
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])
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def test_disable_text_recog_aug_test(cfg_file):
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tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
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config_file = os.path.join(tmp_dir, cfg_file)
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cfg = Config.fromfile(config_file)
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cfg1 = copy.deepcopy(cfg)
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test = cfg1.data.test.datasets[0]
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test.pipeline = cfg1.data.test.pipeline
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cfg1.data.test = test
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disable_text_recog_aug_test(cfg1, set_types=['test'])
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cfg2 = copy.deepcopy(cfg)
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cfg2.data.test.pipeline = None
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disable_text_recog_aug_test(cfg2, set_types=['test'])
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cfg2 = copy.deepcopy(cfg)
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cfg2.data.test = Config(dict(type='ConcatDataset', datasets=[test]))
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disable_text_recog_aug_test(cfg2, set_types=['test'])
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