mirror of https://github.com/open-mmlab/mmocr.git
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import os
|
|
|
|
import pytest
|
|
from mmcv.image import imread
|
|
|
|
from mmocr.apis.inference import init_detector, model_inference
|
|
from mmocr.datasets import build_dataset # noqa: F401
|
|
from mmocr.models import build_detector # noqa: F401
|
|
from mmocr.utils import revert_sync_batchnorm
|
|
|
|
|
|
def build_model(config_file):
|
|
device = 'cpu'
|
|
model = init_detector(config_file, checkpoint=None, device=device)
|
|
model = revert_sync_batchnorm(model)
|
|
|
|
if model.cfg.data.test['type'] == 'ConcatDataset':
|
|
model.cfg.data.test.pipeline = model.cfg.data.test['datasets'][
|
|
0].pipeline
|
|
|
|
return model
|
|
|
|
|
|
@pytest.mark.parametrize('cfg_file', [
|
|
'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
|
|
'../configs/textrecog/abinet/abinet_academic.py',
|
|
'../configs/textrecog/crnn/crnn_academic_dataset.py',
|
|
'../configs/textrecog/seg/seg_r31_1by16_fpnocr_academic.py',
|
|
'../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'
|
|
])
|
|
def test_model_inference(cfg_file):
|
|
tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
|
config_file = os.path.join(tmp_dir, cfg_file)
|
|
model = build_model(config_file)
|
|
with pytest.raises(AssertionError):
|
|
model_inference(model, 1)
|
|
|
|
sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
|
|
model_inference(model, sample_img_path)
|
|
|
|
# numpy inference
|
|
img = imread(sample_img_path)
|
|
|
|
model_inference(model, img)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'cfg_file',
|
|
['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'])
|
|
def test_model_batch_inference_det(cfg_file):
|
|
tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
|
config_file = os.path.join(tmp_dir, cfg_file)
|
|
model = build_model(config_file)
|
|
|
|
sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
|
|
results = model_inference(model, [sample_img_path], batch_mode=True)
|
|
|
|
assert len(results) == 1
|
|
|
|
# numpy inference
|
|
img = imread(sample_img_path)
|
|
results = model_inference(model, [img], batch_mode=True)
|
|
|
|
assert len(results) == 1
|
|
|
|
|
|
@pytest.mark.parametrize('cfg_file', [
|
|
'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
|
|
])
|
|
def test_model_batch_inference_raises_exception_error_aug_test_recog(cfg_file):
|
|
tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
|
config_file = os.path.join(tmp_dir, cfg_file)
|
|
model = build_model(config_file)
|
|
|
|
with pytest.raises(
|
|
Exception,
|
|
match='aug test does not support inference with batch size'):
|
|
sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_det.jpg')
|
|
model_inference(model, [sample_img_path, sample_img_path])
|
|
|
|
with pytest.raises(
|
|
Exception,
|
|
match='aug test does not support inference with batch size'):
|
|
img = imread(sample_img_path)
|
|
model_inference(model, [img, img])
|
|
|
|
|
|
@pytest.mark.parametrize('cfg_file', [
|
|
'../configs/textrecog/sar/sar_r31_parallel_decoder_academic.py',
|
|
])
|
|
def test_model_batch_inference_recog(cfg_file):
|
|
tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
|
config_file = os.path.join(tmp_dir, cfg_file)
|
|
model = build_model(config_file)
|
|
|
|
sample_img_path = os.path.join(tmp_dir, '../demo/demo_text_recog.jpg')
|
|
results = model_inference(
|
|
model, [sample_img_path, sample_img_path], batch_mode=True)
|
|
|
|
assert len(results) == 2
|
|
|
|
# numpy inference
|
|
img = imread(sample_img_path)
|
|
results = model_inference(model, [img, img], batch_mode=True)
|
|
|
|
assert len(results) == 2
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
'cfg_file',
|
|
['../configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2017.py'])
|
|
def test_model_batch_inference_empty_detection(cfg_file):
|
|
tmp_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
|
|
config_file = os.path.join(tmp_dir, cfg_file)
|
|
model = build_model(config_file)
|
|
|
|
empty_detection = []
|
|
|
|
with pytest.raises(
|
|
Exception,
|
|
match='empty imgs provided, please check and try again'):
|
|
|
|
model_inference(model, empty_detection, batch_mode=True)
|