Spaces:
Runtime error
Runtime error
File size: 4,313 Bytes
2366e36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
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)
return model
@pytest.mark.skipif(
platform.system() == 'Windows',
reason='Win container on Github Action does not have enough RAM to run')
@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.skipif(
platform.system() == 'Windows',
reason='Win container on Github Action does not have enough RAM to run')
@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)
|