def setUp(self):
hash_type = testing_data_config("images", FILE_KEY, "hash_type")
hash_val = testing_data_config("images", FILE_KEY, "hash_val")
download_url_or_skip_test(FILE_URL,
FILE_PATH,
hash_type=hash_type,
hash_val=hash_val)
def setUpModule(): # noqa: N802
hash_type = testing_data_config("images", FILE_KEY, "hash_type")
hash_val = testing_data_config("images", FILE_KEY, "hash_val")
download_url_or_skip_test(FILE_URL,
FILE_PATH,
hash_type=hash_type,
hash_val=hash_val)
def setUpModule():
for info in [(ARRAY_SMALL_0, FILE_PATH_SMALL_0),
(ARRAY_SMALL_1, FILE_PATH_SMALL_1)]:
array = info[0].transpose([1, 2, 0])
imwrite(info[1], array, shape=array.shape, photometric="rgb")
hash_type = testing_data_config("images", FILE_KEY, "hash_type")
hash_val = testing_data_config("images", FILE_KEY, "hash_val")
download_url_or_skip_test(FILE_URL,
FILE_PATH,
hash_type=hash_type,
hash_val=hash_val)
def setUp(self):
self.fnames = []
for key in ("0000_t2_tse_tra_4", "0000_ep2d_diff_tra_7"):
fname = os.path.join(os.path.dirname(__file__), "testing_data",
f"test_{key}.nii.gz")
url = testing_data_config("images", key, "url")
hash_type = testing_data_config("images", key, "hash_type")
hash_val = testing_data_config("images", key, "hash_val")
download_url_or_skip_test(url=url,
filepath=fname,
hash_type=hash_type,
hash_val=hash_val)
self.fnames.append(fname)
def setUpClass(cls):
super(__class__, cls).setUpClass()
cls.fnames = []
cls.tmpdir = tempfile.mkdtemp()
for key in ("0000_t2_tse_tra_4", "0000_ep2d_diff_tra_7"):
fname = os.path.join(cls.tmpdir, f"test_{key}.nii.gz")
url = testing_data_config("images", key, "url")
hash_type = testing_data_config("images", key, "hash_type")
hash_val = testing_data_config("images", key, "hash_val")
download_url_or_skip_test(url=url,
filepath=fname,
hash_type=hash_type,
hash_val=hash_val)
cls.fnames.append(fname)
def test_actions(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
config_dict = testing_data_config("images", "mednist")
url = config_dict["url"]
filepath = Path(testing_dir) / "MedNIST.tar.gz"
output_dir = Path(testing_dir)
hash_val, hash_type = config_dict["hash_val"], config_dict["hash_type"]
with skip_if_downloading_fails():
download_and_extract(url,
filepath,
output_dir,
hash_val=hash_val,
hash_type=hash_type)
download_and_extract(url,
filepath,
output_dir,
hash_val=hash_val,
hash_type=hash_type)
wrong_md5 = "0"
with self.assertLogs(logger="monai.apps", level="ERROR"):
try:
download_url(url, filepath, wrong_md5)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
if isinstance(e, RuntimeError):
# FIXME: skip MD5 check as current downloading method may fail
self.assertTrue(str(e).startswith("md5 check"))
return # skipping this test due the network connection errors
try:
extractall(filepath, output_dir, wrong_md5)
except RuntimeError as e:
self.assertTrue(str(e).startswith("md5 check"))
def setUp(self):
self.config = testing_data_config("configs", "test_meta_file")
download_url_or_skip_test(
url=self.config["url"],
filepath=SCHEMA_FILE,
hash_val=self.config.get("hash_val"),
hash_type=self.config.get("hash_type", "sha256"),
)
def setUp(self):
config = testing_data_config("images", "Prostate_T2W_AX_1")
download_url_or_skip_test(
url=config["url"],
filepath=FILE_PATH,
hash_val=config.get("hash_val"),
hash_type=config.get("hash_type", "sha256"),
)
def setUpClass(cls):
super().setUpClass()
for k, n in ((key, FILE_PATH), (key_1, FILE_PATH_1)):
config = testing_data_config("images", f"{k}")
download_url_or_skip_test(filepath=n, **config)
cls.files = [{
keys[0]: x,
keys[1]: y
} for (x, y) in [[FILE_PATH, FILE_PATH_1]] * 4]
def test_default(self, key, file_type):
with tempfile.TemporaryDirectory() as tmp_dir:
with skip_if_downloading_fails():
img_spec = testing_data_config("images", key)
download_and_extract(
img_spec["url"],
output_dir=tmp_dir,
hash_val=img_spec["hash_val"],
hash_type=img_spec["hash_type"],
file_type=file_type,
)
def setUp(self):
set_determinism(seed=0)
self.data_dir = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "testing_data")
data_dir = os.path.join(self.data_dir, "MedNIST")
dataset_file = os.path.join(self.data_dir, "MedNIST.tar.gz")
if not os.path.exists(data_dir):
with skip_if_downloading_fails():
data_spec = testing_data_config("images", "mednist")
download_and_extract(
data_spec["url"],
dataset_file,
self.data_dir,
hash_val=data_spec["hash_val"],
hash_type=data_spec["hash_type"],
)
assert os.path.exists(data_dir)
class_names = sorted(x for x in os.listdir(data_dir)
if os.path.isdir(os.path.join(data_dir, x)))
image_files = [[
os.path.join(data_dir, class_name, x)
for x in sorted(os.listdir(os.path.join(data_dir, class_name)))
] for class_name in class_names]
image_file_list, image_classes = [], []
for i, _ in enumerate(class_names):
image_file_list.extend(image_files[i])
image_classes.extend([i] * len(image_files[i]))
# split train, val, test
valid_frac, test_frac = 0.1, 0.1
self.train_x, self.train_y = [], []
self.val_x, self.val_y = [], []
self.test_x, self.test_y = [], []
for i in range(len(image_classes)):
rann = np.random.random()
if rann < valid_frac:
self.val_x.append(image_file_list[i])
self.val_y.append(image_classes[i])
elif rann < test_frac + valid_frac:
self.test_x.append(image_file_list[i])
self.test_y.append(image_classes[i])
else:
self.train_x.append(image_file_list[i])
self.train_y.append(image_classes[i])
self.device = "cuda:0" if torch.cuda.is_available() else "cpu:0"
def setUp(self):
self.original_urls = se_mod.SE_NET_MODELS.copy()
replace_url = test_is_quick()
if not replace_url:
try:
SEResNet50(pretrained=True, spatial_dims=2, in_channels=3, num_classes=2)
except OSError as rt_e:
print(rt_e)
if "certificate" in str(rt_e): # [SSL: CERTIFICATE_VERIFY_FAILED]
replace_url = True
if replace_url:
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "testing_data")
testing_data_urls = {
"senet154": {
"url": testing_data_config("models", "senet154-c7b49a05", "url"),
"filename": "senet154-c7b49a05.pth",
},
"se_resnet50": {
"url": testing_data_config("models", "se_resnet50-ce0d4300", "url"),
"filename": "se_resnet50-ce0d4300.pth",
},
"se_resnet101": {
"url": testing_data_config("models", "se_resnet101-7e38fcc6", "url"),
"filename": "se_resnet101-7e38fcc6.pth",
},
"se_resnet152": {
"url": testing_data_config("models", "se_resnet152-d17c99b7", "url"),
"filename": "se_resnet152-d17c99b7.pth",
},
"se_resnext50_32x4d": {
"url": testing_data_config("models", "se_resnext50_32x4d-a260b3a4", "url"),
"filename": "se_resnext50_32x4d-a260b3a4.pth",
},
"se_resnext101_32x4d": {
"url": testing_data_config("models", "se_resnext101_32x4d-3b2fe3d8", "url"),
"filename": "se_resnext101_32x4d-3b2fe3d8.pth",
},
}
for item in testing_data_urls:
testing_data_urls[item]["filename"] = os.path.join(testing_dir, testing_data_urls[item]["filename"])
se_mod.SE_NET_MODELS = testing_data_urls
import unittest
from unittest import skipUnless
import numpy as np
from numpy.testing import assert_array_equal
from parameterized import parameterized
from monai.apps.pathology.data import MaskedInferenceWSIDataset
from monai.utils import optional_import
from tests.utils import download_url_or_skip_test, skip_if_quick, testing_data_config
_, has_cim = optional_import("cucim", name="CuImage")
_, has_osl = optional_import("openslide")
FILE_KEY = "wsi_img"
FILE_URL = testing_data_config("images", FILE_KEY, "url")
base_name, extension = os.path.basename(f"{FILE_URL}"), ".tiff"
FILE_NAME = f"temp_{base_name}"
FILE_PATH = os.path.join(os.path.dirname(__file__), "testing_data",
FILE_NAME + extension)
MASK1 = os.path.join(os.path.dirname(__file__), "testing_data",
"temp_tissue_mask1.npy")
MASK2 = os.path.join(os.path.dirname(__file__), "testing_data",
"temp_tissue_mask2.npy")
MASK4 = os.path.join(os.path.dirname(__file__), "testing_data",
"temp_tissue_mask4.npy")
HEIGHT = 32914
WIDTH = 46000
def setUpClass(cls):
super().setUpClass()
for k, n in ((key, FILE_PATH), (key_1, FILE_PATH_1)):
config = testing_data_config("images", f"{k}")
download_url_or_skip_test(filepath=n, **config)
