def test_actions(self):
tempdir = tempfile.mkdtemp()
url = "https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1"
filepath = os.path.join(tempdir, "MedNIST.tar.gz")
output_dir = tempdir
md5_value = "0bc7306e7427e00ad1c5526a6677552d"
try:
download_and_extract(url, filepath, output_dir, md5_value)
download_and_extract(url, filepath, output_dir, md5_value)
except (ContentTooShortError, HTTPError):
pass # ignore remote errors in this test
wrong_md5 = "0"
try:
download_url(url, filepath, wrong_md5)
except RuntimeError as e:
self.assertTrue(str(e).startswith("MD5 check"))
shutil.rmtree(os.path.join(tempdir, "MedNIST"))
try:
extractall(filepath, output_dir, wrong_md5)
except RuntimeError as e:
self.assertTrue(str(e).startswith("MD5 check"))
shutil.rmtree(tempdir)
def test_actions(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
url = "https://drive.google.com/uc?id=13MhoPsNgI6qboJfLicFf_jNvsFUbIYsd"
filepath = os.path.join(testing_dir, "MedNIST.tar.gz")
output_dir = testing_dir
md5_value = "0bc7306e7427e00ad1c5526a6677552d"
try:
download_and_extract(url, filepath, output_dir, md5_value)
download_and_extract(url, filepath, output_dir, md5_value)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(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
wrong_md5 = "0"
try:
download_url(url, filepath, wrong_md5)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(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 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 run_main():
dataset_file = os.path.join(TEST_DATA, "dataset.zip")
dataset_url = "https://github.com/Project-MONAI/MONAILabel/releases/download/data/test_dataset.zip"
if not os.path.exists(os.path.join(TEST_DATA, "dataset")):
if not os.path.exists(dataset_file):
download_url(url=dataset_url, filepath=dataset_file)
extractall(filepath=dataset_file, output_dir=TEST_DATA)
def download_file(url, path, delay=1, skip_on_exists=True):
if skip_on_exists and os.path.exists(path):
return
os.makedirs(os.path.dirname(path), exist_ok=True)
logger.info(f"Downloading resource: {path} from {url}")
download_url(url, path)
if delay > 0:
time.sleep(delay)
def download(resources):
if not resources:
return
for resource in resources:
if not os.path.exists(resource[0]):
os.makedirs(os.path.dirname(resource[0]), exist_ok=True)
logger.info(
f"Downloading resource: {resource[0]} from {resource[1]}")
download_url(resource[1], resource[0])
time.sleep(1)
classification_report,
confusion_matrix,
)
print_config()
# Setup data directory
root_dir = "./"
data_dir = os.path.join(root_dir, "data")
res_dir = os.path.join(root_dir, "results")
os.makedirs(res_dir, exist_ok=True)
trained_model_path = os.path.join(res_dir, "net_key_metric=0.7314.pt")
if not os.path.exists(trained_model_path):
resource = "https://github.com/Thibault-Pelletier/MonaiWorkshop/raw/e29cdaa46e0097db909478e95c001d303ae963ab/results/net_key_metric%3D0.7314.pt"
download_url(url=resource, filepath=trained_model_path)
# Download data if necessary
resource = "https://drive.google.com/uc?id=1aMc9eW_fGCphGBjAKDedxu8-aJVcSczd" # Full 2.9 GB dataset
# resource = "https://drive.google.com/uc?id=1rZwPR3CFlFmYTev2YkxTJDfmLrbCiy63" # Small 200 MB dataset subset
compressed_file = os.path.join(root_dir, "brats2015 - data.zip")
if not os.path.exists(data_dir):
os.makedirs(data_dir)
download_url(url=resource, filepath=compressed_file)
extractall(filepath=compressed_file, output_dir=data_dir)
# Set dataset path
train_dirs = [
os.path.join(data_dir, "train", "HGG"),
os.path.join(data_dir, "train", "LGG")