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 get_data(keys):
"""Get the example data to be used.
Use MarsAtlas as it only contains 1 image for quick download and
that image is parcellated.
"""
cache_dir = os.environ.get("MONAI_DATA_DIRECTORY") or tempfile.mkdtemp()
fname = "MarsAtlas-MNI-Colin27.zip"
url = "https://www.dropbox.com/s/ndz8qtqblkciole/" + fname + "?dl=1"
out_path = os.path.join(cache_dir, "MarsAtlas-MNI-Colin27")
zip_path = os.path.join(cache_dir, fname)
download_and_extract(url, zip_path, out_path)
image, label = sorted(glob(os.path.join(out_path, "*.nii")))
data = {CommonKeys.IMAGE: image, CommonKeys.LABEL: label}
transforms = Compose([
LoadImaged(keys),
AddChanneld(keys),
ScaleIntensityd(CommonKeys.IMAGE),
Rotate90d(keys, spatial_axes=[0, 2])
])
data = transforms(data)
max_size = max(data[keys[0]].shape)
padder = SpatialPadd(keys, (max_size, max_size, max_size))
return padder(data)
def _download_dcmqi_tools(self):
target = os.path.join(self.app_dir, "bin")
os.makedirs(target, exist_ok=True)
dcmqi_tools = [
"segimage2itkimage", "itkimage2segimage", "segimage2itkimage.exe",
"itkimage2segimage.exe"
]
existing = [
tool for tool in dcmqi_tools
if shutil.which(tool) or os.path.exists(os.path.join(target, tool))
]
logger.debug(f"Existing Tools: {existing}")
if len(existing) in [len(dcmqi_tools), len(dcmqi_tools) // 2]:
logger.debug("No need to download dcmqi tools")
return
target_os = "win64.zip" if any(
platform.win32_ver()) else "linux.tar.gz"
with tempfile.TemporaryDirectory() as tmp:
download_and_extract(
url=
f"https://github.com/QIICR/dcmqi/releases/download/v1.2.4/dcmqi-1.2.4-{target_os}",
output_dir=tmp)
for root, _, files in os.walk(tmp):
for f in files:
if f in dcmqi_tools:
shutil.copy(os.path.join(root, f), target)
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 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):
try:
download_and_extract(TEST_DATA_URL, dataset_file,
self.data_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
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 = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu:0")
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 main():
parser = argparse.ArgumentParser(description="training")
parser.add_argument(
"--msd_task",
action="store",
default="Task07_Pancreas",
help="msd task",
)
parser.add_argument(
"--root",
action="store",
default="./data_msd",
help="data root",
)
args = parser.parse_args()
resource = "https://msd-for-monai.s3-us-west-2.amazonaws.com/" + args.msd_task + ".tar"
compressed_file = os.path.join(args.root, args.msd_task + ".tar")
if not os.path.exists(args.root):
download_and_extract(resource, compressed_file, args.root)
def test_default(self):
with tempfile.TemporaryDirectory() as tmp_dir:
try:
# icon.tar.gz https://drive.google.com/file/d/1aHztVYbynCCq-byTf6cRI8ViETpBWHyC/view?usp=sharing
download_and_extract(
"https://drive.google.com/uc?id=1aHztVYbynCCq-byTf6cRI8ViETpBWHyC",
output_dir=tmp_dir,
hash_val="a55d11ad26ed9eb7277905d796205531",
file_type="tar",
)
# favicon.ico.zip https://drive.google.com/file/d/1YGa8N3LBddOkxBnnLndGicoW0klsAH6D/view?usp=sharing
download_and_extract(
"https://drive.google.com/uc?id=1YGa8N3LBddOkxBnnLndGicoW0klsAH6D",
output_dir=tmp_dir,
hash_val="ac6e167ee40803577d98237f2b0241e5",
file_type="zip",
)
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
def test_default(self):
with tempfile.TemporaryDirectory() as tmp_dir:
try:
# icon.tar.gz https://drive.google.com/file/d/1HrQd-AKPbts9jkTNN4pT8vLZyhM5irVn/view?usp=sharing
download_and_extract(
"https://drive.google.com/uc?id=1HrQd-AKPbts9jkTNN4pT8vLZyhM5irVn",
output_dir=tmp_dir,
hash_val="a55d11ad26ed9eb7277905d796205531",
file_type="tar",
)
# favicon.ico.zip https://drive.google.com/file/d/1TqBTJap621NO9arzXRrYi04lr9NTVF8H/view?usp=sharing
download_and_extract(
"https://drive.google.com/uc?id=1TqBTJap621NO9arzXRrYi04lr9NTVF8H",
output_dir=tmp_dir,
hash_val="ac6e167ee40803577d98237f2b0241e5",
file_type="zip",
)
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
from monai.utils.nvtx import Range
nvidia_dlprof_pytorch_nvtx.init()
# set directories
random.seed(0)
root_dir = "/tmp/tmp" + ''.join(random.choice(string.ascii_lowercase) for i in range(16))
print(f"root dir is: {root_dir}")
resource = "https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar"
md5 = "410d4a301da4e5b2f6f86ec3ddba524e"
compressed_file = os.path.join(root_dir, "Task09_Spleen.tar")
data_root = os.path.join(root_dir, "Task09_Spleen")
if not os.path.exists(data_root):
download_and_extract(resource, compressed_file, root_dir, md5)
out_dir = "./outputs_fast"
train_images = sorted(glob.glob(os.path.join(data_root, "imagesTr", "*.nii.gz")))
train_labels = sorted(glob.glob(os.path.join(data_root, "labelsTr", "*.nii.gz")))
data_dicts = [
{"image": image_name, "label": label_name}
for image_name, label_name in zip(train_images, train_labels)
]
train_files, val_files = data_dicts[:-9], data_dicts[-9:]
set_determinism(seed=0)
train_transforms = Compose(
[
