3725 fixes download test (#3728)

* fixes test

Signed-off-by: Wenqi Li <[email protected]>

* skip when downloading fails

Signed-off-by: Wenqi Li <[email protected]>

Author: wyli

tests/test_cross_validation.py

@@ -11,12 +11,11 @@
 
 import os
 import unittest
-from urllib.error import ContentTooShortError, HTTPError
 
 from monai.apps import CrossValidation, DecathlonDataset
 from monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord
 from monai.utils.enums import PostFix
-from tests.utils import skip_if_quick
+from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 
 class TestCrossValidation(unittest.TestCase):
@@ -51,14 +50,8 @@ def _test_dataset(dataset):
             download=True,
         )
 
-        try:  # will start downloading if testing_dir doesn't have the Decathlon files
+        with skip_if_downloading_fails():
             data = cvdataset.get_dataset(folds=0)
-        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
 
         _test_dataset(data)
 

tests/test_decathlondataset.py

@@ -13,12 +13,11 @@
 import shutil
 import unittest
 from pathlib import Path
-from urllib.error import ContentTooShortError, HTTPError
 
 from monai.apps import DecathlonDataset
 from monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord
 from monai.utils.enums import PostFix
-from tests.utils import skip_if_quick
+from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 
 class TestDecathlonDataset(unittest.TestCase):
@@ -41,7 +40,7 @@ def _test_dataset(dataset):
             self.assertTrue(PostFix.meta("image") in dataset[0])
             self.assertTupleEqual(dataset[0]["image"].shape, (1, 36, 47, 44))
 
-        try:  # will start downloading if testing_dir doesn't have the Decathlon files
+        with skip_if_downloading_fails():
             data = DecathlonDataset(
                 root_dir=testing_dir,
                 task="Task04_Hippocampus",
@@ -50,12 +49,6 @@ def _test_dataset(dataset):
                 download=True,
                 copy_cache=False,
             )
-        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
 
         _test_dataset(data)
         data = DecathlonDataset(

tests/test_download_and_extract.py

@@ -16,7 +16,7 @@
 from urllib.error import ContentTooShortError, HTTPError
 
 from monai.apps import download_and_extract, download_url, extractall
-from tests.utils import skip_if_quick
+from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 
 class TestDownloadAndExtract(unittest.TestCase):
@@ -27,22 +27,15 @@ def test_actions(self):
         filepath = Path(testing_dir) / "MedNIST.tar.gz"
         output_dir = Path(testing_dir)
         md5_value = "0bc7306e7427e00ad1c5526a6677552d"
-        try:
+        with skip_if_downloading_fails():
             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"
         with self.assertLogs(logger="monai.apps", level="ERROR"):
             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"))
@@ -56,7 +49,7 @@ def test_actions(self):
     @skip_if_quick
     def test_default(self):
         with tempfile.TemporaryDirectory() as tmp_dir:
-            try:
+            with skip_if_downloading_fails():
                 # 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",
@@ -71,12 +64,6 @@ def test_default(self):
                     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
 
 
 if __name__ == "__main__":

tests/test_efficientnet.py

@@ -13,7 +13,6 @@
 import unittest
 from typing import TYPE_CHECKING
 from unittest import skipUnless
-from urllib.error import ContentTooShortError, HTTPError
 
 import torch
 from parameterized import parameterized
@@ -27,7 +26,7 @@
     get_efficientnet_image_size,
 )
 from monai.utils import optional_import
-from tests.utils import skip_if_quick, test_pretrained_networks, test_script_save
+from tests.utils import skip_if_downloading_fails, skip_if_quick, test_pretrained_networks, test_script_save
 
 if TYPE_CHECKING:
     import torchvision
@@ -251,12 +250,8 @@ class TestEFFICIENTNET(unittest.TestCase):
     def test_shape(self, input_param, input_shape, expected_shape):
         device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        try:
-            # initialize model
+        with skip_if_downloading_fails():
             net = EfficientNetBN(**input_param).to(device)
-        except (ContentTooShortError, HTTPError, RuntimeError) as e:
-            print(str(e))
-            return  # skipping the tests because of http errors
 
         # run inference with random tensor
         with eval_mode(net):
@@ -269,12 +264,8 @@ def test_shape(self, input_param, input_shape, expected_shape):
     def test_non_default_shapes(self, input_param, input_shape, expected_shape):
         device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        try:
-            # initialize model
+        with skip_if_downloading_fails():
             net = EfficientNetBN(**input_param).to(device)
-        except (ContentTooShortError, HTTPError, RuntimeError) as e:
-            print(str(e))
-            return  # skipping the tests because of http errors
 
         # override input shape with different variations
         num_dims = len(input_shape) - 2
@@ -387,12 +378,8 @@ class TestExtractFeatures(unittest.TestCase):
     def test_shape(self, input_param, input_shape, expected_shapes):
         device = "cuda" if torch.cuda.is_available() else "cpu"
 
-        try:
-            # initialize model
+        with skip_if_downloading_fails():
             net = EfficientNetBNFeatures(**input_param).to(device)
-        except (ContentTooShortError, HTTPError, RuntimeError) as e:
-            print(str(e))
-            return  # skipping the tests because of http errors
 
         # run inference with random tensor
         with eval_mode(net):

tests/test_integration_classification_2d.py

@@ -12,7 +12,6 @@
 import os
 import unittest
 import warnings
-from urllib.error import ContentTooShortError, HTTPError
 
 import numpy as np
 import torch
@@ -39,7 +38,7 @@
 )
 from monai.utils import set_determinism
 from tests.testing_data.integration_answers import test_integration_value
-from tests.utils import DistTestCase, TimedCall, skip_if_quick
+from tests.utils import DistTestCase, TimedCall, skip_if_downloading_fails, skip_if_quick
 
 TEST_DATA_URL = "https://drive.google.com/uc?id=1QsnnkvZyJPcbRoV_ArW8SnE1OTuoVbKE"
 MD5_VALUE = "0bc7306e7427e00ad1c5526a6677552d"
@@ -186,14 +185,8 @@ def setUp(self):
         dataset_file = os.path.join(self.data_dir, "MedNIST.tar.gz")
 
         if not os.path.exists(data_dir):
-            try:
+            with skip_if_downloading_fails():
                 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)
 

tests/test_lr_finder.py

@@ -24,6 +24,7 @@
 from monai.optimizers import LearningRateFinder
 from monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord
 from monai.utils import optional_import, set_determinism
+from tests.utils import skip_if_downloading_fails
 
 if TYPE_CHECKING:
     import matplotlib.pyplot as plt
@@ -61,14 +62,15 @@ def setUp(self):
 
     def test_lr_finder(self):
         # 0.001 gives 54 examples
-        train_ds = MedNISTDataset(
-            root_dir=self.root_dir,
-            transform=self.transforms,
-            section="validation",
-            val_frac=0.001,
-            download=True,
-            num_workers=10,
-        )
+        with skip_if_downloading_fails():
+            train_ds = MedNISTDataset(
+                root_dir=self.root_dir,
+                transform=self.transforms,
+                section="validation",
+                val_frac=0.001,
+                download=True,
+                num_workers=10,
+            )
         train_loader = DataLoader(train_ds, batch_size=300, shuffle=True, num_workers=10)
         num_classes = train_ds.get_num_classes()
 

tests/test_mednistdataset.py

@@ -13,12 +13,11 @@
 import shutil
 import unittest
 from pathlib import Path
-from urllib.error import ContentTooShortError, HTTPError
 
 from monai.apps import MedNISTDataset
 from monai.transforms import AddChanneld, Compose, LoadImaged, ScaleIntensityd, ToTensord
 from monai.utils.enums import PostFix
-from tests.utils import skip_if_quick
+from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 MEDNIST_FULL_DATASET_LENGTH = 58954
 
@@ -43,16 +42,10 @@ def _test_dataset(dataset):
             self.assertTrue(PostFix.meta("image") in dataset[0])
             self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
 
-        try:  # will start downloading if testing_dir doesn't have the MedNIST files
+        with skip_if_downloading_fails():
             data = MedNISTDataset(
                 root_dir=testing_dir, transform=transform, section="test", download=True, copy_cache=False
             )
-        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
 
         _test_dataset(data)
 

tests/test_mmar_download.py

@@ -13,7 +13,6 @@
 import tempfile
 import unittest
 from pathlib import Path
-from urllib.error import ContentTooShortError, HTTPError
 
 import numpy as np
 import torch
@@ -22,7 +21,7 @@
 from monai.apps import RemoteMMARKeys, download_mmar, get_model_spec, load_from_mmar
 from monai.apps.mmars import MODEL_DESC
 from monai.apps.mmars.mmars import _get_val
-from tests.utils import skip_if_quick
+from tests.utils import skip_if_downloading_fails, skip_if_quick
 
 TEST_CASES = [["clara_pt_prostate_mri_segmentation_1"], ["clara_pt_covid19_ct_lesion_segmentation_1"]]
 TEST_EXTRACT_CASES = [
@@ -105,7 +104,7 @@ class TestMMMARDownload(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     @skip_if_quick
     def test_download(self, idx):
-        try:
+        with skip_if_downloading_fails():
             # test model specification
             cand = get_model_spec(idx)
             self.assertEqual(cand[RemoteMMARKeys.ID], idx)
@@ -116,22 +115,12 @@ def test_download(self, idx):
                 download_mmar(idx, mmar_dir=tmp_dir, progress=False)
                 download_mmar(idx, mmar_dir=Path(tmp_dir), progress=False, version=1)  # repeated to check caching
                 self.assertTrue(os.path.exists(os.path.join(tmp_dir, idx)))
-        except (ContentTooShortError, HTTPError, RuntimeError) as e:
-            print(str(e))
-            if isinstance(e, HTTPError):
-                self.assertTrue("500" in str(e))  # http error has the code 500
-            return  # skipping this test due the network connection errors
 
     @parameterized.expand(TEST_EXTRACT_CASES)
     @skip_if_quick
     def test_load_ckpt(self, input_args, expected_name, expected_val):
-        try:
+        with skip_if_downloading_fails():
             output = load_from_mmar(**input_args)
-        except (ContentTooShortError, HTTPError, RuntimeError) as e:
-            print(str(e))
-            if isinstance(e, HTTPError):
-                self.assertTrue("500" in str(e))  # http error has the code 500
-            return
         self.assertEqual(output.__class__.__name__, expected_name)
         x = next(output.parameters())  # verify the first element
         np.testing.assert_allclose(x[0][0].detach().cpu().numpy(), expected_val, rtol=1e-3, atol=1e-3)

tests/utils.py

@@ -21,10 +21,11 @@
 import traceback
 import unittest
 import warnings
+from contextlib import contextmanager
 from functools import partial
 from subprocess import PIPE, Popen
 from typing import Callable, Optional, Tuple
-from urllib.error import ContentTooShortError, HTTPError, URLError
+from urllib.error import ContentTooShortError, HTTPError
 
 import numpy as np
 import torch
@@ -93,17 +94,25 @@ def assert_allclose(
     np.testing.assert_allclose(actual, desired, *args, **kwargs)
 
 
-def test_pretrained_networks(network, input_param, device):
+@contextmanager
+def skip_if_downloading_fails():
     try:
+        yield
+    except (ContentTooShortError, HTTPError, ConnectionError) as e:
+        raise unittest.SkipTest(f"error while downloading: {e}") from e
+    except RuntimeError as rt_e:
+        if "network issue" in str(rt_e):
+            raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e
+        if "gdown dependency" in str(rt_e):  # no gdown installed
+            raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e
+        if "md5 check" in str(rt_e):
+            raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e
+        raise rt_e
+
+
+def test_pretrained_networks(network, input_param, device):
+    with skip_if_downloading_fails():
         return network(**input_param).to(device)
-    except (URLError, HTTPError) as e:
-        raise unittest.SkipTest(e) from e
-    except RuntimeError as r_error:
-        if "unexpected EOF" in f"{r_error}":  # The file might be corrupted.
-            raise unittest.SkipTest(f"{r_error}") from r_error
-        if "network issue" in f"{r_error}":  # The network is not available.
-            raise unittest.SkipTest(f"{r_error}") from r_error
-        raise
 
 
 def test_is_quick():
@@ -651,16 +660,8 @@ def test_script_save(net, *inputs, device=None, rtol=1e-4, atol=0.0):
 
 def download_url_or_skip_test(*args, **kwargs):
     """``download_url`` and skip the tests if any downloading error occurs."""
-    try:
+    with skip_if_downloading_fails():
         download_url(*args, **kwargs)
-    except (ContentTooShortError, HTTPError) as e:
-        raise unittest.SkipTest(f"error while downloading: {e}") from e
-    except RuntimeError as rt_e:
-        if "network issue" in str(rt_e):
-            raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e
-        if "gdown dependency" in str(rt_e):  # no gdown installed
-            raise unittest.SkipTest(f"error while downloading: {rt_e}") from rt_e
-        raise rt_e
 
 
 def query_memory(n=2):