Tests Cleanup

tests/__init__.py

@@ -8,32 +8,3 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-from __future__ import annotations
-
-import sys
-import unittest
-import warnings
-
-
-def _enter_pr_4800(self):
-    """
-    code from https://github.com/python/cpython/pull/4800
-    """
-    # The __warningregistry__'s need to be in a pristine state for tests
-    # to work properly.
-    for v in list(sys.modules.values()):
-        if getattr(v, "__warningregistry__", None):
-            v.__warningregistry__ = {}
-    self.warnings_manager = warnings.catch_warnings(record=True)
-    self.warnings = self.warnings_manager.__enter__()
-    warnings.simplefilter("always", self.expected)
-    return self
-
-
-# FIXME: workaround for https://bugs.python.org/issue29620
-try:
-    # Suppression for issue #494:  tests/__init__.py:34: error: Cannot assign to a method
-    unittest.case._AssertWarnsContext.__enter__ = _enter_pr_4800  # type: ignore
-except AttributeError:
-    pass

tests/apps/detection/networks/test_retinanet.py

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets import resnet10, resnet18, resnet34, resnet50, resnet101, resnet152, resnet200
 from monai.utils import ensure_tuple, optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion, dict_product, skip_if_quick, test_onnx_save, test_script_save
+from tests.test_utils import dict_product, skip_if_quick, test_onnx_save, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -94,7 +94,6 @@
 TEST_CASES_TS = [[params["model"], *params["case"]] for params in dict_product(model=MODEL_LIST, case=[TEST_CASE_1])]
 
 
-@SkipIfBeforePyTorchVersion((1, 12))
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNet(unittest.TestCase):

tests/apps/detection/networks/test_retinanet_detector.py

@@ -21,7 +21,7 @@
 from monai.apps.detection.utils.anchor_utils import AnchorGeneratorWithAnchorShape
 from monai.networks import eval_mode, train_mode
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion, skip_if_quick, test_script_save
+from tests.test_utils import skip_if_quick, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -110,7 +110,6 @@ def forward(self, images):
         return {self.cls_key: [torch.randn(out_cls_shape)], self.box_reg_key: [torch.randn(out_box_reg_shape)]}
 
 
-@SkipIfBeforePyTorchVersion((1, 11))
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 @skip_if_quick
 class TestRetinaNetDetector(unittest.TestCase):

tests/apps/detection/utils/test_anchor_box.py

@@ -18,7 +18,7 @@
 
 from monai.apps.detection.utils.anchor_utils import AnchorGenerator, AnchorGeneratorWithAnchorShape
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion, assert_allclose, test_script_save
+from tests.test_utils import assert_allclose, test_script_save
 
 _, has_torchvision = optional_import("torchvision")
 
@@ -39,7 +39,6 @@
 ]
 
 
-@SkipIfBeforePyTorchVersion((1, 11))
 @unittest.skipUnless(has_torchvision, "Requires torchvision")
 class TestAnchorGenerator(unittest.TestCase):
     @parameterized.expand(TEST_CASES_2D)

tests/apps/maisi/networks/test_autoencoderkl_maisi.py

@@ -19,7 +19,6 @@
 from monai.apps.generation.maisi.networks.autoencoderkl_maisi import AutoencoderKlMaisi
 from monai.networks import eval_mode
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion
 
 tqdm, has_tqdm = optional_import("tqdm", name="tqdm")
 _, has_einops = optional_import("einops")
@@ -87,7 +86,6 @@ def test_shape(self, input_param, input_shape, expected_shape, expected_latent_s
             self.assertEqual(result[2].shape, expected_latent_shape)
 
     @parameterized.expand(CASES)
-    @SkipIfBeforePyTorchVersion((1, 11))
     def test_shape_with_convtranspose_and_checkpointing(
         self, input_param, input_shape, expected_shape, expected_latent_shape
     ):
@@ -152,7 +150,6 @@ def test_shape_reconstruction(self):
             result = net.reconstruct(torch.randn(input_shape).to(device))
             self.assertEqual(result.shape, expected_shape)
 
-    @SkipIfBeforePyTorchVersion((1, 11))
     def test_shape_reconstruction_with_convtranspose_and_checkpointing(self):
         input_param, input_shape, expected_shape, _ = CASES[0]
         input_param = input_param.copy()
@@ -170,7 +167,6 @@ def test_shape_encode(self):
             self.assertEqual(result[0].shape, expected_latent_shape)
             self.assertEqual(result[1].shape, expected_latent_shape)
 
-    @SkipIfBeforePyTorchVersion((1, 11))
     def test_shape_encode_with_convtranspose_and_checkpointing(self):
         input_param, input_shape, _, expected_latent_shape = CASES[0]
         input_param = input_param.copy()
@@ -190,7 +186,6 @@ def test_shape_sampling(self):
             )
             self.assertEqual(result.shape, expected_latent_shape)
 
-    @SkipIfBeforePyTorchVersion((1, 11))
     def test_shape_sampling_convtranspose_and_checkpointing(self):
         input_param, _, _, expected_latent_shape = CASES[0]
         input_param = input_param.copy()
@@ -209,7 +204,6 @@ def test_shape_decode(self):
             result = net.decode(torch.randn(latent_shape).to(device))
             self.assertEqual(result.shape, expected_input_shape)
 
-    @SkipIfBeforePyTorchVersion((1, 11))
     def test_shape_decode_convtranspose_and_checkpointing(self):
         input_param, expected_input_shape, _, latent_shape = CASES[0]
         input_param = input_param.copy()

tests/apps/maisi/networks/test_controlnet_maisi.py

@@ -20,7 +20,6 @@
 from monai.apps.generation.maisi.networks.controlnet_maisi import ControlNetMaisi
 from monai.networks import eval_mode
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion
 
 _, has_einops = optional_import("einops")
 
@@ -127,7 +126,6 @@
 ]
 
 
-@SkipIfBeforePyTorchVersion((2, 0))
 class TestControlNet(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     @skipUnless(has_einops, "Requires einops")

tests/apps/test_auto3dseg_bundlegen.py

@@ -26,13 +26,7 @@
 from monai.bundle.config_parser import ConfigParser
 from monai.data import create_test_image_3d
 from monai.utils import set_determinism
-from tests.test_utils import (
-    SkipIfBeforePyTorchVersion,
-    get_testing_algo_template_path,
-    skip_if_downloading_fails,
-    skip_if_no_cuda,
-    skip_if_quick,
-)
+from tests.test_utils import get_testing_algo_template_path, skip_if_downloading_fails, skip_if_no_cuda, skip_if_quick
 
 num_images_perfold = max(torch.cuda.device_count(), 4)
 num_images_per_batch = 2
@@ -104,7 +98,6 @@ def run_auto3dseg_before_bundlegen(test_path, work_dir):
 
 
 @skip_if_no_cuda
-@SkipIfBeforePyTorchVersion((1, 11, 1))
 @skip_if_quick
 class TestBundleGen(unittest.TestCase):
     def setUp(self) -> None:

tests/apps/vista3d/test_point_based_window_inferer.py

@@ -20,7 +20,7 @@
 from monai.networks import eval_mode
 from monai.networks.nets.vista3d import vista3d132
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion, skip_if_quick
+from tests.test_utils import skip_if_quick
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -60,7 +60,6 @@
 ]
 
 
-@SkipIfBeforePyTorchVersion((1, 11))
 @skip_if_quick
 class TestPointBasedWindowInferer(unittest.TestCase):
     @parameterized.expand(TEST_CASES)

tests/bundle/test_bundle_download.py

@@ -28,12 +28,12 @@
 from monai.bundle.scripts import _examine_monai_version, _list_latest_versions, download
 from monai.utils import optional_import
 from tests.test_utils import (
-    SkipIfBeforePyTorchVersion,
     assert_allclose,
     command_line_tests,
     skip_if_downloading_fails,
     skip_if_no_cuda,
     skip_if_quick,
+    skip_if_windows,
 )
 
 _, has_huggingface_hub = optional_import("huggingface_hub")
@@ -95,6 +95,47 @@
     {"model.pt": "27952767e2e154e3b0ee65defc5aed38", "model.ts": "97746870fe591f69ac09827175b00675"},
 ]
 
+TEST_CASE_NGC_1 = [
+    "spleen_ct_segmentation",
+    "0.3.7",
+    None,
+    "monai_spleen_ct_segmentation",
+    "models/model.pt",
+    "b418a2dc8672ce2fd98dc255036e7a3d",
+]
+TEST_CASE_NGC_2 = [
+    "monai_spleen_ct_segmentation",
+    "0.3.7",
+    "monai_",
+    "spleen_ct_segmentation",
+    "models/model.pt",
+    "b418a2dc8672ce2fd98dc255036e7a3d",
+]
+
+TESTCASE_NGC_WEIGHTS = {
+    "key": "model.0.conv.unit0.adn.N.bias",
+    "value": torch.tensor(
+        [
+            -0.0705,
+            -0.0937,
+            -0.0422,
+            -0.2068,
+            0.1023,
+            -0.2007,
+            -0.0883,
+            0.0018,
+            -0.1719,
+            0.0116,
+            0.0285,
+            -0.0044,
+            0.1223,
+            -0.1287,
+            -0.1858,
+            0.0460,
+        ]
+    ),
+}
+
 
 class TestDownload(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
@@ -356,7 +397,6 @@ def test_load_weights_with_net_override(self, bundle_name, device, net_override)
 
     @parameterized.expand([TEST_CASE_9])
     @skip_if_quick
-    @SkipIfBeforePyTorchVersion((1, 7, 1))
     def test_load_ts_module(self, bundle_files, bundle_name, version, repo, device, model_file):
         with skip_if_downloading_fails():
             # load ts module
@@ -419,5 +459,31 @@ def test_url_download_large_files(self, bundle_files, bundle_name, url, hash_val
                     self.assertTrue(check_hash(filepath=file_path, val=hash_val[file]))
 
 
+@skip_if_windows
+class TestNgcBundleDownload(unittest.TestCase):
+    @parameterized.expand([TEST_CASE_NGC_1, TEST_CASE_NGC_2])
+    @skip_if_quick
+    def test_ngc_download_bundle(self, bundle_name, version, remove_prefix, download_name, file_path, hash_val):
+        with skip_if_downloading_fails():
+            with tempfile.TemporaryDirectory() as tempdir:
+                download(
+                    name=bundle_name, source="ngc", version=version, bundle_dir=tempdir, remove_prefix=remove_prefix
+                )
+                full_file_path = os.path.join(tempdir, download_name, file_path)
+                self.assertTrue(os.path.exists(full_file_path))
+                self.assertTrue(check_hash(filepath=full_file_path, val=hash_val))
+
+                model = load(
+                    name=bundle_name, source="ngc", version=version, bundle_dir=tempdir, remove_prefix=remove_prefix
+                )
+                assert_allclose(
+                    model.state_dict()[TESTCASE_NGC_WEIGHTS["key"]],
+                    TESTCASE_NGC_WEIGHTS["value"],
+                    atol=1e-4,
+                    rtol=1e-4,
+                    type_test=False,
+                )
+
+
 if __name__ == "__main__":
     unittest.main()

tests/data/meta_tensor/test_meta_tensor.py

@@ -32,7 +32,7 @@
 from monai.data.utils import decollate_batch, list_data_collate
 from monai.transforms import BorderPadd, Compose, DivisiblePadd, FromMetaTensord, ToMetaTensord
 from monai.utils.enums import PostFix
-from tests.test_utils import TEST_DEVICES, SkipIfBeforePyTorchVersion, assert_allclose, dict_product, skip_if_no_cuda
+from tests.test_utils import TEST_DEVICES, assert_allclose, dict_product, skip_if_no_cuda
 
 DTYPES = [[torch.float32], [torch.float64], [torch.float16], [torch.int64], [torch.int32], [None]]
 
@@ -297,7 +297,6 @@ def test_dataset(self, device, dtype):
             self.check(im, ims[i], ids=True)
 
     @parameterized.expand(DTYPES)
-    @SkipIfBeforePyTorchVersion((1, 8))
     def test_dataloader(self, dtype):
         batch_size = 5
         ims = [self.get_im(dtype=dtype)[0] for _ in range(batch_size * 2)]
@@ -314,7 +313,6 @@ def test_dataloader(self, dtype):
             self.assertTupleEqual(tuple(batch.affine.shape), expected_affine_shape)
             self.assertEqual(len(batch.applied_operations), batch_size)
 
-    @SkipIfBeforePyTorchVersion((1, 9))
     def test_indexing(self):
         """
         Check the metadata is returned in the expected format depending on whether
@@ -406,7 +404,6 @@ def test_slicing(self):
         self.assertEqual(x[[True, False, True]].shape, (2, 3, 4))
 
     @parameterized.expand(DTYPES)
-    @SkipIfBeforePyTorchVersion((1, 8))
     def test_decollate(self, dtype):
         batch_size = 3
         ims = [self.get_im(dtype=dtype)[0] for _ in range(batch_size * 2)]

tests/data/test_shuffle_buffer.py

@@ -18,10 +18,8 @@
 
 from monai.data import DataLoader, ShuffleBuffer
 from monai.utils import convert_data_type
-from tests.test_utils import SkipIfBeforePyTorchVersion
 
 
-@SkipIfBeforePyTorchVersion((1, 12))
 class TestShuffleBuffer(unittest.TestCase):
     def test_shape(self):
         buffer = ShuffleBuffer([1, 2, 3, 4], seed=0)

tests/fl/monai_algo/test_fl_monai_algo_dist.py

@@ -23,7 +23,7 @@
 from monai.fl.utils.constants import ExtraItems
 from monai.fl.utils.exchange_object import ExchangeObject
 from monai.networks import get_state_dict
-from tests.test_utils import DistCall, DistTestCase, SkipIfBeforePyTorchVersion, SkipIfNoModule, skip_if_no_cuda
+from tests.test_utils import DistCall, DistTestCase, SkipIfNoModule, skip_if_no_cuda
 
 TESTS_PATH = TESTS_PATH = Path(__file__).parents[2].as_posix()
 _root_dir = os.path.abspath(pathjoin(TESTS_PATH))
@@ -32,7 +32,6 @@
 
 
 @SkipIfNoModule("ignite")
-@SkipIfBeforePyTorchVersion((1, 11, 1))
 class TestFLMonaiAlgo(DistTestCase):
     @DistCall(nnodes=1, nproc_per_node=2, init_method="no_init")
     @skip_if_no_cuda

tests/handlers/test_handler_tb_image.py

@@ -23,15 +23,13 @@
 from monai.data import decollate_batch
 from monai.handlers import TensorBoardImageHandler
 from monai.utils import optional_import
-from tests.test_utils import SkipIfBeforePyTorchVersion
 
 _, has_tb = optional_import("torch.utils.tensorboard", name="SummaryWriter")
 
 TEST_CASES = [[[20, 20]], [[2, 20, 20]], [[3, 20, 20]], [[20, 20, 20]], [[2, 20, 20, 20]], [[2, 2, 20, 20, 20]]]
 
 
 @unittest.skipUnless(has_tb, "Requires SummaryWriter installation")
-@SkipIfBeforePyTorchVersion((1, 13))  # issue 6683
 class TestHandlerTBImage(unittest.TestCase):
     @parameterized.expand(TEST_CASES)
     def test_tb_image_shape(self, shape):