Merge branch 'dev' into dev

monai/__init__.py

@@ -13,9 +13,51 @@
 
 import os
 import sys
-
+import logging
+import warnings
 from ._version import get_versions
 
+
+old_showwarning = warnings.showwarning
+
+
+def custom_warning_handler(message, category, filename, lineno, file=None, line=None):
+    ignore_files = ["ignite/handlers/checkpoint", "modelopt/torch/quantization/tensor_quant"]
+    if any(ignore in filename for ignore in ignore_files):
+        return
+    old_showwarning(message, category, filename, lineno, file, line)
+
+
+class DeprecatedTypesWarningFilter(logging.Filter):
+    def filter(self, record):
+        message_bodies_to_ignore = [
+            "np.bool8",
+            "np.object0",
+            "np.int0",
+            "np.uint0",
+            "np.void0",
+            "np.str0",
+            "np.bytes0",
+            "@validator",
+            "@root_validator",
+            "class-based `config`",
+            "pkg_resources",
+            "Implicitly cleaning up",
+        ]
+        for message in message_bodies_to_ignore:
+            if message in record.getMessage():
+                return False
+        return True
+
+
+# workaround for https://github.com/Project-MONAI/MONAI/issues/8060
+# TODO: remove this workaround after upstream fixed the warning
+# Set the custom warning handler to filter warning
+warnings.showwarning = custom_warning_handler
+# Get the logger for warnings and add the filter to the logger
+logging.getLogger("py.warnings").addFilter(DeprecatedTypesWarningFilter())
+
+
 PY_REQUIRED_MAJOR = 3
 PY_REQUIRED_MINOR = 9
 

monai/data/image_reader.py

@@ -34,9 +34,6 @@
 )
 from monai.utils import MetaKeys, SpaceKeys, TraceKeys, ensure_tuple, optional_import, require_pkg
 
-# workaround for https://github.com/Project-MONAI/MONAI/issues/8061
-warnings.filterwarnings("ignore", category=DeprecationWarning, module="nptyping")
-
 if TYPE_CHECKING:
     import itk
     import nibabel as nib

pyproject.toml

@@ -4,6 +4,7 @@ requires = [
   "setuptools",
   "torch>=1.9",
   "ninja",
+  "packaging"
 ]
 
 [tool.black]

setup.cfg

@@ -40,6 +40,7 @@ python_requires = >= 3.9
 setup_requires =
     torch
     ninja
+    packaging
 install_requires =
     torch>=1.9
     numpy>=1.24,<2.0

tests/min_tests.py

@@ -212,6 +212,7 @@ def run_testsuit():
         "test_ultrasound_confidence_map_transform",
         "test_vista3d_utils",
         "test_vista3d_transforms",
+        "test_matshow3d",
     ]
     assert sorted(exclude_cases) == sorted(set(exclude_cases)), f"Duplicated items in {exclude_cases}"
 

tests/test_gdsdataset.py

@@ -23,7 +23,7 @@
 from monai.data import GDSDataset, json_hashing
 from monai.transforms import Compose, Flip, Identity, LoadImaged, SimulateDelayd, Transform
 from monai.utils import optional_import
-from tests.utils import TEST_NDARRAYS, assert_allclose
+from tests.utils import TEST_NDARRAYS, assert_allclose, skip_if_no_cuda
 
 _, has_cp = optional_import("cupy")
 nib, has_nib = optional_import("nibabel")
@@ -70,9 +70,9 @@ def __call__(self, data):
         return data
 
 
+@skip_if_no_cuda
 @unittest.skipUnless(has_cp, "Requires CuPy library.")
-@unittest.skipUnless(has_nib, "Requires nibabel package.")
-@unittest.skipUnless(has_kvikio_numpy, "Requires scikit-image library.")
+@unittest.skipUnless(has_cp and has_kvikio_numpy, "Requires CuPy and kvikio library.")
 class TestDataset(unittest.TestCase):
 
     def test_cache(self):
@@ -131,6 +131,7 @@ def test_dtype(self):
                 self.assertEqual(ds[0].dtype, DTYPES[_dtype])
                 self.assertEqual(ds1[0].dtype, DTYPES[_dtype])
 
+    @unittest.skipUnless(has_nib, "Requires nibabel package.")
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3])
     def test_shape(self, transform, expected_shape):
         test_image = nib.Nifti1Image(np.random.randint(0, 2, size=[128, 128, 128]).astype(float), np.eye(4))