adding premerge tests for py3.10 (#5370)

### Description
adding tests for python 3.10

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [ ] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [ ] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

.github/workflows/pythonapp-min.yml

@@ -74,7 +74,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9, '3.10']
     timeout-minutes: 40
     steps:
     - uses: actions/checkout@v3

.github/workflows/setupapp.yml

@@ -78,7 +78,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9, '3.10']
     steps:
     - uses: actions/checkout@v3
       with:

requirements-dev.txt

@@ -3,7 +3,7 @@
 pytorch-ignite==0.4.10
 gdown>=4.4.0
 scipy
-itk>=5.2
+itk>=5.2; python_version < "3.10"
 nibabel
 pillow!=8.3.0  # https://github.com/python-pillow/Pillow/issues/5571
 tensorboard

tests/test_ensure_channel_first.py

@@ -13,36 +13,39 @@
 import tempfile
 import unittest
 
-import itk
 import nibabel as nib
 import numpy as np
 import torch
 from parameterized import parameterized
 from PIL import Image
 
-from monai.data import ITKReader
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import EnsureChannelFirst, LoadImage
+from monai.utils import optional_import
+
+itk, has_itk = optional_import("itk", allow_namespace_pkg=True)
+ITKReader, _ = optional_import("monai.data", name="ITKReader", as_type="decorator")
 
 TEST_CASE_1 = [{}, ["test_image.nii.gz"], None]
 
 TEST_CASE_2 = [{}, ["test_image.nii.gz"], -1]
 
 TEST_CASE_3 = [{}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], None]
 
-TEST_CASE_4 = [{"reader": ITKReader()}, ["test_image.nii.gz"], None]
-
-TEST_CASE_5 = [{"reader": ITKReader()}, ["test_image.nii.gz"], -1]
-
-TEST_CASE_6 = [{"reader": ITKReader()}, ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"], None]
+TEST_CASE_4 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], None]
 
-TEST_CASE_7 = [{"reader": ITKReader(pixel_type=itk.UC)}, "tests/testing_data/CT_DICOM", None]
+TEST_CASE_5 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], -1]
 
-itk.ProcessObject.SetGlobalWarningDisplay(False)
+TEST_CASE_6 = [
+    {"reader": ITKReader() if has_itk else "itkreader"},
+    ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
+    None,
+]
 
 
 class TestEnsureChannelFirst(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4, TEST_CASE_5, TEST_CASE_6])
+    @unittest.skipUnless(has_itk, "itk not installed")
     def test_load_nifti(self, input_param, filenames, original_channel_dim):
         if original_channel_dim is None:
             test_image = np.random.rand(8, 8, 8)
@@ -58,9 +61,11 @@ def test_load_nifti(self, input_param, filenames, original_channel_dim):
             result = EnsureChannelFirst()(result)
             self.assertEqual(result.shape[0], len(filenames))
 
-    @parameterized.expand([TEST_CASE_7])
-    def test_itk_dicom_series_reader(self, input_param, filenames, _):
-        result = LoadImage(image_only=True, **input_param)(filenames)
+    @unittest.skipUnless(has_itk, "itk not installed")
+    def test_itk_dicom_series_reader(self):
+        filenames = "tests/testing_data/CT_DICOM"
+        itk.ProcessObject.SetGlobalWarningDisplay(False)
+        result = LoadImage(image_only=True, reader=ITKReader(pixel_type=itk.UC))(filenames)
         result = EnsureChannelFirst()(result)
         self.assertEqual(result.shape[0], 1)
 

tests/test_image_rw.py

@@ -23,10 +23,13 @@
 from monai.data.image_writer import ITKWriter, NibabelWriter, PILWriter, register_writer, resolve_writer
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import LoadImage, SaveImage, moveaxis
-from monai.utils import MetaKeys, OptionalImportError
+from monai.utils import MetaKeys, OptionalImportError, optional_import
 from tests.utils import TEST_NDARRAYS, assert_allclose
 
+_, has_itk = optional_import("itk", allow_namespace_pkg=True)
 
+
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadSaveNifti(unittest.TestCase):
     def setUp(self):
         self.test_dir = tempfile.mkdtemp()
@@ -82,6 +85,7 @@ def test_4d(self, reader, writer):
         self.nifti_rw(test_data, reader, writer, np.float16)
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadSavePNG(unittest.TestCase):
     def setUp(self):
         self.test_dir = tempfile.mkdtemp()
@@ -137,6 +141,7 @@ def test_1_new(self):
         self.assertEqual(resolve_writer("new")[0](0), 1)
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadSaveNrrd(unittest.TestCase):
     def setUp(self):
         self.test_dir = tempfile.mkdtemp()

tests/test_load_image.py

@@ -15,19 +15,23 @@
 import unittest
 from pathlib import Path
 
-import itk
 import nibabel as nib
 import numpy as np
 import torch
 from parameterized import parameterized
 from PIL import Image
 
-from monai.data import ITKReader, NibabelReader, PydicomReader
+from monai.data import NibabelReader, PydicomReader
 from monai.data.meta_obj import set_track_meta
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import LoadImage
+from monai.utils import optional_import
 from tests.utils import assert_allclose
 
+itk, has_itk = optional_import("itk", allow_namespace_pkg=True)
+ITKReader, _ = optional_import("monai.data", name="ITKReader", as_type="decorator")
+itk_uc, _ = optional_import("itk", name="UC", allow_namespace_pkg=True)
+
 
 class _MiniReader:
     """a test case customised reader"""
@@ -67,34 +71,39 @@ def get_data(self, _obj):
 
 TEST_CASE_5 = [{"reader": NibabelReader(mmap=False)}, ["test_image.nii.gz"], (128, 128, 128)]
 
-TEST_CASE_6 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
+TEST_CASE_6 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (128, 128, 128)]
 
-TEST_CASE_7 = [{"reader": ITKReader()}, ["test_image.nii.gz"], (128, 128, 128)]
+TEST_CASE_7 = [{"reader": ITKReader() if has_itk else "itkreader"}, ["test_image.nii.gz"], (128, 128, 128)]
 
 TEST_CASE_8 = [
-    {"reader": ITKReader()},
+    {"reader": ITKReader() if has_itk else "itkreader"},
     ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
     (3, 128, 128, 128),
 ]
 
 TEST_CASE_8_1 = [
-    {"reader": ITKReader(channel_dim=0)},
+    {"reader": ITKReader(channel_dim=0) if has_itk else "itkreader"},
     ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
     (384, 128, 128),
 ]
 
 TEST_CASE_9 = [
-    {"reader": ITKReader()},
+    {"reader": ITKReader() if has_itk else "itkreader"},
     ["test_image.nii.gz", "test_image2.nii.gz", "test_image3.nii.gz"],
     (3, 128, 128, 128),
 ]
 
-TEST_CASE_10 = [{"reader": ITKReader(pixel_type=itk.UC)}, "tests/testing_data/CT_DICOM", (16, 16, 4), (16, 16, 4)]
+TEST_CASE_10 = [
+    {"reader": ITKReader(pixel_type=itk_uc) if has_itk else "itkreader"},
+    "tests/testing_data/CT_DICOM",
+    (16, 16, 4),
+    (16, 16, 4),
+]
 
-TEST_CASE_11 = [{"reader": "ITKReader", "pixel_type": itk.UC}, "tests/testing_data/CT_DICOM", (16, 16, 4), (16, 16, 4)]
+TEST_CASE_11 = [{"reader": "ITKReader", "pixel_type": itk_uc}, "tests/testing_data/CT_DICOM", (16, 16, 4), (16, 16, 4)]
 
 TEST_CASE_12 = [
-    {"reader": "ITKReader", "pixel_type": itk.UC, "reverse_indexing": True},
+    {"reader": "ITKReader", "pixel_type": itk_uc, "reverse_indexing": True},
     "tests/testing_data/CT_DICOM",
     (16, 16, 4),
     (4, 16, 16),
@@ -124,14 +133,14 @@ def get_data(self, _obj):
 TEST_CASE_19 = [{"reader": PydicomReader()}, "tests/testing_data/CT_DICOM", (16, 16, 4), (16, 16, 4)]
 
 TEST_CASE_20 = [
-    {"reader": "PydicomReader", "ensure_channel_first": True},
+    {"reader": "PydicomReader", "ensure_channel_first": True, "force": True},
     "tests/testing_data/CT_DICOM",
     (16, 16, 4),
     (1, 16, 16, 4),
 ]
 
 TEST_CASE_21 = [
-    {"reader": "PydicomReader", "affine_lps_to_ras": True, "defer_size": "2 MB"},
+    {"reader": "PydicomReader", "affine_lps_to_ras": True, "defer_size": "2 MB", "force": True},
     "tests/testing_data/CT_DICOM",
     (16, 16, 4),
     (16, 16, 4),
@@ -146,6 +155,7 @@ def get_data(self, _obj):
     TESTS_META.append([{"reader": "ITKReader", "fallback_only": False}, (128, 128, 128), track_meta])
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadImage(unittest.TestCase):
     @parameterized.expand(
         [TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_3_1, TEST_CASE_4, TEST_CASE_4_1, TEST_CASE_5]
@@ -290,7 +300,7 @@ def test_my_reader(self):
 
     def test_itk_meta(self):
         """test metadata from a directory"""
-        out = LoadImage(image_only=True, reader="ITKReader", pixel_type=itk.UC, series_meta=True)(
+        out = LoadImage(image_only=True, reader="ITKReader", pixel_type=itk_uc, series_meta=True)(
             "tests/testing_data/CT_DICOM"
         )
         idx = "0008|103e"
@@ -313,6 +323,7 @@ def test_channel_dim(self, input_param, filename, expected_shape):
         self.assertEqual(result.meta["original_channel_dim"], input_param["channel_dim"])
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadImageMeta(unittest.TestCase):
     @classmethod
     def setUpClass(cls):

tests/test_load_imaged.py

@@ -15,7 +15,6 @@
 import unittest
 from pathlib import Path
 
-import itk
 import nibabel as nib
 import numpy as np
 import torch
@@ -26,8 +25,11 @@
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import Compose, EnsureChannelFirstD, FromMetaTensord, LoadImaged, SaveImageD
 from monai.transforms.meta_utility.dictionary import ToMetaTensord
+from monai.utils import optional_import
 from tests.utils import assert_allclose
 
+itk, has_itk = optional_import("itk", allow_namespace_pkg=True)
+
 KEYS = ["image", "label", "extra"]
 
 TEST_CASE_1 = [{"keys": KEYS}, (128, 128, 128)]
@@ -40,6 +42,7 @@
     TESTS_META.append([{"keys": KEYS, "reader": "ITKReader", "fallback_only": False}, (128, 128, 128), track_meta])
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadImaged(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2])
     def test_shape(self, input_param, expected_shape):
@@ -87,6 +90,7 @@ def test_no_file(self):
             LoadImaged(keys="img", reader="nibabelreader", image_only=True)({"img": "unknown"})
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestConsistency(unittest.TestCase):
     def _cmp(self, filename, ch_shape, reader_1, reader_2, outname, ext):
         data_dict = {"img": filename}
@@ -147,6 +151,7 @@ def test_png(self):
             self._cmp(filename, (3, 224, 256), "itkreader", "nibabelreader", output_name, ".png")
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestLoadImagedMeta(unittest.TestCase):
     @classmethod
     def setUpClass(cls):

tests/test_meta_tensor.py

@@ -499,7 +499,7 @@ def test_construct_with_pre_applied_transforms(self):
     def test_multiprocessing(self, device=None, dtype=None):
         """multiprocessing sharing with 'device' and 'dtype'"""
         buf = io.BytesIO()
-        t = MetaTensor([0.0, 0.0], device=device, dtype=dtype)
+        t = MetaTensor([0, 0] if dtype in (torch.int32, torch.int64) else [0.0, 0.0], device=device, dtype=dtype)
         t.is_batch = True
         if t.is_cuda:
             with self.assertRaises(NotImplementedError):
@@ -518,7 +518,9 @@ def test_array_function(self, device="cpu", dtype=float):
         assert_allclose(np.sum(a), np.sum(b))
         assert_allclose(np.sum(a, axis=1), np.sum(b, axis=1))
         assert_allclose(np.linalg.qr(a), np.linalg.qr(b))
-        c = MetaTensor([1.0, 2.0, 3.0], device=device, dtype=dtype)
+        c = MetaTensor(
+            [1, 2, 3] if dtype in (torch.int32, torch.int64) else [1.0, 2.0, 3.0], device=device, dtype=dtype
+        )
         assert_allclose(np.argwhere(c == 1.0).astype(int).tolist(), [[0]])
         assert_allclose(np.concatenate([c, c]), np.asarray([1.0, 2.0, 3.0, 1.0, 2.0, 3.0]))
         if pytorch_after(1, 8, 1):
@@ -530,7 +532,7 @@ def test_array_function(self, device="cpu", dtype=float):
     @parameterized.expand(TESTS)
     def test_numpy(self, device=None, dtype=None):
         """device, dtype"""
-        t = MetaTensor([0.0], device=device, dtype=dtype)
+        t = MetaTensor([0 if dtype in (torch.int32, torch.int64) else 0.0], device=device, dtype=dtype)
         self.assertIsInstance(t, MetaTensor)
         assert_allclose(t.array, np.asarray([0.0]))
         t.array = np.asarray([1.0])
@@ -540,7 +542,7 @@ def test_numpy(self, device=None, dtype=None):
         self.check_meta(t, MetaTensor([2.0]))
         assert_allclose(t.as_tensor(), torch.as_tensor([2.0]))
         if not t.is_cuda:
-            t.array[0] = torch.as_tensor(3.0, device=device, dtype=dtype)
+            t.array[0] = torch.as_tensor(3 if dtype in (torch.int32, torch.int64) else 3.0, device=device, dtype=dtype)
             self.check_meta(t, MetaTensor([3.0]))
             assert_allclose(t.as_tensor(), torch.as_tensor([3.0]))
 

tests/test_resample_to_match.py

@@ -24,8 +24,11 @@
 from monai.data.image_reader import ITKReader, NibabelReader
 from monai.data.image_writer import ITKWriter
 from monai.transforms import Compose, EnsureChannelFirstd, LoadImaged, ResampleToMatch, SaveImaged
+from monai.utils import optional_import
 from tests.utils import assert_allclose, download_url_or_skip_test, testing_data_config
 
+_, has_itk = optional_import("itk", allow_namespace_pkg=True)
+
 TEST_CASES = ["itkreader", "nibabelreader"]
 
 
@@ -36,6 +39,7 @@ def get_rand_fname(len=10, suffix=".nii.gz"):
     return out
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestResampleToMatch(unittest.TestCase):
     @classmethod
     def setUpClass(cls):

tests/test_save_image.py

@@ -18,6 +18,9 @@
 
 from monai.data.meta_tensor import MetaTensor
 from monai.transforms import SaveImage
+from monai.utils import optional_import
+
+_, has_itk = optional_import("itk", allow_namespace_pkg=True)
 
 TEST_CASE_1 = [torch.randint(0, 255, (1, 2, 3, 4)), {"filename_or_obj": "testfile0.nii.gz"}, ".nii.gz", False]
 
@@ -33,6 +36,7 @@
 ]
 
 
+@unittest.skipUnless(has_itk, "itk not installed")
 class TestSaveImage(unittest.TestCase):
     @parameterized.expand([TEST_CASE_1, TEST_CASE_2, TEST_CASE_3, TEST_CASE_4])
     def test_saved_content(self, test_data, meta_data, output_ext, resample):