Merge branch 'dev' into numpy2_reintro

Signed-off-by: Eric Kerfoot <[email protected]>

Author: ericspod

.pre-commit-config.yaml

@@ -66,7 +66,7 @@ repos:
           )$
 
   - repo: https://github.com/hadialqattan/pycln
-    rev: v2.4.0
+    rev: v2.5.0
     hooks:
       - id: pycln
         args: [--config=pyproject.toml]

monai/bundle/scripts.py

@@ -174,7 +174,7 @@ def _get_git_release_url(repo_owner: str, repo_name: str, tag_name: str, filenam
 
 
 def _get_ngc_bundle_url(model_name: str, version: str) -> str:
-    return f"{NGC_BASE_URL}/{model_name.lower()}/versions/{version}/zip"
+    return f"{NGC_BASE_URL}/{model_name.lower()}/versions/{version}/files"
 
 
 def _get_ngc_private_base_url(repo: str) -> str:
@@ -218,6 +218,21 @@ def _remove_ngc_prefix(name: str, prefix: str = "monai_") -> str:
     return name
 
 
+def _get_all_download_files(request_url: str, headers: dict | None = None) -> list[str]:
+    if not has_requests:
+        raise ValueError("requests package is required, please install it.")
+    headers = {} if headers is None else headers
+    response = requests_get(request_url, headers=headers)
+    response.raise_for_status()
+    model_info = json.loads(response.text)
+
+    if not isinstance(model_info, dict) or "modelFiles" not in model_info:
+        raise ValueError("The data is not a dictionary or it does not have the key 'modelFiles'.")
+
+    model_files = model_info["modelFiles"]
+    return [f["path"] for f in model_files]
+
+
 def _download_from_ngc(
     download_path: Path,
     filename: str,
@@ -229,12 +244,12 @@ def _download_from_ngc(
     # ensure prefix is contained
     filename = _add_ngc_prefix(filename, prefix=prefix)
     url = _get_ngc_bundle_url(model_name=filename, version=version)
-    filepath = download_path / f"{filename}_v{version}.zip"
     if remove_prefix:
         filename = _remove_ngc_prefix(filename, prefix=remove_prefix)
-    extract_path = download_path / f"{filename}"
-    download_url(url=url, filepath=filepath, hash_val=None, progress=progress)
-    extractall(filepath=filepath, output_dir=extract_path, has_base=True)
+    filepath = download_path / filename
+    filepath.mkdir(parents=True, exist_ok=True)
+    for file in _get_all_download_files(url):
+        download_url(url=f"{url}/{file}", filepath=f"{filepath}/{file}", hash_val=None, progress=progress)
 
 
 def _download_from_ngc_private(

monai/data/image_reader.py

@@ -607,3 +607,8 @@ def print_verbose(self) -> None:
         print(self)
         if self.meta is not None:
             print(self.meta.__repr__())
+
+
+# needed in later versions of Pytorch to indicate the class is safe for serialisation
+if hasattr(torch.serialization, "add_safe_globals"):
+    torch.serialization.add_safe_globals([MetaTensor])

monai/data/meta_tensor.py

@@ -15,12 +15,13 @@
 from abc import ABC, abstractmethod
 from collections.abc import Sequence
 from contextlib import nullcontext
+from tempfile import TemporaryDirectory
 from typing import TYPE_CHECKING, Any
 
 import numpy as np
 import torch
 
-from monai.utils import ensure_tuple_size, optional_import, require_pkg
+from monai.utils import ensure_tuple_size, get_package_version, optional_import, require_pkg, version_geq
 
 if TYPE_CHECKING:
     import zarr
@@ -233,7 +234,7 @@ def __init__(
         store: zarr.storage.Store | str = "merged.zarr",
         value_store: zarr.storage.Store | str | None = None,
         count_store: zarr.storage.Store | str | None = None,
-        compressor: str = "default",
+        compressor: str | None = None,
         value_compressor: str | None = None,
         count_compressor: str | None = None,
         chunks: Sequence[int] | bool = True,
@@ -246,8 +247,22 @@ def __init__(
         self.value_dtype = value_dtype
         self.count_dtype = count_dtype
         self.store = store
-        self.value_store = zarr.storage.TempStore() if value_store is None else value_store
-        self.count_store = zarr.storage.TempStore() if count_store is None else count_store
+        self.tmpdir: TemporaryDirectory | None
+        if version_geq(get_package_version("zarr"), "3.0.0"):
+            if value_store is None:
+                self.tmpdir = TemporaryDirectory()
+                self.value_store = zarr.storage.LocalStore(self.tmpdir.name)
+            else:
+                self.value_store = value_store
+            if count_store is None:
+                self.tmpdir = TemporaryDirectory()
+                self.count_store = zarr.storage.LocalStore(self.tmpdir.name)
+            else:
+                self.count_store = count_store
+        else:
+            self.tmpdir = None
+            self.value_store = zarr.storage.TempStore() if value_store is None else value_store
+            self.count_store = zarr.storage.TempStore() if count_store is None else count_store
         self.chunks = chunks
         self.compressor = compressor
         self.value_compressor = value_compressor

monai/inferers/merger.py

@@ -234,7 +234,7 @@ def plot_engine_status(
 
 
 def _get_loss_from_output(
-    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor
+    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor,
 ) -> torch.Tensor:
     """Returns a single value from the network output, which is a dict or tensor."""
 

monai/utils/jupyter_utils.py

@@ -65,11 +65,11 @@ def _image3_animated_gif(
     img_str = b""
     for b_data in PIL.GifImagePlugin.getheader(ims[0])[0]:
         img_str += b_data
-    img_str += b"\x21\xFF\x0B\x4E\x45\x54\x53\x43\x41\x50" b"\x45\x32\x2E\x30\x03\x01\x00\x00\x00"
+    img_str += b"\x21\xff\x0b\x4e\x45\x54\x53\x43\x41\x50" b"\x45\x32\x2e\x30\x03\x01\x00\x00\x00"
     for i in ims:
         for b_data in PIL.GifImagePlugin.getdata(i):
             img_str += b_data
-    img_str += b"\x3B"
+    img_str += b"\x3b"
 
     summary = SummaryX if has_tensorboardx and isinstance(writer, SummaryWriterX) else Summary
     summary_image_str = summary.Image(height=10, width=10, colorspace=1, encoded_image_string=img_str)

monai/visualize/img2tensorboard.py

@@ -18,7 +18,7 @@ pep8-naming
 pycodestyle
 pyflakes
 black>=22.12
-isort>=5.1
+isort>=5.1, <6.0
 ruff
 pytype>=2020.6.1; platform_system != "Windows"
 types-setuptools

requirements-dev.txt

@@ -1,2 +1,2 @@
-torch>=1.9
+torch>=1.9,<2.6
 numpy>=1.24,<3.0

requirements.txt

@@ -168,6 +168,16 @@ def get_data(self, _obj):
 # test reader consistency between PydicomReader and ITKReader on dicom data
 TEST_CASE_22 = ["tests/testing_data/CT_DICOM"]
 
+# test pydicom gpu reader
+TEST_CASE_GPU_5 = [{"reader": "PydicomReader", "to_gpu": True}, "tests/testing_data/CT_DICOM", (16, 16, 4), (16, 16, 4)]
+
+TEST_CASE_GPU_6 = [
+    {"reader": "PydicomReader", "ensure_channel_first": True, "force": True, "to_gpu": True},
+    "tests/testing_data/CT_DICOM",
+    (16, 16, 4),
+    (1, 16, 16, 4),
+]
+
 TESTS_META = []
 for track_meta in (False, True):
     TESTS_META.append([{}, (128, 128, 128), track_meta])
@@ -242,16 +252,17 @@ def test_nibabel_reader_gpu(self, input_param, filenames, expected_shape):
 
     @parameterized.expand([TEST_CASE_6, TEST_CASE_7, TEST_CASE_8, TEST_CASE_8_1, TEST_CASE_9])
     def test_itk_reader(self, input_param, filenames, expected_shape):
-        test_image = np.random.rand(128, 128, 128)
+        test_image = torch.randint(0, 256, (128, 128, 128), dtype=torch.uint8).numpy()
+        print("Test image value range:", test_image.min(), test_image.max())
         with tempfile.TemporaryDirectory() as tempdir:
             for i, name in enumerate(filenames):
                 filenames[i] = os.path.join(tempdir, name)
-                itk_np_view = itk.image_view_from_array(test_image)
-                itk.imwrite(itk_np_view, filenames[i])
+                nib.save(nib.Nifti1Image(test_image, np.eye(4)), filenames[i])
             result = LoadImage(image_only=True, **input_param)(filenames)
-            self.assertEqual(result.meta["filename_or_obj"], os.path.join(tempdir, "test_image.nii.gz"))
-            diag = torch.as_tensor(np.diag([-1, -1, 1, 1]))
-            np.testing.assert_allclose(result.affine, diag)
+            ext = "".join(Path(name).suffixes)
+            self.assertEqual(result.meta["filename_or_obj"], os.path.join(tempdir, "test_image" + ext))
+            self.assertEqual(result.meta["space"], "RAS")
+            assert_allclose(result.affine, torch.eye(4))
             self.assertTupleEqual(result.shape, expected_shape)
 
     @parameterized.expand([TEST_CASE_10, TEST_CASE_11, TEST_CASE_12, TEST_CASE_19, TEST_CASE_20, TEST_CASE_21])
@@ -271,6 +282,26 @@ def test_itk_dicom_series_reader(self, input_param, filenames, expected_shape, e
         )
         self.assertTupleEqual(result.shape, expected_np_shape)
 
+    @SkipIfNoModule("pydicom")
+    @SkipIfNoModule("cupy")
+    @SkipIfNoModule("kvikio")
+    @parameterized.expand([TEST_CASE_GPU_5, TEST_CASE_GPU_6])
+    def test_pydicom_gpu_reader(self, input_param, filenames, expected_shape, expected_np_shape):
+        result = LoadImage(image_only=True, **input_param)(filenames)
+        self.assertEqual(result.meta["filename_or_obj"], f"{Path(filenames)}")
+        assert_allclose(
+            result.affine,
+            torch.tensor(
+                [
+                    [-0.488281, 0.0, 0.0, 125.0],
+                    [0.0, -0.488281, 0.0, 128.100006],
+                    [0.0, 0.0, 68.33333333, -99.480003],
+                    [0.0, 0.0, 0.0, 1.0],
+                ]
+            ),
+        )
+        self.assertTupleEqual(result.shape, expected_np_shape)
+
     def test_no_files(self):
         with self.assertRaisesRegex(RuntimeError, "list index out of range"):  # fname_regex excludes everything
             LoadImage(image_only=True, reader="PydicomReader", fname_regex=r"^(?!.*).*")("tests/testing_data/CT_DICOM")
@@ -317,6 +348,21 @@ def test_dicom_reader_consistency(self, filenames):
             np.testing.assert_allclose(pydicom_result, itk_result)
             np.testing.assert_allclose(pydicom_result.affine, itk_result.affine)
 
+    @SkipIfNoModule("pydicom")
+    @SkipIfNoModule("cupy")
+    @SkipIfNoModule("kvikio")
+    @parameterized.expand([TEST_CASE_22])
+    def test_pydicom_reader_gpu_cpu_consistency(self, filenames):
+        gpu_param = {"reader": "PydicomReader", "to_gpu": True}
+        cpu_param = {"reader": "PydicomReader", "to_gpu": False}
+        for affine_flag in [True, False]:
+            gpu_param["affine_lps_to_ras"] = affine_flag
+            cpu_param["affine_lps_to_ras"] = affine_flag
+            gpu_result = LoadImage(image_only=True, **gpu_param)(filenames)
+            cpu_result = LoadImage(image_only=True, **cpu_param)(filenames)
+            np.testing.assert_allclose(gpu_result.cpu(), cpu_result)
+            np.testing.assert_allclose(gpu_result.affine.cpu(), cpu_result.affine)
+
     def test_dicom_reader_consistency_single(self):
         itk_param = {"reader": "ITKReader"}
         pydicom_param = {"reader": "PydicomReader"}