Merge branch 'dev' into fix-compose

Author: KumoLiu

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/transforms/intensity/array.py

@@ -821,6 +821,7 @@ class NormalizeIntensity(Transform):
     mean and std on each channel separately.
     When `channel_wise` is True, the first dimension of `subtrahend` and `divisor` should
     be the number of image channels if they are not None.
+    If the input is not of floating point type, it will be converted to float32
 
     Args:
         subtrahend: the amount to subtract by (usually the mean).
@@ -907,6 +908,9 @@ def __call__(self, img: NdarrayOrTensor) -> NdarrayOrTensor:
             if self.divisor is not None and len(self.divisor) != len(img):
                 raise ValueError(f"img has {len(img)} channels, but divisor has {len(self.divisor)} components.")
 
+            if not img.dtype.is_floating_point:
+                img, *_ = convert_data_type(img, dtype=torch.float32)
+
             for i, d in enumerate(img):
                 img[i] = self._normalize(  # type: ignore
                     d,

tests/test_normalize_intensity.py

@@ -108,6 +108,27 @@ def test_channel_wise(self, im_type):
         normalized = normalizer(input_data)
         assert_allclose(normalized, im_type(expected), type_test="tensor")
 
+    @parameterized.expand([[p] for p in TEST_NDARRAYS])
+    def test_channel_wise_int(self, im_type):
+        normalizer = NormalizeIntensity(nonzero=True, channel_wise=True)
+        input_data = im_type(torch.arange(1, 25).reshape(2, 3, 4))
+        expected = np.array(
+            [
+                [
+                    [-1.593255, -1.3035723, -1.0138896, -0.7242068],
+                    [-0.4345241, -0.1448414, 0.1448414, 0.4345241],
+                    [0.7242068, 1.0138896, 1.3035723, 1.593255],
+                ],
+                [
+                    [-1.593255, -1.3035723, -1.0138896, -0.7242068],
+                    [-0.4345241, -0.1448414, 0.1448414, 0.4345241],
+                    [0.7242068, 1.0138896, 1.3035723, 1.593255],
+                ],
+            ]
+        )
+        normalized = normalizer(input_data)
+        assert_allclose(normalized, im_type(expected), type_test="tensor", rtol=1e-7, atol=1e-7)  # tolerance
+
     @parameterized.expand([[p] for p in TEST_NDARRAYS])
     def test_value_errors(self, im_type):
         input_data = im_type(np.array([[0.0, 3.0, 0.0, 4.0], [0.0, 4.0, 0.0, 5.0]]))

tests/test_zarr_avg_merger.py

@@ -19,11 +19,18 @@
 from torch.nn.functional import pad
 
 from monai.inferers import ZarrAvgMerger
-from monai.utils import optional_import
+from monai.utils import get_package_version, optional_import, version_geq
 from tests.utils import assert_allclose
 
 np.seterr(divide="ignore", invalid="ignore")
 zarr, has_zarr = optional_import("zarr")
+if has_zarr:
+    if version_geq(get_package_version("zarr"), "3.0.0"):
+        directory_store = zarr.storage.LocalStore("test.zarr")
+    else:
+        directory_store = zarr.storage.DirectoryStore("test.zarr")
+else:
+    directory_store = None
 numcodecs, has_numcodecs = optional_import("numcodecs")
 
 TENSOR_4x4 = torch.randint(low=0, high=255, size=(2, 3, 4, 4), dtype=torch.float32)
@@ -154,7 +161,7 @@
 
 # explicit directory store
 TEST_CASE_10_DIRECTORY_STORE = [
-    dict(merged_shape=TENSOR_4x4.shape, store=zarr.storage.DirectoryStore("test.zarr")),
+    dict(merged_shape=TENSOR_4x4.shape, store=directory_store),
     [
         (TENSOR_4x4[..., :2, :2], (0, 0)),
         (TENSOR_4x4[..., :2, 2:], (0, 2)),