Nic-Ma: /black
diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 692108f9..d5bb39bb 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -183,6 +183,12 @@ IO
     :members:
     :special-members: __call__
 
+`LoadNumpy`
+"""""""""""
+.. autoclass:: LoadNumpy
+    :members:
+    :special-members: __call__
+
 Post-processing
 ^^^^^^^^^^^^^^^
 
@@ -570,6 +576,12 @@ IO (Dict)
     :members:
     :special-members: __call__
 
+`LoadNumpyd`
+""""""""""""
+.. autoclass:: LoadNumpyd
+    :members:
+    :special-members: __call__
+
 Post-processing (Dict)
 ^^^^^^^^^^^^^^^^^^^^^^
 
diff --git a/monai/transforms/io/array.py b/monai/transforms/io/array.py
index bcaf91f2..6ba1acff 100644
--- a/monai/transforms/io/array.py
+++ b/monai/transforms/io/array.py
@@ -17,7 +17,7 @@ from typing import Optional
 
 import numpy as np
 from torch.utils.data._utils.collate import np_str_obj_array_pattern
-
+from monai.config import KeysCollection
 from monai.data.utils import correct_nifti_header_if_necessary
 from monai.transforms.compose import Transform
 from monai.utils import optional_import, ensure_tuple
@@ -111,6 +111,8 @@ class LoadNifti(Transform):
 class LoadPNG(Transform):
     """
     Load common 2D image format (PNG, JPG, etc. using PIL) file or files from provided path.
+    If loading a list of files, stack them together and add a new dimension as first dimension,
+    and use the meta data of the first image to represent the stacked result.
     It's based on the Image module in PIL library:
     https://pillow.readthedocs.io/en/stable/reference/Image.html
     """
@@ -138,6 +140,10 @@ class LoadPNG(Transform):
             if self.dtype:
                 data = data.astype(self.dtype)
             img_array.append(data)
+
+            if self.image_only:
+                continue
+
             meta = dict()
             meta["filename_or_obj"] = name
             meta["spatial_shape"] = data.shape[:2]
@@ -146,10 +152,6 @@ class LoadPNG(Transform):
             meta["width"] = img.width
             meta["height"] = img.height
             meta["info"] = img.info
-
-            if self.image_only:
-                continue
-
             if not compatible_meta:
                 compatible_meta = meta
             else:
@@ -159,3 +161,73 @@ class LoadPNG(Transform):
 
         img_array = np.stack(img_array, axis=0) if len(img_array) > 1 else img_array[0]
         return img_array if self.image_only else (img_array, compatible_meta)
+
+
+class LoadNumpy(Transform):
+    """
+    Load arrays or pickled objects from .npy, .npz or pickled files, file or files are from provided path.
+    A typical usage is to load the `mask` data for classification task.
+    If loading a list of files or laoding npz file, stack results together and add a new dimension as first dimension,
+    and use the meta data of the first file to represent the stacked result.
+    It can load part of the npz file with specified `npz_keys`.
+    It's based on the Numpy load/read API:
+    https://numpy.org/doc/stable/reference/generated/numpy.load.html
+
+    """
+
+    def __init__(
+        self, data_only: bool = False, dtype: Optional[np.dtype] = np.float32, npz_keys: Optional[KeysCollection] = None
+    ) -> None:
+        """
+        Args:
+            data_only: if True return only the data array, otherwise return data array and metadata.
+            dtype: if not None convert the loaded data to this data type.
+            npz_keys: if loading npz file, only load the specified keys, if None, load all the items.
+                stack the loaded items together to construct a new first dimension.
+
+        """
+        self.data_only = data_only
+        self.dtype = dtype
+        if npz_keys is not None:
+            npz_keys = ensure_tuple(npz_keys)
+        self.npz_keys = npz_keys
+
+    def __call__(self, filename):
+        """
+        Args:
+            filename (str, list, tuple, file): path file or file-like object or a list of files.
+        """
+        if isinstance(filename, (tuple, list)):
+            for name in filename:
+                if name.endswith(".npz"):
+                    raise TypeError("can not load a list of npz file.")
+        filename = ensure_tuple(filename)
+        data_array = list()
+        compatible_meta = None
+
+        def _save_data_meta(data_array, name, data, compatible_meta):
+            data_array.append(data if self.dtype is None else data.astype(self.dtype))
+            if not self.data_only:
+                meta = dict()
+                meta["filename_or_obj"] = name
+                meta["spatial_shape"] = data.shape
+                if not compatible_meta:
+                    compatible_meta = meta
+                else:
+                    assert np.allclose(
+                        meta["spatial_shape"], compatible_meta["spatial_shape"]
+                    ), "all the data in the list should have same shape."
+            return compatible_meta
+
+        for name in filename:
+            data = np.load(name, allow_pickle=True)
+            if name.endswith(".npz"):
+                # load expected items from NPZ file
+                npz_keys = [f"arr_{i}" for i in range(len(data))] if self.npz_keys is None else self.npz_keys
+                for k in npz_keys:
+                    compatible_meta = _save_data_meta(data_array, name, data[k], compatible_meta)
+            else:
+                compatible_meta = _save_data_meta(data_array, name, data, compatible_meta)
+
+        data_array = np.stack(data_array, axis=0) if len(data_array) > 1 else data_array[0]
+        return data_array if self.data_only else (data_array, compatible_meta)
diff --git a/monai/transforms/io/dictionary.py b/monai/transforms/io/dictionary.py
index faf4060a..5a04b440 100644
--- a/monai/transforms/io/dictionary.py
+++ b/monai/transforms/io/dictionary.py
@@ -21,7 +21,7 @@ import numpy as np
 
 from monai.config import KeysCollection
 from monai.transforms.compose import MapTransform
-from monai.transforms.io.array import LoadNifti, LoadPNG
+from monai.transforms.io.array import LoadNifti, LoadPNG, LoadNumpy
 
 
 class LoadNiftid(MapTransform):
@@ -47,8 +47,8 @@ class LoadNiftid(MapTransform):
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
             as_closest_canonical: if True, load the image as closest to canonical axis format.
-            dtype: if not None convert the loaded image to this data type.
-            meta_key_postfix: use `key_{postfix}` to to store meta data of the nifti image,
+            dtype: if not None convert the loaded image data to this data type.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the nifti image,
                 default is `meta_dict`. The meta data is a dictionary object.
                 For example, load nifti file for `image`, store the metadata into `image_meta_dict`.
             overwriting: whether allow to overwrite existing meta data of same key.
@@ -95,10 +95,10 @@ class LoadPNGd(MapTransform):
         Args:
             keys: keys of the corresponding items to be transformed.
                 See also: :py:class:`monai.transforms.compose.MapTransform`
-            dtype: if not None convert the loaded image to this data type.
-            meta_key_postfix: use `key_{postfix}` to to store meta data of the nifti image,
+            dtype: if not None convert the loaded image data to this data type.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the PNG image,
                 default is `meta_dict`. The meta data is a dictionary object.
-                For example, load nifti file for `image`, store the metadata into `image_meta_dict`.
+                For example, load PNG file for `image`, store the metadata into `image_meta_dict`.
             overwriting: whether allow to overwrite existing meta data of same key.
                 default is False, which will raise exception if encountering existing key.
 
@@ -127,5 +127,57 @@ class LoadPNGd(MapTransform):
         return d
 
 
+class LoadNumpyd(MapTransform):
+    """
+    Dictionary-based wrapper of :py:class:`monai.transforms.LoadNumpy`.
+    """
+
+    def __init__(
+        self,
+        keys: KeysCollection,
+        dtype: Optional[np.dtype] = np.float32,
+        npz_keys: Optional[KeysCollection] = None,
+        meta_key_postfix: str = "meta_dict",
+        overwriting: bool = False,
+    ) -> None:
+        """
+        Args:
+            keys: keys of the corresponding items to be transformed.
+                See also: :py:class:`monai.transforms.compose.MapTransform`
+            dtype: if not None convert the loaded data to this data type.
+            npz_keys: if loading npz file, only load the specified keys, if None, load all the items.
+                stack the loaded items together to construct a new first dimension.
+            meta_key_postfix: use `key_{postfix}` to store the metadata of the Numpy data,
+                default is `meta_dict`. The meta data is a dictionary object.
+                For example, load Numpy file for `mask`, store the metadata into `mask_meta_dict`.
+            overwriting: whether allow to overwrite existing meta data of same key.
+                default is False, which will raise exception if encountering existing key.
+
+        Raises:
+            ValueError: meta_key_postfix must be a string.
+
+        """
+        super().__init__(keys)
+        self.loader = LoadNumpy(data_only=False, dtype=dtype, npz_keys=npz_keys)
+        if not isinstance(meta_key_postfix, str):
+            raise ValueError("meta_key_postfix must be a string.")
+        self.meta_key_postfix = meta_key_postfix
+        self.overwriting = overwriting
+
+    def __call__(self, data):
+        d = dict(data)
+        for key in self.keys:
+            data = self.loader(d[key])
+            assert isinstance(data, (tuple, list)), "loader must return a tuple or list."
+            d[key] = data[0]
+            assert isinstance(data[1], dict), "metadata must be a dict."
+            key_to_add = f"{key}_{self.meta_key_postfix}"
+            if key_to_add in d and not self.overwriting:
+                raise KeyError(f"meta data with key {key_to_add} already exists.")
+            d[key_to_add] = data[1]
+        return d
+
+
 LoadNiftiD = LoadNiftiDict = LoadNiftid
 LoadPNGD = LoadPNGDict = LoadPNGd
+LoadNumpyD = LoadNumpyDict = LoadNumpyd

IO formats 
Hello

How do I perform multi class semantic (instance) segmentation over my set of 2D PNG/TIF grayscale images and their corresponding masks file (NPY numpy array, TIF or any other) matrix representation with # channels corresponding to the classes?
Do I need to use NIFTI_interface? If so, how do I convert my PNG/TIF/NPY files to this format?

Thank you
diff --git a/tests/test_load_numpy.py b/tests/test_load_numpy.py
new file mode 100644
index 00000000..6332e624
--- /dev/null
+++ b/tests/test_load_numpy.py
@@ -0,0 +1,89 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import unittest
+import os
+import shutil
+import numpy as np
+import tempfile
+from monai.transforms import LoadNumpy
+
+
+class TestLoadNumpy(unittest.TestCase):
+    def test_npy(self):
+        test_data = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data)
+
+        result = LoadNumpy()(filepath)
+        self.assertTupleEqual(result[1]["spatial_shape"], test_data.shape)
+        self.assertTupleEqual(result[0].shape, test_data.shape)
+        np.testing.assert_allclose(result[0], test_data)
+
+        shutil.rmtree(tempdir)
+
+    def test_npz1(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data1)
+
+        result = LoadNumpy()(filepath)
+        self.assertTupleEqual(result[1]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result[0].shape, test_data1.shape)
+        np.testing.assert_allclose(result[0], test_data1)
+
+        shutil.rmtree(tempdir)
+
+    def test_npz2(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npz")
+        np.savez(filepath, test_data1, test_data2)
+
+        result = LoadNumpy()(filepath)
+        self.assertTupleEqual(result[1]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result[0].shape, (2, 3, 4, 4))
+        np.testing.assert_allclose(result[0], np.stack([test_data1, test_data2]))
+
+        shutil.rmtree(tempdir)
+
+    def test_npz3(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npz")
+        np.savez(filepath, test1=test_data1, test2=test_data2)
+
+        result = LoadNumpy(npz_keys=["test1", "test2"])(filepath)
+        self.assertTupleEqual(result[1]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result[0].shape, (2, 3, 4, 4))
+        np.testing.assert_allclose(result[0], np.stack([test_data1, test_data2]))
+
+        shutil.rmtree(tempdir)
+
+    def test_npy_pickle(self):
+        test_data = {"test": np.random.randint(0, 256, size=[3, 4, 4])}
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data, allow_pickle=True)
+
+        result = LoadNumpy(data_only=True, dtype=None)(filepath).item()
+        self.assertTupleEqual(result["test"].shape, test_data["test"].shape)
+        np.testing.assert_allclose(result["test"], test_data["test"])
+
+        shutil.rmtree(tempdir)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_load_numpyd.py b/tests/test_load_numpyd.py
new file mode 100644
index 00000000..4e68e1b3
--- /dev/null
+++ b/tests/test_load_numpyd.py
@@ -0,0 +1,89 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import unittest
+import os
+import shutil
+import numpy as np
+import tempfile
+from monai.transforms import LoadNumpyd
+
+
+class TestLoadNumpyd(unittest.TestCase):
+    def test_npy(self):
+        test_data = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data)
+
+        result = LoadNumpyd(keys="mask")({"mask": filepath})
+        self.assertTupleEqual(result["mask_meta_dict"]["spatial_shape"], test_data.shape)
+        self.assertTupleEqual(result["mask"].shape, test_data.shape)
+        np.testing.assert_allclose(result["mask"], test_data)
+
+        shutil.rmtree(tempdir)
+
+    def test_npz1(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data1)
+
+        result = LoadNumpyd(keys="mask")({"mask": filepath})
+        self.assertTupleEqual(result["mask_meta_dict"]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result["mask"].shape, test_data1.shape)
+        np.testing.assert_allclose(result["mask"], test_data1)
+
+        shutil.rmtree(tempdir)
+
+    def test_npz2(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npz")
+        np.savez(filepath, test_data1, test_data2)
+
+        result = LoadNumpyd(keys="mask")({"mask": filepath})
+        self.assertTupleEqual(result["mask_meta_dict"]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result["mask"].shape, (2, 3, 4, 4))
+        np.testing.assert_allclose(result["mask"], np.stack([test_data1, test_data2]))
+
+        shutil.rmtree(tempdir)
+
+    def test_npz3(self):
+        test_data1 = np.random.randint(0, 256, size=[3, 4, 4])
+        test_data2 = np.random.randint(0, 256, size=[3, 4, 4])
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npz")
+        np.savez(filepath, test1=test_data1, test2=test_data2)
+
+        result = LoadNumpyd(keys="mask", npz_keys=["test1", "test2"])({"mask": filepath})
+        self.assertTupleEqual(result["mask_meta_dict"]["spatial_shape"], test_data1.shape)
+        self.assertTupleEqual(result["mask"].shape, (2, 3, 4, 4))
+        np.testing.assert_allclose(result["mask"], np.stack([test_data1, test_data2]))
+
+        shutil.rmtree(tempdir)
+
+    def test_npy_pickle(self):
+        test_data = {"test": np.random.randint(0, 256, size=[3, 4, 4])}
+        tempdir = tempfile.mkdtemp()
+        filepath = os.path.join(tempdir, "test_data.npy")
+        np.save(filepath, test_data, allow_pickle=True)
+
+        result = LoadNumpyd(keys="mask", dtype=None)({"mask": filepath})["mask"].item()
+        self.assertTupleEqual(result["test"].shape, test_data["test"].shape)
+        np.testing.assert_allclose(result["test"], test_data["test"])
+
+        shutil.rmtree(tempdir)
+
+
+if __name__ == "__main__":
+    unittest.main()

pip install -e .[all]
parse_log_pytest
requirements.txt
pytest black flake8 mypy pytype
apt-get update apt-get install -y gcc
3.6
requirements.txt
pytest --no-header -rA --tb=line --color=no -p no:cacheprovider -W ignore::DeprecationWarning
absl-py==1.4.0
attrs==22.2.0
beautifulsoup4==4.12.3
black==22.8.0
cachetools==4.2.4
certifi==2021.5.30
charset-normalizer==2.0.12
click==8.0.4
cycler==0.11.0
dataclasses==0.8
decorator==4.4.2
filelock==3.4.1
flake8==5.0.4
gdown==4.7.3
google-auth==2.22.0
google-auth-oauthlib==0.4.6
grpcio==1.48.2
idna==3.10
imageio==2.15.0
importlab==0.8.1
importlib-metadata==4.8.3
importlib-resources==5.4.0
iniconfig==1.1.1
kiwisolver==1.3.1
libcst==0.4.1
Markdown==3.3.7
matplotlib==3.3.4
mccabe==0.7.0
-e git+https://github.com/Project-MONAI/MONAI.git@44fcf772760e7296787a63182e462e331ace31f8#egg=monai
mypy==0.971
mypy-extensions==1.0.0
networkx==2.5.1
nibabel==3.2.2
ninja==1.11.1.1
numpy==1.19.5
oauthlib==3.2.2
packaging==21.3
pathspec==0.9.0
Pillow==8.4.0
platformdirs==2.4.0
pluggy==1.0.0
protobuf==3.19.6
py==1.11.0
pyasn1==0.5.1
pyasn1-modules==0.3.0
pycodestyle==2.9.1
pyflakes==2.5.0
pyparsing==3.1.4
PySocks==1.7.1
pytest==7.0.1
python-dateutil==2.9.0.post0
pytorch-ignite==0.3.0
pytype==2022.1.5
PyWavelets==1.1.1
PyYAML==6.0.1
requests==2.27.1
requests-oauthlib==2.0.0
rsa==4.9
scikit-image==0.17.2
scipy==1.5.4
six==1.17.0
soupsieve==2.3.2.post1
tabulate==0.8.10
tensorboard==2.10.1
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tifffile==2020.9.3
toml==0.10.2
tomli==1.2.3
torch==1.10.2
tqdm==4.64.1
typed-ast==1.5.5
typing-inspect==0.9.0
typing_extensions==4.1.1
urllib3==1.26.20
Werkzeug==2.0.3
zipp==3.6.0

name: MONAI
channels:
  - defaults
  - https://repo.anaconda.com/pkgs/main
  - https://repo.anaconda.com/pkgs/r
  - conda-forge
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - ca-certificates=2025.2.25=h06a4308_0
  - certifi=2021.5.30=py36h06a4308_0
  - ld_impl_linux-64=2.40=h12ee557_0
  - libffi=3.3=he6710b0_2
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libstdcxx-ng=11.2.0=h1234567_1
  - ncurses=6.4=h6a678d5_0
  - openssl=1.1.1w=h7f8727e_0
  - pip=21.2.2=py36h06a4308_0
  - python=3.6.13=h12debd9_1
  - readline=8.2=h5eee18b_0
  - setuptools=58.0.4=py36h06a4308_0
  - sqlite=3.45.3=h5eee18b_0
  - tk=8.6.14=h39e8969_0
  - wheel=0.37.1=pyhd3eb1b0_0
  - xz=5.6.4=h5eee18b_1
  - zlib=1.2.13=h5eee18b_1
  - pip:
      - absl-py==1.4.0
      - attrs==22.2.0
      - beautifulsoup4==4.12.3
      - black==22.8.0
      - cachetools==4.2.4
      - charset-normalizer==2.0.12
      - click==8.0.4
      - cycler==0.11.0
      - dataclasses==0.8
      - decorator==4.4.2
      - filelock==3.4.1
      - flake8==5.0.4
      - gdown==4.7.3
      - google-auth==2.22.0
      - google-auth-oauthlib==0.4.6
      - grpcio==1.48.2
      - idna==3.10
      - imageio==2.15.0
      - importlab==0.8.1
      - importlib-metadata==4.8.3
      - importlib-resources==5.4.0
      - iniconfig==1.1.1
      - kiwisolver==1.3.1
      - libcst==0.4.1
      - markdown==3.3.7
      - matplotlib==3.3.4
      - mccabe==0.7.0
      - mypy==0.971
      - mypy-extensions==1.0.0
      - networkx==2.5.1
      - nibabel==3.2.2
      - ninja==1.11.1.1
      - numpy==1.19.5
      - oauthlib==3.2.2
      - packaging==21.3
      - pathspec==0.9.0
      - pillow==8.4.0
      - platformdirs==2.4.0
      - pluggy==1.0.0
      - protobuf==3.19.6
      - py==1.11.0
      - pyasn1==0.5.1
      - pyasn1-modules==0.3.0
      - pycodestyle==2.9.1
      - pyflakes==2.5.0
      - pyparsing==3.1.4
      - pysocks==1.7.1
      - pytest==7.0.1
      - python-dateutil==2.9.0.post0
      - pytorch-ignite==0.3.0
      - pytype==2022.1.5
      - pywavelets==1.1.1
      - pyyaml==6.0.1
      - requests==2.27.1
      - requests-oauthlib==2.0.0
      - rsa==4.9
      - scikit-image==0.17.2
      - scipy==1.5.4
      - six==1.17.0
      - soupsieve==2.3.2.post1
      - tabulate==0.8.10
      - tensorboard==2.10.1
      - tensorboard-data-server==0.6.1
      - tensorboard-plugin-wit==1.8.1
      - tifffile==2020.9.3
      - toml==0.10.2
      - tomli==1.2.3
      - torch==1.10.2
      - tqdm==4.64.1
      - typed-ast==1.5.5
      - typing-extensions==4.1.1
      - typing-inspect==0.9.0
      - urllib3==1.26.20
      - werkzeug==2.0.3
      - zipp==3.6.0
prefix: /opt/conda/envs/MONAI

tests/test_load_numpy.py::TestLoadNumpy::test_npy tests/test_load_numpy.py::TestLoadNumpy::test_npy_pickle tests/test_load_numpy.py::TestLoadNumpy::test_npz1 tests/test_load_numpy.py::TestLoadNumpy::test_npz2 tests/test_load_numpy.py::TestLoadNumpy::test_npz3 tests/test_load_numpyd.py::TestLoadNumpyd::test_npy tests/test_load_numpyd.py::TestLoadNumpyd::test_npy_pickle tests/test_load_numpyd.py::TestLoadNumpyd::test_npz1 tests/test_load_numpyd.py::TestLoadNumpyd::test_npz2 tests/test_load_numpyd.py::TestLoadNumpyd::test_npz3
monai/transforms/io/array.py monai/transforms/io/dictionary.py docs/source/transforms.rst
monai/transforms/io/array.py monai/transforms/io/dictionary.py docs/source/transforms.rst