2453 Enhance ToTensor transform to support data with no dim (#2454)

* [DLMED] enhance ToTensor

Signed-off-by: Nic Ma <nma@nvidia.com>

* [DLMED] update according to comments

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

Author: Nic-Ma

monai/transforms/utility/array.py

@@ -25,7 +25,7 @@
 from monai.config import DtypeLike, NdarrayTensor
 from monai.transforms.transform import Randomizable, Transform
 from monai.transforms.utils import extreme_points_to_image, get_extreme_points, map_binary_to_indices
-from monai.utils import ensure_tuple, min_version, optional_import
+from monai.utils import ensure_tuple, issequenceiterable, min_version, optional_import
 
 PILImageImage, has_pil = optional_import("PIL.Image", name="Image")
 pil_image_fromarray, _ = optional_import("PIL.Image", name="fromarray")
@@ -320,7 +320,12 @@ def __call__(self, img) -> torch.Tensor:
         """
         if isinstance(img, torch.Tensor):
             return img.contiguous()
-        return torch.as_tensor(np.ascontiguousarray(img))
+        if issequenceiterable(img):
+            # numpy array with 0 dims is also sequence iterable
+            if not (isinstance(img, np.ndarray) and img.ndim == 0):
+                # `ascontiguousarray` will add 1 dim if img has no dim, so we only apply on data with dims
+                img = np.ascontiguousarray(img)
+        return torch.as_tensor(img)
 
 
 class ToNumpy(Transform):

tests/test_to_tensor.py

@@ -0,0 +1,35 @@
+# Copyright 2020 - 2021 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 numpy as np
+import torch
+
+from monai.transforms import ToTensor
+
+
+class TestToTensor(unittest.TestCase):
+    def test_array_input(self):
+        for test_data in ([[1, 2], [3, 4]], np.array([[1, 2], [3, 4]]), torch.as_tensor([[1, 2], [3, 4]])):
+            result = ToTensor()(test_data)
+            torch.testing.assert_allclose(result, test_data)
+            self.assertTupleEqual(result.shape, (2, 2))
+
+    def test_single_input(self):
+        for test_data in (5, np.asarray(5), torch.tensor(5)):
+            result = ToTensor()(test_data)
+            torch.testing.assert_allclose(result, test_data)
+            self.assertEqual(result.ndim, 0)
+
+
+if __name__ == "__main__":
+    unittest.main()