From 5807f5df1de11387f3e7f0fecbdb1cc53a0a2a36 Mon Sep 17 00:00:00 2001
From: Wenqi Li <wenqil@nvidia.com>
Date: Mon, 10 Jan 2022 14:38:59 +0000
Subject: [PATCH] 3628 0-d array to_contiguous (#3629)

* 0-d array

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

* handling string types

Signed-off-by: Wenqi Li <wenqil@nvidia.com>
---
 monai/transforms/utils.py                     |  8 ++--
 .../utils_pytorch_numpy_unification.py        |  6 ++-
 tests/test_to_contiguous.py                   | 44 +++++++++++++++++++
 3 files changed, 53 insertions(+), 5 deletions(-)
 create mode 100644 tests/test_to_contiguous.py

diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
index f81099c48a..fd795f6ca9 100644
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -14,7 +14,7 @@
 import warnings
 from contextlib import contextmanager
 from inspect import getmembers, isclass
-from typing import Any, Callable, Hashable, Iterable, List, Optional, Sequence, Tuple, Union
+from typing import Any, Callable, Hashable, Iterable, List, Mapping, Optional, Sequence, Tuple, Union
 
 import numpy as np
 import torch
@@ -1508,11 +1508,11 @@ def convert_to_contiguous(data, **kwargs):
             https://pytorch.org/docs/stable/generated/torch.Tensor.contiguous.html#torch.Tensor.contiguous.
 
     """
-    if isinstance(data, (np.ndarray, torch.Tensor)):
+    if isinstance(data, (np.ndarray, torch.Tensor, str, bytes)):
         return ascontiguousarray(data, **kwargs)
-    if isinstance(data, dict):
+    if isinstance(data, Mapping):
         return {k: convert_to_contiguous(v, **kwargs) for k, v in data.items()}
-    if isinstance(data, (list, tuple)):
+    if isinstance(data, Sequence):
         return [convert_to_contiguous(i, **kwargs) for i in data]
     return data
 
diff --git a/monai/transforms/utils_pytorch_numpy_unification.py b/monai/transforms/utils_pytorch_numpy_unification.py
index 93066a132e..5085ded2b5 100644
--- a/monai/transforms/utils_pytorch_numpy_unification.py
+++ b/monai/transforms/utils_pytorch_numpy_unification.py
@@ -334,5 +334,9 @@ def ascontiguousarray(x: NdarrayOrTensor, **kwargs):
 
     """
     if isinstance(x, np.ndarray):
+        if x.ndim == 0:
+            return x
         return np.ascontiguousarray(x)
-    return x.contiguous(**kwargs)
+    if isinstance(x, torch.Tensor):
+        return x.contiguous(**kwargs)
+    return x
diff --git a/tests/test_to_contiguous.py b/tests/test_to_contiguous.py
new file mode 100644
index 0000000000..a9c2a78278
--- /dev/null
+++ b/tests/test_to_contiguous.py
@@ -0,0 +1,44 @@
+# Copyright (c) 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 convert_to_contiguous
+from tests.utils import assert_allclose
+
+
+class TestToContiguous(unittest.TestCase):
+    def test_decollation_dict(self):
+        tochange = np.moveaxis(np.zeros((2, 3, 4)), 0, -1)
+        test_dict = {"test_key": [[1]], 0: np.array(0), 1: np.array([0]), "nested": {"nested": [tochange]}}
+        output = convert_to_contiguous(test_dict)
+        self.assertEqual(output["test_key"], [[1]])
+        assert_allclose(output[0], np.array(0))
+        assert_allclose(output[1], np.array([0]))
+        self.assertTrue(output["nested"]["nested"][0].flags.c_contiguous)
+
+    def test_decollation_seq(self):
+        tochange = torch.zeros(2, 3, 4).transpose(0, 1)
+        test_dict = [[[1]], np.array(0), np.array([0]), torch.tensor(1.0), [[tochange]], "test_string"]
+        output = convert_to_contiguous(test_dict)
+        self.assertEqual(output[0], [[1]])
+        assert_allclose(output[1], np.array(0))
+        assert_allclose(output[2], np.array([0]))
+        assert_allclose(output[3], torch.tensor(1.0))
+        self.assertTrue(output[4][0][0].is_contiguous())
+        self.assertEqual(output[5], "test_string")
+
+
+if __name__ == "__main__":
+    unittest.main()