3465 Support string as dtype

monai/config/type_definitions.py

@@ -60,8 +60,8 @@
 # container must be iterable.
 IndexSelection = Union[Iterable[int], int]
 
-#: Type of datatypes: Adapted from https://github.com/numpy/numpy/blob/master/numpy/typing/_dtype_like.py
-DtypeLike = Union[np.dtype, type, None]
+#: Type of datatypes: Adapted from https://github.com/numpy/numpy/blob/v1.21.4/numpy/typing/_dtype_like.py#L121
+DtypeLike = Union[np.dtype, type, str, None]
 
 #: NdarrayTensor
 #

monai/transforms/utility/array.py

@@ -446,14 +446,16 @@ class ToCupy(Transform):
 
     Args:
         dtype: data type specifier. It is inferred from the input by default.
+            if not None, must be an argument of `numpy.dtype`, for more details:
+            https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.
         wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
             E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.
 
     """
 
     backend = [TransformBackends.TORCH, TransformBackends.NUMPY]
 
-    def __init__(self, dtype=None, wrap_sequence: bool = True) -> None:
+    def __init__(self, dtype: Optional[np.dtype] = None, wrap_sequence: bool = True) -> None:
         super().__init__()
         self.dtype = dtype
         self.wrap_sequence = wrap_sequence

monai/transforms/utility/dictionary.py

@@ -592,6 +592,8 @@ class ToCupyd(MapTransform):
         keys: keys of the corresponding items to be transformed.
             See also: :py:class:`monai.transforms.compose.MapTransform`
         dtype: data type specifier. It is inferred from the input by default.
+            if not None, must be an argument of `numpy.dtype`, for more details:
+            https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.
         wrap_sequence: if `False`, then lists will recursively call this function, default to `True`.
             E.g., if `False`, `[1, 2]` -> `[array(1), array(2)]`, if `True`, then `[1, 2]` -> `array([1, 2])`.
         allow_missing_keys: don't raise exception if key is missing.
@@ -600,7 +602,11 @@ class ToCupyd(MapTransform):
     backend = ToCupy.backend
 
     def __init__(
-        self, keys: KeysCollection, dtype=None, wrap_sequence: bool = True, allow_missing_keys: bool = False
+        self,
+        keys: KeysCollection,
+        dtype: Optional[np.dtype] = None,
+        wrap_sequence: bool = True,
+        allow_missing_keys: bool = False,
     ) -> None:
         super().__init__(keys, allow_missing_keys)
         self.converter = ToCupy(dtype=dtype, wrap_sequence=wrap_sequence)

monai/utils/type_conversion.py

@@ -59,7 +59,7 @@ def dtype_torch_to_numpy(dtype):
 def dtype_numpy_to_torch(dtype):
     """Convert a numpy dtype to its torch equivalent."""
     # np dtypes can be given as np.float32 and np.dtype(np.float32) so unify them
-    dtype = np.dtype(dtype) if isinstance(dtype, type) else dtype
+    dtype = np.dtype(dtype) if isinstance(dtype, (type, str)) else dtype
     return look_up_option(dtype, _np_to_torch_dtype)
 
 
@@ -151,8 +151,8 @@ def convert_to_numpy(data, dtype: DtypeLike = None, wrap_sequence: bool = False)
             will convert Tensor, Numpy array, float, int, bool to numpy arrays, strings and objects keep the original.
             for dictionary, list or tuple, convert every item to a numpy array if applicable.
         dtype: target data type when converting to numpy array.
-        wrap_sequence: if `False`, then lists will recursively call this function. E.g., `[1, 2]` -> `[array(1), array(2)]`.
-            If `True`, then `[1, 2]` -> `array([1, 2])`.
+        wrap_sequence: if `False`, then lists will recursively call this function.
+            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.
     """
     if isinstance(data, torch.Tensor):
         data = data.detach().to(dtype=get_equivalent_dtype(dtype, torch.Tensor), device="cpu").numpy()
@@ -175,19 +175,19 @@ def convert_to_numpy(data, dtype: DtypeLike = None, wrap_sequence: bool = False)
     return data
 
 
-def convert_to_cupy(data, dtype, wrap_sequence: bool = False):
+def convert_to_cupy(data, dtype: Optional[np.dtype] = None, wrap_sequence: bool = False):
     """
     Utility to convert the input data to a cupy array. If passing a dictionary, list or tuple,
     recursively check every item and convert it to cupy array.
 
     Args:
         data: input data can be PyTorch Tensor, numpy array, cupy array, list, dictionary, int, float, bool, str, etc.
-            Tensor, numpy array, cupy array, float, int, bool are converted to cupy arrays
-
+            Tensor, numpy array, cupy array, float, int, bool are converted to cupy arrays,
             for dictionary, list or tuple, convert every item to a numpy array if applicable.
-        dtype: target data type when converting to Cupy array.
-        wrap_sequence: if `False`, then lists will recursively call this function. E.g., `[1, 2]` -> `[array(1), array(2)]`.
-            If `True`, then `[1, 2]` -> `array([1, 2])`.
+        dtype: target data type when converting to Cupy array, tt must be an argument of `numpy.dtype`,
+            for more details: https://docs.cupy.dev/en/stable/reference/generated/cupy.array.html.
+        wrap_sequence: if `False`, then lists will recursively call this function.
+            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.
     """
 
     # direct calls
@@ -227,8 +227,8 @@ def convert_data_type(
         dtype: dtype of output data. Converted to correct library type (e.g.,
             `np.float32` is converted to `torch.float32` if output type is `torch.Tensor`).
             If left blank, it remains unchanged.
-        wrap_sequence: if `False`, then lists will recursively call this function. E.g., `[1, 2]` -> `[array(1), array(2)]`.
-            If `True`, then `[1, 2]` -> `array([1, 2])`.
+        wrap_sequence: if `False`, then lists will recursively call this function.
+            E.g., `[1, 2]` -> `[array(1), array(2)]`. If `True`, then `[1, 2]` -> `array([1, 2])`.
     Returns:
         modified data, orig_type, orig_device
 

tests/test_normalize_intensity.py

@@ -41,23 +41,23 @@
     TESTS.append(
         [
             p,
-            {"nonzero": False, "channel_wise": True, "subtrahend": [1, 2, 3]},
+            {"nonzero": False, "channel_wise": True, "subtrahend": [1, 2, 3], "dtype": np.float32},
             p(np.ones((3, 2, 2))),
             p(np.array([[[0.0, 0.0], [0.0, 0.0]], [[-1.0, -1.0], [-1.0, -1.0]], [[-2.0, -2.0], [-2.0, -2.0]]])),
         ]
     )
     TESTS.append(
         [
             p,
-            {"nonzero": True, "channel_wise": True, "subtrahend": [1, 2, 3], "divisor": [0, 0, 2]},
+            {"nonzero": True, "channel_wise": True, "subtrahend": [1, 2, 3], "divisor": [0, 0, 2], "dtype": "float32"},
             p(np.ones((3, 2, 2))),
             p(np.array([[[0.0, 0.0], [0.0, 0.0]], [[-1.0, -1.0], [-1.0, -1.0]], [[-1.0, -1.0], [-1.0, -1.0]]])),
         ]
     )
     TESTS.append(
         [
             p,
-            {"nonzero": True, "channel_wise": False, "subtrahend": 2, "divisor": 0},
+            {"nonzero": True, "channel_wise": False, "subtrahend": 2, "divisor": 0, "dtype": torch.float32},
             p(np.ones((3, 2, 2))),
             p(np.ones((3, 2, 2)) * -1.0),
         ]

tests/test_to_numpy.py

@@ -47,7 +47,7 @@ def test_tensor_input(self):
         test_data = torch.tensor([[1, 2], [3, 4]])
         test_data = test_data.rot90()
         self.assertFalse(test_data.is_contiguous())
-        result = ToNumpy()(test_data)
+        result = ToNumpy(dtype=torch.uint8)(test_data)
         self.assertTrue(isinstance(result, np.ndarray))
         self.assertTrue(result.flags["C_CONTIGUOUS"])
         assert_allclose(result, test_data, type_test=False)
@@ -73,7 +73,7 @@ def test_list_tuple(self):
 
     def test_single_value(self):
         for test_data in [5, np.array(5), torch.tensor(5)]:
-            result = ToNumpy()(test_data)
+            result = ToNumpy(dtype=np.uint8)(test_data)
             self.assertTrue(isinstance(result, np.ndarray))
             assert_allclose(result, np.asarray(test_data), type_test=False)
             self.assertEqual(result.ndim, 0)