2231 Fixes tutorial 353

.github/workflows/pythonapp.yml

@@ -152,6 +152,7 @@ jobs:
         python -c 'import torch; print(torch.__version__); print(torch.rand(5,3))'
         python -c "import monai; monai.config.print_config()"
         ./runtests.sh --min
+      shell: bash
       env:
         QUICKTEST: True
 

monai/transforms/spatial/array.py

@@ -471,7 +471,7 @@ def __call__(
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
         align_corners: Optional[bool] = None,
         dtype: Union[DtypeLike, torch.dtype] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: channel first array, must have shape: [chns, H, W] or [chns, H, W, D].
@@ -526,13 +526,11 @@ def __call__(
             align_corners=self.align_corners if align_corners is None else align_corners,
             reverse_indexing=True,
         )
-        output: torch.Tensor = xform(
-            img_t.unsqueeze(0),
-            transform_t,
-            spatial_size=output_shape,
-        )
+        output: torch.Tensor = xform(img_t.unsqueeze(0), transform_t, spatial_size=output_shape).squeeze(0)
         self._rotation_matrix = transform
-        return output.squeeze(0).detach().float()
+        out: NdarrayOrTensor
+        out, *_ = convert_to_dst_type(output, dst=img, dtype=output.dtype)
+        return out
 
     def get_rotation_matrix(self) -> Optional[np.ndarray]:
         """
@@ -799,7 +797,7 @@ def __call__(
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
         align_corners: Optional[bool] = None,
         dtype: Union[DtypeLike, torch.dtype] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: channel first array, must have shape 2D: (nchannels, H, W), or 3D: (nchannels, H, W, D).
@@ -1290,7 +1288,7 @@ def __call__(
         grid: Optional[NdarrayOrTensor] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: shape must be (num_channels, H, W[, D]).
@@ -1344,8 +1342,9 @@ def __call__(
                 padding_mode=self.padding_mode.value if padding_mode is None else GridSamplePadMode(padding_mode).value,
                 align_corners=True,
             )[0]
-
-        return out
+        out_val: NdarrayOrTensor
+        out_val, *_ = convert_to_dst_type(out, dst=img, dtype=out.dtype)
+        return out_val
 
 
 class Affine(Transform):
@@ -1425,7 +1424,7 @@ def __call__(
         spatial_size: Optional[Union[Sequence[int], int]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> Union[torch.Tensor, Tuple[torch.Tensor, NdarrayOrTensor]]:
+    ) -> Union[NdarrayOrTensor, Tuple[NdarrayOrTensor, NdarrayOrTensor]]:
         """
         Args:
             img: shape must be (num_channels, H, W[, D]),
@@ -1589,7 +1588,7 @@ def __call__(
         spatial_size: Optional[Union[Sequence[int], int]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: shape must be (num_channels, H, W[, D]),
@@ -1615,7 +1614,7 @@ def __call__(
         grid = self.get_identity_grid(sp_size)
         if self._do_transform:
             grid = self.rand_affine_grid(grid=grid)
-        out: torch.Tensor = self.resampler(
+        out: NdarrayOrTensor = self.resampler(
             img=img, grid=grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
         )
         return out
@@ -1727,7 +1726,7 @@ def __call__(
         spatial_size: Optional[Union[Tuple[int, int], int]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: shape must be (num_channels, H, W),
@@ -1756,7 +1755,7 @@ def __call__(
             grid = CenterSpatialCrop(roi_size=sp_size)(grid[0])
         else:
             grid = create_grid(spatial_size=sp_size)
-        out: torch.Tensor = self.resampler(
+        out: NdarrayOrTensor = self.resampler(
             img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
         )
         return out
@@ -1877,7 +1876,7 @@ def __call__(
         spatial_size: Optional[Union[Tuple[int, int, int], int]] = None,
         mode: Optional[Union[GridSampleMode, str]] = None,
         padding_mode: Optional[Union[GridSamplePadMode, str]] = None,
-    ) -> torch.Tensor:
+    ) -> NdarrayOrTensor:
         """
         Args:
             img: shape must be (num_channels, H, W, D),
@@ -1902,7 +1901,7 @@ def __call__(
             offset = torch.as_tensor(self.rand_offset, device=self.device).unsqueeze(0)
             grid[:3] += gaussian(offset)[0] * self.magnitude
             grid = self.rand_affine_grid(grid=grid)
-        out: torch.Tensor = self.resampler(
+        out: NdarrayOrTensor = self.resampler(
             img, grid, mode=mode or self.mode, padding_mode=padding_mode or self.padding_mode
         )
         return out

monai/transforms/spatial/dictionary.py

@@ -820,10 +820,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
             if do_resampling:
                 d[key] = self.rand_affine.resampler(d[key], grid, mode=mode, padding_mode=padding_mode)
 
-            # if not doing transform and spatial size is unchanged, only need to do convert to torch
-            else:
-                d[key], *_ = convert_data_type(d[key], torch.Tensor, dtype=torch.float32, device=device)
-
         return d
 
     def inverse(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, NdarrayOrTensor]:
@@ -1442,10 +1438,7 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
         self.randomize()
         d = dict(data)
         angle: Union[Sequence[float], float] = self.x if d[self.keys[0]].ndim == 3 else (self.x, self.y, self.z)
-        rotator = Rotate(
-            angle=angle,
-            keep_size=self.keep_size,
-        )
+        rotator = Rotate(angle=angle, keep_size=self.keep_size)
         for key, mode, padding_mode, align_corners, dtype in self.key_iterator(
             d, self.mode, self.padding_mode, self.align_corners, self.dtype
         ):
@@ -1460,7 +1453,6 @@ def __call__(self, data: Mapping[Hashable, NdarrayOrTensor]) -> Dict[Hashable, N
                 )
                 rot_mat = rotator.get_rotation_matrix()
             else:
-                d[key], *_ = convert_data_type(d[key], torch.Tensor)
                 rot_mat = np.eye(d[key].ndim)
             self.push_transform(
                 d,

monai/utils/type_conversion.py

@@ -247,11 +247,14 @@ def convert_data_type(
     return data, orig_type, orig_device
 
 
-def convert_to_dst_type(src: Any, dst: NdarrayOrTensor) -> Tuple[NdarrayOrTensor, type, Optional[torch.device]]:
+def convert_to_dst_type(
+    src: Any, dst: NdarrayOrTensor, dtype: Optional[Union[DtypeLike, torch.dtype]] = None
+) -> Tuple[NdarrayOrTensor, type, Optional[torch.device]]:
     """
-    If `dst` is `torch.Tensor` or its subclass, convert `src` to `torch.Tensor` with the same data type as `dst`,
-    if `dst` is `numpy.ndarray` or its subclass, convert to `numpy.ndarray` with the same data type as `dst`,
+    If `dst` is an instance of `torch.Tensor` or its subclass, convert `src` to `torch.Tensor` with the same data type as `dst`,
+    if `dst` is an instance of `numpy.ndarray` or its subclass, convert to `numpy.ndarray` with the same data type as `dst`,
     otherwise, convert to the type of `dst` directly.
+    `dtype` is an optional argument if the target `dtype` is different from the original `dst`'s data type.
 
     See Also:
         :func:`convert_data_type`
@@ -260,11 +263,14 @@ def convert_to_dst_type(src: Any, dst: NdarrayOrTensor) -> Tuple[NdarrayOrTensor
     if isinstance(dst, torch.Tensor):
         device = dst.device
 
+    if dtype is None:
+        dtype = dst.dtype
+
     output_type: Any
     if isinstance(dst, torch.Tensor):
         output_type = torch.Tensor
     elif isinstance(dst, np.ndarray):
         output_type = np.ndarray
     else:
         output_type = type(dst)
-    return convert_data_type(data=src, output_type=output_type, device=device, dtype=dst.dtype)
+    return convert_data_type(data=src, output_type=output_type, device=device, dtype=dtype)

tests/test_affine_grid.py

@@ -115,7 +115,7 @@ def test_affine_grid(self, input_param, input_data, expected_val):
         result, _ = g(**input_data)
         if "device" in input_data:
             self.assertEqual(result.device, input_data[device])
-        assert_allclose(result, expected_val, rtol=1e-4, atol=1e-4)
+        assert_allclose(result, expected_val, type_test=False, rtol=1e-4, atol=1e-4)
 
 
 if __name__ == "__main__":

tests/test_as_channel_first.py

@@ -34,7 +34,7 @@ def test_value(self, in_type, input_param, expected_shape):
         if isinstance(test_data, torch.Tensor):
             test_data = test_data.cpu().numpy()
         expected = np.moveaxis(test_data, input_param["channel_dim"], 0)
-        assert_allclose(expected, result)
+        assert_allclose(result, expected, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_ensure_type.py

@@ -27,7 +27,7 @@ def test_array_input(self):
             for dtype in ("tensor", "NUMPY"):
                 result = EnsureType(data_type=dtype)(test_data)
                 self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray))
-                assert_allclose(result, test_data)
+                assert_allclose(result, test_data, type_test=False)
                 self.assertTupleEqual(result.shape, (2, 2))
 
     def test_single_input(self):
@@ -41,7 +41,7 @@ def test_single_input(self):
                 if isinstance(test_data, bool):
                     self.assertFalse(result)
                 else:
-                    assert_allclose(result, test_data)
+                    assert_allclose(result, test_data, type_test=False)
                 self.assertEqual(result.ndim, 0)
 
     def test_string(self):

tests/test_ensure_typed.py

@@ -27,7 +27,7 @@ def test_array_input(self):
             for dtype in ("tensor", "NUMPY"):
                 result = EnsureTyped(keys="data", data_type=dtype)({"data": test_data})["data"]
                 self.assertTrue(isinstance(result, torch.Tensor if dtype == "tensor" else np.ndarray))
-                assert_allclose(result, test_data)
+                assert_allclose(result, test_data, type_test=False)
                 self.assertTupleEqual(result.shape, (2, 2))
 
     def test_single_input(self):
@@ -41,7 +41,7 @@ def test_single_input(self):
                 if isinstance(test_data, bool):
                     self.assertFalse(result)
                 else:
-                    assert_allclose(result, test_data)
+                    assert_allclose(result, test_data, type_test=False)
                 self.assertEqual(result.ndim, 0)
 
     def test_string(self):

tests/test_flip.py

@@ -34,12 +34,10 @@ def test_correct_results(self, _, spatial_axis):
         for p in TEST_NDARRAYS:
             im = p(self.imt[0])
             flip = Flip(spatial_axis=spatial_axis)
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.flip(channel, spatial_axis))
+            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]
             expected = np.stack(expected)
             result = flip(im)
-            assert_allclose(expected, result)
+            assert_allclose(result, p(expected))
 
 
 if __name__ == "__main__":

tests/test_flipd.py

@@ -33,12 +33,10 @@ def test_invalid_cases(self, _, spatial_axis, raises):
     def test_correct_results(self, _, spatial_axis):
         for p in TEST_NDARRAYS:
             flip = Flipd(keys="img", spatial_axis=spatial_axis)
-            expected = []
-            for channel in self.imt[0]:
-                expected.append(np.flip(channel, spatial_axis))
+            expected = [np.flip(channel, spatial_axis) for channel in self.imt[0]]
             expected = np.stack(expected)
             result = flip({"img": p(self.imt[0])})["img"]
-            assert_allclose(expected, result)
+            assert_allclose(result, p(expected))
 
 
 if __name__ == "__main__":

tests/test_inverse_collation.py

@@ -115,10 +115,7 @@ def tearDown(self):
 
     @parameterized.expand(TESTS_2D + TESTS_3D)
     def test_collation(self, _, transform, collate_fn, ndim):
-        if ndim == 3:
-            data = self.data_3d
-        else:
-            data = self.data_2d
+        data = self.data_3d if ndim == 3 else self.data_2d
         if collate_fn:
             modified_transform = transform
         else:

tests/test_label_to_mask.py

@@ -64,7 +64,7 @@ def test_value(self, argments, image, expected_data):
         self.assertEqual(type(result), type(image))
         if isinstance(result, torch.Tensor):
             self.assertEqual(result.device, image.device)
-        assert_allclose(result, expected_data)
+        assert_allclose(result, expected_data, type_test=False)
 
 
 if __name__ == "__main__":

tests/test_label_to_maskd.py

@@ -65,7 +65,7 @@ def test_value(self, argments, input_data, expected_data):
         self.assertEqual(type(r), type(i))
         if isinstance(r, torch.Tensor):
             self.assertEqual(r.device, i.device)
-        assert_allclose(r, expected_data)
+        assert_allclose(r, expected_data, type_test=False)
 
 
 if __name__ == "__main__":