Remove unnecessary use of comprehension

monai/networks/blocks/feature_pyramid_network.py

@@ -258,6 +258,6 @@ def forward(self, x: Dict[str, Tensor]) -> Dict[str, Tensor]:
             results, names = self.extra_blocks(results, x_values, names)
 
         # make it back an OrderedDict
-        out = OrderedDict([(k, v) for k, v in zip(names, results)])
+        out = OrderedDict(list(zip(names, results)))
 
         return out

monai/networks/blocks/fft_utils_t.py

@@ -139,12 +139,12 @@ def ifftn_centered_t(ksp: Tensor, spatial_dims: int, is_complex: bool = True) ->
             output2 = ifftn_centered(ksp, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and ifft
-    shift = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+    shift = list(range(-spatial_dims, 0))  # noqa: C416
     if is_complex:
         if ksp.shape[-1] != 2:
             raise ValueError(f"ksp.shape[-1] is not 2 ({ksp.shape[-1]}).")
-        shift = [i for i in range(-spatial_dims - 1, -1)]  # noqa: C416
-    dims = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+        shift = list(range(-spatial_dims - 1, -1))  # noqa: C416
+    dims = list(range(-spatial_dims, 0))  # noqa: C416
 
     x = ifftshift(ksp, shift)
 
@@ -187,12 +187,12 @@ def fftn_centered_t(im: Tensor, spatial_dims: int, is_complex: bool = True) -> T
             output2 = fftn_centered(im, spatial_dims=2, is_complex=True)
     """
     # define spatial dims to perform ifftshift, fftshift, and fft
-    shift = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+    shift = list(range(-spatial_dims, 0))  # noqa: C416
     if is_complex:
         if im.shape[-1] != 2:
             raise ValueError(f"img.shape[-1] is not 2 ({im.shape[-1]}).")
-        shift = [i for i in range(-spatial_dims - 1, -1)]  # noqa: C416
-    dims = [i for i in range(-spatial_dims, 0)]  # noqa: C416
+        shift = list(range(-spatial_dims - 1, -1))  # noqa: C416
+    dims = list(range(-spatial_dims, 0))  # noqa: C416
 
     x = ifftshift(im, shift)
 

monai/transforms/utility/dictionary.py

@@ -423,7 +423,7 @@ def __call__(
             output = []
             results = [self.splitter(d[key]) for key in all_keys]
             for row in zip(*results):
-                new_dict = {k: v for k, v in zip(all_keys, row)}
+                new_dict = dict(zip(all_keys, row))
                 # fill in the extra keys with unmodified data
                 for k in set(d.keys()).difference(set(all_keys)):
                     new_dict[k] = deepcopy(d[k])

tests/test_k_space_spike_noised.py

@@ -43,7 +43,7 @@ def get_data(im_shape, im_type):
         create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d
         ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)
         ims = [im_type(im[None]) for im in ims]
-        return {k: v for k, v in zip(KEYS, ims)}
+        return dict(zip(KEYS, ims))
 
     @parameterized.expand(TESTS)
     def test_same_result(self, im_shape, im_type):

tests/test_rand_k_space_spike_noised.py

@@ -40,7 +40,7 @@ def get_data(im_shape, im_type):
         create_test_image = create_test_image_2d if len(im_shape) == 2 else create_test_image_3d
         ims = create_test_image(*im_shape, rad_max=20, noise_max=0.0, num_seg_classes=5)
         ims = [im_type(im[None]) for im in ims]
-        return {k: v for k, v in zip(KEYS, ims)}
+        return dict(zip(KEYS, ims))
 
     @parameterized.expand(TESTS)
     def test_same_result(self, im_shape, im_type):