Suppress mypy warnings about incompatible superclass signature

Related to Project-MONAI#495

Author: hjmjohnson

monai/transforms/croppad/array.py

@@ -53,7 +53,7 @@ def _determine_data_pad_width(self, data_shape):
         else:
             return [(0, max(self.spatial_size[i] - data_shape[i], 0)) for i in range(len(self.spatial_size))]
 
-    def __call__(self, img, mode: Optional[str] = None):
+    def __call__(self, img, mode: Optional[str] = None):  # type: ignore # see issue #495
         data_pad_width = self._determine_data_pad_width(img.shape[1:])
         all_pad_width = [(0, 0)] + data_pad_width
         img = np.pad(img, all_pad_width, mode=mode or self.mode)

monai/transforms/post/array.py

@@ -38,7 +38,7 @@ def __init__(self, to_onehot: bool = False, num_classes: Optional[int] = None):
         self.to_onehot = to_onehot
         self.num_classes = num_classes
 
-    def __call__(self, img, to_onehot: Optional[bool] = None, num_classes: Optional[int] = None):
+    def __call__(self, img, to_onehot: Optional[bool] = None, num_classes: Optional[int] = None):  # type: ignore # see issue #495
         if to_onehot or self.to_onehot:
             if num_classes is None:
                 num_classes = self.num_classes
@@ -69,7 +69,7 @@ def __init__(self, sigmoid: bool = False, softmax: bool = False, other: Optional
         self.softmax = softmax
         self.other = other
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self, img, sigmoid: Optional[bool] = None, softmax: Optional[bool] = None, other: Optional[Callable] = None
     ):
         if sigmoid is True and softmax is True:
@@ -118,7 +118,7 @@ def __init__(
         self.threshold_values = threshold_values
         self.logit_thresh = logit_thresh
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         argmax: Optional[bool] = None,

monai/transforms/spatial/array.py

@@ -79,7 +79,7 @@ def __init__(
         self.cval = cval
         self.dtype = dtype
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         data_array: np.ndarray,
         affine=None,
@@ -162,7 +162,7 @@ def __init__(self, axcodes=None, as_closest_canonical: bool = False, labels=tupl
         self.as_closest_canonical = as_closest_canonical
         self.labels = labels
 
-    def __call__(self, data_array: np.ndarray, affine=None):
+    def __call__(self, data_array: np.ndarray, affine=None):  # type: ignore # see issue #495
         """
         original orientation of `data_array` is defined by `affine`.
 
@@ -261,7 +261,7 @@ def __init__(
         self.preserve_range = preserve_range
         self.anti_aliasing = anti_aliasing
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -329,7 +329,7 @@ def __init__(
         self.cval = cval
         self.prefilter = prefilter
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -471,7 +471,7 @@ def __init__(self, k: int = 1, spatial_axes=(0, 1)):
         self.k = k
         self.spatial_axes = spatial_axes
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         """
         Args:
             img (ndarray): channel first array, must have shape: (num_channels, H[, W, ..., ]),
@@ -567,7 +567,7 @@ def randomize(self):
         self._do_transform = self.R.random_sample() < self.prob
         self.angle = self.R.uniform(low=self.degrees[0], high=self.degrees[1])
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -671,7 +671,7 @@ def randomize(self):
         else:
             self._zoom = self.R.uniform(self.min_zoom, self.max_zoom)
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         order=None,
@@ -887,7 +887,7 @@ def __init__(
         self.as_tensor_output = as_tensor_output
         self.device = device
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         grid: Union[np.ndarray, torch.Tensor],
@@ -977,7 +977,7 @@ def __init__(
         self.padding_mode = padding_mode
         self.mode = mode
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,
@@ -1061,7 +1061,7 @@ def randomize(self):
         self.do_transform = self.R.rand() < self.prob
         self.rand_affine_grid.randomize()
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,
@@ -1158,7 +1158,7 @@ def randomize(self, spatial_size):
         self.deform_grid.randomize(spatial_size)
         self.rand_affine_grid.randomize()
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img: Union[np.ndarray, torch.Tensor],
         spatial_size=None,

monai/transforms/utility/array.py

@@ -118,7 +118,7 @@ def __init__(self, dtype: np.dtype = np.float32):
         """
         self.dtype = dtype
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         assert isinstance(img, np.ndarray), "image must be numpy array."
         return img.astype(self.dtype)
 
@@ -161,7 +161,7 @@ def __init__(self, dim: Optional[int] = None):
             assert isinstance(dim, int) and dim >= -1, "invalid channel dimension."
         self.dim = dim
 
-    def __call__(self, img: np.ndarray):
+    def __call__(self, img: np.ndarray):  # type: ignore # see issue #495
         """
         Args:
             img (ndarray): numpy arrays with required dimension `dim` removed
@@ -209,7 +209,7 @@ def __init__(
         if logger_handler is not None:
             self._logger.addHandler(logger_handler)
 
-    def __call__(
+    def __call__(  # type: ignore # see issue #495
         self,
         img,
         prefix: Optional[str] = None,