Refactor unnecessary else / elif when if block has a return statement

monai/data/utils.py

@@ -403,7 +403,7 @@ def _detect_batch_size(batch_data: Sequence):
                 dict_batch[k] = v
 
         return dict_batch
-    elif isinstance(batch_data, list):
+    if isinstance(batch_data, list):
         batch_size = _detect_batch_size(batch_data)
         list_batch = []
         for b in batch_data:

monai/handlers/utils.py

@@ -259,7 +259,7 @@ def from_engine(keys: KeysCollection, first: bool = False):
     def _wrapper(data):
         if isinstance(data, dict):
             return tuple(data[k] for k in keys)
-        elif isinstance(data, list) and isinstance(data[0], dict):
+        if isinstance(data, list) and isinstance(data[0], dict):
             # if data is a list of dictionaries, extract expected keys and construct lists,
             # if `first=True`, only extract keys from the first item of the list
             ret = [data[0][k] if first else [i[k] for i in data] for k in keys]

monai/networks/blocks/fcn.py

@@ -191,25 +191,24 @@ def forward(self, x: torch.Tensor):
             fs3 = self.refine8(self.up_conv(fs2) + gcfm4)
             fs4 = self.refine9(self.up_conv(fs3) + gcfm5)
             return self.refine10(self.up_conv(fs4))
-        else:
-            fs1 = self.refine6(
-                F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2
-            )
-            fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3)
-            fs3 = self.refine8(
-                F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4
-            )
-            fs4 = self.refine9(
-                F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5
-            )
-            return self.refine10(
-                F.interpolate(
-                    fs4,
-                    org_input.size()[2:],
-                    mode=self.upsample_mode,
-                    align_corners=True,
-                )
+        fs1 = self.refine6(
+            F.interpolate(gcfm1, fm3.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm2
+        )
+        fs2 = self.refine7(F.interpolate(fs1, fm2.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm3)
+        fs3 = self.refine8(
+            F.interpolate(fs2, pool_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm4
+        )
+        fs4 = self.refine9(
+            F.interpolate(fs3, conv_x.size()[2:], mode=self.upsample_mode, align_corners=True) + gcfm5
+        )
+        return self.refine10(
+            F.interpolate(
+                fs4,
+                org_input.size()[2:],
+                mode=self.upsample_mode,
+                align_corners=True,
             )
+        )
 
 
 class MCFCN(FCN):

monai/transforms/intensity/array.py

@@ -1330,7 +1330,7 @@ def __init__(
                 raise AssertionError(
                     "If a sequence is passed to k_intensity, then a sequence of locations must be passed to loc"
                 )
-            elif len(k_intensity) != len(loc):
+            if len(k_intensity) != len(loc):
                 raise AssertionError("There must be one intensity_factor value for each tuple of indices in loc.")
         if isinstance(self.loc[0], Sequence) and k_intensity is not None:
             if not isinstance(self.k_intensity, Sequence):
@@ -1541,8 +1541,7 @@ def _make_sequence(self, x: torch.Tensor) -> Sequence[Sequence[float]]:
             if not isinstance(self.intensity_range[0], Sequence):
                 intensity_range = (ensure_tuple(self.intensity_range),) * x.shape[0]
                 return intensity_range
-            else:
-                return ensure_tuple(self.intensity_range)
+            return ensure_tuple(self.intensity_range)
         else:
             # set default range if one not provided
             return self._set_default_range(x)

monai/transforms/post/array.py

@@ -334,12 +334,11 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
         """
         if isinstance(img, np.ndarray):
             return np.asarray(np.where(np.isin(img, self.applied_labels), img, 0))
-        elif isinstance(img, torch.Tensor):
+        if isinstance(img, torch.Tensor):
             img_arr = img.detach().cpu().numpy()
             img_arr = self(img_arr)
             return torch.as_tensor(img_arr, device=img.device)
-        else:
-            raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.")
+        raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.")
 
 
 class FillHoles(Transform):
@@ -415,12 +414,11 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
         """
         if isinstance(img, np.ndarray):
             return fill_holes(img, self.applied_labels, self.connectivity)
-        elif isinstance(img, torch.Tensor):
+        if isinstance(img, torch.Tensor):
             img_arr = img.detach().cpu().numpy()
             img_arr = self(img_arr)
             return torch.as_tensor(img_arr, device=img.device)
-        else:
-            raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.")
+        raise NotImplementedError(f"{self.__class__} can not handle data of type {type(img)}.")
 
 
 class LabelToContour(Transform):

monai/transforms/utility/array.py

@@ -1001,8 +1001,7 @@ def __call__(
         def _compute(op: Callable, data: np.ndarray):
             if self.channel_wise:
                 return [op(c) for c in data]
-            else:
-                return op(data)
+            return op(data)
 
         custom_index = 0
         for o in self.ops:

monai/transforms/utils.py

@@ -1043,7 +1043,7 @@ def convert_to_tensor(data):
     """
     if isinstance(data, torch.Tensor):
         return data.contiguous()
-    elif isinstance(data, np.ndarray):
+    if isinstance(data, np.ndarray):
         # skip array of string classes and object, refer to:
         # https://github.com/pytorch/pytorch/blob/v1.9.0/torch/utils/data/_utils/collate.py#L13
         if re.search(r"[SaUO]", data.dtype.str) is None:
@@ -1107,11 +1107,11 @@ def tensor_to_numpy(data):
     if isinstance(data, torch.Tensor):
         # invert Tensor to numpy, if scalar data, convert to number
         return data.item() if data.ndim == 0 else np.ascontiguousarray(data.detach().cpu().numpy())
-    elif isinstance(data, dict):
+    if isinstance(data, dict):
         return {k: tensor_to_numpy(v) for k, v in data.items()}
-    elif isinstance(data, list):
+    if isinstance(data, list):
         return [tensor_to_numpy(i) for i in data]
-    elif isinstance(data, tuple):
+    if isinstance(data, tuple):
         return tuple(tensor_to_numpy(i) for i in data)
 
     return data

monai/utils/deprecated.py

@@ -100,9 +100,8 @@ def _wrapper(*args, **kwargs):
 
         if is_func:
             return _wrapper
-        else:
-            obj.__init__ = _wrapper
-            return obj
+        obj.__init__ = _wrapper
+        return obj
 
     return _decorator
 

monai/utils/dist.py

@@ -34,7 +34,7 @@ def get_dist_device():
         backend = dist.get_backend()
         if backend == "nccl" and torch.cuda.is_available():
             return torch.device(f"cuda:{torch.cuda.current_device()}")
-        elif backend == "gloo":
+        if backend == "gloo":
             return torch.device("cpu")
     return None
 

monai/utils/jupyter_utils.py

@@ -224,8 +224,7 @@ def _get_loss(data):
 
     if isinstance(output, list):
         return _get_loss(output[0])
-    else:
-        return _get_loss(output)
+    return _get_loss(output)
 
 
 class StatusMembers(Enum):