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

references/classification/transforms.py

@@ -40,11 +40,11 @@ def forward(self, batch: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
         """
         if batch.ndim != 4:
             raise ValueError("Batch ndim should be 4. Got {}".format(batch.ndim))
-        elif target.ndim != 1:
+        if target.ndim != 1:
             raise ValueError("Target ndim should be 1. Got {}".format(target.ndim))
-        elif not batch.is_floating_point():
+        if not batch.is_floating_point():
             raise TypeError("Batch dtype should be a float tensor. Got {}.".format(batch.dtype))
-        elif target.dtype != torch.int64:
+        if target.dtype != torch.int64:
             raise TypeError("Target dtype should be torch.int64. Got {}".format(target.dtype))
 
         if not self.inplace:
@@ -116,11 +116,11 @@ def forward(self, batch: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
         """
         if batch.ndim != 4:
             raise ValueError("Batch ndim should be 4. Got {}".format(batch.ndim))
-        elif target.ndim != 1:
+        if target.ndim != 1:
             raise ValueError("Target ndim should be 1. Got {}".format(target.ndim))
-        elif not batch.is_floating_point():
+        if not batch.is_floating_point():
             raise TypeError("Batch dtype should be a float tensor. Got {}.".format(batch.dtype))
-        elif target.dtype != torch.int64:
+        if target.dtype != torch.int64:
             raise TypeError("Target dtype should be torch.int64. Got {}".format(target.dtype))
 
         if not self.inplace:

references/detection/transforms.py

@@ -104,7 +104,7 @@ def forward(
         if isinstance(image, torch.Tensor):
             if image.ndimension() not in {2, 3}:
                 raise ValueError("image should be 2/3 dimensional. Got {} dimensions.".format(image.ndimension()))
-            elif image.ndimension() == 2:
+            if image.ndimension() == 2:
                 image = image.unsqueeze(0)
 
         orig_w, orig_h = F.get_image_size(image)
@@ -186,7 +186,7 @@ def forward(
         if isinstance(image, torch.Tensor):
             if image.ndimension() not in {2, 3}:
                 raise ValueError("image should be 2/3 dimensional. Got {} dimensions.".format(image.ndimension()))
-            elif image.ndimension() == 2:
+            if image.ndimension() == 2:
                 image = image.unsqueeze(0)
 
         if torch.rand(1) < self.p:
@@ -245,7 +245,7 @@ def forward(
         if isinstance(image, torch.Tensor):
             if image.ndimension() not in {2, 3}:
                 raise ValueError("image should be 2/3 dimensional. Got {} dimensions.".format(image.ndimension()))
-            elif image.ndimension() == 2:
+            if image.ndimension() == 2:
                 image = image.unsqueeze(0)
 
         r = torch.rand(7)

test/datasets_utils.py

@@ -486,7 +486,7 @@ def _inject_fake_data(self, tmpdir, config):
                 "The method 'inject_fake_data' needs to return at least an integer indicating the number of "
                 "examples for the current configuration."
             )
-        elif isinstance(info, int):
+        if isinstance(info, int):
             info = dict(num_examples=info)
         elif not isinstance(info, dict):
             raise UsageError(

torchvision/datasets/imagenet.py

@@ -48,7 +48,7 @@ def __init__(self, root: str, split: str = "train", download: Optional[str] = No
                 "directory."
             )
             raise RuntimeError(msg)
-        elif download is False:
+        if download is False:
             msg = "The use of the download flag is deprecated, since the dataset " "is no longer publicly accessible."
             warnings.warn(msg, RuntimeWarning)
 

torchvision/datasets/inaturalist.py

@@ -214,11 +214,10 @@ def category_name(self, category_type: str, category_id: int) -> str:
         else:
             if category_type not in self.categories_index:
                 raise ValueError(f"Invalid category type '{category_type}'")
-            else:
-                for name, id in self.categories_index[category_type].items():
-                    if id == category_id:
-                        return name
-                raise ValueError(f"Invalid category id {category_id} for {category_type}")
+            for name, id in self.categories_index[category_type].items():
+                if id == category_id:
+                    return name
+            raise ValueError(f"Invalid category id {category_id} for {category_type}")
 
     def _check_integrity(self) -> bool:
         return os.path.exists(self.root) and len(os.listdir(self.root)) > 0

torchvision/models/detection/_utils.py

@@ -276,8 +276,7 @@ def __call__(self, match_quality_matrix: Tensor) -> Tensor:
             # empty targets or proposals not supported during training
             if match_quality_matrix.shape[0] == 0:
                 raise ValueError("No ground-truth boxes available for one of the images " "during training")
-            else:
-                raise ValueError("No proposal boxes available for one of the images " "during training")
+            raise ValueError("No proposal boxes available for one of the images " "during training")
 
         # match_quality_matrix is M (gt) x N (predicted)
         # Max over gt elements (dim 0) to find best gt candidate for each prediction

torchvision/models/efficientnet.py

@@ -172,7 +172,7 @@ def __init__(
 
         if not inverted_residual_setting:
             raise ValueError("The inverted_residual_setting should not be empty")
-        elif not (
+        if not (
             isinstance(inverted_residual_setting, Sequence)
             and all([isinstance(s, MBConvConfig) for s in inverted_residual_setting])
         ):

torchvision/models/mobilenetv3.py

@@ -151,7 +151,7 @@ def __init__(
 
         if not inverted_residual_setting:
             raise ValueError("The inverted_residual_setting should not be empty")
-        elif not (
+        if not (
             isinstance(inverted_residual_setting, Sequence)
             and all([isinstance(s, InvertedResidualConfig) for s in inverted_residual_setting])
         ):

torchvision/models/segmentation/segmentation.py

@@ -100,9 +100,8 @@ def _load_weights(model: nn.Module, arch_type: str, backbone: str, progress: boo
     model_url = model_urls.get(arch, None)
     if model_url is None:
         raise NotImplementedError("pretrained {} is not supported as of now".format(arch))
-    else:
-        state_dict = load_state_dict_from_url(model_url, progress=progress)
-        model.load_state_dict(state_dict)
+    state_dict = load_state_dict_from_url(model_url, progress=progress)
+    model.load_state_dict(state_dict)
 
 
 def _segm_lraspp_mobilenetv3(backbone_name: str, num_classes: int, pretrained_backbone: bool = True) -> LRASPP:

torchvision/models/shufflenetv2.py

@@ -161,9 +161,8 @@ def _shufflenetv2(arch: str, pretrained: bool, progress: bool, *args: Any, **kwa
         model_url = model_urls[arch]
         if model_url is None:
             raise NotImplementedError("pretrained {} is not supported as of now".format(arch))
-        else:
-            state_dict = load_state_dict_from_url(model_url, progress=progress)
-            model.load_state_dict(state_dict)
+        state_dict = load_state_dict_from_url(model_url, progress=progress)
+        model.load_state_dict(state_dict)
 
     return model
 

torchvision/prototype/datasets/benchmark.py

@@ -250,7 +250,7 @@ def __exit__(self, exc_type, exc_val, exc_tb):
         def delta(self):
             if self._start is None:
                 raise RuntimeError()
-            elif self._stop is None:
+            if self._stop is None:
                 raise RuntimeError()
             return self._stop - self._start
 

torchvision/transforms/functional.py

@@ -228,11 +228,11 @@ def to_pil_image(pic, mode=None):
     if not (isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
         raise TypeError("pic should be Tensor or ndarray. Got {}.".format(type(pic)))
 
-    elif isinstance(pic, torch.Tensor):
+    if isinstance(pic, torch.Tensor):
         if pic.ndimension() not in {2, 3}:
             raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndimension()))
 
-        elif pic.ndimension() == 2:
+        if pic.ndimension() == 2:
             # if 2D image, add channel dimension (CHW)
             pic = pic.unsqueeze(0)
 
@@ -244,7 +244,7 @@ def to_pil_image(pic, mode=None):
         if pic.ndim not in {2, 3}:
             raise ValueError("pic should be 2/3 dimensional. Got {} dimensions.".format(pic.ndim))
 
-        elif pic.ndim == 2:
+        if pic.ndim == 2:
             # if 2D image, add channel dimension (HWC)
             pic = np.expand_dims(pic, 2)
 

torchvision/utils.py

@@ -176,11 +176,11 @@ def draw_bounding_boxes(
 
     if not isinstance(image, torch.Tensor):
         raise TypeError(f"Tensor expected, got {type(image)}")
-    elif image.dtype != torch.uint8:
+    if image.dtype != torch.uint8:
         raise ValueError(f"Tensor uint8 expected, got {image.dtype}")
-    elif image.dim() != 3:
+    if image.dim() != 3:
         raise ValueError("Pass individual images, not batches")
-    elif image.size(0) not in {1, 3}:
+    if image.size(0) not in {1, 3}:
         raise ValueError("Only grayscale and RGB images are supported")
 
     if image.size(0) == 1:
@@ -254,11 +254,11 @@ def draw_segmentation_masks(
 
     if not isinstance(image, torch.Tensor):
         raise TypeError(f"The image must be a tensor, got {type(image)}")
-    elif image.dtype != torch.uint8:
+    if image.dtype != torch.uint8:
         raise ValueError(f"The image dtype must be uint8, got {image.dtype}")
-    elif image.dim() != 3:
+    if image.dim() != 3:
         raise ValueError("Pass individual images, not batches")
-    elif image.size()[0] != 3:
+    if image.size()[0] != 3:
         raise ValueError("Pass an RGB image. Other Image formats are not supported")
     if masks.ndim == 2:
         masks = masks[None, :, :]