[*.py] Rename "Arguments:" to "Args:" (#3203)

Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/ops/deform_conv.py

@@ -27,7 +27,7 @@ def deform_conv2d(
     `Deformable Convolutional Networks
     <https://arxiv.org/abs/1703.06211>`__ if :attr:`mask` is ``None``.
 
-    Arguments:
+    Args:
         input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
         offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,
             out_height, out_width]): offsets to be applied for each position in the
@@ -154,7 +154,7 @@ class DeformConv2d(nn.Module):
 
     def forward(self, input: Tensor, offset: Tensor, mask: Tensor = None) -> Tensor:
         """
-        Arguments:
+        Args:
             input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
             offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,
                 out_height, out_width]): offsets to be applied for each position in the

torchvision/ops/feature_pyramid_network.py

@@ -10,7 +10,7 @@ class ExtraFPNBlock(nn.Module):
     """
     Base class for the extra block in the FPN.
 
-    Arguments:
+    Args:
         results (List[Tensor]): the result of the FPN
         x (List[Tensor]): the original feature maps
         names (List[str]): the names for each one of the
@@ -41,7 +41,7 @@ class FeaturePyramidNetwork(nn.Module):
     The input to the model is expected to be an OrderedDict[Tensor], containing
     the feature maps on top of which the FPN will be added.
 
-    Arguments:
+    Args:
         in_channels_list (list[int]): number of channels for each feature map that
             is passed to the module
         out_channels (int): number of channels of the FPN representation
@@ -134,7 +134,7 @@ class FeaturePyramidNetwork(nn.Module):
         """
         Computes the FPN for a set of feature maps.
 
-        Arguments:
+        Args:
             x (OrderedDict[Tensor]): feature maps for each feature level.
 
         Returns:

torchvision/ops/focal_loss.py

@@ -13,7 +13,7 @@ def sigmoid_focal_loss(
     Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py .
     Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
 
-    Arguments:
+    Args:
         inputs: A float tensor of arbitrary shape.
                 The predictions for each example.
         targets: A float tensor with the same shape as inputs. Stores the binary

torchvision/ops/poolers.py

@@ -45,7 +45,7 @@ class LevelMapper(object):
     """Determine which FPN level each RoI in a set of RoIs should map to based
     on the heuristic in the FPN paper.
 
-    Arguments:
+    Args:
         k_min (int)
         k_max (int)
         canonical_scale (int)
@@ -69,7 +69,7 @@ class LevelMapper(object):
 
     def __call__(self, boxlists: List[Tensor]) -> Tensor:
         """
-        Arguments:
+        Args:
             boxlists (list[BoxList])
         """
         # Compute level ids
@@ -87,7 +87,7 @@ class MultiScaleRoIAlign(nn.Module):
 
     It infers the scale of the pooling via the heuristics present in the FPN paper.
 
-    Arguments:
+    Args:
         featmap_names (List[str]): the names of the feature maps that will be used
             for the pooling.
         output_size (List[Tuple[int, int]] or List[int]): output size for the pooled region
@@ -182,7 +182,7 @@ class MultiScaleRoIAlign(nn.Module):
         image_shapes: List[Tuple[int, int]],
     ) -> Tensor:
         """
-        Arguments:
+        Args:
             x (OrderedDict[Tensor]): feature maps for each level. They are assumed to have
                 all the same number of channels, but they can have different sizes.
             boxes (List[Tensor[N, 4]]): boxes to be used to perform the pooling operation, in

torchvision/ops/ps_roi_align.py

@@ -18,7 +18,7 @@ def ps_roi_align(
     Performs Position-Sensitive Region of Interest (RoI) Align operator
     mentioned in Light-Head R-CNN.
 
-    Arguments:
+    Args:
         input (Tensor[N, C, H, W]): input tensor
         boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
             format where the regions will be taken from. If a single Tensor is passed,

torchvision/ops/ps_roi_pool.py

@@ -17,7 +17,7 @@ def ps_roi_pool(
     Performs Position-Sensitive Region of Interest (RoI) Pool operator
     described in R-FCN
 
-    Arguments:
+    Args:
         input (Tensor[N, C, H, W]): input tensor
         boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
             format where the regions will be taken from. If a single Tensor is passed,

torchvision/ops/roi_align.py

@@ -19,7 +19,7 @@ def roi_align(
     """
     Performs Region of Interest (RoI) Align operator described in Mask R-CNN
 
-    Arguments:
+    Args:
         input (Tensor[N, C, H, W]): input tensor
         boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
             format where the regions will be taken from. If a single Tensor is passed,

torchvision/ops/roi_pool.py

@@ -17,7 +17,7 @@ def roi_pool(
     """
     Performs Region of Interest (RoI) Pool operator described in Fast R-CNN
 
-    Arguments:
+    Args:
         input (Tensor[N, C, H, W]): input tensor
         boxes (Tensor[K, 5] or List[Tensor[L, 4]]): the box coordinates in (x1, y1, x2, y2)
             format where the regions will be taken from. If a single Tensor is passed,