Reorder weight and offset args in deform_conv2d

We place offset arg before the weight arg, to be more
consistent with DeformConv2d.forward(input, offset)

Author: pedrofreire

test/test_ops.py

@@ -474,18 +474,18 @@ def _test_forward(self, device, contiguous):
     def _test_backward(self, device, contiguous):
         x, weight, offset, bias, stride, padding, dilation = self.get_fn_args(device, contiguous)
 
-        def func(x_, weight_, offset_, bias_):
-            return ops.deform_conv2d(x_, weight_, offset_, bias_, stride=stride, padding=padding, dilation=dilation)
+        def func(x_, offset_, weight_, bias_):
+            return ops.deform_conv2d(x_, offset_, weight_, bias_, stride=stride, padding=padding, dilation=dilation)
 
-        gradcheck(func, (x, weight, offset, bias), nondet_tol=1e-5)
+        gradcheck(func, (x, offset, weight, bias), nondet_tol=1e-5)
 
         @torch.jit.script
-        def script_func(x_, weight_, offset_, bias_, stride_, pad_, dilation_):
+        def script_func(x_, offset_, weight_, bias_, stride_, pad_, dilation_):
             # type: (Tensor, Tensor, Tensor, Tensor, Tuple[int, int], Tuple[int, int], Tuple[int, int]) -> Tensor
-            return ops.deform_conv2d(x_, weight_, offset_, bias_, stride=stride_, padding=pad_, dilation=dilation_)
+            return ops.deform_conv2d(x_, offset_, weight_, bias_, stride=stride_, padding=pad_, dilation=dilation_)
 
-        gradcheck(lambda z, wei, off, bi: script_func(z, wei, off, bi, stride, padding, dilation),
-                  (x, weight, offset, bias), nondet_tol=1e-5)
+        gradcheck(lambda z, off, wei, bi: script_func(z, off, wei, bi, stride, padding, dilation),
+                  (x, offset, weight, bias), nondet_tol=1e-5)
 
 
 if __name__ == '__main__':

torchvision/ops/deform_conv.py

@@ -8,18 +8,18 @@
 from torch.jit.annotations import Optional, Tuple
 
 
-def deform_conv2d(input, weight, offset, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
+def deform_conv2d(input, offset, weight, bias=None, stride=(1, 1), padding=(0, 0), dilation=(1, 1)):
     # type: (Tensor, Tensor, Tensor, Optional[Tensor], Tuple[int, int], Tuple[int, int], Tuple[int, int]) -> Tensor
     """
     Performs Deformable Convolution, described in Deformable Convolutional Networks
 
     Arguments:
         input (Tensor[batch_size, in_channels, in_height, in_width]): input tensor
-        weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]):
-            convolution weights, split into groups of size (in_channels // groups)
         offset (Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,
             out_height, out_width]): offsets to be applied for each position in the
             convolution kernel.
+        weight (Tensor[out_channels, in_channels // groups, kernel_height, kernel_width]):
+            convolution weights, split into groups of size (in_channels // groups)
         bias (Tensor[out_channels]): optional bias of shape (out_channels,). Default: None
         stride (int or Tuple[int, int]): distance between convolution centers. Default: 1
         padding (int or Tuple[int, int]): height/width of padding of zeroes around
@@ -105,7 +105,7 @@ def forward(self, input, offset):
                 out_height, out_width]): offsets to be applied for each position in the
                 convolution kernel.
         """
-        return deform_conv2d(input, self.weight, offset, self.bias, stride=self.stride,
+        return deform_conv2d(input, offset, self.weight, self.bias, stride=self.stride,
                              padding=self.padding, dilation=self.dilation)
 
     def __repr__(self):