dafdc9b5cb8fa4c65285aad22b1429549d06d71a,networks/resample2d_package/functions/resample2d.py,Resample2dFunction,backward,#Resample2dFunction#Any#,27

Before Change

        assert(gradOutput.is_contiguous() == True)
        
        with torch.cuda.device_of(input1):
            b, c, h, w = input1.size()
            gradInput1 = input1.new().resize_(b,c,h,w).zero_()

            b, c, h, w = input2.size()
            gradInput2 = input2.new().resize_(b,c,h,w).zero_()

            resample2d.Resample2d_cuda_backward(input1, input2, gradOutput, gradInput1, gradInput2, self.kernel_size)

        return gradInput1, gradInput2

After Change

        input1, input2 = ctx.saved_tensors

        grad_input1 = Variable(input1.new(input1.size()).zero_())
        grad_input2 = Variable(input1.new(input2.size()).zero_())

        resample2d.Resample2d_cuda_backward(input1, input2, grad_output.data,
                                            grad_input1.data, grad_input2.data,
                                            ctx.kernel_size)

        return grad_input1, grad_input2, None

Instances