7929d68e8d79bae487858fef93e81d28aa708d40,lib/model/roi_crop/functions/roi_crop.py,RoICropFunction,forward,#RoICropFunction#Any#Any#,9

Before Change

        else:
            self.device = -1
        self.device_c[0] = self.device
        if not input1.is_cuda:
            roi_crop.BilinearSamplerBHWD_updateOutput(input1, input2, output)
        else:
            output = output.cuda(self.device)
            roi_crop.BilinearSamplerBHWD_updateOutput_cuda(input1, input2, output)
        return output

    def backward(self, grad_output):
        grad_input1 = torch.zeros(self.input1.size())

After Change

        self.input2 = input2.clone()
        output = input2.new(input2.size()[0], input1.size()[1], input2.size()[1], input2.size()[2]).zero_()
        assert output.get_device() == input1.get_device(), "output and input1 must on the same device"
        assert output.get_device() == input2.get_device(), "output and input2 must on the same device"
        roi_crop.BilinearSamplerBHWD_updateOutput_cuda(input1, input2, output)
        return output

    def backward(self, grad_output):

Instances