densenet_efficient_multi_gpu.py 文件源码

def forward(self, weight, bias, input):
        # Assert we're using cudnn
        for i in ([weight, bias, input]):
            if i is not None and not(cudnn.is_acceptable(i)):
                raise Exception('You must be using CUDNN to use _EfficientBatchNorm')

        res = input.new(*self._output_size(input, weight))
        self._cudnn_info = torch._C._cudnn_convolution_full_forward(
            input, weight, bias, res,
            (self.padding, self.padding),
            (self.stride, self.stride),
            (self.dilation, self.dilation),
            self.groups, cudnn.benchmark
        )

        return res