def forward()

in Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py [0:0]

• 26 lines of code
• 10 McCabe index (conditional complexity)

    def forward(self, inputs, drop_connect_rate=None):
        """
        :param inputs: input tensor
        :param drop_connect_rate: drop connect rate (float, between 0 and 1)
        :return: output of block
        """

        # Expansion and Depthwise Convolution
        x = inputs
        if self._block_args.expand_ratio != 1:
            x = self._swish(self._bn0(self._expand_conv(inputs)))
        x = self._swish(self._bn1(self._depthwise_conv(x)))

        # Squeeze and Excitation
        if self.has_se:
            x_squeezed = F.adaptive_avg_pool2d(x, 1)
            x_squeezed = self._se_expand(
                self._swish(self._se_reduce(x_squeezed))
            )
            x = torch.sigmoid(x_squeezed) * x

        x = self._bn2(self._project_conv(x))

        # Skip connection and drop connect
        input_filters, output_filters = (
            self._block_args.input_filters,
            self._block_args.output_filters,
        )
        if (
            self.id_skip and self._block_args.stride == 1
            and input_filters == output_filters
        ):
            if drop_connect_rate:
                x = drop_connect(
                    x, p=drop_connect_rate, training=self.training
                )
            x = x + inputs  # skip connection
        return x