def forward()

in pytorchvideo/models/resnet.py [0:0]

• 23 lines of code
• 13 McCabe index (conditional complexity)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # Explicitly forward every layer.
        # Branch2a, for example Tx1x1, BN, ReLU.
        if self.conv_a is not None:
            x = self.conv_a(x)
        if self.norm_a is not None:
            x = self.norm_a(x)
        if self.act_a is not None:
            x = self.act_a(x)

        # Branch2b, for example 1xHxW, BN, ReLU.
        output = []
        for ind in range(len(self.conv_b)):
            x_ = self.conv_b[ind](x)
            if self.norm_b[ind] is not None:
                x_ = self.norm_b[ind](x_)
            if self.act_b[ind] is not None:
                x_ = self.act_b[ind](x_)
            output.append(x_)
        if self.reduce_method == "sum":
            x = torch.stack(output, dim=0).sum(dim=0, keepdim=False)
        elif self.reduce_method == "cat":
            x = torch.cat(output, dim=1)

        # Branch2c, for example 1x1x1, BN.
        x = self.conv_c(x)
        if self.norm_c is not None:
            x = self.norm_c(x)
        return x