def forward()

in networks/deep_sdf_decoder.py [0:0]

• 31 lines of code
• 16 McCabe index (conditional complexity)

    def forward(self, input):
        xyz = input[:, -3:]

        if input.shape[1] > 3 and self.latent_dropout:
            latent_vecs = input[:, :-3]
            latent_vecs = F.dropout(latent_vecs, p=0.2, training=self.training)
            x = torch.cat([latent_vecs, xyz], 1)
        else:
            x = input

        for layer in range(0, self.num_layers - 1):
            lin = getattr(self, "lin" + str(layer))
            if layer in self.latent_in:
                x = torch.cat([x, input], 1)
            elif layer != 0 and self.xyz_in_all:
                x = torch.cat([x, xyz], 1)
            x = lin(x)
            # last layer Tanh
            if layer == self.num_layers - 2 and self.use_tanh:
                x = self.tanh(x)
            if layer < self.num_layers - 2:
                if (
                    self.norm_layers is not None
                    and layer in self.norm_layers
                    and not self.weight_norm
                ):
                    bn = getattr(self, "bn" + str(layer))
                    x = bn(x)
                x = self.relu(x)
                if self.dropout is not None and layer in self.dropout:
                    x = F.dropout(x, p=self.dropout_prob, training=self.training)

        if hasattr(self, "th"):
            x = self.th(x)

        return x