def forward()

in src/modules/transformer_decoder.py [0:0]

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

    def forward(self, features, mask, captions, incremental_state=None):

        if features is not None:
            features = features.permute(0, 2, 1)
            features = features.transpose(0, 1)
            features = self.layer_norm(features)

        if mask is not None:
            mask = (1 - mask.squeeze(1)).byte()

        # embed positions
        if self.embed_positions is not None:
            positions = self.embed_positions(captions, incremental_state=incremental_state)
        if incremental_state is not None:
            if self.embed_positions is not None:
                positions = positions[:, -1:]
            captions = captions[:, -1:]

        # embed tokens and positions
        x = self.embed_scale * self.embed_tokens(captions)
        if self.embed_positions is not None:
            x += positions

        x = F.dropout(x, p=self.dropout, training=self.training)

        # B x T x C -> T x B x C
        x = x.transpose(0, 1)

        # decoder layers
        for layer in self.layers:
            x = layer(x, features, mask, incremental_state)
        # T x B x C -> B x T x C
        x = x.transpose(0, 1)

        x = self.linear(x)
        _, predicted = x.max(dim=-1)

        if incremental_state is None:
            return x, predicted
        else:
            return x