9ea14875825b2ab63a43ad2f0f5159d99df86d02,model.py,Lookahead,forward,#Lookahead#Any#,122

Before Change

        // add lookahead windows (with context+1 width) as a fourth dimension
        // for each seq-batch-feature combination
        x = [x[i:i + self.context + 1] for i in range(seq_len)]  // TxLxNxH - sequence, context, batch, feature
        x = torch.stack(x)
        x = x.permute(0, 2, 3, 1)  // TxNxHxL - sequence, batch, feature, context

After Change

        x = x.transpose(0, 1).transpose(1, 2)
        x = F.pad(x, pad=self.pad, value=0)
        x = self.conv(x)
        x = x.transpose(1, 2).transpose(0, 1).contiguous()
        return x

    def __repr__(self):
        return self.__class__.__name__ + "(" \

Instances