128ea5274db96163c58a99405e7205abcab36192,onmt/Translator.py,Translator,_runTarget,#Translator#Any#Any#,58

Before Change

        decOut, decStates, attn = self.model.decoder(
            batch.tgt[:-1], batch.src, context, decStates)

        aeq(decOut.size(), batch.tgt[1:].data.size())
        for dec, tgt in zip(decOut, batch.tgt[1:].data):
            // Log prob of each word.
            out = self.model.generator.forward(dec)

After Change

        //  (2) if a target is specified, compute the "goldScore"
        //  (i.e. log likelihood) of the target under the model
        goldScores = torch.FloatTensor(batch.batch_size).fill_(0)
        decOut, decStates, attn = self.model.decoder(
            batch.tgt[:-1], src, context, decStates)

        // print(decOut.size(), batch.tgt[1:].data.size())

Instances