e73fddf45377e8a3c0ea2e8281fae18f7b498dd6,tests/utils.py,TestIncrementalDecoder,forward,#TestIncrementalDecoder#Any#Any#Any#,99

Before Change

        // define output in terms of raw probs
        probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
        for i, step in enumerate(steps):
            // args.beam_probs gives the probability for every vocab element,
            // starting with eos, then unknown, and then the rest of the vocab
            if step < len(self.args.beam_probs):
                probs[:, i, self.dictionary.eos():] = self.args.beam_probs[step]
            else:
                probs[:, i, self.dictionary.eos()] = 1.0

        // random attention
        attn = torch.rand(bbsz, src_len, tgt_len)

        return Variable(probs), Variable(attn)

After Change

            steps = list(range(tgt_len))

        // define output in terms of raw probs
        if hasattr(self.args, "probs"):
            assert self.args.probs.dim() == 3, \
                "expected probs to have size bsz*steps*vocab"
            probs = self.args.probs.index_select(1, torch.LongTensor(steps))
        else:
            probs = torch.FloatTensor(bbsz, len(steps), vocab).zero_()
            for i, step in enumerate(steps):
                // args.beam_probs gives the probability for every vocab element,
                // starting with eos, then unknown, and then the rest of the vocab
                if step < len(self.args.beam_probs):
                    probs[:, i, self.dictionary.eos():] = self.args.beam_probs[step]
                else:
                    probs[:, i, self.dictionary.eos()] = 1.0

        // random attention
        attn = torch.rand(bbsz, src_len, tgt_len)

        return Variable(probs), Variable(attn)

Instances