def predict()

in drqa/reader/model.py [0:0]

• 23 lines of code
• 8 McCabe index (conditional complexity)

    def predict(self, ex, candidates=None, top_n=1, async_pool=None):
        """Forward a batch of examples only to get predictions.

        Args:
            ex: the batch
            candidates: batch * variable length list of string answer options.
              The model will only consider exact spans contained in this list.
            top_n: Number of predictions to return per batch element.
            async_pool: If provided, non-gpu post-processing will be offloaded
              to this CPU process pool.
        Output:
            pred_s: batch * top_n predicted start indices
            pred_e: batch * top_n predicted end indices
            pred_score: batch * top_n prediction scores

        If async_pool is given, these will be AsyncResult handles.
        """
        # Eval mode
        self.network.eval()

        # Transfer to GPU
        if self.use_cuda:
            inputs = [e if e is None else e.cuda(non_blocking=True)
                      for e in ex[:5]]
        else:
            inputs = [e for e in ex[:5]]

        # Run forward
        with torch.no_grad():
            score_s, score_e = self.network(*inputs)

        # Decode predictions
        score_s = score_s.data.cpu()
        score_e = score_e.data.cpu()
        if candidates:
            args = (score_s, score_e, candidates, top_n, self.args.max_len)
            if async_pool:
                return async_pool.apply_async(self.decode_candidates, args)
            else:
                return self.decode_candidates(*args)
        else:
            args = (score_s, score_e, top_n, self.args.max_len)
            if async_pool:
                return async_pool.apply_async(self.decode, args)
            else:
                return self.decode(*args)