da1e8c2de9b265dcb18256a0a087165faf138b42,python/baseline/pytorch/seq2seq/model.py,EncoderDecoderModelBase,predict,#EncoderDecoderModelBase#Any#,149

Before Change

                if isinstance(value, list):
                    example[k] = np.array([value[b]])
                else:
                    example[k] = value[b].reshape((1,) + value[b].shape)
            inputs = self.make_input(example)
            encoder_outputs = self.encode(inputs, inputs["src_len"])
            batch.append(self.decoder.predict_one(inputs[src_field], encoder_outputs, **kwargs)[0])
        return batch

After Change

        are already transformed.
        
        self.eval()
        make = kwargs.get("make_input", True)
        if make:
            inputs, perm_idx = self.make_input(batch_dict, perm=True)
        else:
            inputs = batch_dict

Instances