def tune_embeddings()

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

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

    def tune_embeddings(self, words):
        """Unfix the embeddings of a list of words. This is only relevant if
        only some of the embeddings are being tuned (tune_partial = N).

        Shuffles the N specified words to the front of the dictionary, and saves
        the original vectors of the other N + 1:vocab words in a fixed buffer.

        Args:
            words: iterable of tokens contained in dictionary.
        """
        words = {w for w in words if w in self.word_dict}

        if len(words) == 0:
            logger.warning('Tried to tune embeddings, but no words given!')
            return

        if len(words) == len(self.word_dict):
            logger.warning('Tuning ALL embeddings in dictionary')
            return

        # Shuffle words and vectors
        embedding = self.network.embedding.weight.data
        for idx, swap_word in enumerate(words, self.word_dict.START):
            # Get current word + embedding for this index
            curr_word = self.word_dict[idx]
            curr_emb = embedding[idx].clone()
            old_idx = self.word_dict[swap_word]

            # Swap embeddings + dictionary indices
            embedding[idx].copy_(embedding[old_idx])
            embedding[old_idx].copy_(curr_emb)
            self.word_dict[swap_word] = idx
            self.word_dict[idx] = swap_word
            self.word_dict[curr_word] = old_idx
            self.word_dict[old_idx] = curr_word

        # Save the original, fixed embeddings
        self.network.register_buffer(
            'fixed_embedding', embedding[idx + 1:].clone()
        )