batchbuilder.py 文件源码

def reconstruct_batch(self, output, batch_id, chosen_labels=None):
        """ Create the song associated with the network output
        Args:
            output (list[np.Array]): The ouput of the network (size batch_size*output_dim)
            batch_id (int): The batch id
            chosen_labels (list[np.Array[batch_size, int]]): the sampled class at each timestep (useful to reconstruct the generated song)
        Return:
            Song: The reconstructed song
        """
        assert Relative.HAS_EMPTY == True

        processed_song = Relative.RelativeSong()
        processed_song.first_note = music.Note()
        processed_song.first_note.note = 56  # TODO: Define what should be the first note
        print('Reconstruct')
        for i, note in enumerate(output):
            relative = Relative.RelativeNote()
            # Here if we did sample the output, we should get which has heen the selected output
            if not chosen_labels or i == len(chosen_labels):  # If chosen_labels, the last generated note has not been sampled
                chosen_label = int(np.argmax(note[batch_id,:]))  # Cast np.int64 to int to avoid compatibility with mido
            else:
                chosen_label = int(chosen_labels[i][batch_id])
            print(chosen_label, end=' ')  # TODO: Add a text output connector
            if chosen_label == 0:  # <next> token
                relative.pitch_class = None
                #relative.scale = # Note used
                #relative.prev_tick =
            else:
                relative.pitch_class = chosen_label-1
                #relative.scale =
                #relative.prev_tick =
            processed_song.notes.append(relative)
        print()
        return self.reconstruct_song(processed_song)