[PYTHON] Class when inferring with fairseq

I've been modifying interactive.py for a long time, but officially there was a good function.

Previous method Classify fairseq interactive

code

from fairseq.models.transformer import TransformerModel

class Interactive:
    def __init__(self, spm_path, data_path, checkpoint_path, checkpoint_name):
        #Number of sentences processed at the same time
        self.num = 32

        self.ltos = TransformerModel.from_pretrained(
            checkpoint_path,
            checkpoint_file=checkpoint_name,
            data_name_or_path=data_path,
            bpe='sentencepiece',
            sentencepiece_model=spm_path,
            no_repeat_ngram_size=2
            )

    def inference(self, texts: list):
        result = []
        n = self.num
        for t in [texts[i*n:(i+1)*n] for i in range(len(texts))]:
            result += self.ltos.translate(t)
        return [r.replace("_", " ") for r in result]

Commentary

--Basically, you just infer with fairseq.models.transformer.from_pretrained.translate. ――If you try to process about 100 sentences at the same time, it will be slow because it consumes memory, so it is divided into multiple sentences. --It seems that the CPU uses only half of the installed number.

It's about 5 times faster than the previous code.