utils/eval_utils.py

import string
import math

import torch

from data import data_utils


def get_symbols_to_strip_from_output(generator):
    if hasattr(generator, "symbols_to_strip_from_output"):
        return generator.symbols_to_strip_from_output
    else:
        return {generator.bos, generator.eos}


def decode_fn(x, tgt_dict, bpe, generator, tokenizer=None):
    x = tgt_dict.string(x.int().cpu(), extra_symbols_to_ignore=get_symbols_to_strip_from_output(generator))
    if bpe is not None:
        x = bpe.decode(x)
    if tokenizer is not None:
        x = tokenizer.decode(x)
    return x


def eval_caption(task, generator, models, sample):
    transtab = str.maketrans({key: None for key in string.punctuation})
    hypos = task.inference_step(generator, models, sample)
    results = []
    for i, sample_id in enumerate(sample["id"].tolist()):
        detok_hypo_str = decode_fn(hypos[i][0]["tokens"], task.tgt_dict, task.bpe, generator)
        results.append({"image_id": str(sample_id), "caption": detok_hypo_str.translate(transtab).strip()})
    return results, None


def eval_step(task, generator, models, sample):
    if task.cfg._name == 'caption':
        return eval_caption(task, generator, models, sample)
    else:
        raise NotImplementedError