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Runtime error
| 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 | |