Spaces:
Running
Running
| # ------------------------------------------------------------------------ | |
| # Modified from OFA (https://github.com/OFA-Sys/OFA) | |
| # Copyright 2022 The OFA-Sys Team. | |
| # All rights reserved. | |
| # This source code is licensed under the Apache 2.0 license | |
| # found in the LICENSE file in the root directory. | |
| # ------------------------------------------------------------------------ | |
| # Modifications Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| 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_vqa_gen(task, generator, models, sample, **kwargs): | |
| 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({"question_id": sample_id, "answer": detok_hypo_str.strip()}) | |
| scores = [ref_dict.get(result['answer'], 0) for ref_dict, result in zip(sample['ref_dict'], results)] | |
| return results, scores | |
| def zero_shot_step(task, generator, models, sample, **kwargs): | |
| generator.zero_shot = True | |
| if task.cfg._name == 'vqa_gen': | |
| generator.constraint_trie = None | |
| return eval_vqa_gen(task, generator, models, sample, **kwargs) | |
| else: | |
| raise NotImplementedError | |