Specialgfhdhdh/OFA-Visual_Grounding

1#!/usr/bin/env python3 -u2# Copyright (c) Facebook, Inc. and its affiliates.3#4# This source code is licensed under the MIT license found in the5# LICENSE file in the root directory of this source tree.6 7import logging8import os9import sys10import json11from itertools import chain12 13import numpy as np14import torch15import torch.distributed as dist16from fairseq import distributed_utils, options, tasks, utils17from fairseq.dataclass.utils import convert_namespace_to_omegaconf18from fairseq.logging import progress_bar19from fairseq.utils import reset_logging20from omegaconf import DictConfig21 22from utils import checkpoint_utils23from utils.eval_utils import eval_step24 25logging.basicConfig(26    format="%(asctime)s | %(levelname)s | %(name)s | %(message)s",27    datefmt="%Y-%m-%d %H:%M:%S",28    level=os.environ.get("LOGLEVEL", "INFO").upper(),29    stream=sys.stdout,30)31logger = logging.getLogger("ofa.evaluate")32 33 34def apply_half(t):35    if t.dtype is torch.float32:36        return t.to(dtype=torch.half)37    return t38 39 40def main(cfg: DictConfig):41    utils.import_user_module(cfg.common)42 43    reset_logging()44    logger.info(cfg)45 46    assert (47        cfg.dataset.max_tokens is not None or cfg.dataset.batch_size is not None48    ), "Must specify batch size either with --max-tokens or --batch-size"49 50    # Fix seed for stochastic decoding51    if cfg.common.seed is not None and not cfg.generation.no_seed_provided:52        np.random.seed(cfg.common.seed)53        utils.set_torch_seed(cfg.common.seed)54 55    use_fp16 = cfg.common.fp1656    use_cuda = torch.cuda.is_available() and not cfg.common.cpu57 58    if use_cuda:59        torch.cuda.set_device(cfg.distributed_training.device_id)60 61    # Load ensemble62    overrides = eval(cfg.common_eval.model_overrides)63    logger.info("loading model(s) from {}".format(cfg.common_eval.path))64    models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(65        utils.split_paths(cfg.common_eval.path),66        arg_overrides=overrides,67        suffix=cfg.checkpoint.checkpoint_suffix,68        strict=(cfg.checkpoint.checkpoint_shard_count == 1),69        num_shards=cfg.checkpoint.checkpoint_shard_count,70    )71 72    # loading the dataset should happen after the checkpoint has been loaded so we can give it the saved task config73    task.load_dataset(cfg.dataset.gen_subset, task_cfg=saved_cfg.task)74 75    # Move models to GPU76    for model in models:77        model.eval()78        if use_fp16:79            model.half()80        if use_cuda and not cfg.distributed_training.pipeline_model_parallel:81            model.cuda()82        model.prepare_for_inference_(cfg)83 84    # Load dataset (possibly sharded)85    itr = task.get_batch_iterator(86        dataset=task.dataset(cfg.dataset.gen_subset),87        max_tokens=cfg.dataset.max_tokens,88        max_sentences=cfg.dataset.batch_size,89        max_positions=utils.resolve_max_positions(90            task.max_positions(), *[m.max_positions() for m in models]91        ),92        ignore_invalid_inputs=cfg.dataset.skip_invalid_size_inputs_valid_test,93        required_batch_size_multiple=cfg.dataset.required_batch_size_multiple,94        seed=cfg.common.seed,95        num_shards=cfg.distributed_training.distributed_world_size,96        shard_id=cfg.distributed_training.distributed_rank,97        num_workers=cfg.dataset.num_workers,98        data_buffer_size=cfg.dataset.data_buffer_size,99    ).next_epoch_itr(shuffle=False)100    progress = progress_bar.progress_bar(101        itr,102        log_format=cfg.common.log_format,103        log_interval=cfg.common.log_interval,104        default_log_format=("tqdm" if not cfg.common.no_progress_bar else "simple"),105    )106 107    # Initialize generator108    generator = task.build_generator(models, cfg.generation)109 110    results = []111    score_sum = torch.FloatTensor([0]).cuda()112    score_cnt = torch.FloatTensor([0]).cuda()113    for sample in progress:114        if "net_input" not in sample:115            continue116        sample = utils.move_to_cuda(sample) if use_cuda else sample117        sample = utils.apply_to_sample(apply_half, sample) if cfg.common.fp16 else sample118        with torch.no_grad():119            result, scores = eval_step(task, generator, models, sample)120        results += result121        score_sum += sum(scores) if scores is not None else 0122        score_cnt += len(scores) if scores is not None else 0123        progress.log({"sentences": sample["nsentences"]})124 125    gather_results = None126    if cfg.distributed_training.distributed_world_size > 1:127        gather_results = [None for _ in range(dist.get_world_size())]128        dist.all_gather_object(gather_results, results)129        dist.all_reduce(score_sum.data)130        dist.all_reduce(score_cnt.data)131    if score_cnt.item() > 0:132        logger.info("score_sum: {}, score_cnt: {}, score: {}".format(133            score_sum, score_cnt, round(score_sum.item() / score_cnt.item(), 4)134        ))135 136    if cfg.distributed_training.distributed_world_size == 1 or dist.get_rank() == 0:137        os.makedirs(cfg.common_eval.results_path, exist_ok=True)138        output_path = os.path.join(cfg.common_eval.results_path, "{}_predict.json".format(cfg.dataset.gen_subset))139        gather_results = list(chain(*gather_results)) if gather_results is not None else results140        with open(output_path, 'w') as fw:141            json.dump(gather_results, fw)142 143 144def cli_main():145    parser = options.get_generation_parser()146    args = options.parse_args_and_arch(parser)147    cfg = convert_namespace_to_omegaconf(args)148    distributed_utils.call_main(cfg, main)149 150 151if __name__ == "__main__":152    cli_main()