main_onlinetest.py

"""
单模型在MSCOCO Online Test数据集测试
"""
import os
import sys
import pprint
import random
import time
import tqdm
import logging
import argparse
import numpy as np

import torch
import torch.nn as nn
import torch.multiprocessing as mp
import torch.distributed as dist

import losses
import models
import datasets
import lib.utils as utils
from lib.utils import AverageMeter
from optimizer.optimizer import Optimizer
from evaluation.online_tester import OnlineTester
from scorer.scorer import Scorer
from lib.config import cfg, cfg_from_file

class Tester(object):
    def __init__(self, args):
        super(Tester, self).__init__()
        self.args = args
        self.device = torch.device("cuda")

        self.setup_logging()
        self.setup_network()
        self.evaler = OnlineTester(
            eval_ids = cfg.DATA_LOADER.TEST_4W_ID,   # MSCOCO 在线测试图片ID文件
            gv_feat = cfg.DATA_LOADER.TEST_GV_FEAT,
            att_feats = cfg.DATA_LOADER.TEST_ATT_FEATS
        )

    def setup_logging(self):
        self.logger = logging.getLogger(cfg.LOGGER_NAME)
        self.logger.setLevel(logging.INFO)
        
        ch = logging.StreamHandler(stream=sys.stdout)
        ch.setLevel(logging.INFO)
        formatter = logging.Formatter("[%(levelname)s: %(asctime)s] %(message)s")
        ch.setFormatter(formatter)
        self.logger.addHandler(ch)

        if not os.path.exists(cfg.ROOT_DIR):
            os.makedirs(cfg.ROOT_DIR)
        
        # 在线测试日志文件存储在"./experiments/{MODELNAME}/OnlineTest_log.txt"中
        fh = logging.FileHandler(os.path.join(cfg.ROOT_DIR, 'OnlineTest_' + cfg.LOGGER_NAME + '.txt'))
        fh.setLevel(logging.INFO)
        fh.setFormatter(formatter)
        self.logger.addHandler(fh)

    def setup_network(self):
        model = models.create(cfg.MODEL.TYPE)
        print(model)
        self.model = torch.nn.DataParallel(model).cuda()
        # 导入模型参数
        if self.args.resume > 0:
            self.model.load_state_dict(
                torch.load(self.snapshot_path("caption_model", self.args.resume),
                    map_location=lambda storage, loc: storage)
            )
        
    def eval(self, epoch):
        self.evaler(self.model, 'online_test_' + str(epoch))
        self.logger.info('######## Epoch ' + str(epoch) + ' ########')
        self.logger.info('Inference result saved to result_online_test_%d.json' % epoch)

    def snapshot_path(self, name, epoch):
        snapshot_folder = os.path.join(cfg.ROOT_DIR, 'snapshot')
        return os.path.join(snapshot_folder, name + "_" + str(epoch) + ".pth")

def parse_args():
    """
    Parse input arguments
    """
    parser = argparse.ArgumentParser(description='Image Captioning')
    parser.add_argument('--folder', dest='folder', default=None, type=str)
    parser.add_argument("--resume", type=int, default=-1)

    if len(sys.argv) == 1:
        parser.print_help()
        sys.exit(1)

    args = parser.parse_args()
    return args

if __name__ == '__main__':
    args = parse_args()
    print('Called with args:')
    print(args)

    if args.folder is not None:
        cfg_from_file(os.path.join(args.folder, 'config.yml'))
    # ./experiments/MODELNAME
    cfg.ROOT_DIR = args.folder

    tester = Tester(args)
    tester.eval(args.resume)