119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
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# encoding:utf-8
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# -----------------------------------------------------------
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# "Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information"
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# Yuan, Zhiqiang and Zhang, Wenkai and Changyuan Tian and Xuee, Rong and Zhengyuan Zhang and Wang, Hongqi and Fu, Kun and Sun, Xian
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# Writen by YuanZhiqiang, 2021. Our code is depended on AMFMN
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# ------------------------------------------------------------
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import os, random, copy
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import numpy as np
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import torch
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import torch.nn as nn
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import argparse
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import yaml
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import shutil
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import tensorboard_logger as tb_logger
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import logging
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import click
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import utils
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import data
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import engine
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from vocab import deserialize_vocab
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def parser_options():
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# Hyper Parameters setting
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parser = argparse.ArgumentParser()
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parser.add_argument('--path_opt', default='option/RSITMD/ablation_q/RSITMD_GAC_decay0.5_m0.2_q0.1.yaml', type=str,
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help='path to a yaml options file')
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parser.add_argument('--resume', default='checkpoint/RSITMD_GAC_decay0.5_m0.2_q0.1/3/GAC_best.pth.tar', type=str,
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help='path to a yaml options file')
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opt = parser.parse_args()
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# load model options
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with open(opt.path_opt, 'r') as handle:
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options = yaml.load(handle)
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options['optim']['resume'] = opt.resume
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return options
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def main(options):
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# choose model
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if options['model']['name'] == "GAC":
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from model import GAC as models
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elif options['model']['name'] == "GAC_mca":
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from model import GAC_mca as models
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else:
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raise NotImplementedError
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# make vocab
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vocab = deserialize_vocab(options['dataset']['vocab_path'])
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vocab_word = sorted(vocab.word2idx.items(), key=lambda x: x[1], reverse=False)
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vocab_word = [tup[0] for tup in vocab_word]
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# Create dataset, model, criterion and optimizer
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test_loader = data.get_test_loader(vocab, options)
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model = models.factory(options['model'],
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vocab_word,
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cuda=True,
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data_parallel=False)
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print('Model has {} parameters'.format(utils.params_count(model)))
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# optionally resume from a checkpoint
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if os.path.isfile(options['optim']['resume']):
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print("=> loading checkpoint '{}'".format(options['optim']['resume']))
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checkpoint = torch.load(options['optim']['resume'])
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start_epoch = checkpoint['epoch']
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best_rsum = checkpoint['best_rsum']
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model.load_state_dict(checkpoint['model'])
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else:
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print("=> no checkpoint found at '{}'".format(options['optim']['resume']))
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# evaluate on test set
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sims = engine.validate_test(test_loader, model)
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return sims
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def update_options_savepath(options, k):
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updated_options = copy.deepcopy(options)
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updated_options['optim']['resume'] = options['logs']['ckpt_save_path'] + options['k_fold']['experiment_name'] + "/" \
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+ str(k) + "/" + options['model']['name'] + '_best.pth.tar'
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return updated_options
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if __name__ == '__main__':
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options = parser_options()
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# run experiment
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one_sims = main(options)
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print(one_sims)
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# import mytools
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# mytools.save_to_npy(one_sims, "rsicd_2.npy")
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# # ave
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# last_sims = one_sims
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# # get indicators
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# (r1i, r5i, r10i, medri, meanri), _ = utils.acc_i2t2(last_sims)
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# logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
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# (r1i, r5i, r10i, medri, meanri))
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# (r1t, r5t, r10t, medrt, meanrt), _ = utils.acc_t2i2(last_sims)
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# logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
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# (r1t, r5t, r10t, medrt, meanrt))
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# currscore = (r1t + r5t + r10t + r1i + r5i + r10i)/6.0
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# all_score = "r1i:{} r5i:{} r10i:{} medri:{} meanri:{}\n r1t:{} r5t:{} r10t:{} medrt:{} meanrt:{}\n sum:{}\n ------\n".format(
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# r1i, r5i, r10i, medri, meanri, r1t, r5t, r10t, medrt, meanrt, currscore
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# )
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# print(all_score)
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