├── figures
    ├── HVSA.jpg
    └── performance.jpg
├── requirements.txt
├── layers
    ├── seq2vec.py
    ├── HVSA.py
    └── HVSA_Modules.py
├── dataset
    ├── vocab.py
    ├── data.py
    └── data_handlers.py
├── LICENSE
├── configs
    ├── HVSA_rsicd.yaml
    └── HVSA_rsitmd.yaml
├── loss
    └── losses.py
├── README.md
├── eval.py
├── train.py
├── controller.py
├── utils.py
├── vocab
    ├── rsicd_splits_vocab.json
    └── rsitmd_splits_vocab.json
└── data
    └── rsitmd_raw
        └── test_filename.txt


/figures/HVSA.jpg:
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https://raw.githubusercontent.com/ZhangWeihang99/HVSA/HEAD/figures/HVSA.jpg


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/figures/performance.jpg:
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https://raw.githubusercontent.com/ZhangWeihang99/HVSA/HEAD/figures/performance.jpg


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/requirements.txt:
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1 | python==3.8.8
2 | torch==1.9.0
3 | torchvision==0.9.0
4 | nltk==3.5
5 | numpy==1.19.5


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/layers/seq2vec.py:
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 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | # A revision version from Skip-thoughs
 8 | import skipthoughts
 9 | 
10 | def factory(vocab_words, cfg , dropout=0.25):
11 | 
12 |     if cfg['arch'] == 'skipthoughts':
13 |         st_class = getattr(skipthoughts, cfg['type'])
14 |         # vocab: ['list', 'of', 'words',]
15 |         seq2vec = st_class(cfg['dir_st'],
16 |                            vocab_words,
17 |                            dropout=dropout,
18 |                            fixed_emb=cfg['fixed_emb'])
19 | 
20 |     else:
21 |         raise NotImplementedError
22 |     return seq2vec
23 | 


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/dataset/vocab.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | import json
 8 | 
 9 | 
10 | class Vocabulary(object):
11 |     """Simple vocabulary wrapper."""
12 | 
13 |     def __init__(self):
14 |         self.word2idx = {}
15 |         self.idx2word = {}
16 |         self.idx = 1
17 | 
18 |     def add_word(self, word):
19 |         if word not in self.word2idx:
20 |             self.word2idx[word] = self.idx
21 |             self.idx2word[self.idx] = word
22 |             self.idx += 1
23 | 
24 |     def __call__(self, word):
25 |         if word not in self.word2idx:
26 |             return self.word2idx['<unk>']
27 |         return self.word2idx[word]
28 | 
29 |     def __len__(self):
30 |         return len(self.word2idx)
31 | 
32 | 
33 | def deserialize_vocab(src):
34 |     """get vocab from json file"""
35 |     with open(src) as f:
36 |         d = json.load(f)
37 |     vocab = Vocabulary()
38 |     vocab.word2idx = d['word2idx']
39 |     vocab.idx2word = d['idx2word']
40 |     vocab.idx = d['idx']
41 |     return vocab
42 | 
43 | 
44 | 
45 | 
46 | 
47 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2023 Zhang Weihang
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/configs/HVSA_rsicd.yaml:
--------------------------------------------------------------------------------
 1 | experiment_name: 'HVSA_RSICD'
 2 | 
 3 | optim:
 4 |     epochs: 50
 5 |     lr: 0.001
 6 |     lr_decay_param: 0.1
 7 |     lr_update_epoch: 20
 8 |     grad_clip: 0
 9 |     alpha: 0.2
10 |     resume: False
11 | 
12 | loss:
13 |     adaptive_alignment:
14 |         beta: 0.5
15 |     feature_uniformity:
16 |         gamma: 2
17 | 
18 | model:
19 |     name: HVSA
20 |     embed:
21 |         embed_dim: 512
22 |     finetune: True
23 |     seq2vec:
24 |         arch: skipthoughts
25 |         dir_st: /mnt/data/seq2vec
26 |         type: BayesianUniSkip
27 |         dropout: 0.25
28 |         fixed_emb: False
29 | 
30 | logs:
31 |     eval_step: 1
32 |     print_freq: 10
33 |     ckpt_save_path: "/workspace/HVSA/checkpoint/"
34 |     logger_name: 'logs/'
35 | 
36 | 
37 | random_seed:
38 |     seed: [2023,2024,2025]
39 |     nums: 3
40 |     current_num: 0
41 | 
42 | dataset:
43 |     datatype: rsicd
44 |     data_path: '/workspace/HVSA/data/rsicd_raw/'
45 |     image_path: '/mnt/data/dataset/RSICD/images/'
46 |     vocab_path: '/workspace/HVSA/vocab/rsicd_splits_vocab.json'
47 |     batch_size: 256
48 |     batch_size_val: 256
49 |     workers: 2
50 | 
51 | GPU: V100


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/configs/HVSA_rsitmd.yaml:
--------------------------------------------------------------------------------
 1 | experiment_name: 'HVSA_RSITMD'
 2 | 
 3 | optim:
 4 |     epochs: 50
 5 |     lr: 0.0005
 6 |     lr_decay_param: 0.1
 7 |     lr_update_epoch: 20
 8 |     grad_clip: 0
 9 |     alpha: 0.2
10 |     resume: False
11 | 
12 | loss:
13 |     adaptive_alignment:
14 |         beta: 0.5
15 |     feature_uniformity:
16 |         gamma: 2
17 | 
18 | model:
19 |     name: HVSA
20 |     embed:
21 |         embed_dim: 512
22 |     finetune: True
23 |     seq2vec:
24 |         arch: skipthoughts
25 |         dir_st: /mnt/data/seq2vec
26 |         type: BayesianUniSkip
27 |         dropout: 0.25
28 |         fixed_emb: False
29 | 
30 | 
31 | logs:
32 |     eval_step: 1
33 |     print_freq: 10
34 |     ckpt_save_path: "/workspace/HVSA/checkpoint/"
35 |     logger_name: 'logs/'
36 | 
37 | 
38 | random_seed:
39 |     seed: [2022,2023,2024]
40 |     nums: 3
41 |     current_num: 0
42 | 
43 | dataset:
44 |     datatype: rsitmd
45 |     data_path: '/workspace/HVSA/data/rsitmd_raw/'
46 |     image_path: '/mnt/data/dataset/RSITMD/images/'
47 |     vocab_path: '/workspace/HVSA/vocab/rsitmd_splits_vocab.json'
48 |     batch_size: 256
49 |     batch_size_val: 256
50 |     workers: 2
51 | 
52 | GPU: V100


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/loss/losses.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | import torch
 8 | from torch.autograd import Variable
 9 | 
10 | 
11 | def difficulty_weighted_loss(scores, size, margin, beta):
12 |     """ Difficulty Weighted Loss inspired by CL"""
13 | 
14 |     diagonal = scores.diag().view(size, 1)
15 |     d1 = diagonal.expand_as(scores)
16 |     d2 = diagonal.t().expand_as(scores)
17 | 
18 |     # triplet_loss indicates the difficulty of samples
19 | 
20 |     # caption retrieval
21 |     cost_s = (margin + scores - d1).clamp(min=0)
22 |     # image retrieval
23 |     cost_im = (margin + scores - d2).clamp(min=0)
24 |     mask = torch.eye(scores.size(0)) > .5
25 |     I = Variable(mask)
26 |     if torch.cuda.is_available():
27 |         I = I.cuda()
28 |     cost_s = cost_s.masked_fill_(I, 0)
29 |     cost_im = cost_im.masked_fill_(I, 0)
30 | 
31 |     # from easy to hard
32 |     weight_s = torch.exp(-beta*cost_s)
33 |     weight_im = torch.exp(-beta*cost_im)
34 | 
35 |     cost_s = cost_s * weight_s
36 |     cost_im = cost_im * weight_im
37 |     return cost_s.sum() + cost_im.sum()
38 | 
39 | 
40 | def feature_unif_loss(x, gamma=2):
41 | 
42 |     """ get image features uniformity loss"""
43 | 
44 |     x = x[::5, :]
45 |     x0 = x / x.norm(p=2, dim=1, keepdim=True)
46 |     return torch.pdist(x0, p=2).pow(2).mul(-gamma).exp().mean()
47 | 


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/layers/HVSA.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | from .HVSA_Modules import *
 8 | import copy
 9 | from utils import cosine_similarity
10 | 
11 | class HVSA(nn.Module):
12 | 
13 |     def __init__(self, config={}, vocab_words=[]):
14 |         super(HVSA, self).__init__()
15 |         self.Eiters = 0
16 |         self.extract_img_feature = EncoderImageFull(cfg=config)
17 |         self.extract_text_feature = Skipthoughts_Embedding_Module(
18 |             vocab=vocab_words,
19 |             cfg=config
20 |         )
21 | 
22 |         self.weight_loss = AutoWeightedModule(2)
23 | 
24 |     def forward(self, img, text, text_lens):
25 |         if self.training is True:
26 |             self.Eiters += 1
27 | 
28 |         batch_img = img.shape[0]
29 |         batch_text = text.shape[0]
30 | 
31 |         # extract features
32 |         img_feature = self.extract_img_feature(img)
33 | 
34 |         # text features
35 |         text_feature = self.extract_text_feature(text, text_lens)
36 |         dual_sim = cosine_similarity(img_feature.unsqueeze(dim=1).expand(-1, batch_text, -1),
37 |                                      text_feature.unsqueeze(dim=0).expand(batch_img, -1, -1))
38 | 
39 |         return dual_sim, img_feature, text_feature
40 | 
41 | 
42 | def factory(cfg, vocab_words, cuda=True):
43 |     """
44 |     get the model and set on device
45 |     Args:
46 |         vocab_words: all words sorted by frequency
47 |     """
48 | 
49 |     cfg = copy.copy(cfg)
50 | 
51 |     # choose model
52 |     if cfg['name'] == "HVSA":
53 |         model = HVSA(cfg, vocab_words)
54 |     else:
55 |         raise NotImplementedError
56 | 
57 |     # use gpu
58 |     if cuda:
59 |         model.cuda()
60 | 
61 |     return model
62 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | # HVSA
 2 | 
 3 | Official PyTorch implementation for [**Hypersphere-Based Remote Sensing Cross-Modal Text–Image Retrieval via Curriculum Learning**](https://ieeexplore.ieee.org/document/10261223?source=authoralert).
 4 | 
 5 | 
 6 | ![HVSA](figures/HVSA.jpg)
 7 | ## News :tada:
 8 | - 📣 Oct 2023 - The code of HVSA has been released.
 9 | - 📣 Oct 2023 - Codes is coming soon (before Nov). 
10 | - 📣 Sep 2023 - Paper Accepted by TGRS.
11 | 
12 | 
13 | ## Dependencies
14 | Download the dataset files and pre-trained models. Files about seq2vec are from [here](https://github.com/xiaoyuan1996/AMFMN/tree/master/AMFMN).
15 | 
16 | Install dependencies using the following command.
17 | 
18 | ```
19 | pip install -r requirements.txt
20 | ```
21 | 
22 | ## Dataset preparation
23 | All experiments are based on [RSITMD](https://github.com/xiaoyuan1996/AMFMN/tree/master/RSITMD) and [RSICD](https://github.com/201528014227051/RSICD_optimal) datasets,
24 | ### RSICD
25 | 
26 | We followed the same split provided by [AMFMN](https://github.com/xiaoyuan1996/AMFMN/tree/master/AMFMN/data/rsicd_precomp).
27 | Dataset splits can be found in [data/rsicd_raw](data/rsicd_raw).
28 | 
29 | ### RSITMD
30 | We followed the same split provided by [AMFMN](https://github.com/xiaoyuan1996/AMFMN/tree/master/AMFMN/data/rsitmd_precomp).
31 | Dataset splits can be found in [data/rsitmd_raw](data/rsitmd_raw).
32 | 
33 | ## Train
34 | ```bash
35 | # RSITMD Dataset
36 | python train.py --config configs/HVSA_rsitmd.yaml
37 | ```
38 | ## Evaluate
39 | ```bash
40 | # RSITMD Dataset
41 | python eval.py --config configs/HVSA_rsitmd.yaml
42 | ```
43 | ## Performance
44 | 
45 | ![Performance](figures/performance.jpg)
46 | 
47 | ## Citing HVSA
48 | If you find this repository useful, please consider giving a star :star: and citation:
49 | ```
50 | @ARTICLE{10261223,
51 |   author={Zhang, Weihang and Li, Jihao and Li, Shuoke and Chen, Jialiang and Zhang, Wenkai and Gao, Xin and Sun, Xian},
52 |   journal={IEEE Transactions on Geoscience and Remote Sensing}, 
53 |   title={Hypersphere-Based Remote Sensing Cross-Modal Text–Image Retrieval via Curriculum Learning}, 
54 |   year={2023},
55 |   volume={61},
56 |   number={},
57 |   pages={1-15},
58 |   doi={10.1109/TGRS.2023.3318227}}
59 | ```
60 | ## Acknowledgment
61 | The implementation of HVSA relies on resources from <a href="https://github.com/fartashf/vsepp">VSE++</a>, and <a href="https://github.com/xiaoyuan1996/AMFMN">AMFMN</a>. We thank the original authors for their open-sourcing. 


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/eval.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | import os
 8 | import copy
 9 | import numpy as np
10 | import controller
11 | import utils
12 | from dataset import data_handlers
13 | 
14 | 
15 | 
16 | def main(configs):
17 | 
18 |     # choose model
19 |     vocab, vocab_word = data_handlers.make_vocab(configs)
20 | 
21 |     # Create dataset, model, criterion and optimizer
22 |     test_loader = data_handlers.get_test_loader(vocab, configs)
23 | 
24 |     model = controller.get_model(configs, vocab_word)
25 | 
26 |     controller.load_model(configs, model)
27 | 
28 |     sims = controller.model_test(test_loader, model)
29 |     # get indicators
30 |     (r1i, r5i, r10i, medri, meanri), _ = utils.acc_i2t(sims)
31 |     (r1t, r5t, r10t, medrt, meanrt), _ = utils.acc_t2i(sims)
32 |     rsum = r1t + r5t + r10t + r1i + r5i + r10i
33 |     all_score = "r1i:{} r5i:{} r10i:{} medri:{} meanri:{}\n r1t:{} r5t:{} r10t:{} medrt:{} meanrt:{}\n rsum:{}\n ------\n".format(
34 |         r1i, r5i, r10i, medri, meanri, r1t, r5t, r10t, medrt, meanrt, rsum
35 |     )
36 | 
37 |     print(all_score)
38 | 
39 |     utils.log_to_txt(
40 |         contexts=all_score,
41 |         filename="experiment/" + str(configs['GPU']) + "/" + "result/" + configs['experiment_name'] + ".txt"
42 |     )
43 | 
44 |     return [r1i, r5i, r10i, r1t, r5t, r10t, rsum]
45 | 
46 | def update_configs_savepath(configs, k):
47 |     updated_configs = copy.deepcopy(configs)
48 | 
49 |     updated_configs['optim']['resume'] = configs['logs']['ckpt_save_path'] + configs['experiment_name'] + "/" \
50 |                                          + str(k) + "/" + configs['model']['name'] + '_best.pth.tar'
51 | 
52 |     return updated_configs
53 | 
54 | if __name__ == '__main__':
55 |     configs = controller.get_config()
56 | 
57 |     # calc ave k results
58 |     last_score = []
59 |     if not os.path.exists("experiment/" + str(configs['GPU']) + "/" + "result/"):
60 |         os.makedirs("experiment/" + str(configs['GPU']) + "/" + "result/")
61 | 
62 |     for k in range(configs['random_seed']['nums']):
63 |         print("=========================================")
64 |         print("Start evaluate random seed {}".format(configs['random_seed']['seed'][k]))
65 | 
66 |         # update the checkpoint path
67 |         update_configs = update_configs_savepath(configs, k)
68 | 
69 |         # get one experiment result
70 |         one_score = main(update_configs)
71 |         last_score.append(one_score)
72 |         
73 |         print("Complete evaluate evaluate random seed {}".format(configs['random_seed']['seed'][k]))
74 | 
75 |     # average result
76 |     print("\n================ Ave Score On {}-random verify) ============".format(configs['random_seed']['nums']))
77 |     last_score = np.average(last_score, axis=0)
78 |     names = ['r1i', 'r5i', 'r10i', 'r1t', 'r5t', 'r10t', 'rsum']
79 |     for name, score in zip(names, last_score):
80 |         print("{}:{}".format(name, score))
81 |         utils.log_to_txt(
82 |             contexts="{}:{}".format(name, score),
83 |             filename="experiment/" + str(configs['GPU']) + "/" + "result/" + configs['experiment_name'] + ".txt"
84 |         )
85 | 
86 | 


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/train.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | import torch
 8 | import tensorboard_logger as tb_logger
 9 | import logging
10 | import json
11 | 
12 | import utils
13 | from dataset import data_handlers
14 | import controller
15 | 
16 | def main(config):
17 |     """
18 |     train and val the model
19 |     """
20 | 
21 |     vocab, vocab_word = data_handlers.make_vocab(config)
22 | 
23 |     # Create dataset, model, criterion and optimizer
24 |     print("load the data {}".format(config['dataset']['image_path']))
25 |     train_loader, val_loader = data_handlers.get_train_val_loaders(vocab, config)
26 |     model = controller.get_model(config, vocab_word)
27 |     optimizer = torch.optim.AdamW(filter(lambda p: p.requires_grad, model.parameters()),
28 |                                   lr=config['optim']['lr'])
29 | 
30 | 
31 |     # optionally resume from a checkpoint
32 |     if config['optim']['resume']:
33 |         start_epoch = controller.load_model(config, model)
34 |     else:
35 |         start_epoch = 0
36 | 
37 |     # start to train
38 |     best_rsum = 0
39 |     best_score = ""
40 |     for epoch in range(start_epoch, config['optim']['epochs']):
41 |         # train for one epoch
42 |         controller.model_train(train_loader, model, optimizer, epoch, config=config)
43 |         # evaluate on validation set
44 |         if epoch % config['logs']['eval_step'] == 0:
45 |             rsum, all_scores = controller.model_validate(val_loader, model, config)
46 |             is_best = rsum > best_rsum
47 |             if is_best:
48 |                 best_score = all_scores
49 |                 best_rsum = rsum
50 |                 # save ckpt
51 |                 utils.save_checkpoint(
52 |                     {
53 |                         'epoch': epoch + 1,
54 |                         'model': model.state_dict(),
55 |                         'config': config,
56 |                     },
57 |                     is_best,
58 |                     filename='best.pth.tar',
59 |                     prefix=config['logs']['ckpt_save_path'],
60 |                     model_name=config['model']['name']
61 |                 )
62 |             print("Current random seed: {}".format(config['random_seed']['current_num']))
63 |             print("Now  score:")
64 |             print(all_scores)
65 |             print("Best score:")
66 |             print(best_score)
67 | 
68 | 
69 | if __name__ == '__main__':
70 | 
71 |     config = controller.get_config()
72 |     # make logger
73 |     tb_logger.configure(config['logs']['logger_name'] +
74 |                         config['experiment_name'] + '/', flush_secs=5)
75 | 
76 |     logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
77 | 
78 |     logging.info("HVSA CONFIG: \n {}".format(json.dumps(config, indent=4)))
79 | 
80 |     # k_fold verify
81 |     for k in range(len(config['random_seed'])):
82 |         random_seed = config['random_seed']['seed'][k]
83 |         print("=========================================")
84 |         print("Start with the random seed: {}".format(random_seed))
85 |         utils.set_seed(random_seed)
86 |         # generate random train and val samples
87 |         data_handlers.generate_random_samples(config)
88 |         # update save path
89 |         update_config = utils.update_config(config, k)
90 | 
91 |         # run experiment
92 |         main(update_config)
93 | 


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/dataset/data.py:
--------------------------------------------------------------------------------
 1 | # encoding:utf-8
 2 | """
 3 | * HVSA
 4 | * By Zhang Weihang
 5 | """
 6 | 
 7 | import torch
 8 | import torch.utils.data as data
 9 | import torchvision.transforms as transforms
10 | import nltk
11 | from PIL import Image
12 | 
13 | class PairwiseDataset(data.Dataset):
14 |     """
15 |     Load precomputed captions and image features
16 |     """
17 | 
18 |     def __init__(self, data_split, vocab, opt):
19 |         self.vocab = vocab
20 |         self.loc = opt['dataset']['data_path']
21 |         self.img_path = opt['dataset']['image_path']
22 | 
23 |         # Captions
24 |         self.captions = []
25 |         self.maxlength = 0
26 | 
27 |         if data_split != 'test':
28 |             with open(self.loc+'%s_caps_verify.txt' % data_split, 'rb') as f:
29 |                 for line in f:
30 |                     self.captions.append(line.strip())
31 | 
32 |             self.images = []
33 |             with open(self.loc + '%s_filename_verify.txt' % data_split, 'rb') as f:
34 |                 for line in f:
35 |                     self.images.append(line.strip())
36 |         else:
37 |             with open(self.loc + '%s_caps.txt' % data_split, 'rb') as f:
38 |                 for line in f:
39 |                     self.captions.append(line.strip())
40 | 
41 |             self.images = []
42 |             with open(self.loc + '%s_filename.txt' % data_split, 'rb') as f:
43 |                 for line in f:
44 |                     self.images.append(line.strip())
45 |         
46 |         self.length = len(self.captions)
47 | 
48 |         if len(self.images) != self.length:
49 |             self.im_div = 5
50 |         else:
51 |             self.im_div = 1
52 | 
53 |         if data_split == "train":
54 |             self.transform = transforms.Compose([
55 |                 transforms.Resize((278, 278)),
56 |                 transforms.RandomRotation((0, 90)),
57 |                 transforms.RandomCrop(256),
58 |                 transforms.ToTensor(),
59 |                 transforms.Normalize((0.485, 0.456, 0.406),
60 |                                      (0.229, 0.224, 0.225))])
61 |         else:
62 |             self.transform = transforms.Compose([
63 |                 transforms.Resize((256, 256)),
64 |                 transforms.ToTensor(),
65 |                 transforms.Normalize((0.485, 0.456, 0.406),
66 |                                      (0.229, 0.224, 0.225))])
67 | 
68 |     def __getitem__(self, index):
69 |         # handle the image redundancy
70 |         img_id = index//self.im_div
71 |         caption = self.captions[index]
72 | 
73 |         vocab = self.vocab
74 | 
75 |         # Convert caption (string) to word ids.
76 |         tokens = nltk.tokenize.word_tokenize(
77 |             caption.lower().decode('utf-8'))
78 |         punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%']
79 |         tokens = [k for k in tokens if k not in punctuations]
80 |         tokens_UNK = [k if k in vocab.word2idx.keys() else '<unk>' for k in tokens]
81 | 
82 | 
83 |         caption = []
84 |         caption.extend([vocab(token) for token in tokens_UNK])
85 |         caption = torch.LongTensor(caption)
86 | 
87 |         image = Image.open(self.img_path  +str(self.images[img_id])[2:-1]).convert('RGB')
88 |         image = self.transform(image)  # torch.Size([3, 256, 256])
89 | 
90 |         return image, caption, tokens_UNK, index, img_id
91 | 
92 |     def __len__(self):
93 |         return self.length
94 | 
95 | 
96 | 
97 | 
98 | 
99 | 


--------------------------------------------------------------------------------
/dataset/data_handlers.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | * HVSA
  4 | * By Zhang Weihang
  5 | """
  6 | 
  7 | import torch
  8 | from dataset.data import PairwiseDataset
  9 | from dataset.vocab import deserialize_vocab
 10 | import utils
 11 | import random
 12 | 
 13 | 
 14 | def collate_fn(data):
 15 | 
 16 |     # Sort the data list by caption length
 17 |     data.sort(key=lambda x: len(x[2]), reverse=True)
 18 |     images, captions, tokens, ids, img_ids = zip(*data)
 19 | 
 20 |     # Merge images (convert tuple of 3D tensor to 4D tensor)
 21 |     images = torch.stack(images, 0)
 22 | 
 23 |     # Merget captions (convert tuple of 1D tensor to 2D tensor)
 24 |     # PAD
 25 |     lengths = [len(cap) for cap in captions]
 26 | 
 27 |     targets = torch.zeros(len(captions), max(lengths)).long()
 28 |     for i, cap in enumerate(captions):
 29 |         end = lengths[i]
 30 |         targets[i, :end] = cap[:end]
 31 | 
 32 |     lengths = [l if l !=0 else 1 for l in lengths]
 33 |     return images, targets, lengths, ids
 34 | 
 35 | 
 36 | def get_one_loader(data_split, vocab, batch_size=100,
 37 |                        shuffle=True, num_workers=0, opt={}):
 38 |     """Returns torch.utils.data.DataLoader for custom coco dataset."""
 39 |     dset = PairwiseDataset(data_split, vocab, opt)
 40 | 
 41 |     data_loader = torch.utils.data.DataLoader(dataset=dset,
 42 |                                               batch_size=batch_size,
 43 |                                               shuffle=shuffle,
 44 |                                               pin_memory=False,
 45 |                                               collate_fn=collate_fn,
 46 |                                               num_workers=num_workers)
 47 |     return data_loader
 48 | 
 49 | 
 50 | def get_train_val_loaders(vocab, opt):
 51 |     train_loader = get_one_loader('train', vocab,
 52 |                                       opt['dataset']['batch_size'], True, opt['dataset']['workers'], opt=opt)
 53 |     val_loader = get_one_loader('val', vocab,
 54 |                                     opt['dataset']['batch_size_val'], False, opt['dataset']['workers'], opt=opt)
 55 |     return train_loader, val_loader
 56 | 
 57 | 
 58 | def get_test_loader(vocab, opt):
 59 |     test_loader = get_one_loader('test', vocab,
 60 |                                     opt['dataset']['batch_size_val'], False, opt['dataset']['workers'], opt=opt)
 61 |     return test_loader
 62 | 
 63 | 
 64 | def make_vocab(options):
 65 |     # make vocab
 66 |     print("Making the vocab")
 67 |     vocab = deserialize_vocab(options['dataset']['vocab_path'])
 68 |     vocab_word = sorted(vocab.word2idx.items(), key=lambda x: x[1], reverse=False)
 69 |     vocab_word = [tup[0] for tup in vocab_word]
 70 |     return vocab, vocab_word
 71 | 
 72 | 
 73 | def generate_random_samples(options):
 74 |     """
 75 |     splite training data for train and val
 76 | 
 77 |     """
 78 |     # load all anns
 79 |     caps = utils.load_from_txt(options['dataset']['data_path'] + 'train_caps.txt')
 80 |     fnames = utils.load_from_txt(options['dataset']['data_path'] + 'train_filename.txt')
 81 | 
 82 |     # merge
 83 |     assert len(caps) // 5 == len(fnames)
 84 |     all_infos = []
 85 |     for img_id in range(len(fnames)):
 86 |         cap_id = [img_id * 5, (img_id + 1) * 5]
 87 |         all_infos.append([caps[cap_id[0]:cap_id[1]], fnames[img_id]])
 88 | 
 89 |     # shuffle
 90 |     random.shuffle(all_infos)
 91 | 
 92 |     # split_trainval
 93 |     percent = 0.9
 94 |     train_infos = all_infos[:int(len(all_infos) * percent)]
 95 |     val_infos = all_infos[int(len(all_infos) * percent):]
 96 | 
 97 |     # save to txt
 98 |     train_caps = []
 99 |     train_fnames = []
100 |     for item in train_infos:
101 |         for cap in item[0]:
102 |             train_caps.append(cap)
103 |         train_fnames.append(item[1])
104 |     utils.log_to_txt(train_caps, options['dataset']['data_path'] + 'train_caps_verify.txt', mode='w')
105 |     utils.log_to_txt(train_fnames, options['dataset']['data_path'] + 'train_filename_verify.txt', mode='w')
106 | 
107 |     val_caps = []
108 |     val_fnames = []
109 |     for item in val_infos:
110 |         for cap in item[0]:
111 |             val_caps.append(cap)
112 |         val_fnames.append(item[1])
113 |     utils.log_to_txt(val_caps, options['dataset']['data_path'] + 'val_caps_verify.txt', mode='w')
114 |     utils.log_to_txt(val_fnames, options['dataset']['data_path'] + 'val_filename_verify.txt', mode='w')
115 | 
116 |     print("Generate random samples to {} complete.".format(options['dataset']['data_path']))
117 | 
118 | 


--------------------------------------------------------------------------------
/layers/HVSA_Modules.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | * HVSA
  4 | * By Zhang Weihang
  5 | """
  6 | 
  7 | import torch
  8 | import torch.nn as nn
  9 | import torch.nn.init
 10 | from torch.autograd import Variable
 11 | from torchvision.models.resnet import resnet18
 12 | import torch.nn.functional as F
 13 | from layers import seq2vec
 14 | 
 15 | from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
 16 | 
 17 | 
 18 | def l2norm(X, dim, eps=1e-8):
 19 |     """
 20 |     L2-normalize columns of X
 21 | 
 22 |     """
 23 |     norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
 24 |     X = torch.div(X, norm)
 25 |     return X
 26 | 
 27 | 
 28 | class L2Norm(nn.Module):
 29 |     """
 30 |     input x [batch_size, embedding_size]
 31 |     return x [batch_size, embedding_size]
 32 |     """
 33 |     def forward(self, x):
 34 | 
 35 |         return x / x.norm(p=2, dim=1, keepdim=True)
 36 | 
 37 | 
 38 | 
 39 | class KEA(nn.Module):
 40 | 
 41 |     """ Key-Entity Attention"""
 42 | 
 43 |     def __init__(self, c, attention_channel=64):
 44 |         """
 45 | 
 46 |         :param c: int, the input and output channel number
 47 |         """
 48 | 
 49 |         super(KEA, self).__init__()
 50 | 
 51 |         self.conv1 = nn.Conv2d(c, c, 1)
 52 |         self.attention_channel = attention_channel
 53 |         self.linear_0 = nn.Conv1d(c, self.attention_channel, 1, bias=False)
 54 |         self.linear_1 = nn.Conv1d(self.attention_channel, c, 1, bias=False)
 55 |         self.linear_1.weight.data = self.linear_0.weight.data.permute(1, 0, 2)
 56 |         self.conv2 = nn.Sequential(
 57 |             nn.Conv2d(c, c, 1, bias=False),
 58 |             nn.BatchNorm2d(c)
 59 |         )
 60 | 
 61 |     def forward(self, x):
 62 |         idn = x
 63 |         # key
 64 |         x = self.conv1(x)
 65 |         b, c, h, w = x.size()
 66 |         x = x.view(b, c, h*w)   # b*c*n
 67 | 
 68 |         # key * query
 69 |         attn = self.linear_0(x)  # b*k*n
 70 |         attn = F.softmax(attn, dim=-1)  # b*k*n
 71 |         attn = attn / (1e-9 + attn.sum(keepdim=True, dim=1))  # b*k*n
 72 | 
 73 |         # value
 74 |         x = self.linear_1(attn)
 75 |         x = x.view(b, c, h, w)
 76 |         x = self.conv2(x)
 77 | 
 78 |         # res-connetction
 79 |         x = x + idn
 80 |         x = F.relu(x)
 81 | 
 82 |         return x
 83 | 
 84 | 
 85 | class AutoWeightedModule(nn.Module):
 86 | 
 87 |     def __init__(self, num_loss):
 88 |         super(AutoWeightedModule, self).__init__()
 89 |         self.num_loss = num_loss
 90 |         self.linear = nn.Sequential(
 91 |             nn.Linear(num_loss, 64, bias=False),
 92 |             nn.PReLU(),
 93 |             nn.Linear(64, 128, bias=False),
 94 |             nn.PReLU(),
 95 |             nn.Linear(128, num_loss, bias=False),
 96 |         )
 97 | 
 98 |         self.softmax = nn.Softmax(dim=0)
 99 | 
100 | 
101 |     def forward(self, loss):
102 |         # [num_loss, 1]
103 | 
104 |         loss = torch.cat(loss, dim=0)
105 |         self.weight = self.softmax(self.linear(loss))
106 |         return (self.weight*loss).sum()
107 | 
108 | 
109 | class Skipthoughts_Embedding_Module(nn.Module):
110 | 
111 |     def __init__(self, vocab, cfg, out_dropout=-1):
112 |         super(Skipthoughts_Embedding_Module, self).__init__()
113 |         self.cfg = cfg
114 |         self.vocab_words = vocab
115 | 
116 |         self.seq2vec = seq2vec.factory(self.vocab_words, self.cfg['seq2vec'], self.cfg['seq2vec']['dropout'])
117 | 
118 |         self.to_out = nn.Linear(in_features=2400, out_features=self.cfg['embed']['embed_dim'])
119 | 
120 |         self.dropout = out_dropout
121 | 
122 |     def forward(self, input_text, text_len):
123 |         x_t_vec = self.seq2vec(input_text, text_len)
124 |         out = F.relu(self.to_out(x_t_vec))
125 |         if self.dropout >= 0:
126 |             out = F.dropout(out, self.dropout)
127 | 
128 |         return out
129 | 
130 | 
131 | 
132 | 
133 | class EncoderImageFull(nn.Module):
134 | 
135 |     def __init__(self, cfg={}):
136 | 
137 |         """Load pretrained VGG19 and replace top fc layer."""
138 |         super(EncoderImageFull, self).__init__()
139 |         self.embed_size = cfg['embed']['embed_dim']
140 | 
141 | 
142 |         #Load a pre-trained model
143 |         self.resnet = resnet18(pretrained=True)
144 | 
145 |         # Replace the last fully connected layer of CNN with a new one
146 |         self.fc = nn.Linear(self.resnet.fc.in_features, cfg['embed']['embed_dim'])
147 | 
148 |         self.key_entity_atten = KEA(512)
149 | 
150 |     def forward(self, images):
151 |         """Extract image feature vectors."""
152 |         # features = self.cnn(images)
153 |         x = self.resnet.conv1(images)
154 |         x = self.resnet.bn1(x)
155 |         x = self.resnet.relu(x)
156 |         x = self.resnet.maxpool(x)
157 | 
158 |         f1 = self.resnet.layer1(x)
159 |         f2 = self.resnet.layer2(f1)
160 |         f3 = self.resnet.layer3(f2)
161 | 
162 |         # use self attention
163 |         f4 = self.resnet.layer4(f3)
164 |         f4 = self.key_entity_atten(f4)
165 | 
166 | 
167 |         features = self.resnet.avgpool(f4)
168 |         features = torch.flatten(features, 1)
169 |         # linear projection to the joint embedding space
170 |         features = self.fc(features)
171 | 
172 |         return features
173 | 


--------------------------------------------------------------------------------
/controller.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | * HVSA
  4 | * By Zhang Weihang
  5 | """
  6 | 
  7 | import os
  8 | import argparse
  9 | import yaml
 10 | import time
 11 | import torch
 12 | import numpy as np
 13 | from torch.autograd import Variable
 14 | import tensorboard_logger as tb_logger
 15 | import logging
 16 | 
 17 | from loss import losses
 18 | import utils
 19 | 
 20 | 
 21 | def get_config():
 22 |     """
 23 |     get the parser to load the config
 24 |     """
 25 |     # Hyper Parameters setting
 26 |     parser = argparse.ArgumentParser()
 27 |     parser.add_argument('--config', default='configs/HVSA_rsitmd.yaml', type=str,
 28 |                         help='path to a yaml options file')
 29 |     args = parser.parse_args()
 30 |     # load model config
 31 |     with open(args.config, 'r') as handle:
 32 |         config = yaml.load(handle, Loader=yaml.FullLoader)
 33 | 
 34 |     return config
 35 | 
 36 | 
 37 | def get_model(options, vocab_word):
 38 |     """
 39 |     get model HVSA
 40 | 
 41 |     """
 42 | 
 43 |     # choose model
 44 |     if options['model']['name'] == "HVSA":
 45 |         from layers import HVSA as models
 46 |     else:
 47 |         raise NotImplementedError
 48 | 
 49 |     # make ckpt save dir
 50 |     if not os.path.exists(options['logs']['ckpt_save_path']):
 51 |         os.makedirs(options['logs']['ckpt_save_path'])
 52 | 
 53 |     model = models.factory(options['model'], vocab_word, cuda=True,)
 54 | 
 55 |     return model
 56 | 
 57 | 
 58 | def model_train(train_loader, model, optimizer, epoch, config):
 59 |     """
 60 |     train one epoch
 61 | 
 62 |     """
 63 | 
 64 |     # extract value
 65 |     utils.adjust_learning_rate(config, optimizer, epoch)
 66 |     grad_clip = config['optim']['grad_clip']
 67 |     margin = config['optim']['alpha']
 68 |     # loss_name = opt['model']['name'] + "_" + opt['dataset']['datatype']
 69 |     print_freq = config['logs']['print_freq']
 70 | 
 71 |     # switch to train mode
 72 |     model.train()
 73 |     batch_time = utils.AverageMeter()
 74 |     data_time = utils.AverageMeter()
 75 |     train_logger = utils.LogCollector()
 76 | 
 77 |     end = time.time()
 78 |     params = list(model.parameters())
 79 | 
 80 |     # get the train data
 81 |     for i, train_data in enumerate(train_loader):
 82 |         images, captions, lengths, ids = train_data  #length: [22,19,19,18....] {list:128}
 83 | 
 84 |         batch_size = images.size(0)
 85 |         margin = float(margin)
 86 |         # measure data loading time
 87 |         data_time.update(time.time() - end)
 88 |         model.logger = train_logger
 89 | 
 90 |         input_visual = Variable(images)   #[128,3,256,256]
 91 |         input_text = Variable(captions)   #[128,22]
 92 | 
 93 |         if torch.cuda.is_available():
 94 |             input_visual = input_visual.cuda()
 95 |             input_text = input_text.cuda()
 96 | 
 97 |         scores, image_feature, text_feature = model(input_visual, input_text, lengths)
 98 |         torch.cuda.synchronize()
 99 | 
100 |         # adaptive alignment
101 |         beta = config['loss']['adaptive_alignment']['beta']
102 |         loss_triplet = losses.difficulty_weighted_loss(
103 |             scores, input_visual.size(0), margin, beta) / (batch_size*batch_size)
104 | 
105 |         # feature uniformity
106 |         gamma = config['loss']['feature_uniformity']['gamma']
107 |         loss_unif_image = losses.feature_unif_loss(image_feature, gamma)
108 | 
109 |         # auto weighted module
110 |         loss = model.weight_loss([loss_triplet.unsqueeze(0), loss_unif_image.unsqueeze(0)])
111 | 
112 |         if grad_clip > 0:
113 |             torch.nn.utils.clip_grad_norm_(params, grad_clip)
114 | 
115 |         train_logger.update('L', loss.item())
116 |         train_logger.update('dwh_loss', loss_triplet.item())
117 |         train_logger.update('unif_loss', loss_unif_image.item())
118 | 
119 |         # set grad zero, compute the grad and update the params
120 |         optimizer.zero_grad()
121 |         loss.backward()
122 |         torch.cuda.synchronize()
123 |         optimizer.step()
124 |         torch.cuda.synchronize()
125 | 
126 |         # measure elapsed time
127 |         batch_time.update(time.time() - end)
128 |         end = time.time()
129 | 
130 |         # print training information
131 |         if i % print_freq == 0:
132 |             logging.info(
133 |                 'Epoch: [{0}][{1}/{2}]\t'
134 |                 'Time {batch_time.val:.3f}\t'
135 |                 '{elog}\t'
136 |                 .format(epoch, i, len(train_loader),
137 |                         batch_time=batch_time,
138 |                         elog=str(train_logger)))
139 | 
140 |         tb_logger.log_value('epoch', epoch, step=model.Eiters)
141 |         tb_logger.log_value('step', i, step=model.Eiters)
142 |         train_logger.tb_log(tb_logger, step=model.Eiters)
143 | 
144 | 
145 | def model_validate(val_loader, model, config):
146 | 
147 |     model.eval()
148 |     val_logger = utils.LogCollector()
149 |     model.logger = val_logger
150 | 
151 |     start = time.time()
152 |     input_visual = np.zeros((len(val_loader.dataset), 3, 256, 256))
153 |     input_text = np.zeros((len(val_loader.dataset), 47), dtype=np.int64)
154 |     input_text_lengeth = [0]*len(val_loader.dataset)
155 |     for i, val_data in enumerate(val_loader):
156 | 
157 |         images, captions, lengths, ids = val_data
158 | 
159 |         for (id, img, cap, key,l) in zip(ids, (images.numpy().copy()), (captions.numpy().copy()), images, lengths):
160 |             input_visual[id] = img
161 |             input_text[id, :captions.size(1)] = cap
162 |             input_text_lengeth[id] = l
163 | 
164 |     input_visual = np.array([input_visual[i] for i in range(0, len(input_visual), 5)])
165 | 
166 |     d = utils.shard_dis(input_visual, input_text, model, shard_size=config['dataset']['batch_size_val']
167 |                         , lengths=input_text_lengeth)
168 |     end = time.time()
169 | 
170 | 
171 |     print("calculate similarity time:", end - start)
172 | 
173 |     (r1i, r5i, r10i, medri, meanri), _ = utils.acc_i2t(d)
174 |     logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
175 |                  (r1i, r5i, r10i, medri, meanri))
176 |     (r1t, r5t, r10t, medrt, meanrt), _ = utils.acc_t2i(d)
177 |     logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
178 |                  (r1t, r5t, r10t, medrt, meanrt))
179 |     rsum = (r1t + r5t + r10t + r1i + r5i + r10i)
180 | 
181 |     all_score = "r1i:{} r5i:{} r10i:{} medri:{} meanri:{}\n r1t:{} r5t:{} r10t:{} medrt:{} meanrt:{}\n sum:{}\n ------\n".format(
182 |         r1i, r5i, r10i, medri, meanri, r1t, r5t, r10t, medrt, meanrt, rsum
183 |     )
184 | 
185 |     # for tensorboard
186 |     tb_logger.log_value('rsum', rsum, step=model.Eiters)
187 |     tb_logger.log_value('r1i', r1i, step=model.Eiters)
188 |     tb_logger.log_value('r5i', r5i, step=model.Eiters)
189 |     tb_logger.log_value('r10i', r10i, step=model.Eiters)
190 |     tb_logger.log_value('medri', medri, step=model.Eiters)
191 |     tb_logger.log_value('meanri', meanri, step=model.Eiters)
192 |     tb_logger.log_value('r1t', r1t, step=model.Eiters)
193 |     tb_logger.log_value('r5t', r5t, step=model.Eiters)
194 |     tb_logger.log_value('r10t', r10t, step=model.Eiters)
195 |     tb_logger.log_value('medrt', medrt, step=model.Eiters)
196 |     tb_logger.log_value('meanrt', meanrt, step=model.Eiters)
197 | 
198 |     return rsum, all_score
199 | 
200 | 
201 | def model_test(test_loader, model):
202 |     model.eval()
203 |     val_logger = utils.LogCollector()
204 |     model.logger = val_logger
205 | 
206 |     start = time.time()
207 |     input_visual = np.zeros((len(test_loader.dataset), 3, 256, 256))
208 |     input_text = np.zeros((len(test_loader.dataset), 47), dtype=np.int64)
209 |     input_text_lengeth = [0] * len(test_loader.dataset)
210 | 
211 |     for i, val_data in enumerate(test_loader):
212 | 
213 |         images, captions, lengths, ids = val_data
214 | 
215 |         for (id, img, cap, key, l) in zip(ids, (images.numpy().copy()), (captions.numpy().copy()), images, lengths):
216 |             input_visual[id] = img
217 |             input_text[id, :captions.size(1)] = cap
218 |             input_text_lengeth[id] = l
219 | 
220 |     input_visual = np.array([input_visual[i] for i in range(0, len(input_visual), 5)])
221 | 
222 |     d = utils.shard_dis(input_visual, input_text, model, lengths=input_text_lengeth)
223 | 
224 |     end = time.time()
225 |     print("calculate infer time:", (end-start)/len(test_loader.dataset))
226 | 
227 |     return d
228 | 
229 | 
230 | def load_model(options, model):
231 |     if os.path.isfile(options['optim']['resume']):
232 |         print("=> loading checkpoint '{}'".format(options['optim']['resume']))
233 |         checkpoint = torch.load(options['optim']['resume'])
234 |         start_epoch = checkpoint['epoch']
235 |         model.load_state_dict(checkpoint['model'])
236 |     else:
237 |         start_epoch = 0
238 |         print("=> no checkpoint found at '{}'".format(options['optim']['resume']))
239 | 
240 |     return start_epoch
241 | 
242 | 
243 | 


--------------------------------------------------------------------------------
/utils.py:
--------------------------------------------------------------------------------
  1 | # encoding:utf-8
  2 | """
  3 | * HVSA
  4 | * By Zhang Weihang
  5 | """
  6 | 
  7 | import torch
  8 | import numpy as np
  9 | import sys
 10 | from torch.autograd import Variable
 11 | from collections import OrderedDict
 12 | import os
 13 | import random
 14 | import copy
 15 | 
 16 | 
 17 | def log_to_txt(contexts=None, filename="save.txt", mark=False, encoding='UTF-8', mode='a'):
 18 |     """save contexts as txt"""
 19 | 
 20 |     f = open(filename, mode, encoding=encoding)
 21 |     if mark:
 22 |         sig = "------------------------------------------------\n"
 23 |         f.write(sig)
 24 |     elif isinstance(contexts, dict):
 25 |         tmp = ""
 26 |         for c in contexts.keys():
 27 |             tmp += str(c)+" | " + str(contexts[c]) + "\n"
 28 |         contexts = tmp
 29 |         f.write(contexts)
 30 |     else:
 31 |         if isinstance(contexts,list):
 32 |             tmp = ""
 33 |             for c in contexts:
 34 |                 tmp += str(c)
 35 |             contexts = tmp
 36 |         else:
 37 |             contexts = contexts + "\n"
 38 |         f.write(contexts)
 39 | 
 40 |     f.close()
 41 | 
 42 | 
 43 | class AverageMeter(object):
 44 |     """Computes and stores the average and current value"""
 45 | 
 46 |     def __init__(self):
 47 |         self.reset()
 48 | 
 49 |     def reset(self):
 50 |         self.val = 0
 51 |         self.avg = 0
 52 |         self.sum = 0
 53 |         self.count = 0
 54 | 
 55 |     def update(self, val, n=0):
 56 |         self.val = val
 57 |         self.sum += val * n
 58 |         self.count += n
 59 |         self.avg = self.sum / (.0001 + self.count)
 60 | 
 61 |     def __str__(self):
 62 |         """String representation for logging
 63 |         """
 64 |         # for values that should be recorded exactly e.g. iteration number
 65 |         if self.count == 0:
 66 |             return str(self.val)
 67 |         # for stats
 68 |         return '%.4f (%.4f)' % (self.val, self.avg)
 69 | 
 70 | 
 71 | class LogCollector(object):
 72 |     """A collection of logging objects that can change from train to val"""
 73 | 
 74 |     def __init__(self):
 75 |         # to keep the order of logged variables deterministic
 76 |         self.meters = OrderedDict()
 77 | 
 78 |     def update(self, k, v, n=0):
 79 |         # create a new meter if previously not recorded
 80 |         if k not in self.meters:
 81 |             self.meters[k] = AverageMeter()
 82 |         self.meters[k].update(v, n)
 83 | 
 84 |     def __str__(self):
 85 |         """Concatenate the meters in one log line
 86 |         """
 87 |         s = ''
 88 |         for i, (k, v) in enumerate(self.meters.items()):
 89 |             if i > 0:
 90 |                 s += '  '
 91 |             s += k + ' ' + str(v)
 92 |         return s
 93 | 
 94 |     def tb_log(self, tb_logger, prefix='', step=None):
 95 |         """Log using tensorboard
 96 |         """
 97 |         for k, v in self.meters.items():
 98 |             tb_logger.log_value(prefix + k, v.val, step=step)
 99 | 
100 | 
101 | def update_values(dict_from, dict_to):
102 |     for key, value in dict_from.items():
103 |         if isinstance(value, dict):
104 |             update_values(dict_from[key], dict_to[key])
105 |         elif value is not None:
106 |             dict_to[key] = dict_from[key]
107 |     return dict_to
108 | 
109 | 
110 | def cosine_similarity(x1, x2, dim=-1, eps=1e-8):
111 |     """Returns cosine similarity between x1 and x2, computed along dim."""
112 | 
113 |     w12 = torch.sum(x1 * x2, dim)
114 |     w1 = torch.norm(x1, 2, dim)
115 |     w2 = torch.norm(x2, 2, dim)
116 | 
117 |     return (w12 / (w1 * w2).clamp(min=eps)).squeeze()
118 | 
119 | 
120 | def shard_dis(images, captions, model, shard_size=256, lengths=None):
121 |     """compute image-caption pairwise distance during validation and test"""
122 | 
123 |     n_im_shard = (len(images) - 1) // shard_size + 1
124 |     n_cap_shard = (len(captions) - 1) // shard_size + 1
125 | 
126 |     d = np.zeros((len(images), len(captions)))
127 | 
128 |     for i in range(n_im_shard):
129 |         im_start, im_end = shard_size*i, min(shard_size*(i+1), len(images))
130 |         for j in range(n_cap_shard):
131 |             sys.stdout.write('\r>> shard_distance batch (%d,%d)' % (i, j))
132 |             cap_start, cap_end = shard_size * j, min(shard_size * (j + 1), len(captions))
133 | 
134 |             with torch.no_grad():
135 |                 im = Variable(torch.from_numpy(images[im_start:im_end])).float().cuda()
136 |                 s = Variable(torch.from_numpy(captions[cap_start:cap_end])).cuda()
137 |                 l = lengths[cap_start:cap_end]
138 | 
139 |                 sim, _, _ = model(im, s, l)
140 |                 sim = sim.squeeze()
141 |                 d[im_start:im_end, cap_start:cap_end] = sim.data.cpu().numpy()
142 |     sys.stdout.write('\n')
143 |     return d
144 | 
145 | 
146 | def acc_i2t(input):
147 |     """Computes the precision@k for the specified values of k of i2t"""
148 |     image_size = input.shape[0]
149 |     ranks = np.zeros(image_size)
150 |     top1 = np.zeros(image_size)
151 | 
152 |     for index in range(image_size):
153 |         inds = np.argsort(input[index])[::-1]
154 |         # Score
155 |         rank = 1e20
156 |         # get ground_truth's rank
157 |         for i in range(5 * index, 5 * index + 5, 1):  # gt:i
158 |             tmp = np.where(inds == i)[0][0]
159 |             if tmp < rank:
160 |                 rank = tmp
161 |         ranks[index] = rank
162 |         top1[index] = inds[0]
163 | 
164 | 
165 |     # Compute metrics
166 |     r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
167 |     r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
168 |     r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
169 |     medr = np.floor(np.median(ranks)) + 1
170 |     meanr = ranks.mean() + 1
171 | 
172 |     return (r1, r5, r10, medr, meanr), (ranks, top1)
173 | 
174 | 
175 | def acc_t2i(input):
176 |     """Computes the precision@k for the specified values of k of t2i"""
177 |     image_size = input.shape[0]
178 |     ranks = np.zeros(5*image_size)
179 |     top1 = np.zeros(5*image_size)
180 | 
181 |     # --> (5N(caption), N(image))
182 |     input = input.T
183 | 
184 |     for index in range(image_size):
185 |         for i in range(5):
186 |             inds = np.argsort(input[5 * index + i])[::-1]
187 |             ranks[5 * index + i] = np.where(inds == index)[0][0]
188 |             top1[5 * index + i] = inds[0]
189 | 
190 |     # Compute metrics
191 |     r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
192 |     r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
193 |     r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
194 |     medr = np.floor(np.median(ranks)) + 1
195 |     meanr = ranks.mean() + 1
196 | 
197 |     return (r1, r5, r10, medr, meanr), (ranks, top1)
198 | 
199 | 
200 | def save_checkpoint(state, is_best, filename, prefix='', model_name=None):
201 |     """
202 |     save the best checkpoint
203 |     :param state: model information
204 |     :param is_best: is or not best
205 |     :param filename: the file name of the checkpoint
206 |     :param prefix: the prefix of the save path
207 |     :param model_name: current model 's name
208 |     :return: None
209 | 
210 |     """
211 |     tries = 15
212 |     error = None
213 | 
214 |     # deal with unstable I/O. Usually not necessary.
215 |     while tries:
216 |         try:
217 |             # torch.save(state, prefix + filename)
218 |             if is_best:
219 |                 torch.save(state, prefix +model_name +'_best.pth.tar')
220 |                 # torch.save(state, prefix + filename)
221 | 
222 |         except IOError as e:
223 |             error = e
224 |             tries -= 1
225 |         else:
226 |             break
227 |         print('model save {} failed, remaining {} trials'.format(filename, tries))
228 |         if not tries:
229 |             raise error
230 | 
231 | 
232 | def adjust_learning_rate(options, optimizer, epoch, warm_up=10):
233 |     """
234 |     Sets the learning rate to the initial LR
235 |     decayed every n epochs
236 | 
237 |     """
238 | 
239 |     for param_group in optimizer.param_groups:
240 |         lr = param_group['lr']
241 |         
242 |         if epoch / warm_up < 1:
243 |             lr = options['optim']['lr'] * (epoch+1)/warm_up
244 |         if epoch % options['optim']['lr_update_epoch'] == options['optim']['lr_update_epoch'] - 1:
245 |             lr = lr * options['optim']['lr_decay_param']
246 | 
247 |         param_group['lr'] = lr
248 | 
249 |     print("Current lr: {}".format(optimizer.state_dict()['param_groups'][0]['lr']))
250 | 
251 | 
252 | def load_from_txt(filename, encoding="utf-8"):
253 |     """
254 |     read txt lines
255 | 
256 |     """
257 |     f = open(filename, 'r', encoding=encoding)
258 |     contexts = f.readlines()
259 |     return contexts
260 | 
261 | 
262 | def set_seed(seed=42):
263 |     """
264 |     set random seeds for reproduction of the code
265 | 
266 |     """
267 | 
268 |     os.environ['PYTHONHASHSEED'] = str(seed)
269 |     np.random.seed(seed)
270 |     random.seed(seed)
271 |     torch.manual_seed(seed)
272 |     torch.cuda.manual_seed(seed)
273 |     torch.cuda.manual_seed_all(seed)
274 |     torch.backends.cudnn.deterministic = True
275 | 
276 | 
277 | def update_config(config, k):
278 |     """
279 |     update the config
280 | 
281 |     """
282 |     updated_config = copy.deepcopy(config)
283 | 
284 |     updated_config['random_seed']['current_num'] = k
285 |     updated_config['logs']['ckpt_save_path'] = config['logs']['ckpt_save_path'] + \
286 |                                                 config['experiment_name'] + "/" + str(k) + "/"
287 |     return updated_config
288 | 


--------------------------------------------------------------------------------
/vocab/rsicd_splits_vocab.json:
--------------------------------------------------------------------------------
1 | {"word2idx": {"many": 1, "planes": 2, "parked": 3, "next": 4, "long": 5, "building": 6, "airport": 7, "full": 8, "airplanes": 9, "containers": 10, "near": 11, "runways": 12, "size": 13, "large": 14, "several": 15, "buildings": 16, "green": 17, "trees": 18, "around": 19, "piece": 20, "bareland": 21, "parking": 22, "lot": 23, "scattered": 24, "every": 25, "plane": 26, "position": 27, "complex": 28, "surrounded": 29, "farmland": 30, "numerous": 31, "grand": 32, "made": 33, "terminal": 34, "middle": 35, "silver": 36, "besides": 37, "black": 38, "road": 39, "either": 40, "hand": 41, "dark": 42, "great": 43, "front": 44, "others": 45, "embraced": 46, "different": 47, "open": 48, "runway": 49, "straight": 50, "wide": 51, "stands": 52, "roads": 53, "regions": 54, "side": 55, "square": 56, "meadow": 57, "seven": 58, "big": 59, "little": 60, "arc": 61, "gray": 62, "ground": 63, "light": 64, "four": 65, "lots": 66, "occupies": 67, "area": 68, "including": 69, "differnet": 70, "sizes": 71, "brown": 72, "white": 73, "sparse": 74, "divide": 75, "lines": 76, "inside": 77, "river": 78, "simple": 79, "small": 80, "crowded": 81, "space": 82, "storage": 83, "tanks": 84, "plants": 85, "neat": 86, "arranged": 87, "red": 88, "spaces": 89, "two": 90, "ocean": 91, "farmlands": 92, "meadows": 93, "five": 94, "empty": 95, "orderly": 96, "sides": 97, "pieces": 98, "extends": 99, "water": 100, "aeroplanes": 101, "apron": 102, "located": 103, "weblike": 104, "flight": 105, "paths": 106, "ploygon": 107, "main": 108, "netlike": 109, "decorated": 110, "lawns": 111, "cross": 112, "polygonal": 113, "passenger": 114, "termial": 115, "crossing": 116, "strip": 117, "shaped": 118, "viaducts": 119, "narrow": 120, "star": 121, "like": 122, "sits": 123, "parallel": 124, "three": 125, "rectangular": 126, "boomerang": 127, "aircraft": 128, "corner": 129, "take": 130, "running": 131, "one": 132, "land": 133, "terminals": 134, "crossed": 135, "tarmac": 136, "boarding": 137, "gate": 138, "separated": 139, "landside": 140, "airside": 141, "oval": 142, "aircrafts": 143, "stay": 144, "tarmacs": 145, "grassy": 146, "fields": 147, "waiting": 148, "alongside": 149, "standing": 150, "beside": 151, "grey": 152, "lying": 153, "circular": 154, "yellow": 155, "stretch": 156, "sparsely": 157, "rounded": 158, "rectangle": 159, "criss": 160, "surrounds": 161, "radial": 162, "stop": 163, "majestic": 164, "symmetrical": 165, "triangle": 166, "curve": 167, "seated": 168, "blue": 169, "adjacent": 170, "wasteland": 171, "polygon": 172, "irregular": 173, "paralleled": 174, "grassland": 175, "vertically": 176, "cruciform": 177, "x": 178, "sandwiched": 179, "field": 180, "surrounding": 181, "strips": 182, "neatly": 183, "built": 184, "trapezoid": 185, "grass": 186, "divided": 187, "outside": 188, "encircled": 189, "complicated": 190, "cars": 191, "rows": 192, "separately": 193, "circle": 194, "respectively": 195, "docked": 196, "number": 197, "plains": 198, "eight": 199, "still": 200, "bare": 201, "highway": 202, "packed": 203, "direction": 204, "good": 205, "order": 206, "broad": 207, "almost": 208, "bald": 209, "covers": 210, "vast": 211, "roofed": 212, "house": 213, "tall": 214, "planted": 215, "triangular": 216, "lawn": 217, "sea": 218, "site": 219, "highways": 220, "scale": 221, "roof": 222, "lake": 223, "nearby": 224, "traces": 225, "huge": 226, "spacious": 227, "line": 228, "mark": 229, "part": 230, "tracks": 231, "totally": 232, "point": 233, "seems": 234, "barren": 235, "nothing": 236, "seen": 237, "zigzag": 238, "squared": 239, "khaki": 240, "streets": 241, "corrugated": 242, "short": 243, "aside": 244, "place": 245, "color": 246, "region": 247, "business": 248, "railway": 249, "ponds": 250, "tract": 251, "shape": 252, "parts": 253, "interspersed": 254, "pond": 255, "pools": 256, "messy": 257, "growing": 258, "goes": 259, "along": 260, "row": 261, "stony": 262, "hills": 263, "surface": 264, "bleak": 265, "shadows": 266, "ruts": 267, "pathways": 268, "mountainous": 269, "lakes": 270, "plain": 271, "wavy": 272, "vein": 273, "sandy": 274, "stripes": 275, "clusters": 276, "vegetations": 277, "dirt": 278, "curly": 279, "stretches": 280, "across": 281, "greenland": 282, "coner": 283, "form": 284, "rugged": 285, "together": 286, "formed": 287, "houses": 288, "grow": 289, "grows": 290, "half": 291, "dry": 292, "wet": 293, "going": 294, "marbled": 295, "pattern": 296, "flat": 297, "path": 298, "traverses": 299, "wild": 300, "curved": 301, "striped": 302, "construction": 303, "colored": 304, "stripe": 305, "reddish": 306, "uneven": 307, "desert": 308, "spots": 309, "signs": 310, "marks": 311, "ripples": 312, "living": 313, "things": 314, "plant": 315, "broken": 316, "stone": 317, "waste": 318, "car": 319, "grind": 320, "2": 321, "expanse": 322, "soil": 323, "grown": 324, "wheel": 325, "grew": 326, "exposed": 327, "amount": 328, "vegetation": 329, "truck": 330, "driving": 331, "factory": 332, "forest": 333, "roofs": 334, "kinds": 335, "growth": 336, "patch": 337, "'s": 338, "something": 339, "flows": 340, "baseball": 341, "surround": 342, "flower": 343, "lush": 344, "base": 345, "ball": 346, "covered": 347, "fan-shaped": 348, "courts": 349, "people": 350, "playing": 351, "bright": 352, "playground": 353, "tree": 354, "semi-surrounded": 355, "tennis": 356, "lane": 357, "track": 358, "freeway": 359, "baseballfield": 360, "fan": 361, "bushy": 362, "fanshaped": 363, "edges": 364, "blocks": 365, "distributed": 366, "sand": 367, "round": 368, "edge": 369, "pentagonal": 370, "see": 371, "bleachers": 372, "baseballfields": 373, "diagonally": 374, "basketball": 375, "court": 376, "looking": 377, "well": 378, "forms": 379, "semicircle": 380, "winding": 381, "another": 382, "sports": 383, "back": 384, "football": 385, "sport": 386, "park": 387, "soccer": 388, "pitch": 389, "grounds": 390, "pitches": 391, "neighborhood": 392, "sit": 393, "woods": 394, "freeways": 395, "relatively": 396, "colors": 397, "consists": 398, "crossroads": 399, "evenly": 400, "waves": 401, "beach": 402, "beautiful": 403, "slapping": 404, "makes": 405, "sharp": 406, "gradient": 407, "clear": 408, "boats": 409, "coastline": 410, "crystal": 411, "colour": 412, "flourishing": 413, "resort": 414, "pretty": 415, "roaring": 416, "lapping": 417, "wave": 418, "shore": 419, "textures": 420, "looks": 421, "rock": 422, "mounds": 423, "rocks": 424, "coast": 425, "stones": 426, "swashes": 427, "spray": 428, "bank": 429, "spoondrift": 430, "series": 431, "come": 432, "calm": 433, "separates": 434, "deep": 435, "blue-green": 436, "surf": 437, "masses": 438, "separate": 439, "set": 440, "towards": 441, "city": 442, "lies": 443, "boundary": 444, "curving": 445, "kind": 446, "layers": 447, "patting": 448, "wharf": 449, "swimming": 450, "pool": 451, "quarter": 452, "mountain": 453, "sapphire": 454, "thr": 455, "jungle": 456, "flourish": 457, "stained": 458, "dam": 459, "ribbon": 460, "jade": 461, "high": 462, "way": 463, "cyan": 464, "quiet": 465, "peaceful": 466, "integrated": 467, "beat": 468, "fish": 469, "vastness": 470, "wind": 471, "pedestrian": 472, "sun": 473, "toward": 474, "golden": 475, "rolling": 476, "play": 477, "tourists": 478, "umbrellas": 479, "chairs": 480, "yachts": 481, "sands": 482, "coconut": 483, "byland": 484, "reaching": 485, "stretching": 486, "stand": 487, "yacht": 488, "shade": 489, "bridges": 490, "bridge": 491, "connects": 492, "steel": 493, "structure": 494, "two-way": 495, "six-line": 496, "connected": 497, "exuberant": 498, "cast": 499, "shadow": 500, "muddy": 501, "similar": 502, "boat": 503, "downtown": 504, "rivers": 505, "turns": 506, "busy": 507, "scene": 508, "branches": 509, "branch": 510, "banks": 511, "ends": 512, "island": 513, "connecting": 514, "traverse": 515, "valley": 516, "mills": 517, "ones": 518, "crosses": 519, "plenty": 520, "spans": 521, "deck": 522, "bridege": 523, "suspension": 524, "cable": 525, "stayed": 526, "dense": 527, "double": 528, "tower": 529, "circles": 530, "groves": 531, "residential": 532, "areas": 533, "contains": 534, "towers": 535, "brightly": 536, "shining": 537, "ships": 538, "station": 539, "cities": 540, "tiny": 541, "stopping": 542, "factories": 543, "joins": 544, "ports": 545, "sailing": 546, "ship": 547, "automobiles": 548, "traveling": 549, "bustling": 550, "u": 551, "low": 552, "saddle": 553, "go": 554, "turbid": 555, "passing": 556, "mountains": 557, "islands": 558, "vans": 559, "cement": 560, "partly": 561, "port": 562, "railways": 563, "voer": 564, "unparallel": 565, "build": 566, "barelands": 567, "alive": 568, "lands": 569, "strong": 570, "passed": 571, "densely": 572, "populated": 573, "traffic": 574, "center": 575, "distinctive": 576, "design": 577, "vehicles": 578, "stadium": 579, "block": 580, "altitude": 581, "shell": 582, "make": 583, "intensive": 584, "street": 585, "ferris": 586, "viaduct": 587, "semi-circle": 588, "squares": 589, "quadrilateral": 590, "top": 591, "gymnasium": 592, "leaf": 593, "eye": 594, "among": 595, "regular": 596, "hexagon": 597, "loop": 598, "hexagonal": 599, "grid": 600, "whose": 601, "close": 602, "lace": 603, "metallic": 604, "designed": 605, "seperated": 606, "elliptical": 607, "bowl": 608, "urban": 609, "flyover": 610, "metal": 611, "esthetic": 612, "semi": 613, "drop": 614, "composed": 615, "aesthetic": 616, "smaller": 617, "zone": 618, "paved": 619, "twelve": 620, "purple": 621, "ring": 622, "incomplete": 623, "strange": 624, "whole": 625, "total": 626, "vivid": 627, "spectacular": 628, "domes": 629, "gym": 630, "honeycomb": 631, "trails": 632, "behind": 633, "central": 634, "sided": 635, "magnificent": 636, "church": 637, "owns": 638, "courtyard": 639, "patterns": 640, "variety": 641, "styles": 642, "coloured": 643, "orange": 644, "pink": 645, "moss": 646, "dome": 647, "directions": 648, "tidy": 649, "`": 650, "parks": 651, "roadside": 652, "colorful": 653, "includes": 654, "pitched": 655, "brick": 656, "yard": 657, "neighborhoods": 658, "junction": 659, "old": 660, "flats": 661, "spherical": 662, "opposite": 663, "cylindrical": 664, "pithed": 665, "owning": 666, "closely": 667, "linked": 668, "commercial": 669, "office": 670, "district": 671, "high-rise": 672, "well-organized": 673, "various": 674, "edifices": 675, "eye-catching": 676, "malls": 677, "six": 678, "passes": 679, "prosperous": 680, "distictive": 681, "compact": 682, "rise": 683, "housing": 684, "medium": 685, "height": 686, "skyscrapers": 687, "redidential": 688, "arterial": 689, "lined": 690, "reflection": 691, "glass": 692, "arrangement": 693, "alley": 694, "distribute": 695, "corners": 696, "crossroad": 697, "sitting": 698, "shapes": 699, "school": 700, "apartment": 701, "overpass": 702, "residence": 703, "enclosed": 704, "upscale": 705, "distance": 706, "left": 707, "right": 708, "trade": 709, "pass": 710, "economic": 711, "developed": 712, "belong": 713, "intersection": 714, "mainly": 715, "gap": 716, "working": 717, "community": 718, "devided": 719, "rectangules": 720, 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"1132": "nested", "1133": "ease", "1134": "ran", "1135": "drive", "1136": "exercise", "1137": "locate", "1138": "surroundededed", "1139": "subdivisions", "1140": "planned", "1141": "semisurrounded", "1142": "residents", "1143": "jars", "1144": "paint", "1145": "forked", "1146": "west", "1147": "sunshine", "1148": "shabby", "1149": "wreathed", "1150": "situates", "1151": "l-shaped", "1152": "surroundeded", "1153": "hemmed", "1154": "neatly-arranged", "1155": "new", "1156": "north", "1157": "playgound", "1158": "rimmed", "1159": "jar", "1160": "quite", "1161": "rim", "1162": "baksetball", "1163": "slender", "1164": "environment", "1165": "raod", "1166": "course", "1167": "tree-lined", "1168": "links", "1169": "convenience", "1170": "away", "1171": "scenery", "1172": "fast", "1173": "silver-gray", "1174": "containing", "1175": "overall", "1176": "pavilion", "1177": "treelined", "1178": "patches", "1179": "aqua", "1180": "complete", "1181": "airfields", "1182": "criss_crossing", "1183": "potholes", "1184": "bumpy", "1185": "slope", "1186": "palm", "1187": "shallow", "1188": "eyot", "1189": "towns", "1190": "past", "1191": "umbrella", "1192": "delicate", "1193": "paddy", "1194": "bule", "1195": "system", "1196": "budding", "1197": "walkway", "1198": "bends", "1199": "retangular", "1200": "blurred", "1201": "hard", "1202": "nebulous", "1203": "twinkling", "1204": "crash", "1205": "beating", "1206": "truss", "1207": "pyramidal", "1208": "faced", "1209": "alleys", "1210": "ranged", "1211": "tightly", "1212": "peaks", "1213": "intersperse", "1214": "sheet", "1215": "airtight", "1216": "facotry", "1217": "unused", "1218": "merging", "1219": "getting", "1220": "forks", "1221": "circumferential", "1222": "ramps", "1223": "cloverleaf", "1224": "ramp", "1225": "<unk>"}, "idx": 1226}


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/vocab/rsitmd_splits_vocab.json:
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1 | {"word2idx": {"baseball": 1, "field": 2, "beside": 3, "green": 4, "amusement": 5, "park": 6, "around": 7, "red": 8, "track": 9, "adjacent": 10, "playground": 11, "square": 12, "long": 13, "path": 14, "next": 15, "runway": 16, "two": 17, "white": 18, "houses": 19, "located": 20, "middle": 21, "lawn": 22, "fields": 23, "different": 24, "sizes": 25, "stadium": 26, "stand": 27, "big": 28, "side-by-side": 29, "road": 30, "side": 31, "football": 32, "passes": 33, "gray": 34, "bare": 35, "soil": 36, "close": 37, "alongside": 38, "piece": 39, "land": 40, "separates": 41, "house": 42, "separating": 43, "octagonal": 44, "buildings": 45, "colors": 46, "building": 47, "parking": 48, "lot": 49, "full": 50, "cars": 51, "tennis": 52, "courts": 53, "grey": 54, "roadside": 55, "one": 56, "intersection": 57, "roads": 58, "crossed": 59, "highway": 60, "parallel": 61, "pass": 62, "streets": 63, "lined": 64, "trees": 65, "empty": 66, "sides": 67, "lots": 68, "deep": 69, "black": 70, "river": 71, "blue": 72, "factory": 73, "boats": 74, "moored": 75, "many": 76, "parked": 77, "bank": 78, "three": 79, "anchored": 80, "respectively": 81, "turquoise": 82, "ships": 83, "area": 84, "goods": 85, "boat": 86, "quietly": 87, "thin": 88, "bridge": 89, "sail": 90, "dark": 91, "sea": 92, "harbor": 93, "small": 94, "floating": 95, "port": 96, "floated": 97, "ship": 98, "near": 99, "dock": 100, "yellow": 101, "lay": 102, "across": 103, "crossroads": 104, "sailed": 105, "fast": 106, "calm": 107, "sailing": 108, "deck": 109, "purple": 110, "dry": 111, "cargo": 112, "striped": 113, "coast": 114, "stripes": 115, "shore": 116, "water": 117, "either": 118, "car": 119, "left": 120, "wave": 121, "heihe": 122, "pools": 123, "swimming": 124, "opposite": 125, "facing": 126, "moving": 127, "colorful": 128, "board": 129, "belt": 130, "curved": 131, "crosses": 132, "directly": 133, "make": 134, "way": 135, "residential": 136, "crossing": 137, "intersects": 138, "areas": 139, "plant": 140, "belts": 141, "rivers": 142, "surrounded": 143, "densely": 144, "populated": 145, "decorated": 146, "hand": 147, "open": 148, "grassland": 149, "high-rise": 150, "grass": 151, "skyscrapers": 152, "sits": 153, "tall": 154, "ellipse": 155, "shapes": 156, "oval": 157, "built": 158, "kind": 159, "light": 160, "bifurcation": 161, "street": 162, "train": 163, "railway": 164, "directions": 165, "tracks": 166, "planes": 167, "plane": 168, "large": 169, "crowded": 170, "separated": 171, "jagged": 172, "zigzag": 173, "lying": 174, "vehicles": 175, "airport": 176, "ground": 177, "stopped": 178, "dust": 179, "five": 180, "docked": 181, "sparkling": 182, "face": 183, "rectangular": 184, "together": 185, "line": 186, "several": 187, "edge": 188, "playgrounds": 189, "six": 190, "table": 191, "balls": 192, "semicircular": 193, "orange": 194, "dense": 195, "forest": 196, "thick": 197, "connected": 198, "lake": 199, "pond": 200, "lush": 201, "tree": 202, "connects": 203, "court": 204, "vacant": 205, "paths": 206, 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"1293": "carefully", "1294": "c-shaped", "1295": "auditorium", "1296": "hot", "1297": "private", "1298": "photo", "1299": "beds", "1300": "hall", "1301": "monument", "1302": "lantern", "1303": "bell", "1304": "x-shaped", "1305": "hold", "1306": "tens", "1307": "new", "1308": "tripods", "1309": "stalls", "1310": "blearchers", "1311": "pipe", "1312": "storagetanks", "1313": "pipelines", "1314": "drums", "1315": "pipes", "1316": "reservoir", "1317": "identical", "1318": "pallets", "1319": "nine", "1320": "trays", "1321": "storerooms", "1322": "barrelan", "1323": "june", "1324": "columnar", "1325": "tanker", "1326": "loaded", "1327": "treatment", "1328": "jars", "1329": "refinery", "1330": "tankers", "1331": "storing", "1332": "jar", "1333": "bicycles", "1334": "barrels", "1335": "shrinking", "1336": "loops", "1337": "auxiliary", "1338": "eight-shaped", "1339": "flyovers", "1340": "ramps", "1341": "three-dimensional", "1342": "brush", "1343": "nested", "1344": "luggage", "1345": "turfs", "1346": "bunkers", "1347": "extremely", "1348": "20", "1349": "<unk>"}, "idx": 1350}


--------------------------------------------------------------------------------
/data/rsitmd_raw/test_filename.txt:
--------------------------------------------------------------------------------
   1 | boat_0.tif
   2 | boat_0.tif
   3 | boat_0.tif
   4 | boat_0.tif
   5 | boat_0.tif
   6 | playground_1.tif
   7 | playground_1.tif
   8 | playground_1.tif
   9 | playground_1.tif
  10 | playground_1.tif
  11 | airport_2.tif
  12 | airport_2.tif
  13 | airport_2.tif
  14 | airport_2.tif
  15 | airport_2.tif
  16 | airport_3.tif
  17 | airport_3.tif
  18 | airport_3.tif
  19 | airport_3.tif
  20 | airport_3.tif
  21 | airport_4.tif
  22 | airport_4.tif
  23 | airport_4.tif
  24 | airport_4.tif
  25 | airport_4.tif
  26 | airport_5.tif
  27 | airport_5.tif
  28 | airport_5.tif
  29 | airport_5.tif
  30 | airport_5.tif
  31 | airport_6.tif
  32 | airport_6.tif
  33 | airport_6.tif
  34 | airport_6.tif
  35 | airport_6.tif
  36 | airport_7.tif
  37 | airport_7.tif
  38 | airport_7.tif
  39 | airport_7.tif
  40 | airport_7.tif
  41 | airport_8.tif
  42 | airport_8.tif
  43 | airport_8.tif
  44 | airport_8.tif
  45 | airport_8.tif
  46 | airport_9.tif
  47 | airport_9.tif
  48 | airport_9.tif
  49 | airport_9.tif
  50 | airport_9.tif
  51 | airport_10.tif
  52 | airport_10.tif
  53 | airport_10.tif
  54 | airport_10.tif
  55 | airport_10.tif
  56 | airport_11.tif
  57 | airport_11.tif
  58 | airport_11.tif
  59 | airport_11.tif
  60 | airport_11.tif
  61 | airport_12.tif
  62 | airport_12.tif
  63 | airport_12.tif
  64 | airport_12.tif
  65 | airport_12.tif
  66 | bareland_13.tif
  67 | bareland_13.tif
  68 | bareland_13.tif
  69 | bareland_13.tif
  70 | bareland_13.tif
  71 | bareland_14.tif
  72 | bareland_14.tif
  73 | bareland_14.tif
  74 | bareland_14.tif
  75 | bareland_14.tif
  76 | bareland_15.tif
  77 | bareland_15.tif
  78 | bareland_15.tif
  79 | bareland_15.tif
  80 | bareland_15.tif
  81 | bareland_16.tif
  82 | bareland_16.tif
  83 | bareland_16.tif
  84 | bareland_16.tif
  85 | bareland_16.tif
  86 | bareland_17.tif
  87 | bareland_17.tif
  88 | bareland_17.tif
  89 | bareland_17.tif
  90 | bareland_17.tif
  91 | baseballfield_18.tif
  92 | baseballfield_18.tif
  93 | baseballfield_18.tif
  94 | baseballfield_18.tif
  95 | baseballfield_18.tif
  96 | baseballfield_19.tif
  97 | baseballfield_19.tif
  98 | baseballfield_19.tif
  99 | baseballfield_19.tif
 100 | baseballfield_19.tif
 101 | baseballfield_20.tif
 102 | baseballfield_20.tif
 103 | baseballfield_20.tif
 104 | baseballfield_20.tif
 105 | baseballfield_20.tif
 106 | baseballfield_21.tif
 107 | baseballfield_21.tif
 108 | baseballfield_21.tif
 109 | baseballfield_21.tif
 110 | baseballfield_21.tif
 111 | baseballfield_22.tif
 112 | baseballfield_22.tif
 113 | baseballfield_22.tif
 114 | baseballfield_22.tif
 115 | baseballfield_22.tif
 116 | baseballfield_23.tif
 117 | baseballfield_23.tif
 118 | baseballfield_23.tif
 119 | baseballfield_23.tif
 120 | baseballfield_23.tif
 121 | baseballfield_24.tif
 122 | baseballfield_24.tif
 123 | baseballfield_24.tif
 124 | baseballfield_24.tif
 125 | baseballfield_24.tif
 126 | baseballfield_25.tif
 127 | baseballfield_25.tif
 128 | baseballfield_25.tif
 129 | baseballfield_25.tif
 130 | baseballfield_25.tif
 131 | baseballfield_26.tif
 132 | baseballfield_26.tif
 133 | baseballfield_26.tif
 134 | baseballfield_26.tif
 135 | baseballfield_26.tif
 136 | beach_27.tif
 137 | beach_27.tif
 138 | beach_27.tif
 139 | beach_27.tif
 140 | beach_27.tif
 141 | beach_28.tif
 142 | beach_28.tif
 143 | beach_28.tif
 144 | beach_28.tif
 145 | beach_28.tif
 146 | beach_29.tif
 147 | beach_29.tif
 148 | beach_29.tif
 149 | beach_29.tif
 150 | beach_29.tif
 151 | beach_30.tif
 152 | beach_30.tif
 153 | beach_30.tif
 154 | beach_30.tif
 155 | beach_30.tif
 156 | beach_31.tif
 157 | beach_31.tif
 158 | beach_31.tif
 159 | beach_31.tif
 160 | beach_31.tif
 161 | beach_32.tif
 162 | beach_32.tif
 163 | beach_32.tif
 164 | beach_32.tif
 165 | beach_32.tif
 166 | beach_33.tif
 167 | beach_33.tif
 168 | beach_33.tif
 169 | beach_33.tif
 170 | beach_33.tif
 171 | beach_34.tif
 172 | beach_34.tif
 173 | beach_34.tif
 174 | beach_34.tif
 175 | beach_34.tif
 176 | beach_35.tif
 177 | beach_35.tif
 178 | beach_35.tif
 179 | beach_35.tif
 180 | beach_35.tif
 181 | beach_36.tif
 182 | beach_36.tif
 183 | beach_36.tif
 184 | beach_36.tif
 185 | beach_36.tif
 186 | beach_37.tif
 187 | beach_37.tif
 188 | beach_37.tif
 189 | beach_37.tif
 190 | beach_37.tif
 191 | beach_38.tif
 192 | beach_38.tif
 193 | beach_38.tif
 194 | beach_38.tif
 195 | beach_38.tif
 196 | beach_39.tif
 197 | beach_39.tif
 198 | beach_39.tif
 199 | beach_39.tif
 200 | beach_39.tif
 201 | beach_40.tif
 202 | beach_40.tif
 203 | beach_40.tif
 204 | beach_40.tif
 205 | beach_40.tif
 206 | beach_41.tif
 207 | beach_41.tif
 208 | beach_41.tif
 209 | beach_41.tif
 210 | beach_41.tif
 211 | bridge_42.tif
 212 | bridge_42.tif
 213 | bridge_42.tif
 214 | bridge_42.tif
 215 | bridge_42.tif
 216 | bridge_43.tif
 217 | bridge_43.tif
 218 | bridge_43.tif
 219 | bridge_43.tif
 220 | bridge_43.tif
 221 | bridge_44.tif
 222 | bridge_44.tif
 223 | bridge_44.tif
 224 | bridge_44.tif
 225 | bridge_44.tif
 226 | bridge_45.tif
 227 | bridge_45.tif
 228 | bridge_45.tif
 229 | bridge_45.tif
 230 | bridge_45.tif
 231 | bridge_46.tif
 232 | bridge_46.tif
 233 | bridge_46.tif
 234 | bridge_46.tif
 235 | bridge_46.tif
 236 | bridge_47.tif
 237 | bridge_47.tif
 238 | bridge_47.tif
 239 | bridge_47.tif
 240 | bridge_47.tif
 241 | bridge_48.tif
 242 | bridge_48.tif
 243 | bridge_48.tif
 244 | bridge_48.tif
 245 | bridge_48.tif
 246 | bridge_49.tif
 247 | bridge_49.tif
 248 | bridge_49.tif
 249 | bridge_49.tif
 250 | bridge_49.tif
 251 | bridge_50.tif
 252 | bridge_50.tif
 253 | bridge_50.tif
 254 | bridge_50.tif
 255 | bridge_50.tif
 256 | bridge_51.tif
 257 | bridge_51.tif
 258 | bridge_51.tif
 259 | bridge_51.tif
 260 | bridge_51.tif
 261 | bridge_52.tif
 262 | bridge_52.tif
 263 | bridge_52.tif
 264 | bridge_52.tif
 265 | bridge_52.tif
 266 | bridge_53.tif
 267 | bridge_53.tif
 268 | bridge_53.tif
 269 | bridge_53.tif
 270 | bridge_53.tif
 271 | bridge_54.tif
 272 | bridge_54.tif
 273 | bridge_54.tif
 274 | bridge_54.tif
 275 | bridge_54.tif
 276 | bridge_55.tif
 277 | bridge_55.tif
 278 | bridge_55.tif
 279 | bridge_55.tif
 280 | bridge_55.tif
 281 | bridge_56.tif
 282 | bridge_56.tif
 283 | bridge_56.tif
 284 | bridge_56.tif
 285 | bridge_56.tif
 286 | center_57.tif
 287 | center_57.tif
 288 | center_57.tif
 289 | center_57.tif
 290 | center_57.tif
 291 | center_58.tif
 292 | center_58.tif
 293 | center_58.tif
 294 | center_58.tif
 295 | center_58.tif
 296 | center_59.tif
 297 | center_59.tif
 298 | center_59.tif
 299 | center_59.tif
 300 | center_59.tif
 301 | center_60.tif
 302 | center_60.tif
 303 | center_60.tif
 304 | center_60.tif
 305 | center_60.tif
 306 | center_61.tif
 307 | center_61.tif
 308 | center_61.tif
 309 | center_61.tif
 310 | center_61.tif
 311 | center_62.tif
 312 | center_62.tif
 313 | center_62.tif
 314 | center_62.tif
 315 | center_62.tif
 316 | center_63.tif
 317 | center_63.tif
 318 | center_63.tif
 319 | center_63.tif
 320 | center_63.tif
 321 | center_64.tif
 322 | center_64.tif
 323 | center_64.tif
 324 | center_64.tif
 325 | center_64.tif
 326 | center_65.tif
 327 | center_65.tif
 328 | center_65.tif
 329 | center_65.tif
 330 | center_65.tif
 331 | center_66.tif
 332 | center_66.tif
 333 | center_66.tif
 334 | center_66.tif
 335 | center_66.tif
 336 | center_67.tif
 337 | center_67.tif
 338 | center_67.tif
 339 | center_67.tif
 340 | center_67.tif
 341 | center_68.tif
 342 | center_68.tif
 343 | center_68.tif
 344 | center_68.tif
 345 | center_68.tif
 346 | center_69.tif
 347 | center_69.tif
 348 | center_69.tif
 349 | center_69.tif
 350 | center_69.tif
 351 | center_70.tif
 352 | center_70.tif
 353 | center_70.tif
 354 | center_70.tif
 355 | center_70.tif
 356 | church_71.tif
 357 | church_71.tif
 358 | church_71.tif
 359 | church_71.tif
 360 | church_71.tif
 361 | church_72.tif
 362 | church_72.tif
 363 | church_72.tif
 364 | church_72.tif
 365 | church_72.tif
 366 | church_73.tif
 367 | church_73.tif
 368 | church_73.tif
 369 | church_73.tif
 370 | church_73.tif
 371 | church_74.tif
 372 | church_74.tif
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 374 | church_74.tif
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 376 | church_75.tif
 377 | church_75.tif
 378 | church_75.tif
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 380 | church_75.tif
 381 | church_76.tif
 382 | church_76.tif
 383 | church_76.tif
 384 | church_76.tif
 385 | church_76.tif
 386 | church_77.tif
 387 | church_77.tif
 388 | church_77.tif
 389 | church_77.tif
 390 | church_77.tif
 391 | church_78.tif
 392 | church_78.tif
 393 | church_78.tif
 394 | church_78.tif
 395 | church_78.tif
 396 | church_79.tif
 397 | church_79.tif
 398 | church_79.tif
 399 | church_79.tif
 400 | church_79.tif
 401 | church_80.tif
 402 | church_80.tif
 403 | church_80.tif
 404 | church_80.tif
 405 | church_80.tif
 406 | church_81.tif
 407 | church_81.tif
 408 | church_81.tif
 409 | church_81.tif
 410 | church_81.tif
 411 | church_82.tif
 412 | church_82.tif
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 414 | church_82.tif
 415 | church_82.tif
 416 | church_83.tif
 417 | church_83.tif
 418 | church_83.tif
 419 | church_83.tif
 420 | church_83.tif
 421 | commercial_84.tif
 422 | commercial_84.tif
 423 | commercial_84.tif
 424 | commercial_84.tif
 425 | commercial_84.tif
 426 | commercial_85.tif
 427 | commercial_85.tif
 428 | commercial_85.tif
 429 | commercial_85.tif
 430 | commercial_85.tif
 431 | commercial_86.tif
 432 | commercial_86.tif
 433 | commercial_86.tif
 434 | commercial_86.tif
 435 | commercial_86.tif
 436 | commercial_87.tif
 437 | commercial_87.tif
 438 | commercial_87.tif
 439 | commercial_87.tif
 440 | commercial_87.tif
 441 | commercial_88.tif
 442 | commercial_88.tif
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 444 | commercial_88.tif
 445 | commercial_88.tif
 446 | commercial_89.tif
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 449 | commercial_89.tif
 450 | commercial_89.tif
 451 | commercial_90.tif
 452 | commercial_90.tif
 453 | commercial_90.tif
 454 | commercial_90.tif
 455 | commercial_90.tif
 456 | commercial_91.tif
 457 | commercial_91.tif
 458 | commercial_91.tif
 459 | commercial_91.tif
 460 | commercial_91.tif
 461 | commercial_92.tif
 462 | commercial_92.tif
 463 | commercial_92.tif
 464 | commercial_92.tif
 465 | commercial_92.tif
 466 | commercial_93.tif
 467 | commercial_93.tif
 468 | commercial_93.tif
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 471 | commercial_94.tif
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 474 | commercial_94.tif
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 476 | commercial_95.tif
 477 | commercial_95.tif
 478 | commercial_95.tif
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 480 | commercial_95.tif
 481 | commercial_96.tif
 482 | commercial_96.tif
 483 | commercial_96.tif
 484 | commercial_96.tif
 485 | commercial_96.tif
 486 | commercial_97.tif
 487 | commercial_97.tif
 488 | commercial_97.tif
 489 | commercial_97.tif
 490 | commercial_97.tif
 491 | denseresidential_98.tif
 492 | denseresidential_98.tif
 493 | denseresidential_98.tif
 494 | denseresidential_98.tif
 495 | denseresidential_98.tif
 496 | denseresidential_99.tif
 497 | denseresidential_99.tif
 498 | denseresidential_99.tif
 499 | denseresidential_99.tif
 500 | denseresidential_99.tif
 501 | denseresidential_100.tif
 502 | denseresidential_100.tif
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 506 | denseresidential_101.tif
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 516 | denseresidential_103.tif
 517 | denseresidential_103.tif
 518 | denseresidential_103.tif
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 521 | denseresidential_104.tif
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 526 | denseresidential_105.tif
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 535 | denseresidential_106.tif
 536 | denseresidential_107.tif
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 541 | denseresidential_108.tif
 542 | denseresidential_108.tif
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 545 | denseresidential_108.tif
 546 | denseresidential_109.tif
 547 | denseresidential_109.tif
 548 | denseresidential_109.tif
 549 | denseresidential_109.tif
 550 | denseresidential_109.tif
 551 | denseresidential_110.tif
 552 | denseresidential_110.tif
 553 | denseresidential_110.tif
 554 | denseresidential_110.tif
 555 | denseresidential_110.tif
 556 | denseresidential_111.tif
 557 | denseresidential_111.tif
 558 | denseresidential_111.tif
 559 | denseresidential_111.tif
 560 | denseresidential_111.tif
 561 | desert_112.tif
 562 | desert_112.tif
 563 | desert_112.tif
 564 | desert_112.tif
 565 | desert_112.tif
 566 | desert_113.tif
 567 | desert_113.tif
 568 | desert_113.tif
 569 | desert_113.tif
 570 | desert_113.tif
 571 | desert_114.tif
 572 | desert_114.tif
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 575 | desert_114.tif
 576 | desert_115.tif
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 578 | desert_115.tif
 579 | desert_115.tif
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 581 | desert_116.tif
 582 | desert_116.tif
 583 | desert_116.tif
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 585 | desert_116.tif
 586 | desert_117.tif
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 596 | desert_119.tif
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 600 | desert_119.tif
 601 | desert_120.tif
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 603 | desert_120.tif
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 607 | desert_121.tif
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 609 | desert_121.tif
 610 | desert_121.tif
 611 | desert_122.tif
 612 | desert_122.tif
 613 | desert_122.tif
 614 | desert_122.tif
 615 | desert_122.tif
 616 | desert_123.tif
 617 | desert_123.tif
 618 | desert_123.tif
 619 | desert_123.tif
 620 | desert_123.tif
 621 | farmland_124.tif
 622 | farmland_124.tif
 623 | farmland_124.tif
 624 | farmland_124.tif
 625 | farmland_124.tif
 626 | farmland_125.tif
 627 | farmland_125.tif
 628 | farmland_125.tif
 629 | farmland_125.tif
 630 | farmland_125.tif
 631 | farmland_126.tif
 632 | farmland_126.tif
 633 | farmland_126.tif
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 635 | farmland_126.tif
 636 | farmland_127.tif
 637 | farmland_127.tif
 638 | farmland_127.tif
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 642 | farmland_128.tif
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 646 | farmland_129.tif
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 649 | farmland_129.tif
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 656 | farmland_131.tif
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 659 | farmland_131.tif
 660 | farmland_131.tif
 661 | farmland_132.tif
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 669 | farmland_133.tif
 670 | farmland_133.tif
 671 | farmland_134.tif
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 674 | farmland_134.tif
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 676 | farmland_135.tif
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 681 | farmland_136.tif
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 685 | farmland_136.tif
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 692 | farmland_138.tif
 693 | farmland_138.tif
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 696 | farmland_139.tif
 697 | farmland_139.tif
 698 | farmland_139.tif
 699 | farmland_139.tif
 700 | farmland_139.tif
 701 | farmland_140.tif
 702 | farmland_140.tif
 703 | farmland_140.tif
 704 | farmland_140.tif
 705 | farmland_140.tif
 706 | forest_141.tif
 707 | forest_141.tif
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1936 | stadium_387.tif
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1942 | stadium_388.tif
1943 | stadium_388.tif
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1946 | stadium_389.tif
1947 | stadium_389.tif
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1955 | stadium_390.tif
1956 | stadium_391.tif
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1963 | stadium_392.tif
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1965 | stadium_392.tif
1966 | stadium_393.tif
1967 | stadium_393.tif
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1971 | stadium_394.tif
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1973 | stadium_394.tif
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1975 | stadium_394.tif
1976 | stadium_395.tif
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1978 | stadium_395.tif
1979 | stadium_395.tif
1980 | stadium_395.tif
1981 | stadium_396.tif
1982 | stadium_396.tif
1983 | stadium_396.tif
1984 | stadium_396.tif
1985 | stadium_396.tif
1986 | stadium_397.tif
1987 | stadium_397.tif
1988 | stadium_397.tif
1989 | stadium_397.tif
1990 | stadium_397.tif
1991 | stadium_398.tif
1992 | stadium_398.tif
1993 | stadium_398.tif
1994 | stadium_398.tif
1995 | stadium_398.tif
1996 | stadium_399.tif
1997 | stadium_399.tif
1998 | stadium_399.tif
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2003 | stadium_400.tif
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2006 | stadium_401.tif
2007 | stadium_401.tif
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2009 | stadium_401.tif
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2011 | stadium_402.tif
2012 | stadium_402.tif
2013 | stadium_402.tif
2014 | stadium_402.tif
2015 | stadium_402.tif
2016 | stadium_403.tif
2017 | stadium_403.tif
2018 | stadium_403.tif
2019 | stadium_403.tif
2020 | stadium_403.tif
2021 | stadium_404.tif
2022 | stadium_404.tif
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2257 | storagetanks_451.tif
2258 | storagetanks_451.tif
2259 | storagetanks_451.tif
2260 | storagetanks_451.tif
2261 | 


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