├── .gitignore ├── LICENSE ├── README.md ├── checkpoints └── .gitkeep ├── data └── dataloader.py ├── logs └── .gitkeep ├── lsh.py ├── run.py └── utils └── evaluate.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Log 2 | *.log 3 | 4 | # Checkpoint 5 | *.pt 6 | 7 | # Byte-compiled / optimized / DLL files 8 | __pycache__/ 9 | *.py[cod] 10 | *$py.class 11 | 12 | # C extensions 13 | *.so 14 | 15 | # Distribution / packaging 16 | .Python 17 | build/ 18 | develop-eggs/ 19 | dist/ 20 | downloads/ 21 | eggs/ 22 | .eggs/ 23 | lib/ 24 | lib64/ 25 | parts/ 26 | sdist/ 27 | var/ 28 | wheels/ 29 | pip-wheel-metadata/ 30 | share/python-wheels/ 31 | *.egg-info/ 32 | .installed.cfg 33 | *.egg 34 | MANIFEST 35 | 36 | # PyInstaller 37 | # Usually these files are written by a python script from a template 38 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 39 | *.manifest 40 | *.spec 41 | 42 | # Installer logs 43 | pip-log.txt 44 | pip-delete-this-directory.txt 45 | 46 | # Unit test / coverage reports 47 | htmlcov/ 48 | .tox/ 49 | .nox/ 50 | .coverage 51 | .coverage.* 52 | .cache 53 | nosetests.xml 54 | coverage.xml 55 | *.cover 56 | *.py,cover 57 | .hypothesis/ 58 | .pytest_cache/ 59 | 60 | # Translations 61 | *.mo 62 | *.pot 63 | 64 | # Django stuff: 65 | *.log 66 | local_settings.py 67 | db.sqlite3 68 | db.sqlite3-journal 69 | 70 | # Flask stuff: 71 | instance/ 72 | .webassets-cache 73 | 74 | # Scrapy stuff: 75 | .scrapy 76 | 77 | # Sphinx documentation 78 | docs/_build/ 79 | 80 | # PyBuilder 81 | target/ 82 | 83 | # Jupyter Notebook 84 | .ipynb_checkpoints 85 | 86 | # IPython 87 | profile_default/ 88 | ipython_config.py 89 | 90 | # pyenv 91 | .python-version 92 | 93 | # pipenv 94 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 95 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 96 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 97 | # install all needed dependencies. 98 | #Pipfile.lock 99 | 100 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 101 | __pypackages__/ 102 | 103 | # Celery stuff 104 | celerybeat-schedule 105 | celerybeat.pid 106 | 107 | # SageMath parsed files 108 | *.sage.py 109 | 110 | # Environments 111 | .env 112 | .venv 113 | env/ 114 | venv/ 115 | ENV/ 116 | env.bak/ 117 | venv.bak/ 118 | 119 | # Spyder project settings 120 | .spyderproject 121 | .spyproject 122 | 123 | # Rope project settings 124 | .ropeproject 125 | 126 | # mkdocs documentation 127 | /site 128 | 129 | # mypy 130 | .mypy_cache/ 131 | .dmypy.json 132 | dmypy.json 133 | 134 | # Pyre type checker 135 | .pyre/ 136 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Similarity Search in High Dimensions via Hashing 2 | 3 | ## REQUIREMENTS 4 | `pip install -r requirements.txt` 5 | 6 | 1. pytorch >= 1.0 7 | 2. loguru 8 | 9 | ## DATASETS 10 | [cifar10-gist.mat](https://pan.baidu.com/s/1qE9KiAOTNs5ORn_WoDDwUg) password: umb6 11 | 12 | [cifar-10_alexnet.t](https://pan.baidu.com/s/1ciJIYGCfS3m0marQvatNjQ) password: f1b7 13 | 14 | [nus-wide-tc21_alexnet.t](https://pan.baidu.com/s/1YglFwoxB-3j7xTEyAc8ykw) password: vfeu 15 | 16 | [imagenet-tc100_alexnet.t](https://pan.baidu.com/s/1ayv4wdtCOzEDsJy01SjRew) password: 6w5i 17 | 18 | ## USAGE 19 | ``` 20 | usage: run.py [-h] [--dataset DATASET] [--root ROOT] 21 | [--code-length CODE_LENGTH] [--topk TOPK] [--gpu GPU] 22 | 23 | LSH_PyTorch 24 | 25 | optional arguments: 26 | -h, --help show this help message and exit 27 | --dataset DATASET Dataset name. 28 | --root ROOT Path of dataset 29 | --code-length CODE_LENGTH 30 | Binary hash code length.(default: 31 | 8,16,24,32,48,64,96,128) 32 | --topk TOPK Calculate top k data map.(default: all) 33 | --gpu GPU Using gpu.(default: False) 34 | ``` 35 | 36 | ## EXPERIMENTS 37 | cifar10-gist dataset. 1000 query images, 59000 retrieval images, MAP@ALL. 38 | 39 | cifar-10-alexnet dataset. Alexnet features, 1000 query images, 59000 retrieval images, MAP@ALL. 40 | 41 | nus-wide-tc21-alexnet dataset. Alexnet features, top 21 classes, 2100 query images, 193734 retrieval images, MAP@5000. 42 | 43 | imagenet-tc100-alexnet dataset. Alexnet features, top 100 classes, 5000 query images, 130000 retrieval images, MAP@1000. 44 | 45 | 46 | Bits | 8 | 16 | 24 | 32 | 48 | 64 | 96 | 128 47 | --- | --- | --- | --- | --- | --- | --- | --- | --- 48 | cifar10-gist@ALL | 0.1138 | 0.1191 | 0.1195 | 0.1288 | 0.1349 | 0.1436 | 0.1536 | 0.1521 49 | cifar10-alexnet@ALL | 0.1463 | 0.1290 | 0.1416 | 0.1588 | 0.1686 | 0.1757 | 0.1860 | 0.2121 50 | nus-wide-tc21-alexnet@5000 | 0.3905 | 0.4632 | 0.4836 | 0.5243 | 0.6012 | 0.6051 | 0.6513 | 0.6921 51 | imagenet-tc100-alexnet@1000 | 0.0536 | 0.0685 | 0.0952 | 0.1290 | 0.1861 | 0.2326 | 0.2909 | 0.3410 52 | -------------------------------------------------------------------------------- /checkpoints/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TreezzZ/LSH_PyTorch/bcaf566d71024ce1483bc04ad61b3d8a3e5d8150/checkpoints/.gitkeep -------------------------------------------------------------------------------- /data/dataloader.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import numpy as np 3 | import scipy.io as sio 4 | 5 | 6 | def load_data(dataset, root): 7 | """ 8 | Load dataset. 9 | 10 | Args 11 | dataset(str): Dataset name. 12 | root(str): Path of dataset. 13 | """ 14 | if dataset == 'cifar10-gist': 15 | return load_data_gist(root) 16 | elif dataset == 'cifar-10' or dataset == 'nus-wide-tc21' or dataset == 'imagenet-tc100': 17 | return _load_data(root) 18 | else: 19 | raise ValueError('Invalid dataset name!') 20 | 21 | 22 | def _load_data(root): 23 | """ 24 | Load alexnet fc7 features. 25 | 26 | Args 27 | root(str): Path of dataset. 28 | 29 | Returns 30 | train_data(torch.Tensor, 5000*4096): Training data. 31 | train_targets(torch.Tensor, 5000*10): One-hot training targets. 32 | query_data(torch.Tensor, 1000*4096): Query data. 33 | query_targets(torch.Tensor, 1000*10): One-hot query targets. 34 | retrieval_data(torch.Tensor, 59000*4096): Retrieval data. 35 | retrieval_targets(torch.Tensor, 59000*10): One-hot retrieval targets. 36 | """ 37 | data = torch.load(root) 38 | train_data = data['train_features'] 39 | train_targets = data['train_targets'] 40 | query_data = data['query_features'] 41 | query_targets = data['query_targets'] 42 | retrieval_data = data['retrieval_features'] 43 | retrieval_targets = data['retrieval_targets'] 44 | 45 | # Normalization 46 | mean = retrieval_data.mean() 47 | std = retrieval_data.std() 48 | train_data = (train_data - mean) / std 49 | query_data = (query_data - mean) / std 50 | retrieval_data = (retrieval_data - mean) / std 51 | 52 | return train_data, train_targets, query_data, query_targets, retrieval_data, retrieval_targets 53 | 54 | 55 | def load_data_gist(root): 56 | """ 57 | Load cifar10-gist dataset. 58 | 59 | Args 60 | root(str): Path of dataset. 61 | 62 | Returns 63 | train_data(torch.Tensor, num_train*512): Training data. 64 | train_targets(torch.Tensor, num_train*10): One-hot training targets. 65 | query_data(torch.Tensor, num_query*512): Query data. 66 | query_targets(torch.Tensor, num_query*10): One-hot query targets. 67 | retrieval_data(torch.Tensor, num_train*512): Retrieval data. 68 | retrieval_targets(torch.Tensor, num_train*10): One-hot retrieval targets. 69 | """ 70 | # Load data 71 | mat_data = sio.loadmat(root) 72 | query_data = mat_data['testdata'] 73 | query_targets = mat_data['testgnd'].astype(np.int) 74 | retrieval_data = mat_data['traindata'] 75 | retrieval_targets = mat_data['traingnd'].astype(np.int) 76 | 77 | # One-hot 78 | query_targets = encode_onehot(query_targets) 79 | retrieval_targets = encode_onehot(retrieval_targets) 80 | 81 | # Normalization 82 | data = np.concatenate((query_data, retrieval_data), axis=0) 83 | data = (data - data.mean()) / data.std() 84 | query_data = data[:query_data.shape[0], :] 85 | retrieval_data = data[query_data.shape[0]:, :] 86 | 87 | # Sample training data 88 | num_train = 5000 89 | train_index = np.random.permutation(len(retrieval_data))[:num_train] 90 | train_data = retrieval_data[train_index, :] 91 | train_targets = retrieval_targets[train_index, :] 92 | 93 | train_data = torch.from_numpy(train_data).float() 94 | train_targets = torch.from_numpy(train_targets).float() 95 | query_data = torch.from_numpy(query_data).float() 96 | query_targets = torch.from_numpy(query_targets).float() 97 | retrieval_data = torch.from_numpy(retrieval_data).float() 98 | retrieval_targets = torch.from_numpy(retrieval_targets).float() 99 | 100 | 101 | 102 | return train_data, train_targets, query_data, query_targets, train_data, train_targets 103 | 104 | 105 | def encode_onehot(labels, num_classes=10): 106 | """ 107 | One-hot labels. 108 | 109 | Args: 110 | labels (numpy.ndarray): labels. 111 | num_classes (int): Number of classes. 112 | 113 | Returns: 114 | onehot_labels (numpy.ndarray): one-hot labels. 115 | """ 116 | onehot_labels = np.zeros((len(labels), num_classes)) 117 | 118 | for i in range(len(labels)): 119 | onehot_labels[i, labels[i]] = 1 120 | 121 | return onehot_labels 122 | -------------------------------------------------------------------------------- /logs/.gitkeep: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TreezzZ/LSH_PyTorch/bcaf566d71024ce1483bc04ad61b3d8a3e5d8150/logs/.gitkeep -------------------------------------------------------------------------------- /lsh.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from utils.evaluate import mean_average_precision, pr_curve 4 | 5 | 6 | def train( 7 | query_data, 8 | query_targets, 9 | retrieval_data, 10 | retrieval_targets, 11 | code_length, 12 | device, 13 | topk, 14 | ): 15 | """ 16 | Training model 17 | 18 | Args 19 | query_data(torch.Tensor): Query data. 20 | query_targets(torch.Tensor): One-hot query targets. 21 | retrieval_data(torch.Tensor): Retrieval data. 22 | retrieval_targets(torch.Tensor): One-hot retrieval targets. 23 | code_length(int): Hash code length. 24 | device(torch.device): GPU or CPU. 25 | topk(int): Calculate top k data map. 26 | 27 | Returns 28 | checkpoint(dict): Checkpoint. 29 | """ 30 | # Initialization 31 | query_data, retrieval_data, query_targets, retrieval_targets = query_data.to(device), retrieval_data.to(device), query_targets.to(device), retrieval_targets.to(device) 32 | 33 | # Generate random projection matrix 34 | W = torch.randn(query_data.shape[1], code_length).to(device) 35 | 36 | # Generate query and retrieval code 37 | query_code = (query_data @ W).sign() 38 | retrieval_code = (retrieval_data @ W).sign() 39 | 40 | # Compute map 41 | mAP = mean_average_precision( 42 | query_code, 43 | retrieval_code, 44 | query_targets, 45 | retrieval_targets, 46 | device, 47 | topk, 48 | ) 49 | 50 | # P-R curve 51 | P, R = pr_curve( 52 | query_code, 53 | retrieval_code, 54 | query_targets, 55 | retrieval_targets, 56 | device, 57 | ) 58 | 59 | # Save checkpoint 60 | checkpoint = { 61 | 'qB': query_code, 62 | 'rB': retrieval_code, 63 | 'qL': query_targets, 64 | 'rL': retrieval_targets, 65 | 'W': W, 66 | 'P': P, 67 | 'R': R, 68 | 'map': mAP, 69 | } 70 | torch.save(checkpoint, 'checkpoints/code_{}_map_{:.4f}.pt'.format(code_length, mAP)) 71 | 72 | return checkpoint 73 | -------------------------------------------------------------------------------- /run.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import torch 3 | import random 4 | import numpy as np 5 | import lsh 6 | 7 | from loguru import logger 8 | from data.dataloader import load_data 9 | 10 | 11 | def run(): 12 | # Load configuration 13 | args = load_config() 14 | logger.add('logs/{}.log'.format(args.dataset), rotation='500 MB', level='INFO') 15 | logger.info(args) 16 | 17 | random.seed(args.seed) 18 | torch.manual_seed(args.seed) 19 | torch.cuda.manual_seed(args.seed) 20 | np.random.seed(args.seed) 21 | 22 | # Load dataset 23 | _, _, query_data, query_targets, retrieval_data, retrieval_targets = load_data(args.dataset, args.root) 24 | 25 | # Training 26 | for code_length in args.code_length: 27 | checkpoint = lsh.train( 28 | query_data, 29 | query_targets, 30 | retrieval_data, 31 | retrieval_targets, 32 | code_length, 33 | args.device, 34 | args.topk, 35 | ) 36 | logger.info('[code length:{}][map:{:.4f}]'.format(code_length, checkpoint['map'])) 37 | 38 | 39 | def load_config(): 40 | """ 41 | Load configuration. 42 | 43 | Args 44 | None 45 | 46 | Returns 47 | args(argparse.ArgumentParser): Configuration. 48 | """ 49 | parser = argparse.ArgumentParser(description='LSH_PyTorch') 50 | parser.add_argument('--dataset', type=str, 51 | help='Dataset name.') 52 | parser.add_argument('--root', type=str, 53 | help='Path of dataset') 54 | parser.add_argument('--code-length', default='8,16,24,32,48,64,96,128', type=str, 55 | help='Binary hash code length.(default: 8,16,24,32,48,64,96,128)') 56 | parser.add_argument('--topk', default=-1, type=int, 57 | help='Calculate top k data map.(default: all)') 58 | parser.add_argument('--gpu', default=None, type=int, 59 | help='Using gpu.(default: False)') 60 | parser.add_argument('--seed', default=3367, type=int, 61 | help='Random seed.(default: 3367)') 62 | 63 | args = parser.parse_args() 64 | 65 | # GPU 66 | if args.gpu is None: 67 | args.device = torch.device("cpu") 68 | else: 69 | args.device = torch.device("cuda:%d" % args.gpu) 70 | 71 | # Hash code length 72 | args.code_length = list(map(int, args.code_length.split(','))) 73 | 74 | return args 75 | 76 | 77 | if __name__ == "__main__": 78 | run() 79 | 80 | -------------------------------------------------------------------------------- /utils/evaluate.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def mean_average_precision(query_code, 5 | retrieval_code, 6 | query_targets, 7 | retrieval_targets, 8 | device, 9 | topk=None, 10 | ): 11 | """ 12 | Calculate mean average precision(map). 13 | 14 | Args: 15 | query_code (torch.Tensor): Query data hash code. 16 | retrieval_code (torch.Tensor): Retrieval data hash code. 17 | query_targets (torch.Tensor): Query data targets, one-hot 18 | retrieval_targets (torch.Tensor): retrieval data targets, one-host 19 | device (torch.device): Using CPU or GPU. 20 | topk (int): Calculate top k data map. 21 | 22 | Returns: 23 | meanAP (float): Mean Average Precision. 24 | """ 25 | num_query = query_targets.shape[0] 26 | mean_AP = 0.0 27 | 28 | for i in range(num_query): 29 | # Retrieve images from database 30 | retrieval = (query_targets[i, :] @ retrieval_targets.t() > 0).float() 31 | 32 | # Calculate hamming distance 33 | hamming_dist = 0.5 * (retrieval_code.shape[1] - query_code[i, :] @ retrieval_code.t()) 34 | 35 | # Arrange position according to hamming distance 36 | retrieval = retrieval[torch.argsort(hamming_dist)][:topk] 37 | 38 | # Retrieval count 39 | retrieval_cnt = retrieval.sum().int().item() 40 | 41 | # Can not retrieve images 42 | if retrieval_cnt == 0: 43 | continue 44 | 45 | # Generate score for every position 46 | score = torch.linspace(1, retrieval_cnt, retrieval_cnt).to(device) 47 | 48 | # Acquire index 49 | index = (torch.nonzero(retrieval == 1).squeeze() + 1.0).float() 50 | 51 | mean_AP += (score / index).mean() 52 | 53 | mean_AP = mean_AP / num_query 54 | return mean_AP.item() 55 | 56 | 57 | def pr_curve(query_code, retrieval_code, query_targets, retrieval_targets, device): 58 | """ 59 | P-R curve. 60 | 61 | Args 62 | query_code(torch.Tensor): Query hash code. 63 | retrieval_code(torch.Tensor): Retrieval hash code. 64 | query_targets(torch.Tensor): Query targets. 65 | retrieval_targets(torch.Tensor): Retrieval targets. 66 | device (torch.device): Using CPU or GPU. 67 | 68 | Returns 69 | P(torch.Tensor): Precision. 70 | R(torch.Tensor): Recall. 71 | """ 72 | num_query = query_code.shape[0] 73 | num_bit = query_code.shape[1] 74 | P = torch.zeros(num_query, num_bit + 1).to(device) 75 | R = torch.zeros(num_query, num_bit + 1).to(device) 76 | for i in range(num_query): 77 | gnd = (query_targets[i].unsqueeze(0).mm(retrieval_targets.t()) > 0).float().squeeze() 78 | tsum = torch.sum(gnd) 79 | if tsum == 0: 80 | continue 81 | hamm = 0.5 * (retrieval_code.shape[1] - query_code[i, :] @ retrieval_code.t()) 82 | tmp = (hamm <= torch.arange(0, num_bit + 1).reshape(-1, 1).float().to(device)).float() 83 | total = tmp.sum(dim=-1) 84 | total = total + (total == 0).float() * 0.1 85 | t = gnd * tmp 86 | count = t.sum(dim=-1) 87 | p = count / total 88 | r = count / tsum 89 | P[i] = p 90 | R[i] = r 91 | mask = (P > 0).float().sum(dim=0) 92 | mask = mask + (mask == 0).float() * 0.1 93 | P = P.sum(dim=0) / mask 94 | R = R.sum(dim=0) / mask 95 | 96 | return P, R 97 | --------------------------------------------------------------------------------