From 519a23ca5080bb57e6c56882de2a5192539a1ff2 Mon Sep 17 00:00:00 2001 From: zz Date: Mon, 14 Oct 2019 19:08:08 +0800 Subject: [PATCH] first commit --- README.md | 29 +++++++ __pycache__/net.cpython-37.pyc | Bin 0 -> 1141 bytes evaluation.py | 135 +++++++++++++++++++++++++++++ mAP.py | 109 +++++++++++++++++++++++ net.py | 26 ++++++ net.pyc | Bin 0 -> 1447 bytes precision_at_k.npy | Bin 0 -> 400128 bytes result/test_binary | Bin 0 -> 1920336 bytes result/test_label | Bin 0 -> 80331 bytes result/train_binary | Bin 0 -> 9600336 bytes result/train_label | Bin 0 -> 400329 bytes train.py | 153 +++++++++++++++++++++++++++++++++ 12 files changed, 452 insertions(+) create mode 100755 README.md create mode 100644 __pycache__/net.cpython-37.pyc create mode 100755 evaluation.py create mode 100755 mAP.py create mode 100755 net.py create mode 100644 net.pyc create mode 100644 precision_at_k.npy create mode 100644 result/test_binary create mode 100644 result/test_label create mode 100644 result/train_binary create mode 100644 result/train_label create mode 100755 train.py diff --git a/README.md b/README.md new file mode 100755 index 0000000..553e503 --- /dev/null +++ b/README.md @@ -0,0 +1,29 @@ +# pytorch_deephash + +## Introduction + +This is the Pytorch implementation of [Deep Learning of Binary Hash Codes for Fast Image Retrieval](https://github.com/kevinlin311tw/caffe-cvprw15), and can achieve more than 93% mAP in CIFAR10 dataset. + +## Environment + +> Pytorch 0.4.0 + +> torchvision 0.2.1 + +## Training + +```python +python train.py +``` + +You will get trained models in model folder by default, and models' names are their test accuracy. + +## Evaluation + +```shell +python mAP.py --pretrained {your saved model name in model folder by default} +``` + +## Tips + +There are some other args, which you can get them by adding '-h' or reading the code. diff --git a/__pycache__/net.cpython-37.pyc b/__pycache__/net.cpython-37.pyc new file mode 100644 index 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/dev/null +++ b/evaluation.py @@ -0,0 +1,135 @@ +import os +import argparse + +import numpy as np +from net import AlexNetPlusLatent + +from timeit import time + +import torch +import torch.nn as nn + +from torchvision import datasets, models, transforms +from torch.autograd import Variable +import torch.backends.cudnn as cudnn +import torch.optim.lr_scheduler + +parser = argparse.ArgumentParser(description='Deep Hashing evaluate mAP') +parser.add_argument('--pretrained', type=str, default=92, metavar='pretrained_model', + help='loading pretrained model(default = None)') +parser.add_argument('--bits', type=int, default=48, metavar='bts', + help='binary bits') +parser.add_argument('--path', type=str, default='model', metavar='P', + help='path directory') +args = parser.parse_args() + +def load_data(): + transform_train = transforms.Compose( + [transforms.Resize(227), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + transform_test = transforms.Compose( + [transforms.Resize(227), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + trainset = datasets.CIFAR10(root='./data', train=True, download=True, + transform=transform_train) + trainloader = torch.utils.data.DataLoader(trainset, batch_size=100, + shuffle=False, num_workers=2) + + testset = datasets.CIFAR10(root='./data', train=False, download=True, + transform=transform_test) + testloader = torch.utils.data.DataLoader(testset, batch_size=100, + shuffle=False, num_workers=2) + return trainloader, testloader + +def binary_output(dataloader): + net = AlexNetPlusLatent(args.bits) + net.load_state_dict(torch.load('./{}/{}'.format(args.path, args.pretrained))) + use_cuda = torch.cuda.is_available() + if use_cuda: + net.cuda() + full_batch_output = torch.cuda.FloatTensor() + full_batch_label = torch.cuda.LongTensor() + net.eval() + for batch_idx, (inputs, targets) in enumerate(dataloader): + if use_cuda: + inputs, targets = inputs.cuda(), targets.cuda() + inputs, targets = Variable(inputs), Variable(targets) + outputs, _ = net(inputs) + full_batch_output = torch.cat((full_batch_output, outputs.data), 0) + full_batch_label = torch.cat((full_batch_label, targets.data), 0) + return torch.round(full_batch_output), full_batch_label + +def precision(trn_binary, trn_label, tst_binary, tst_label): + trn_binary = trn_binary.cpu().numpy() + trn_binary = np.asarray(trn_binary, np.int32) + trn_label = trn_label.cpu().numpy() + tst_binary = tst_binary.cpu().numpy() + tst_binary = np.asarray(tst_binary, np.int32) + tst_label = tst_label.cpu().numpy() + classes = np.max(tst_label) + 1 + + # 写法冗余 + for i in range(classes): + if i == 0: + tst_sample_binary = tst_binary[np.random.RandomState(seed=i).permutation(np.where(tst_label==i)[0])[:100]] + tst_sample_label = np.array([i]).repeat(100) + continue + else: + tst_sample_binary = np.concatenate([tst_sample_binary, tst_binary[np.random.RandomState(seed=i).permutation(np.where(tst_label==i)[0])[:100]]]) + tst_sample_label = np.concatenate([tst_sample_label, np.array([i]).repeat(100)]) + + """ + + for i in range(classes): + tst + """ + query_times = tst_sample_binary.shape[0] + trainset_len = trn_binary.shape[0] + AP = np.zeros(query_times) + precision_radius = np.zeros(query_times) + Ns = np.arange(1, trainset_len + 1) + sum_tp = np.zeros(trainset_len) + for i in range(query_times): + print('Query ', i+1) + query_label = tst_sample_label[i] + query_binary = tst_sample_binary[i,:] + query_result = np.count_nonzero(query_binary != trn_binary, axis=1) #don't need to divide binary length + sort_indices = np.argsort(query_result) + buffer_yes = np.equal(query_label, trn_label[sort_indices]).astype(int) + P = np.cumsum(buffer_yes) / Ns + precision_radius[i] = P[np.where(np.sort(query_result)>2)[0][0]-1] + AP[i] = np.sum(P * buffer_yes) /sum(buffer_yes) + sum_tp = sum_tp + np.cumsum(buffer_yes) + precision_at_k = sum_tp / Ns / query_times + index = [100, 200, 400, 600, 800, 1000] + index = [i - 1 for i in index] + print('precision at k:', precision_at_k[index]) + np.save('precision_at_k', precision_at_k) + print('precision within Hamming radius 2:', np.mean(precision_radius)) + map = np.mean(AP) + print('mAP:', map) + + + +if os.path.exists('./result/train_binary') and os.path.exists('./result/train_label') and \ + os.path.exists('./result/test_binary') and os.path.exists('./result/test_label') and args.pretrained == 0: + train_binary = torch.load('./result/train_binary') + train_label = torch.load('./result/train_label') + test_binary = torch.load('./result/test_binary') + test_label = torch.load('./result/test_label') + +else: + trainloader, testloader = load_data() + train_binary, train_label = binary_output(trainloader) + test_binary, test_label = binary_output(testloader) + if not os.path.isdir('result'): + os.mkdir('result') + torch.save(train_binary, './result/train_binary') + torch.save(train_label, './result/train_label') + torch.save(test_binary, './result/test_binary') + torch.save(test_label, './result/test_label') + + +precision(train_binary, train_label, test_binary, test_label) diff --git a/mAP.py b/mAP.py new file mode 100755 index 0000000..07686a5 --- /dev/null +++ b/mAP.py @@ -0,0 +1,109 @@ +import os +import argparse + +import numpy as np +from scipy.spatial.distance import hamming, cdist +from net import AlexNetPlusLatent + +from timeit import time + +import torch +import torch.nn as nn + +from torchvision import datasets, models, transforms +from torch.autograd import Variable +import torch.backends.cudnn as cudnn +import torch.optim.lr_scheduler + +parser = argparse.ArgumentParser(description='Deep Hashing evaluate mAP') +parser.add_argument('--pretrained', type=int, default=92, metavar='pretrained_model', + help='loading pretrained model(default = None)') +parser.add_argument('--bits', type=int, default=48, metavar='bts', + help='binary bits') +args = parser.parse_args() + +def load_data(): + transform_train = transforms.Compose( + [transforms.Scale(227), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + transform_test = transforms.Compose( + [transforms.Scale(227), + transforms.ToTensor(), + transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]) + trainset = datasets.CIFAR10(root='./data', train=True, download=True, + transform=transform_train) + trainloader = torch.utils.data.DataLoader(trainset, batch_size=100, + shuffle=False, num_workers=2) + + testset = datasets.CIFAR10(root='./data', train=False, download=True, + transform=transform_test) + testloader = torch.utils.data.DataLoader(testset, batch_size=100, + shuffle=False, num_workers=2) + return trainloader, testloader + +def binary_output(dataloader): + net = AlexNetPlusLatent(args.bits) + net.load_state_dict(torch.load('./model/%d' %args.pretrained)) + use_cuda = torch.cuda.is_available() + if use_cuda: + net.cuda() + full_batch_output = torch.cuda.FloatTensor() + full_batch_label = torch.cuda.LongTensor() + net.eval() + for batch_idx, (inputs, targets) in enumerate(dataloader): + if use_cuda: + inputs, targets = inputs.cuda(), targets.cuda() + inputs, targets = Variable(inputs, volatile=True), Variable(targets) + outputs, _ = net(inputs) + full_batch_output = torch.cat((full_batch_output, outputs.data), 0) + full_batch_label = torch.cat((full_batch_label, targets.data), 0) + return torch.round(full_batch_output), full_batch_label + +def precision(trn_binary, trn_label, tst_binary, tst_label): + trn_binary = trn_binary.cpu().numpy() + trn_binary = np.asarray(trn_binary, np.int32) + trn_label = trn_label.cpu().numpy() + tst_binary = tst_binary.cpu().numpy() + tst_binary = np.asarray(tst_binary, np.int32) + tst_label = tst_label.cpu().numpy() + query_times = tst_binary.shape[0] + trainset_len = train_binary.shape[0] + AP = np.zeros(query_times) + Ns = np.arange(1, trainset_len + 1) + total_time_start = time.time() + for i in range(query_times): + print('Query ', i+1) + query_label = tst_label[i] + query_binary = tst_binary[i,:] + query_result = np.count_nonzero(query_binary != trn_binary, axis=1) #don't need to divide binary length + sort_indices = np.argsort(query_result) + buffer_yes= np.equal(query_label, trn_label[sort_indices]).astype(int) + P = np.cumsum(buffer_yes) / Ns + AP[i] = np.sum(P * buffer_yes) /sum(buffer_yes) + map = np.mean(AP) + print(map) + print('total query time = ', time.time() - total_time_start) + + + +if os.path.exists('./result/train_binary') and os.path.exists('./result/train_label') and \ + os.path.exists('./result/test_binary') and os.path.exists('./result/test_label') and args.pretrained == 0: + train_binary = torch.load('./result/train_binary') + train_label = torch.load('./result/train_label') + test_binary = torch.load('./result/test_binary') + test_label = torch.load('./result/test_label') + +else: + trainloader, testloader = load_data() + train_binary, train_label = binary_output(trainloader) + test_binary, test_label = binary_output(testloader) + if not os.path.isdir('result'): + os.mkdir('result') + torch.save(train_binary, './result/train_binary') + torch.save(train_label, './result/train_label') + torch.save(test_binary, './result/test_binary') + torch.save(test_label, './result/test_label') + + +precision(train_binary, train_label, test_binary, test_label) diff --git a/net.py b/net.py new file mode 100755 index 0000000..252d6d6 --- /dev/null +++ b/net.py @@ -0,0 +1,26 @@ +import torch.nn as nn +from torchvision import models + +# pre-trained alex net model +alexnet_model = models.alexnet(pretrained=True) + +# nn.Module: Base class for all neural network modules. +# Custom class should also subclass this class +class AlexNetPlusLatent(nn.Module): + def __init__(self, bits): + super(AlexNetPlusLatent, self).__init__() + self.bits = 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