-
Notifications
You must be signed in to change notification settings - Fork 117
/
DSDH.py
147 lines (112 loc) · 4.61 KB
/
DSDH.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
from utils.tools import *
from network import *
import os
import torch
import torch.optim as optim
import time
import numpy as np
torch.multiprocessing.set_sharing_strategy('file_system')
# DSDH(NIPS2017)
# paper [Deep Supervised Discrete Hashing](https://papers.nips.cc/paper/6842-deep-supervised-discrete-hashing.pdf)
# code [DSDH_PyTorch](https://github.com/TreezzZ/DSDH_PyTorch)
def get_config():
config = {
"alpha": 1,
"nu": 1,
"mu": 1,
"eta": 55,
"dcc_iter": 10,
# "optimizer":{"type": optim.SGD, "optim_params": {"lr": 0.05, "weight_decay": 10 ** -5}, "lr_type": "step"},
"optimizer": {"type": optim.RMSprop, "optim_params": {"lr": 1e-5, "weight_decay": 10 ** -5}, "lr_type": "step"},
"info": "[DSDH]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 64,
"net": AlexNet,
# "net":ResNet,
# "dataset": "cifar10",
"dataset": "cifar10-1",
# "dataset": "cifar10-2",
# "dataset": "coco",
# "dataset": "mirflickr",
# "dataset": "voc2012",
# "dataset": "imagenet",
# "dataset": "nuswide_21",
# "dataset": "nuswide_21_m",
# "dataset": "nuswide_81_m",
"epoch": 150,
"test_map": 15,
# "save_path": "save/DSDH",
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [48],
}
config = config_dataset(config)
return config
class DSDHLoss(torch.nn.Module):
def __init__(self, config, bit):
super(DSDHLoss, self).__init__()
self.U = torch.zeros(bit, config["num_train"]).float().to(config["device"])
self.B = torch.zeros(bit, config["num_train"]).float().to(config["device"])
self.Y = torch.zeros(config["n_class"], config["num_train"]).float().to(config["device"])
def forward(self, u, y, ind, config):
self.U[:, ind] = u.t().data
self.Y[:, ind] = y.t()
# self.updateBandW(config["device"])
inner_product = u @ self.U * 0.5
s = (y @ self.Y > 0).float()
likelihood_loss = (1 + (-inner_product.abs()).exp()).log() + inner_product.clamp(min=0) - s * inner_product
likelihood_loss = likelihood_loss.mean()
# Classification loss
cl_loss = (y.t() - self.W.t() @ self.B[:, ind]).pow(2).mean()
# Regularization loss
reg_loss = self.W.pow(2).mean()
loss = likelihood_loss + config["mu"] * cl_loss + config["nu"] * reg_loss
return loss
def updateBandW(self, device):
B = self.B
for dit in range(config["dcc_iter"]):
# W-step
W = torch.inverse(B @ B.t() + config["nu"] / config["mu"] * torch.eye(bit).to(device)) @ B @ self.Y.t()
for i in range(B.shape[0]):
P = W @ self.Y + config["eta"] / config["mu"] * self.U
p = P[i, :]
w = W[i, :]
W_prime = torch.cat((W[:i, :], W[i + 1:, :]))
B_prime = torch.cat((B[:i, :], B[i + 1:, :]))
B[i, :] = (p - B_prime.t() @ W_prime @ w).sign()
self.B = B
self.W = W
def train_val(config, bit):
device = config["device"]
train_loader, test_loader, dataset_loader, num_train, num_test, num_dataset = get_data(config)
config["num_train"] = num_train
net = config["net"](bit).to(device)
optimizer = config["optimizer"]["type"](net.parameters(), **(config["optimizer"]["optim_params"]))
criterion = DSDHLoss(config, bit)
Best_mAP = 0
for epoch in range(config["epoch"]):
criterion.updateBandW(config["device"])
current_time = time.strftime('%H:%M:%S', time.localtime(time.time()))
print("%s[%2d/%2d][%s] bit:%d, dataset:%s, training...." % (
config["info"], epoch + 1, config["epoch"], current_time, bit, config["dataset"]), end="")
net.train()
train_loss = 0
for image, label, ind in train_loader:
image = image.to(device)
label = label.to(device)
optimizer.zero_grad()
u = net(image)
loss = criterion(u, label.float(), ind, config)
train_loss += loss.item()
loss.backward()
optimizer.step()
train_loss = train_loss / len(train_loader)
print("\b\b\b\b\b\b\b loss:%.3f" % (train_loss))
if (epoch + 1) % config["test_map"] == 0:
Best_mAP = validate(config, Best_mAP, test_loader, dataset_loader, net, bit, epoch, num_dataset)
if __name__ == "__main__":
config = get_config()
print(config)
for bit in config["bit_list"]:
train_val(config, bit)