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QSMIH.py
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QSMIH.py
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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')
# QSMIH(Signal Processing: Image Communication)
# paper [Deep supervised hashing using quadratic spherical mutual information for efficient image retrieval](https://www.sciencedirect.com/science/article/pii/S0923596521000072)
# code [qsmi pytorch](https://github.com/passalis/qsmi)
# "net": AlexNet, "alpha": 0.001
# [QSMIH] epoch:150, bit:48, dataset:cifar10, MAP:0.777, Best MAP: 0.777
# [QSMIH] epoch:30, bit:48, dataset:nuswide_21, MAP:0.803, Best MAP: 0.811
# "net": AlexNet, "alpha": 0.01
# [QSMIH] epoch:45, bit:48, dataset:nuswide_21, MAP:0.821, Best MAP: 0.821
# [QSMIH] epoch:10, bit:48, dataset:coco, MAP:0.639, Best MAP: 0.639
# [QSMIH] epoch:70, bit:48, dataset:cifar10, MAP:0.762, Best MAP: 0.779
def get_config():
config = {
"alpha": 0.01,
"sigma": 0,
"use_square_clamp": True,
# "optimizer": {"type": optim.SGD, "optim_params": {"lr": 0.005, "weight_decay": 10 ** -5}},
"optimizer": {"type": optim.RMSprop, "optim_params": {"lr": 1e-5, "weight_decay": 10 ** -5}},
"info": "[QSMIH]",
"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": 5,
"save_path": "save/QSMIH",
# "device":torch.device("cpu"),
"device": torch.device("cuda:0"),
"bit_list": [48],
}
config = config_dataset(config)
return config
# modify from https://github.com/passalis/qsmi/blob/master/hashing/qmi_hashing.py
class QSMIHLoss(torch.nn.Module):
def __init__(self, config, bit):
super(QSMIHLoss, self).__init__()
def forward(self, u, y, ind, config):
u = u / (torch.sqrt(torch.sum(u ** 2, dim=1, keepdim=True)) + 1e-8)
Y = torch.mm(u, u.t())
Y = 0.5 * (Y + 1)
# Get the indicator matrix \Delta
# D = (y.view(y.shape[0], 1) == y.view(1, y.shape[0]))
D = (y @ y.t() > 0).float()
M = D.size(1) ** 2 / torch.sum(D)
if config["use_square_clamp"]:
Q_in = (D * Y - 1) ** 2
Q_btw = (1.0 / M) * Y ** 2
# Minimize clamped loss
L_QSMI = Q_in + Q_btw
else:
Q_in = D * Y
Q_btw = (1.0 / M) * Y
# Maximize QMI/QSMI
L_QSMI = Q_btw - Q_in
L_QSMI = L_QSMI.mean()
L_hash = config["alpha"] * (u.abs() - 1).abs().mean()
return L_QSMI + L_hash
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 = QSMIHLoss(config, bit)
Best_mAP = 0
for epoch in range(config["epoch"]):
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)