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DBDH.py
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DBDH.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')
# DBDH(Neurocomputing2020)
# paper [Deep balanced discrete hashing for image retrieval](https://www.sciencedirect.com/science/article/abs/pii/S0925231220306032)
# [DBDH] epoch:150, bit:48, dataset:cifar10-1, MAP:0.792, Best MAP: 0.793
# [DBDH] epoch:80, bit:48, dataset:nuswide_21, MAP:0.833, Best MAP: 0.834
def get_config():
config = {
"alpha": 0.1,
# "p": 1,
"p": 2,
# "optimizer": {"type": optim.SGD, "epoch_lr_decrease": 50,
# "optim_params": {"lr": 0.1, "weight_decay": 5e-4, "momentum": 0.9}},
"optimizer": {"type": optim.RMSprop, "epoch_lr_decrease": 50,
"optim_params": {"lr": 1e-5, "weight_decay": 10 ** -5}},
"info": "[DBDH]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 128,
"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/DBDH",
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [48],
}
config = config_dataset(config)
return config
class DPSHLoss(torch.nn.Module):
def __init__(self, config, bit):
super(DPSHLoss, self).__init__()
self.U = torch.zeros(config["num_train"], bit).float().to(config["device"])
self.Y = torch.zeros(config["num_train"], config["n_class"]).float().to(config["device"])
def forward(self, u, y, ind, config):
u = u.clamp(min=-1, max=1)
self.U[ind, :] = u.data
self.Y[ind, :] = y.float()
s = (y @ self.Y.t() > 0).float()
inner_product = u @ self.U.t() * 0.5
likelihood_loss = (1 + (-(inner_product.abs())).exp()).log() + inner_product.clamp(min=0) - s * inner_product
likelihood_loss = likelihood_loss.mean()
if config["p"] == 1:
quantization_loss = config["alpha"] * u.mean(dim=1).abs().mean()
else:
quantization_loss = config["alpha"] * u.mean(dim=1).pow(2).mean()
return likelihood_loss + quantization_loss
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 = DPSHLoss(config, bit)
Best_mAP = 0
for epoch in range(config["epoch"]):
lr = config["optimizer"]["optim_params"]["lr"] * (0.1 ** (epoch // config["optimizer"]["epoch_lr_decrease"]))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
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)