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ADSH.py
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ADSH.py
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from utils.tools import *
from network import *
import os
import torch
import torch.optim as optim
from tqdm import tqdm
import time
import numpy as np
torch.multiprocessing.set_sharing_strategy('file_system')
# ADSH(AAAI2018)
# paper [Asymmetric Deep Supervised Hashing](https://cs.nju.edu.cn/lwj/paper/AAAI18_ADSH.pdf)
# code1 [ADSH matlab + pytorch](https://github.com/jiangqy/ADSH-AAAI2018)
# code2 [ADSH_pytorch](https://github.com/TreezzZ/ADSH_PyTorch)
def get_config():
config = {
"gamma": 200,
"num_samples": 2000,
"max_iter": 150,
"epoch": 3,
"test_map": 10,
# "optimizer": {"type": optim.SGD, "optim_params": {"lr": 0.001, "weight_decay": 5e-4}},
# "optimizer": {"type": optim.RMSprop, "optim_params": {"lr": 1e-5, "weight_decay": 10 ** -5}},
"optimizer": {"type": optim.Adam, "optim_params": {"lr": 1e-4, "weight_decay": 1e-5}},
"info": "[ADSH]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 64,
"net": AlexNet,
"dataset": "cifar10-1",
# "dataset": "nuswide_21",
"save_path": "save/ADSH",
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [48],
}
# if config["dataset"] == "nuswide_21":
# config["gamma"] = 0
config = config_dataset(config)
return config
def calc_sim(database_label, train_label):
S = (database_label @ train_label.t() > 0).float()
# soft constraint
r = S.sum() / (1 - S).sum()
S = S * (1 + r) - r
return S
def train_val(config, bit):
device = config["device"]
num_samples = config["num_samples"]
train_loader, test_loader, dataset_loader, num_train, num_test, num_dataset = get_data(config)
# get database_labels
clses = []
for _, cls, _ in tqdm(dataset_loader):
clses.append(cls)
database_labels = torch.cat(clses).to(device).float()
net = config["net"](bit).to(device)
optimizer = config["optimizer"]["type"](net.parameters(), **(config["optimizer"]["optim_params"]))
Best_mAP = 0
V = torch.zeros((num_dataset, bit)).to(device)
for iter in range(config["max_iter"]):
current_time = time.strftime('%H:%M:%S', time.localtime(time.time()))
print("%s[%2d/%2d][%s] bit:%d, dataset:%s, training...." % (
config["info"], iter + 1, config["max_iter"], current_time, bit, config["dataset"]), end="")
net.train()
# sampling and construct similarity matrix
select_index = np.random.permutation(range(num_dataset))[0: num_samples]
if "cifar" in config["dataset"]:
train_loader.dataset.data = np.array(dataset_loader.dataset.data)[select_index]
train_loader.dataset.targets = np.array(dataset_loader.dataset.targets)[select_index]
else:
train_loader.dataset.imgs = np.array(dataset_loader.dataset.imgs)[select_index].tolist()
sample_label = database_labels[select_index]
Sim = calc_sim(sample_label, database_labels)
U = torch.zeros((num_samples, bit)).to(device)
train_loss = 0
for epoch in range(config["epoch"]):
for image, label, ind in train_loader:
image = image.to(device)
label = label.to(device).float()
net.zero_grad()
S = calc_sim(label, database_labels)
u = net(image)
u = u.tanh()
U[ind, :] = u.data
square_loss = (u @ V.t() - bit * S).pow(2)
quantization_loss = config["gamma"] * (V[select_index[ind]] - u).pow(2)
loss = (square_loss.sum() + quantization_loss.sum()) / (num_train * u.size(0))
train_loss += loss.item()
loss.backward()
optimizer.step()
train_loss = train_loss / len(train_loader) / epoch
print("\b\b\b\b\b\b\b loss:%.3f" % (train_loss))
# learning binary codes: discrete coding
barU = torch.zeros((num_dataset, bit)).to(device)
barU[select_index, :] = U
# calculate Q
Q = -2 * bit * Sim.t() @ U - 2 * config["gamma"] * barU
for k in range(bit):
sel_ind = np.setdiff1d([ii for ii in range(bit)], k)
V_ = V[:, sel_ind]
U_ = U[:, sel_ind]
Uk = U[:, k]
Qk = Q[:, k]
# formula 10
V[:, k] = -(2 * V_ @ (U_.t() @ Uk) + Qk).sign()
if (iter + 1) % config["test_map"] == 0:
Best_mAP = validate(config, Best_mAP, test_loader, dataset_loader, net, bit, epoch, num_dataset)
def main():
config = get_config()
for bit in config["bit_list"]:
config["pr_curve_path"] = f"log/alexnet/ADSH_{config['dataset']}_{bit}.json"
train_val(config, bit)
if __name__ == "__main__":
main()