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IDHN.py
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IDHN.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')
# IDHN(TMM2019)
# paper [Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval](https://arxiv.org/abs/1803.02987)
# code [IDHN-Tensorflow](https://github.com/pectinid16/IDHN)
# [IDHN] epoch:75, bit:48, dataset:mirflickr, MAP:0.740, Best MAP: 0.740
def get_config():
config = {
"alpha": 0.5,
"gamma": 0.1,
"lambda": 0.1,
# "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": "[IDHN]",
"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": 15,
"save_path": "save/IDHN",
# "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.q = bit
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 / (u.abs() + 1)
self.U[ind, :] = u.data
self.Y[ind, :] = y.float()
s = y @ self.Y.t()
norm = y.pow(2).sum(dim=1, keepdim=True).pow(0.5) @ self.Y.pow(2).sum(dim=1, keepdim=True).pow(0.5).t()
s = s / (norm + 0.00001)
M = (s > 0.99).float() + (s < 0.01).float()
inner_product = config["alpha"] * u @ self.U.t()
log_loss = torch.log(1 + torch.exp(-inner_product.abs())) + inner_product.clamp(min=0) - s * inner_product
mse_loss = (inner_product + self.q - 2 * s * self.q).pow(2)
loss1 = (M * log_loss + config["gamma"] * (1 - M) * mse_loss).mean()
loss2 = config["lambda"] * (u.abs() - 1).abs().mean()
return loss1 + loss2
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"]):
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:
# print("calculating test binary code......")
tst_binary, tst_label = compute_result(test_loader, net, device=device)
# print("calculating dataset binary code.......")\
trn_binary, trn_label = compute_result(dataset_loader, net, device=device)
# print("calculating map.......")
mAP = CalcTopMap(trn_binary.numpy(), tst_binary.numpy(), trn_label.numpy(), tst_label.numpy(),
config["topK"])
if mAP > Best_mAP:
Best_mAP = mAP
if "save_path" in config:
if not os.path.exists(config["save_path"]):
os.makedirs(config["save_path"])
print("save in ", config["save_path"])
np.save(os.path.join(config["save_path"], config["dataset"] + str(mAP) + "-" + "trn_binary.npy"),
trn_binary.numpy())
torch.save(net.state_dict(),
os.path.join(config["save_path"], config["dataset"] + "-" + str(mAP) + "-model.pt"))
print("%s epoch:%d, bit:%d, dataset:%s, MAP:%.3f, Best MAP: %.3f" % (
config["info"], epoch + 1, bit, config["dataset"], mAP, Best_mAP))
print(config)
if __name__ == "__main__":
config = get_config()
print(config)
for bit in config["bit_list"]:
train_val(config, bit)