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Unsupervised_BiHalf.py
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Unsupervised_BiHalf.py
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from utils.tools import *
from network import *
import os
import torch
import torch.optim as optim
import torch.nn.functional as F
import time
import numpy as np
torch.multiprocessing.set_sharing_strategy('file_system')
# Deep Unsupervised Image Hashing by Maximizing Bit Entropy(AAAI2021)
# paper [Deep Unsupervised Image Hashing by Maximizing Bit Entropy](https://arxiv.org/pdf/2012.12334.pdf)
# code [Deep-Unsupervised-Image-Hashing](https://github.com/liyunqianggyn/Deep-Unsupervised-Image-Hashing)
def get_config():
config = {
"gamma": 6,
"optimizer": {"type": optim.SGD, "epoch_lr_decrease": 30,
"optim_params": {"lr": 0.0001, "weight_decay": 5e-4, "momentum": 0.9}},
"info": "[BiHalf Unsupervised]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 64,
"net": BiHalfModelUnsupervised,
"dataset": "cifar10-2", # in paper BiHalf is "Cifar-10(I)"
"epoch": 200,
"test_map": 5,
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [64],
}
config = config_dataset(config)
config["topK"] = 1000
return config
class BiHalfModelUnsupervised(nn.Module):
def __init__(self, bit):
super(BiHalfModelUnsupervised, self).__init__()
self.vgg = models.vgg16(pretrained=True)
self.vgg.classifier = nn.Sequential(*list(self.vgg.classifier.children())[:6])
for param in self.vgg.parameters():
param.requires_grad = False
self.fc_encode = nn.Linear(4096, bit)
class Hash(torch.autograd.Function):
@staticmethod
def forward(ctx, U):
# Yunqiang for half and half (optimal transport)
_, index = U.sort(0, descending=True)
N, D = U.shape
B_creat = torch.cat((torch.ones([int(N / 2), D]), -torch.ones([N - int(N / 2), D]))).to(config["device"])
B = torch.zeros(U.shape).to(config["device"]).scatter_(0, index, B_creat)
ctx.save_for_backward(U, B)
return B
@staticmethod
def backward(ctx, g):
U, B = ctx.saved_tensors
add_g = (U - B) / (B.numel())
grad = g + config["gamma"] * add_g
return grad
def forward(self, x):
x = self.vgg.features(x)
x = x.view(x.size(0), -1)
x = self.vgg.classifier(x)
h = self.fc_encode(x)
b = BiHalfModelUnsupervised.Hash.apply(h)
if not self.training:
return b
else:
target_b = F.cosine_similarity(b[:x.size(0) // 2], b[x.size(0) // 2:])
target_x = F.cosine_similarity(x[:x.size(0) // 2], x[x.size(0) // 2:])
loss = F.mse_loss(target_b, target_x)
return 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"]))
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, lr:%.9f, dataset:%s, training...." % (
config["info"], epoch + 1, config["epoch"], current_time, bit, lr, config["dataset"]), end="")
net.train()
train_loss = 0
for image, _, ind in train_loader:
image = image.to(device)
optimizer.zero_grad()
loss = net(image)
train_loss += loss.item()
loss.backward()
optimizer.step()
train_loss = train_loss / len(train_loader)
print("\b\b\b\b\b\b\b loss:%.9f" % (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)