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CNNH.py
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CNNH.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
from itertools import product
from random import shuffle
from tqdm import tqdm
torch.multiprocessing.set_sharing_strategy('file_system')
# CNNH(AAAI2014)
# paper [Supervised Hashing for Image Retrieval via Image Representation Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/download/8137/8861)
# code[CNNH-pytorch](https://github.com/heheqianqian/CNNH)
# [CNNH] epoch:20, bit:48, dataset:cifar10-1, MAP:0.134, Best MAP: 0.134
# [CNNH] epoch:80, bit:48, dataset:nuswide_21, MAP:0.386, Best MAP: 0.386
def get_config():
config = {
"T": 10,
"H_save_path": "save/CNNH/",
"optimizer": {"type": optim.Adam, "optim_params": {"lr": 1e-5, "betas": (0.9, 0.999)}},
"info": "[CNNH]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 128,
"net": AlexNet,
# "net":ResNet,
# "dataset": "cifar10-1",
"dataset": "nuswide_21",
"epoch": 150,
"test_map": 10,
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [48],
}
config = config_dataset(config)
return config
class CNNHLoss(torch.nn.Module):
def __init__(self, config, train_labels, bit):
super(CNNHLoss, self).__init__()
S = (train_labels @ train_labels.t() > 0).float() * 2 - 1
# load H if exists
save_full_path = "%sH_T(%d)_bit(%d)_dataset(%s).pt" % (
config["H_save_path"], config["T"], bit, config["dataset"])
if os.path.exists(save_full_path):
print("loading ", save_full_path)
self.H = torch.load(save_full_path).to(config["device"])
else:
self.H = self.stage_one(config["num_train"], bit, config["T"], S, config["H_save_path"], config["dataset"],
config["device"])
def stage_one(self, n, q, T, S, H_save_path, dataset, device):
if not os.path.exists(H_save_path):
os.makedirs(H_save_path)
H = 2 * torch.rand((n, q)).to(device) - 1
L = H @ H.t() - q * S
permutation = list(product(range(n), range(q)))
for t in range(T):
H_temp = H.clone()
L_temp = L.clone()
shuffle(permutation)
for i, j in tqdm(permutation):
# formula 7
g_prime_Hij = 4 * L[i, :] @ H[:, j]
g_prime_prime_Hij = 4 * (H[:, j].t() @ H[:, j] + H[i, j].pow(2) + L[i, i])
# formula 6
d = (-g_prime_Hij / g_prime_prime_Hij).clamp(min=-1 - H[i, j], max=1 - H[i, j])
# formula 8
L[i, :] = L[i, :] + d * H[:, j].t()
L[:, i] = L[:, i] + d * H[:, j]
L[i, i] = L[i, i] + d * d
H[i, j] = H[i, j] + d
if L.pow(2).mean() >= L_temp.pow(2).mean():
H = H_temp
L = L_temp
save_full_path = "%sH_T(%d)_bit(%d)_dataset(%s).pt" % (H_save_path, t + 1, bit, dataset)
torch.save(H.sign().cpu(), save_full_path)
print("[CNNH stage 1][%d/%d] reconstruction loss:%.7f ,H save in %s" % (
t + 1, T, L.pow(2).mean().item(), save_full_path))
return H.sign()
def forward(self, u, y, ind, config):
loss = (u - self.H[ind]).pow(2).mean()
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)
# get database_labels
clses = []
for _, cls, _ in tqdm(train_loader):
clses.append(cls)
train_labels = torch.cat(clses).to(device).float()
optimizer = config["optimizer"]["type"](net.parameters(), **(config["optimizer"]["optim_params"]))
print("Stage 1: learning approximate hash codes.")
criterion = CNNHLoss(config, train_labels, bit)
print("Stage 2: learning images feature representation and hash functions.")
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)