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GreedyHash.py
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GreedyHash.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')
# GreedyHash(NIPS2018)
# paper [Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN](https://papers.nips.cc/paper/7360-greedy-hash-towards-fast-optimization-for-accurate-hash-coding-in-cnn.pdf)
# code [GreedyHash](https://github.com/ssppp/GreedyHash)
def get_config():
config = {
"alpha": 0.1,
"optimizer": {"type": optim.SGD, "epoch_lr_decrease": 30,
"optim_params": {"lr": 0.001, "weight_decay": 5e-4, "momentum": 0.9}},
# "optimizer": {"type": optim.RMSprop, "epoch_lr_decrease": 30,
# "optim_params": {"lr": 5e-5, "weight_decay": 5e-4}},
"info": "[GreedyHash]",
"resize_size": 256,
"crop_size": 224,
"batch_size": 64,
"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": 200,
"test_map": 3,
# "device":torch.device("cpu"),
"device": torch.device("cuda:1"),
"bit_list": [48],
}
config = config_dataset(config)
if config["dataset"] == "imagenet":
config["alpha"] = 1
config["optimizer"]["epoch_lr_decrease"] = 80
return config
class GreedyHashLoss(torch.nn.Module):
def __init__(self, config, bit):
super(GreedyHashLoss, self).__init__()
self.fc = torch.nn.Linear(bit, config["n_class"], bias=False).to(config["device"])
self.criterion = torch.nn.CrossEntropyLoss().to(config["device"])
def forward(self, u, onehot_y, ind, config):
b = GreedyHashLoss.Hash.apply(u)
# one-hot to label
y = onehot_y.argmax(axis=1)
y_pre = self.fc(b)
loss1 = self.criterion(y_pre, y)
loss2 = config["alpha"] * (u.abs() - 1).pow(3).abs().mean()
return loss1 + loss2
class Hash(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
# ctx.save_for_backward(input)
return input.sign()
@staticmethod
def backward(ctx, grad_output):
# input, = ctx.saved_tensors
# grad_output = grad_output.data
return grad_output
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)
criterion = GreedyHashLoss(config, bit)
optimizer = config["optimizer"]["type"](list(net.parameters())+list(criterion.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, 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)