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train_autoencoder.py
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train_autoencoder.py
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# -*- coding:utf-8 -*-
#@Project: NestFuse for image fusion
#@Author: Li Hui, Jiangnan University
#@Email: hui_li_jnu@163.com
#@File : train_autoencoder.py
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import sys
import time
import numpy as np
from tqdm import tqdm, trange
import scipy.io as scio
import random
import torch
from torch.optim import Adam
from torch.autograd import Variable
import utils
from net import NestFuse_autoencoder
from args_fusion import args
import pytorch_msssim
def main():
original_imgs_path = utils.list_images(args.dataset)
train_num = 80000
original_imgs_path = original_imgs_path[:train_num]
random.shuffle(original_imgs_path)
for i in range(2,3):
# i = 3
train(i, original_imgs_path)
def train(i, original_imgs_path):
batch_size = args.batch_size
# load network model
# nest_model = FusionNet_gra()
input_nc = 1
output_nc = 1
# true for deeply supervision
# In our paper, deeply supervision strategy was not used.
deepsupervision = False
nb_filter = [64, 112, 160, 208, 256]
nest_model = NestFuse_autoencoder(nb_filter, input_nc, output_nc, deepsupervision)
if args.resume is not None:
print('Resuming, initializing using weight from {}.'.format(args.resume))
nest_model.load_state_dict(torch.load(args.resume))
print(nest_model)
optimizer = Adam(nest_model.parameters(), args.lr)
mse_loss = torch.nn.MSELoss()
ssim_loss = pytorch_msssim.msssim
if args.cuda:
nest_model.cuda()
tbar = trange(args.epochs)
print('Start training.....')
Loss_pixel = []
Loss_ssim = []
Loss_all = []
count_loss = 0
all_ssim_loss = 0.
all_pixel_loss = 0.
for e in tbar:
print('Epoch %d.....' % e)
# load training database
image_set_ir, batches = utils.load_dataset(original_imgs_path, batch_size)
nest_model.train()
count = 0
for batch in range(batches):
image_paths = image_set_ir[batch * batch_size:(batch * batch_size + batch_size)]
img = utils.get_train_images_auto(image_paths, height=args.HEIGHT, width=args.WIDTH, flag=False)
count += 1
optimizer.zero_grad()
img = Variable(img, requires_grad=False)
if args.cuda:
img = img.cuda()
# get fusion image
# encoder
en = nest_model.encoder(img)
# decoder
outputs = nest_model.decoder_train(en)
# resolution loss: between fusion image and visible image
x = Variable(img.data.clone(), requires_grad=False)
ssim_loss_value = 0.
pixel_loss_value = 0.
for output in outputs:
pixel_loss_temp = mse_loss(output, x)
ssim_loss_temp = ssim_loss(output, x, normalize=True)
ssim_loss_value += (1-ssim_loss_temp)
pixel_loss_value += pixel_loss_temp
ssim_loss_value /= len(outputs)
pixel_loss_value /= len(outputs)
# total loss
total_loss = pixel_loss_value + args.ssim_weight[i] * ssim_loss_value
total_loss.backward()
optimizer.step()
all_ssim_loss += ssim_loss_value.item()
all_pixel_loss += pixel_loss_value.item()
if (batch + 1) % args.log_interval == 0:
mesg = "{}\t SSIM weight {}\tEpoch {}:\t[{}/{}]\t pixel loss: {:.6f}\t ssim loss: {:.6f}\t total: {:.6f}".format(
time.ctime(), i, e + 1, count, batches,
all_pixel_loss / args.log_interval,
(args.ssim_weight[i] * all_ssim_loss) / args.log_interval,
(args.ssim_weight[i] * all_ssim_loss + all_pixel_loss) / args.log_interval
)
tbar.set_description(mesg)
Loss_pixel.append(all_pixel_loss / args.log_interval)
Loss_ssim.append(all_ssim_loss / args.log_interval)
Loss_all.append((args.ssim_weight[i] * all_ssim_loss + all_pixel_loss) / args.log_interval)
count_loss = count_loss + 1
all_ssim_loss = 0.
all_pixel_loss = 0.
if (batch + 1) % (200 * args.log_interval) == 0:
# save model
nest_model.eval()
nest_model.cpu()
save_model_filename = args.ssim_path[i] + '/' + "Epoch_" + str(e) + "_iters_" + str(count) + "_" + \
str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + args.ssim_path[
i] + ".model"
save_model_path = os.path.join(args.save_model_dir_autoencoder, save_model_filename)
torch.save(nest_model.state_dict(), save_model_path)
# save loss data
# pixel loss
loss_data_pixel = Loss_pixel
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "loss_pixel_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'loss_pixel': loss_data_pixel})
# SSIM loss
loss_data_ssim = Loss_ssim
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "loss_ssim_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'loss_ssim': loss_data_ssim})
# all loss
loss_data = Loss_all
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "loss_all_epoch_" + str(e) + "_iters_" + \
str(count) + "-" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'loss_all': loss_data})
nest_model.train()
nest_model.cuda()
tbar.set_description("\nCheckpoint, trained model saved at", save_model_path)
# pixel loss
loss_data_pixel = Loss_pixel
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "Final_loss_pixel_epoch_" + str(
args.epochs) + "_" + str(
time.ctime()).replace(' ', '_').replace(':', '_') + "_" + args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'final_loss_pixel': loss_data_pixel})
loss_data_ssim = Loss_ssim
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "Final_loss_ssim_epoch_" + str(
args.epochs) + "_" + str(
time.ctime()).replace(' ', '_').replace(':', '_') + "_" + args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'final_loss_ssim': loss_data_ssim})
# SSIM loss
loss_data = Loss_all
loss_filename_path = args.save_loss_dir + args.ssim_path[i] + '/' + "Final_loss_all_epoch_" + str(
args.epochs) + "_" + str(
time.ctime()).replace(' ', '_').replace(':', '_') + "_" + args.ssim_path[i] + ".mat"
scio.savemat(loss_filename_path, {'final_loss_all': loss_data})
# save model
nest_model.eval()
nest_model.cpu()
save_model_filename = args.ssim_path[i] + '/' "Final_epoch_" + str(args.epochs) + "_" + \
str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + args.ssim_path[i] + ".model"
save_model_path = os.path.join(args.save_model_dir_autoencoder, save_model_filename)
torch.save(nest_model.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
def check_paths(args):
try:
if not os.path.exists(args.vgg_model_dir):
os.makedirs(args.vgg_model_dir)
if not os.path.exists(args.save_model_dir):
os.makedirs(args.save_model_dir)
except OSError as e:
print(e)
sys.exit(1)
if __name__ == "__main__":
main()