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train.py
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# Training DenseFuse network
# auto-encoder
import os
import sys
import time
from utils import gradient
import numpy as np
from tqdm import tqdm, trange
import scipy.io as scio
import random
import torch
from torch.optim import Adam
from testMat import showLossChart
from torch.autograd import Variable
import utils
from net import GhostFusion_net
from args_fusion import args
import pytorch_msssim
def main():
# os.environ["CUDA_VISIBLE_DEVICES"] = "3"
original_imgs_path = utils.list_images(args.dataset)
#train_num = 40000
train_num = 70000
#train_num = 50;
original_imgs_path = original_imgs_path[:train_num]
random.shuffle(original_imgs_path)
# for i in range(5):
i = 2
train(i, original_imgs_path)
def train(i, original_imgs_path):
batch_size = args.batch_size
# load network model, RGB
in_c = 1 # 1 - gray; 3 - RGB
if in_c == 1:
img_model = 'L'
else:
img_model = 'RGB'
input_nc = in_c
output_nc = in_c
densefuse_model = GhostFusion_net(input_nc, output_nc)
if args.resume is not None:
print('Resuming, initializing using weight from {}.'.format(args.resume))
densefuse_model.load_state_dict(torch.load(args.resume))
print(densefuse_model)
optimizer = Adam(densefuse_model.parameters(), args.lr)
mse_loss = torch.nn.MSELoss(reduction="mean")
ssim_loss = pytorch_msssim.msssim
if (args.cuda):
densefuse_model.cuda(int(args.device));
tbar = trange(args.epochs)
print('Start training.....')
# creating save path
temp_path_model = os.path.join(args.save_model_dir, args.ssim_path[i])
if os.path.exists(temp_path_model) is False:
os.mkdir(temp_path_model)
temp_path_loss = os.path.join(args.save_loss_dir, args.ssim_path[i])
if os.path.exists(temp_path_loss) is False:
os.mkdir(temp_path_loss)
Loss_pixel = []
Loss_ssim = []
Loss_grad = []
Loss_all = []
all_ssim_loss = 0.
all_pixel_loss = 0.
all_grad_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)
densefuse_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, mode=img_model)
count += 1
optimizer.zero_grad()
img = Variable(img, requires_grad=False)
if args.cuda:
img = img.cuda(args.device)
en = densefuse_model.encoder(img)
outputs = densefuse_model.decoder(en)
x = Variable(img.data.clone(), requires_grad=False)
ssim_loss_value = 0.
pixel_loss_value = 0.
grad_loss_value = 0.;
for output in outputs:
grad_loss_temp = mse_loss(gradient(output),gradient(img));
pixel_loss_temp = mse_loss(output, img)
ssim_loss_temp = ssim_loss(output, img, normalize=True)
grad_loss_value += grad_loss_temp
ssim_loss_value += (1-ssim_loss_temp)
pixel_loss_value += pixel_loss_temp
ssim_loss_value /= len(outputs)
pixel_loss_value /= len(outputs)
grad_loss_value /= len(outputs)
# total loss
total_loss = pixel_loss_value + args.ssim_weight[i] * ssim_loss_value + grad_loss_value;
total_loss.backward()
optimizer.step()
all_ssim_loss += ssim_loss_value.item()
all_pixel_loss += pixel_loss_value.item()
all_grad_loss += grad_loss_value.item();
if (batch + 1) % args.log_interval == 0:
print("hi");
mesg = "{}\tEpoch {}:\t[{}/{}]\t pixel loss: {:.6f}\t ssim loss: {:.6f}\t grad loss: {:.6f}\t total: {:.6f}".format(
time.ctime(), e + 1, count, batches,
all_pixel_loss / args.log_interval,
all_ssim_loss / args.log_interval,all_grad_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_grad.append(all_grad_loss/args.log_interval);
Loss_all.append((args.ssim_weight[i] * all_ssim_loss + all_pixel_loss) / args.log_interval)
all_ssim_loss = 0.
all_pixel_loss = 0.
all_grad_loss = 0.
if (batch + 1) % (200 * args.log_interval) == 0:
# save model
densefuse_model.eval()
densefuse_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, save_model_filename)
torch.save(densefuse_model.state_dict(), save_model_path)
# save loss data
# pixel loss
loss_data_pixel = np.array(Loss_pixel)
loss_filename_path = args.ssim_path[i] + '/' + "loss_pixel_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_pixel})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+'/loss_pixel.png')
# grad loss
loss_data_grad = np.array(Loss_grad)
loss_filename_path = args.ssim_path[i] + '/' + "loss_pixel_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_grad})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/loss_grad.png");
# SSIM loss
loss_data_ssim = np.array(Loss_ssim)
loss_filename_path = args.ssim_path[i] + '/' + "loss_ssim_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_ssim})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/loss_ssim.png");
# all loss
loss_data_total = np.array(Loss_all)
loss_filename_path = args.ssim_path[i] + '/' + "loss_total_epoch_" + str(
args.epochs) + "_iters_" + str(count) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_total})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/allLoss.png");
densefuse_model.train()
if (args.cuda):
densefuse_model.cuda(int(args.device));
tbar.set_description("\nCheckpoint, trained model saved at", save_model_path)
# pixel loss
loss_data_pixel = np.array(Loss_pixel)
loss_filename_path = args.ssim_path[i] + '/' + "Final_loss_pixel_epoch_" + str(
args.epochs) + "_" + str(time.ctime()).replace(' ', '_').replace(':','_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_pixel})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/loss_pixel.png");
# SSIM loss
loss_data_ssim = np.array(Loss_ssim)
loss_filename_path = args.ssim_path[i] + '/' + "Final_loss_ssim_epoch_" + str(
args.epochs) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_ssim})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/loss_ssim.png");
# grad loss
loss_data_grad = np.array(Loss_grad)
loss_filename_path = args.ssim_path[i] + '/' + "Final_loss_grad_epoch_" + str(
args.epochs) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_ssim})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/loss_grad.png");
# all loss
loss_data_total = np.array(Loss_all)
loss_filename_path = args.ssim_path[i] + '/' + "Final_loss_total_epoch_" + str(
args.epochs) + "_" + str(time.ctime()).replace(' ', '_').replace(':', '_') + "_" + \
args.ssim_path[i] + ".mat"
save_loss_path = os.path.join(args.save_loss_dir, loss_filename_path)
scio.savemat(save_loss_path, {'Loss': loss_data_total})
showLossChart(save_loss_path,args.save_loss_dir+"/"+args.ssim_path[i]+"/allLoss.png");
# save model
densefuse_model.eval()
densefuse_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, save_model_filename)
torch.save(densefuse_model.state_dict(), save_model_path)
print("\nDone, trained model saved at", save_model_path)
if __name__ == "__main__":
main()