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train_SRGAN.py
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train_SRGAN.py
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from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
from util import util
from math import log10
from util.visualizer import save_sr_result
import time
import os
import pytorch_ssim
if __name__ == '__main__':
# 加载设置
opt = TrainOptions().parse()
opt.upscale_factor = 4
# 设置显示验证结果存储的设置
web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'val')
image_dir = os.path.join(web_dir, 'images')
util.mkdirs([web_dir, image_dir])
# 加载训练数据集
dataset_train = create_dataset(opt)
dataset_train_size = len(dataset_train)
print('The number of training images = %d' % dataset_train_size)
# 加载验证数据集
opt1 = TrainOptions().parse()
opt1.upscale_factor = 4
opt1.phase = "val"
opt1.batch_size = 1
opt1.serial_batches = True
dataset_val = create_dataset(opt1)
dataset_val_size = len(dataset_val)
print('The number of valling images = %d' % dataset_val_size)
# 构建model
model = create_model(opt)
# 设置学习率,和恢复权重
model.setup(opt)
# 设置显示训练结果的类
visualizer = Visualizer(opt)
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_iters = 0
epoch_start_time = time.time()
model.train()
for i, data in enumerate(dataset_train):
iter_start_time = time.time()
epoch_iters += 1
# 训练一次
model.set_input(data)
model.optimize_parameters()
# 保存训练出来的图像
if epoch_iters % opt.display_freq == 0:
visualizer.display_current_results_sr(model.get_current_visuals(), epoch)
# 控制台打印loss的值,存储log信息到磁盘
if epoch_iters % opt.print_freq == 0:
losses = model.get_current_losses()
t_comp = (time.time() - iter_start_time) / epoch_iters
visualizer.print_current_losses(epoch, epoch_iters, losses, t_comp)
# 在验证数据集上验证结果
if epoch % opt.save_epoch_freq == 0: # cache our model every <save_epoch_freq> epochs
print('saving the model at the end of epoch %d' % epoch)
model.save_networks(epoch)
model.eval()
batch_sizes = 0
mse = 0
ssims = 0
psnr = 0
psnr = 0
ssim = 0
for i, data in enumerate(dataset_val): # batchsize = 1
batch_sizes += 1 # because batch_size = 1
model.set_input(data)
model.forward()
sr = model.sr_img
hr = model.hr_image
batch_mse = ((sr - hr) ** 2).data.mean()
batch_ssim = pytorch_ssim.ssim(sr, hr).data
mse += batch_mse * 1 # because batch_size = 1
ssims += batch_ssim * 1 # because batch_size = 1
psnr = 10 * log10(1 / (mse / batch_sizes))
ssim = ssims / batch_sizes
# 保存结果
if i % opt.display_freq == 0:
save_sr_result(model.get_current_visuals(), epoch, opt.display_winsize, image_dir, web_dir,
opt.name)
print("[converting LR images to SR images] PSNR: %.4f dB SSIM: %.4f" %(psnr, ssim))
print('End of epoch %d / %d \t Time Taken: %d sec' % (
epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
model.update_learning_rate()