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train.py
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train.py
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import time
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
from util.metrics import PSNR, SSIM
from logger import Logger
import pytorch_ssim
def train(opt, data_loader, model, visualizer):
logger = Logger('./checkpoints/log/')
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
results = model.get_current_visuals()
ssim = pytorch_ssim.ssim(results['fake_B'], results['real_B']).item()
psnrMetric = PSNR(results['Restored_Train'],results['Sharp_Train'])
print('PSNR = %f, SSIM = %.4f' % (psnrMetric, ssim))
results.pop('fake_B') # 计算完SSIM,就去掉多余项
results.pop('real_B')
visualizer.display_current_results(results,epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
for tag, value in errors.items():
logger.scalar_summary(tag, value, epoch)
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
if __name__ == '__main__':
opt = TrainOptions().parse()
opt.continue_train = True # 选择是否继续上次的训练
if opt.continue_train:
opt.which_epoch = 25
opt.epoch_count = opt.which_epoch + 1
data_loader = CreateDataLoader(opt)
model = create_model(opt)
visualizer = Visualizer(opt)
train(opt, data_loader, model, visualizer)
# train(opt, data_loader, model, None)