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train.py
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train.py
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import os
import time
import torch
from data import CreateDataLoader
from models import create_model
from options.train_options import TrainOptions
from options.test_options import TestOptions
from util.visualizer import Visualizer
from pytorch_msssim import ssim
import numpy as np
def test(model, val_dataset):
model.eval()
cur_score = 0.0
with torch.no_grad():
for i, images in enumerate(val_dataset):
model.set_input(images)
real_A, fake_B, real_B = model.test(encode=True)
cur_score += ssim(fake_B, real_B, val_range=1.0)
cur_score /= len(val_dataset)
return cur_score
if __name__ == '__main__':
opt = TrainOptions().parse()
val_opt = TestOptions().parse()
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
val_opt.phase = 'test'
val_opt.batch_size = 1
val_opt.num_threads = 1
val_opt.serial_batches = True # no shuffle
val_dataloader = CreateDataLoader(val_opt)
val_dataset = val_dataloader.load_data()
print('val images = %d' % len(val_dataloader))
model = create_model(opt)
model.setup(opt)
visualizer = Visualizer(opt)
total_steps = 0
best_score = 0
log_dir = os.path.join(opt.checkpoints_dir, "val_ssim.txt")
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
iter_data_time = time.time()
epoch_iter = 0
t_data = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
if total_steps % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
visualizer.reset()
total_steps += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data)
if not model.is_train():
continue
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
save_result = total_steps % opt.update_html_freq == 0
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_steps % opt.print_freq == 0:
losses = model.get_current_losses()
tensors = model.get_tensor_encoded()
t = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_Tensor_encoded(epoch, epoch_iter, tensors)
visualizer.print_current_losses(epoch, epoch_iter, losses, t, t_data)
if opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, opt, losses)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save_networks('latest')
# ========== Validation ========
print('Validation with ssim..')
cur_score = test(model, val_dataset)
print(" ssim = %.6f" % cur_score)
with open(log_dir, 'a') as log_file:
log_file.write("ssim value of epoch %d is %.6f \n" % (epoch, cur_score))
if cur_score > best_score:
best_score = cur_score
model.save_networks('best')
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save_networks('latest')
model.save_networks(epoch)
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()