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train_baseline.py
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import time
import os
from options.train_options import TrainOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
import torchvision.utils as vutils
import torch as th
from util.metrics import PSNR, SSIM
import matplotlib
import matplotlib.image as mpimg
from PIL import Image
#from util.utils_mkdir import prepare_dirs_and_logger, save_config
def train(opt, data_loader, model, visualizer):
save_dir = os.path.join(opt.checkpoints_dir, opt.name)
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):
# save valid
if opt.epoch_count == epoch:
valid_cat = th.cat((data['A'], data['B']), 0)
vutils.save_image(
valid_cat, '{}/deblur{}.jpg'.format(save_dir, total_steps), nrow=opt.batchSize)
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()
# psnrMetric = PSNR(
# results['Restored_Train'], results['Sharp_Train'])
# print('PSNR on Train = %f' %
# (psnrMetric))
#visualizer.display_current_results(results, epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
with open("./errors.txt","a+") as f:
f.write(str(errors)+"\n")
results = model.get_current_visuals()
if(os.path.exists(str(save_dir)+'/blur/')):
print('exist')
else:
os.makedirs(str(save_dir)+'/blur/')
if(os.path.exists(str(save_dir)+'/sharp/')):
print('exist')
else:
os.makedirs(str(save_dir)+'/sharp/')
if(os.path.exists(str(save_dir)+'/restored/')):
print('exist')
else:
os.makedirs(str(save_dir)+'/restored/')
import cv2
results['Blurred_Train'].save('{}/blur/blur{}.jpg'.format(save_dir, total_steps))
results['Sharp_Train'].save('{}/sharp/sharp{}.jpg'.format(save_dir, total_steps))
results['Restored_Train'].save('{}/restored/restored{}.jpg'.format(save_dir, total_steps))
# cv2.imwrite('{}/blur/blur{}.jpg'.format(save_dir, total_steps),results['Blurred_Train'].reshape((256,256,3)))
# cv2.imwrite('{}/sharp/sharp{}.jpg'.format(save_dir, total_steps),results['Sharp_Train'].reshape((256, 256, 3)))
# cv2.imwrite('{}/restored/restored{}.jpg'.format(save_dir, total_steps),results['Restored_Train'].reshape((256, 256, 3)))
# matplotlib.image.imsave('{}/blur/blur{}_1.jpg'.format(save_dir, total_steps), results['Blurred_Train'])
# matplotlib.image.imsave('{}/sharp/sharp{}.jpg'.format(save_dir, total_steps), results['Sharp_Train'])
# matplotlib.image.imsave('{}/restored/restored{}.jpg'.format(save_dir, total_steps), results['Restored_Train'])
# result = th.cat((th.FloatTensor(results['Blurred_Train']/255),th.FloatTensor(results['Sharp_Train']/255)),0)
# torch_result = th.FloatTensor(result)
# print(type(torch_result))
# print(type(result),"jjjjjjjjjjjjjjjjj")
# vutils.save_image(result, '{}/deblur{}.jpg'.format(save_dir, total_steps), nrow=opt.batchSize)
# 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()
opt = TrainOptions().parse()
print("ss")
print(opt)
print("sss")
# prepare_dirs_and_logger(opt)
# save_config(opt)
data_loader = CreateDataLoader(opt)
model = create_model(opt)
visualizer = Visualizer(opt)
train(opt, data_loader, model, visualizer)