def get_logger(self, opts):
self.logger = Logger(self.run_dir, self.epoch, self.run_name)
self.logger.add_iter_visual_log(self.get_visuals, 1, "test_visuals")
self.logger.add_metric_log(self.get_pairs,
(("ssim", self.get_metric(ssim)), ("psnr", self.get_metric(psnr))))
return self.logger
def get_logger(self, opts):
self.logger = Logger(self.run_dir, self.epoch, self.run_name)
return self.logger
dataset_opts = opts['dataset']
train_dataset = get_dataset(**dataset_opts)
train_loader = DataLoader(train_dataset,
batch_size=opts["batch_size"], num_workers=opts['num_workers'], shuffle=True)
train_loader = add_post(train_loader, get_image)
# Get checkpoint
if opts['last_epoch'] == 'last':
checkpoint, start_epoch = get_last_checkpoint(run_dir)
else:
start_epoch = opts['last_epoch']
checkpoint = path.join(run_dir, "net_{}".format(start_epoch))
if type(start_epoch) is not int: start_epoch = 0
# Get model
model = ADNTrain(opts['learn'], opts['loss'], **opts['model'])
if opts['use_gpu']: model.cuda()
if path.isfile(checkpoint): model.resume(checkpoint)
# Get logger
logger = Logger(run_dir, start_epoch, args.run_name)
logger.add_loss_log(model.get_loss, opts["print_step"], opts['window_size'])
logger.add_iter_visual_log(model.get_visuals, opts['visualize_step'], "train_visuals")
logger.add_save_log(model.save, opts['save_step'])
# Train the model
for epoch in range(start_epoch, opts['num_epochs']):
for data in logger(train_loader):
model.optimize(*data)
model.update_lr()