def main():
display_config()
dataset_root = get_full_path(args.scale, args.train_set)
print('Contructing dataset...')
dataset_factory = DatasetFactory()
train_dataset = dataset_factory.create_dataset(args.model, dataset_root)
model_factory = ModelFactory()
model = model_factory.create_model(args.model)
loss_fn = get_loss_fn(model.name)
check_point = os.path.join('check_point', model.name,
str(args.scale) + 'x')
solver = Solver(model,
check_point,
loss_fn=loss_fn,
batch_size=args.batch_size,
num_epochs=args.num_epochs,
learning_rate=args.learning_rate,
fine_tune=args.fine_tune,
verbose=args.verbose)
print('Training...')
solver.train(train_dataset)
def main():
display_config()
useGPU = True
print('Contructing dataset...')
dataset_root = os.path.join('preprocessed_data', args.test_set)
dataset_factory = DatasetFactory()
val_dataset = dataset_factory.create_dataset('VALID', dataset_root)
print('Loading model...')
model_path = os.path.join('check_point', args.model, str(args.scale) + 'x', 'model.pt')
if not os.path.exists(model_path):
raise Exception('Cannot find %s.' % model_path)
model = torch.load(model_path)
if useGPU:
model = model.cuda()
else:
model = model.cpu()
# needed for forward pass! save memory
for param in model.parameters():
param.requires_grad = False
dataloader = DataLoader(val_dataset, batch_size=1, shuffle=False, num_workers=6)
print('Testing...')
avrtime = 0
cnt = 0
for i, (input_batch, imgname) in enumerate(dataloader):
if useGPU:
input_batch = Variable(input_batch.cuda(), requires_grad=False)
else:
input_batch = Variable(input_batch, requires_grad=False)
start = time.time()
output_batch, _ = model(input_batch)
elapsed_time = time.time() - start
avrtime += elapsed_time
output_batch = (output_batch.data + 0.5) * 255 # change into pixel domain
output_batch = output_batch.cpu().numpy()
export(args.scale, model.name, output_batch, imgname[0])
cnt += 1
avrtime = avrtime / cnt
print('Average time: %f\n' % avrtime)
print('..Finish..')
def main():
display_config()
dataset_root = get_full_path(args.scale, args.test_set)
print('Contructing dataset...')
dataset_factory = DatasetFactory()
train_dataset = dataset_factory.create_dataset(args.model, dataset_root)
model_factory = ModelFactory()
model = model_factory.create_model(args.model)
check_point = os.path.join('check_point', model.name,
str(args.scale) + 'x')
solver = Solver(model, check_point)
print('Testing...')
stats, outputs = solver.test(train_dataset)
export(args.scale, model.name, stats, outputs)