Sorry about that I can not show all the codes. Here are some snippets.
# This is the snippet in main function of training network
best_measure = 0.
t = 0.
for e in range(1, numEpochs+1):
	# Update the learning rate
	scheduler.step()
	# Here `train` and `test` is function for training and testing network, respectively. 
	t = train(e, t)
	loss, c_measure, data, logits, label = test(e, t)
	torch.save({'epoch': e,
			'state_dict': model.state_dict(),
			'rsnr': c_measure,
			'loss': loss,
			'optimizer': optimizer.state_dict()}, save_model_path)
	if c_measure >= best_measure:
		shutil.copyfile(save_model_path, best_model_path)
		best_measure = c_measure

# This is the test function code
def test(epoch, ttot):
	model.eval()
	with torch.no_grad():
		test_loss = AverageMeter()
		test_measure = AverageMeter()
		for batch_idx, (data, target) in enumerate(val_loader, 1):	
			model.eval()
			# where are we.
			dataset_size = len(train_set)
			dataset_batches = len(train_loader)
			iteration = (epoch-1) * (dataset_size // config['batch-size']) + batch_idx + 1

			data, target = data.to(device), target.to(device)
			logits = model(data)

			loss = criterion(logits, target)
			l_measure = rsnr(logits, target)
			test_measure.update(l_measure, 1)
			test_loss.update(loss.data.item(), data.size(0))

			testing_logger(epoch, test_loss.avg, test_measure.avg, optimizer)
	print('[Epoch %2d] Average test loss: %.3f, Average test RSNR: %.3f'
		%(epoch, test_loss.avg, test_measure.avg))

	return test_loss.avg, test_measure.avg, data, logits, target

And the following is the snippets for testing the network after loading the saved model
ave_measure = 0
model.eval()
with torch.no_grad():
	for i in range(nums):
		model.eval()
		data, target = torch.from_numpy(sparse[i,0:]).float().unsqueeze(0).to(device), \
						torch.from_numpy(label[i,0:]).float().unsqueeze(0).to(device)
        logits = model(data)
		loss = criterion(logits, target) # `criterion` is the loss function
		l_measure = rsnr(logits, target) # `rsnr` is the measure function
		print('Sample %d:  , loss: %.3f, rsnr: %.3f'%(i, loss.item(), l_measure))
		ave_measure = ave_measure + l_measure
print('Average rsnr: %.3f'%(ave_measure/nums))


Thank you, Arul.
Edit: I posted the test funtion.