def test(args, shared_model, dataset, targets, log):
start_time = time.time()
log.info('Test time ' + time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)) + ', ' + 'Start testing.')
local_model = SVM()
local_model.load_state_dict(shared_model.state_dict())
if args.gpu:
local_model = local_model.cuda()
correct_cnt = 0
predictions = np.zeros([targets.shape[0]], dtype=np.int64)
for idx in range(targets.shape[0]):
data = dataset[idx]
data = Variable(torch.from_numpy(data))
if args.gpu:
data = data.cuda()
target = targets[idx]
output = local_model(data)
if args.gpu:
output = output.cpu()
predict_class = output.max(0)[1].data.numpy()[0]
predictions[idx] = predict_class
if target == predict_class:
correct_cnt += 1
# else:
# print(predict_class)
# if (idx + 1) % 100 == 0:
# log.info('Test time ' + time.strftime("%Hh %Mm %Ss", time.gmtime(time.time() - start_time)) + ', ' + 'Accuracy: %d / %d\t%0.4f' % (correct_cnt, idx + 1, correct_cnt / (idx + 1)))
log.info('Overall f1 score = %0.4f' % (f1_score(list(targets), list(predictions), average='weighted')))
log.info('Overall accuracy = %0.2f%%' % (100 * correct_cnt / targets.shape[0]))
return correct_cnt / targets.shape[0]
if not os.path.exists(args.model_dir):
os.mkdir(args.model_dir)
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
if args.train:
model = SVM()
if args.model_load:
try:
saved_state = torch.load(
os.path.join(args.model_dir, 'best_model.dat'))
model.load_state_dict(saved_state)
except:
print('Cannot load existing model from file!')
if args.gpu:
model = model.cuda()
dataset = torch.from_numpy(np.load("../output/data/dataset_train.npy"))
targets = torch.from_numpy(
np.int64(np.load("../output/data/target_train.npy")))
dataset_test = np.load(dataset_path)
targets_test = np.int64(np.load(target_path))
if args.L2norm:
log_test = setup_logger(
0, 'test_log_norm',
os.path.join(args.log_dir, 'test_log_norm.txt'))
log = setup_logger(
0, 'train_log_norm',
os.path.join(args.log_dir, 'train_log_norm.txt'))
optimizer = Adam(model.parameters(), lr=args.lr, weight_decay=10)
else:
