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+import torch
+import torch.nn as nn
+import torchvision
+from torch.utils.data import DataLoader
+import torch.optim as optim
+from torch.optim import lr_scheduler
+import argparse
+import os
+import cv2
+from network.models import model_selection
+from dataset.transform import xception_default_data_transforms
+from dataset.mydataset import MyDataset
+def main():
+	args = parse.parse_args()
+	test_list = args.test_list
+	batch_size = args.batch_size
+	model_path = args.model_path
+	torch.backends.cudnn.benchmark=True
+	test_dataset = MyDataset(txt_path=test_list, transform=xception_default_data_transforms['test'])
+	test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True, drop_last=True, num_workers=8)
+	test_dataset_size = len(test_dataset)
+	corrects = 0
+	acc = 0
+	#model = torchvision.models.densenet121(num_classes=2)
+	model = model_selection(modelname='xception', num_out_classes=2, dropout=0.5)
+	model.load_state_dict(torch.load(model_path))
+	if isinstance(model, torch.nn.DataParallel):
+		model = model.module
+	model = model.cuda()
+	model.eval()
+	with torch.no_grad():
+		for (image, labels) in test_loader:
+			image = image.cuda()
+			labels = labels.cuda()
+			outputs = model(image)
+			_, preds = torch.max(outputs.data, 1)
+			corrects += torch.sum(preds == labels.data).to(torch.float32)
+			print('Iteration Acc {:.4f}'.format(torch.sum(preds == labels.data).to(torch.float32)/batch_size))
+		acc = corrects / test_dataset_size
+		print('Test Acc: {:.4f}'.format(acc))
+
+
+
+if __name__ == '__main__':
+	parse = argparse.ArgumentParser(
+		formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+	parse.add_argument('--batch_size', '-bz', type=int, default=32)
+	parse.add_argument('--test_list', '-tl', type=str, default='./data_list/Deepfakes_c0_test.txt')
+	parse.add_argument('--model_path', '-mp', type=str, default='./pretrained_model/df_c0_best.pkl')
+	main()
+	print('Hello world!!!')