--- a
+++ b/benchmark.py
@@ -0,0 +1,103 @@
+import torch
+from torchmetrics.image.inception import InceptionScore
+from glob import glob
+from natsort import natsorted
+from tqdm import tqdm
+import os
+import cv2
+import numpy as np
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from torchvision.models import inception_v3, Inception3
+from torchvision.datasets import ImageFolder
+import pathlib
+from torch.utils.data import Dataset
+from PIL import Image
+from pytorch_gan_metrics import get_inception_score, get_fid
+
+os.environ['TF_XLA_FLAGS'] = '--tf_xla_enable_xla_devices'
+os.environ["CUDA_DEVICE_ORDER"]= "PCI_BUS_ID"
+os.environ["CUDA_VISIBLE_DEVICES"]= '1'
+# if __name__ == '__main__':
+# 	path = natsorted(glob('experiments/inception/294/*'))
+# 	for sub_path in path:
+# 		cls_name  = os.path.split(sub_path)[-1]
+# 		imgs_path = natsorted(glob(sub_path+'/*'))
+# 		inception = InceptionScore(splits=1)
+# 		for img in tqdm(imgs_path):
+# 			img = cv2.imread(img, 1)
+# 			img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# 			img = torch.permute(torch.Tensor(img).to(torch.uint8), (2, 0, 1))
+# 			inception.update(torch.unsqueeze(img, axis=0))
+# 			mean, std = inception.compute()
+# 		print('Inception score for class {}: {}'.format(cls_name, mean, std))
+# 		break
+class MyDataset(Dataset):
+	def __init__(self, image_paths, transform=None):
+		self.image_paths = image_paths
+		self.transform = transform
+
+	def __getitem__(self, index):
+		image_path = self.image_paths[index]
+		x = Image.open(image_path)
+		# y = self.get_class_label(image_path.split('/')[-1])
+		if self.transform is not None:
+			x = self.transform(x)
+		# print(x.shape)
+		return x
+
+	def __len__(self):
+		return len(self.image_paths)
+
+
+@torch.no_grad()
+def extract_features(loader, inception, device):
+    pbar = tqdm(loader)
+
+    feature_list = []
+
+    for img in pbar:
+        img = img.to(device)
+        img = img.to(torch.uint8)
+        # print(img.dtype)
+        # feature = inception(img)[0].view(img.shape[0], -1)
+        # feature_list.append(feature.to('cpu'))
+        inception.update(img)
+
+    # features = torch.cat(feature_list, 0)
+
+    return inception.compute()
+
+
+if __name__ == '__main__':
+
+	paths = natsorted(glob('experiments/inception/*'))
+
+	for path in paths:
+	    # device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
+	    # inception = InceptionScore(splits=10)
+	    # inception = InceptionScore(feature=2048, splits=1)
+	    # inception = inception.to(device)
+
+	    transform = transforms.Compose(
+	        [
+	            # transforms.Resize( (299, 299) ),
+	            # transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
+	            transforms.ToTensor(),
+	            # transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
+	        ]
+	    )
+
+	    dset   = MyDataset(natsorted(glob(path+'/*')), transform)
+	    loader = DataLoader(dset, batch_size=256, num_workers=8, shuffle=False)
+
+	    # mean, std = extract_features(loader, inception, device)
+	    mean, std = get_inception_score(loader)
+
+	    print('Inception score for epoch {}: ({}, {})'.format(os.path.split(path)[-1], mean, std))
+
+	    with open('experiments/inceptionscore_torch.txt', 'a') as file:
+	    	file.write('Inception score for epoch {}: ({}, {})\n'.format(os.path.split(path)[-1], mean, std))
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