diff --git a/opensora/models/causalvideovae/dataset/video_dataset.py b/opensora/models/causalvideovae/dataset/video_dataset.py
index a375d3a0..bdc406bf 100644
--- a/opensora/models/causalvideovae/dataset/video_dataset.py
+++ b/opensora/models/causalvideovae/dataset/video_dataset.py
@@ -246,5 +246,5 @@ def _load_video(self, video_path, sample_rate=None):
         frame_id_list = np.linspace(s, e - 1, num_frames, dtype=int)
         video_data = decord_vr.get_batch(frame_id_list).asnumpy()
         video_data = torch.from_numpy(video_data)
-        video_data = video_data.permute(3, 0, 1, 2)
+        video_data = video_data.permute(0, 3, 1, 2)
         return video_data
diff --git a/opensora/models/causalvideovae/eval/cal_fvd.py b/opensora/models/causalvideovae/eval/cal_fvd.py
index 1f1a9806..f7c55239 100755
--- a/opensora/models/causalvideovae/eval/cal_fvd.py
+++ b/opensora/models/causalvideovae/eval/cal_fvd.py
@@ -13,7 +13,8 @@ def trans(x):
     return x
 
 def calculate_fvd(videos1, videos2, device, method='styleganv'):
-
+    videos1 = videos1.to(device)
+    videos2 = videos2.to(device)
     if method == 'styleganv':
         from fvd.styleganv.fvd import get_fvd_feats, frechet_distance, load_i3d_pretrained
     elif method == 'videogpt':
diff --git a/opensora/models/causalvideovae/eval/eval.py b/opensora/models/causalvideovae/eval/eval.py
index ec773200..46aa194f 100755
--- a/opensora/models/causalvideovae/eval/eval.py
+++ b/opensora/models/causalvideovae/eval/eval.py
@@ -84,25 +84,18 @@ def __getitem__(self, index):
 
 def calculate_common_metric(args, dataloader, device):
     score_list = []
-    for batch_data in tqdm(dataloader):
-        real_videos = batch_data["real"].to(device)
-        generated_videos = batch_data["generated"].to(device)
-
+    for batch_data in tqdm(dataloader): # {'real': real_video_tensor, 'generated':generated_video_tensor }
+        real_videos = batch_data['real'] 
+        generated_videos = batch_data['generated']
         assert real_videos.shape[2] == generated_videos.shape[2]
-        if args.metric == "fvd":
-            tmp_list = list(
-                calculate_fvd(
-                    real_videos, generated_videos, args.device, method=args.fvd_method
-                )["value"].values()
-            )
-        elif args.metric == "ssim":
-            tmp_list = list(
-                calculate_ssim(real_videos, generated_videos)["value"].values()
-            )
-        elif args.metric == "psnr":
-            tmp_list = [calculate_psnr(real_videos, generated_videos)]
+        if args.metric == 'fvd':
+            tmp_list = list(calculate_fvd(real_videos, generated_videos, args.device, method=args.fvd_method)['value'].values())
+        elif args.metric == 'ssim':
+            tmp_list = list(calculate_ssim(real_videos, generated_videos)['value'].values())
+        elif args.metric == 'psnr':
+            tmp_list = list(calculate_psnr(real_videos, generated_videos)['value'].values())
         else:
-            tmp_list = [calculate_lpips(real_videos, generated_videos, args.device)]
+            tmp_list  = list(calculate_lpips(real_videos, generated_videos, args.device)['value'].values())
         score_list += tmp_list
     return np.mean(score_list)
 
diff --git a/opensora/models/causalvideovae/eval/fvd/styleganv/fvd.py b/opensora/models/causalvideovae/eval/fvd/styleganv/fvd.py
index 3043a2a4..68ef36fa 100755
--- a/opensora/models/causalvideovae/eval/fvd/styleganv/fvd.py
+++ b/opensora/models/causalvideovae/eval/fvd/styleganv/fvd.py
@@ -14,7 +14,7 @@ def load_i3d_pretrained(device=torch.device('cpu')):
         print(f"preparing for download {i3D_WEIGHTS_URL}, you can download it by yourself.")
         os.system(f"wget {i3D_WEIGHTS_URL} -O {filepath}")
     i3d = torch.jit.load(filepath).eval().to(device)
-    i3d = torch.nn.DataParallel(i3d)
+    #i3d = torch.nn.DataParallel(i3d)
     return i3d
     
 
@@ -87,4 +87,4 @@ def frechet_distance(feats_fake: np.ndarray, feats_real: np.ndarray) -> float:
         fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
     else:
         fid = np.real(m)
-    return float(fid)
\ No newline at end of file
+    return float(fid)