resolved device reference

yolov5.py

@@ -101,7 +101,6 @@ import requests
 import yaml
 import matplotlib.patches as patches
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 def dff_l(activations,  model,  n_components):
     batch_size, channels, h, w = activations.shape
     print('activation', activations.shape)
@@ -175,6 +174,7 @@ class DeepFeatureFactorization:
 
 
 def dff_nmf(image, target_lyr, n_components):
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     mean = [0.485, 0.456, 0.406]  # Mean for RGB channels
     std = [0.229, 0.224, 0.225]   # Standard deviation for RGB channels
     img = cv2.resize(image, (640, 640))