Update app

app.py

@@ -34,6 +34,7 @@ CHECKPOINT_PATH = "model_rgb.pth"
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 use_cuda = torch.cuda.is_available()
 n_negative_samples = 100
+print("Using CUDA: ", use_cuda, device)
 
 def preprocess_video(path, result_folder, apply_preprocess, padding=20):
 
@@ -225,7 +226,9 @@ def load_checkpoint(path, model):
 	for k, v in s.items():
 		new_s[k.replace('module.', '')] = v
 	model.load_state_dict(new_s)
-	model.cuda()
+
+	if use_cuda:
+		model.cuda()
 
 	print("Loaded checkpoint from: {}".format(path))
 
@@ -721,10 +724,6 @@ def process_video(video_path, num_avg_frames, apply_preprocess):
 
 		# Convert frames to tensor
 		rgb_frames = np.transpose(rgb_frames, (4, 0, 1, 2, 3))
-		print("Transposed array: ", rgb_frames.shape)
-		a = np.array([2,2])
-		b = torch.tensor(a)
-		print("checking torch: ", b.shape)
 		rgb_frames = torch.FloatTensor(rgb_frames).unsqueeze(0)
 		B = rgb_frames.size(0)
 		print("Successfully converted the frames to tensor")