Update detect_from_videos.py

detect_from_videos.py

@@ -12,7 +12,7 @@ from model_core import Two_Stream_Net
 from torchvision import transforms
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
+map_location=torch.device('cpu')
 
 xception_default_data_transforms_256 = {
     'train': transforms.Compose([
@@ -115,7 +115,7 @@ def predict_with_model(image, model, post_function=nn.Softmax(dim=1),
 
 
 def test_full_image_network(video_path, model_path, output_path,
-                            start_frame=0, end_frame=None, cuda=True):
+                            start_frame=0, end_frame=None, cuda=False):
     """
     Reads a video and evaluates a subset of frames with the a detection network
     that takes in a full frame. Outputs are only given if a face is present
@@ -150,7 +150,7 @@ def test_full_image_network(video_path, model_path, output_path,
     # Load model
     # model, *_ = model_selection(modelname='xception', num_out_classes=2)
     model = Two_Stream_Net()
-    model.load_state_dict(torch.load(model_path))
+    model.load_state_dict(torch.load(model_path,map_location))
     model = model.to(device)
     model.eval()