changed path

bob/learn/pytorch/architectures/MCCNN.py

@@ -88,25 +88,25 @@ class MCCNN(nn.Module):
 
     light_cnn_model_file = os.path.join(MCCNN.get_mccnnpath(), "LightCNN_29Layers_checkpoint.pth.tar")
 
-    url='https://www.idiap.ch/software/bob/data/bob/bob.learn.pytorch/master/LightCNN_29Layers_checkpoint.pth.tar'
+    url='http://www.idiap.ch/software/bob/data/bob/bob.learn.pytorch/master/LightCNN_29Layers_checkpoint.pth.tar'
 
     print("light_cnn_model_file",light_cnn_model_file)
 
 
-    # if not os.path.exists(light_cnn_model_file):
+    if not os.path.exists(light_cnn_model_file):
 
-    #   bob.io.base.create_directories_safe(os.path.split(light_cnn_model_file)[0])
+      bob.io.base.create_directories_safe(os.path.split(light_cnn_model_file)[0])
 
-    #   print('Downloading the LightCNN model')
+      print('Downloading the LightCNN model')
 
-    #   bob.extension.download.download_file(url,light_cnn_model_file)
+      bob.extension.download.download_file(url,light_cnn_model_file)
 
-    #   print('Downloaded LightCNN model to {}'.format(light_cnn_model_file))
+      print('Downloaded LightCNN model to {}'.format(light_cnn_model_file))
 
 
-    # ## Loding the pretrained model for ch_0
+    ## Loding the pretrained model for ch_0
 
-    # self.load_state_dict(self.get_model_state_dict(light_cnn_model_file),strict=False)
+    self.load_state_dict(self.get_model_state_dict(light_cnn_model_file),strict=False)
 
     # copy over the weights to all other layers
 
@@ -206,7 +206,7 @@ class MCCNN(nn.Module):
     pretrained_model_path: str
       Absolute path to the LightCNN model file
 
-    new_state_dict:pyclass:dict
+    new_state_dict: dict
       Dictionary with LightCNN weights
 
     """