Rename global model var

opennsfw2/_inference.py

@@ -16,7 +16,7 @@
 from ._model import make_open_nsfw_model
 from ._typing import NDFloat32Array
 
-model: Optional[Model] = None
+global_model: Optional[Model] = None
 
 
 def predict_image(
@@ -32,17 +32,17 @@ def predict_image(
     """
     pil_image = Image.open(image_path)
     image = preprocess_image(pil_image, preprocessing)
-    global model
-    if model is None:
-        model = make_open_nsfw_model(weights_path=weights_path)
-    nsfw_probability = float(model(np.expand_dims(image, 0))[0][1])
+    global global_model
+    if global_model is None:
+        global_model = make_open_nsfw_model(weights_path=weights_path)
+    nsfw_probability = float(global_model(np.expand_dims(image, 0))[0][1])
 
     if grad_cam_path is not None:
         # TensorFlow will only be imported here.
         from ._inspection import make_and_save_nsfw_grad_cam
 
         make_and_save_nsfw_grad_cam(
-            pil_image, preprocessing, model, grad_cam_path, alpha
+            pil_image, preprocessing, global_model, grad_cam_path, alpha
         )
 
     return nsfw_probability
@@ -82,10 +82,10 @@ def predict_images(
         preprocess_image(Image.open(image_path), preprocessing)
         for image_path in image_paths
     ]
-    global model
-    if model is None:
-        model = make_open_nsfw_model(weights_path=weights_path)
-    predictions = _predict_images_in_batches(model, images, batch_size)
+    global global_model
+    if global_model is None:
+        global_model = make_open_nsfw_model(weights_path=weights_path)
+    predictions = _predict_images_in_batches(global_model, images, batch_size)
     nsfw_probabilities: List[float] = predictions[:, 1].tolist()
 
     if grad_cam_paths is not None:
@@ -94,7 +94,7 @@ def predict_images(
 
         for image_path, grad_cam_path in zip(image_paths, grad_cam_paths):
             make_and_save_nsfw_grad_cam(
-                Image.open(image_path), preprocessing, model,
+                Image.open(image_path), preprocessing, global_model,
                 grad_cam_path, alpha
             )
 
@@ -141,9 +141,9 @@ def predict_video_frames(
     cap = cv2.VideoCapture(video_path)  # pylint: disable=no-member
     fps = cap.get(cv2.CAP_PROP_FPS)  # pylint: disable=no-member
 
-    global model
-    if model is None:
-        model = make_open_nsfw_model(weights_path=weights_path)
+    global global_model
+    if global_model is None:
+        global_model = make_open_nsfw_model(weights_path=weights_path)
 
     video_writer: Optional[cv2.VideoWriter] = None  # pylint: disable=no-member
     input_frames: List[NDFloat32Array] = []
@@ -181,7 +181,7 @@ def predict_video_frames(
 
             if frame_count == 1 or len(input_frames) >= aggregation_size:
                 predictions = _predict_images_in_batches(
-                    model, input_frames, batch_size
+                    global_model, input_frames, batch_size
                 )
                 agg_fn = _get_aggregation_fn(aggregation)
                 nsfw_probability = agg_fn(predictions[:, 1])