Replace functool lru cache with dict cache

frigate/util/model.py

@@ -1,6 +1,5 @@
 """Model Utils"""
 
-import functools
 import logging
 import os
 from typing import Any
@@ -18,10 +17,13 @@ logger = logging.getLogger(__name__)
 ### Post Processing
 
 
-@functools.lru_cache
-def nanodet_center_priors(
+def calculate_nanodet_center_priors(
     input_height: int, input_width: int, strides: tuple, dtype: type
 ):
+    """
+    Adapted from https://github.com/RangiLyu/nanodet/blob/be9b4a9001d7f9b6fc89c2df31ae8d428e35b4f0/nanodet/model/head/nanodet_plus_head.py
+    """
+
     def get_single_level_center_priors(featmap_size, stride, dtype):
         """Generate centers of a single stage feature map.
         Args:
@@ -63,6 +65,9 @@ def nanodet_center_priors(
     return center_priors
 
 
+nanodet_center_priors: dict[(int, int, tuple, type), np.ndarray] = {}
+
+
 def post_process_dfine(
     tensor_output: np.ndarray, width: int, height: int
 ) -> np.ndarray:
@@ -332,6 +337,10 @@ def post_process_nanodet_plus(
     width: int,
     height: int,
 ):
+    """
+    Adapted from https://github.com/RangiLyu/nanodet/blob/be9b4a9001d7f9b6fc89c2df31ae8d428e35b4f0/nanodet/model/head/nanodet_plus_head.py
+    """
+
     def distance2bbox(points, distance, max_shape=None):
         """Decode distance prediction to bounding box.
 
@@ -357,14 +366,24 @@ def post_process_nanodet_plus(
 
     predictions = predictions[0]
 
-    # TODO From parameters
+    # Below two parameters are consistent with all nanodet **plus** models
     reg_max = 7
     strides = (8, 16, 32, 64)
 
     num_classes = predictions.shape[-1] - 4 * (reg_max + 1)
     cls_scores, bbox_preds = predictions[:, :num_classes], predictions[:, num_classes:]
 
-    center_priors = nanodet_center_priors(height, width, strides, predictions[0].dtype)
+    try:
+        center_priors = nanodet_center_priors[
+            (height, width, strides, predictions[0].dtype)
+        ]
+    except KeyError:
+        center_priors = calculate_nanodet_center_priors(
+            height, width, strides, predictions[0].dtype
+        )
+        nanodet_center_priors[(height, width, strides, predictions[0].dtype)] = (
+            center_priors
+        )
 
     x = bbox_preds.reshape(bbox_preds.shape[0], 4, reg_max + 1)
     x = scipy.special.softmax(x, axis=-1)