Change the model input tensor config to use an enumeration

docs/docs/configuration/advanced.md

@@ -61,7 +61,7 @@ Custom models may also require different input tensor formats. The colorspace co
 
 ```yaml
 model:
-  input_tensor: ["B", "H", "W", "C"]
+  input_tensor: "nhwc"
 ```
 
 The labelmap can be customized to your needs. A common reason to do this is to combine multiple object types that are easily confused when you don't need to be as granular such as car/truck. By default, truck is renamed to car because they are often confused. You cannot add new object types, but you can change the names of existing objects in the model.
@@ -85,6 +85,7 @@ Note that if you rename objects in the labelmap, you will also need to update yo
 Included with Frigate is a build of ffmpeg that works for the vast majority of users. However, there exists some hardware setups which have incompatibilities with the included build. In this case, a docker volume mapping can be used to overwrite the included ffmpeg build with an ffmpeg build that works for your specific hardware setup.
 
 To do this:
+
 1. Download your ffmpeg build and uncompress to a folder on the host (let's use `/home/appdata/frigate/custom-ffmpeg` for this example).
 2. Update your docker-compose or docker CLI to include `'/home/appdata/frigate/custom-ffmpeg':'/usr/lib/btbn-ffmpeg':'ro'` in the volume mappings.
 3. Restart frigate and the custom version will be used if the mapping was done correctly.

docs/docs/configuration/index.md

@@ -101,7 +101,7 @@ model:
   # Valid values are rgb, bgr, or yuv. (default: shown below)
   input_pixel_format: rgb
   # Optional: Object detection model input tensor format (default: shown below)
-  input_tensor: ["B", "H", "W", "C"]
+  input_tensor: "nhwc"
   # Optional: Label name modifications. These are merged into the standard labelmap.
   labelmap:
     2: vehicle

frigate/config.py

@@ -693,6 +693,11 @@ class PixelFormatEnum(str, Enum):
     yuv = "yuv"
 
 
+class InputTensorEnum(str, Enum):
+    nchw = "nchw"
+    nhwc = "nhwc"
+
+
 class ModelConfig(FrigateBaseModel):
     path: Optional[str] = Field(title="Custom Object detection model path.")
     labelmap_path: Optional[str] = Field(title="Label map for custom object detector.")
@@ -701,8 +706,8 @@ class ModelConfig(FrigateBaseModel):
     labelmap: Dict[int, str] = Field(
         default_factory=dict, title="Labelmap customization."
     )
-    input_tensor: List[str] = Field(
+    input_tensor: InputTensorEnum = Field(
-        default=["B", "H", "W", "C"], title="Model Input Tensor Shape"
+        default=InputTensorEnum.nhwc, title="Model Input Tensor Shape"
     )
     input_pixel_format: PixelFormatEnum = Field(
         default=PixelFormatEnum.rgb, title="Model Input Pixel Color Format"

frigate/object_detection.py

@@ -10,7 +10,7 @@ from abc import ABC, abstractmethod
 import numpy as np
 from setproctitle import setproctitle
 
-from frigate.config import DetectorTypeEnum
+from frigate.config import DetectorTypeEnum, InputTensorEnum
 from frigate.detectors.edgetpu_tfl import EdgeTpuTfl
 from frigate.detectors.cpu_tfl import CpuTfl
 
@@ -27,14 +27,10 @@ class ObjectDetector(ABC):
 
 def tensor_transform(desired_shape):
     # Currently this function only supports BHWC permutations
-    if desired_shape == ["B", "H", "W", "C"]:
+    if desired_shape == InputTensorEnum.nhwc:
         return None
-    else:
+    elif desired_shape == InputTensorEnum.nchw:
-        transform = [0] * 4
+        return (0, 3, 1, 2)
-        transform[desired_shape.index("H")] = 1
-        transform[desired_shape.index("W")] = 2
-        transform[desired_shape.index("C")] = 3
-        return tuple(transform)
 
 
 class LocalObjectDetector(ObjectDetector):

frigate/test/test_object_detector.py

@@ -2,7 +2,7 @@ import unittest
 from unittest.mock import patch
 
 import numpy as np
-from frigate.config import DetectorTypeEnum, ModelConfig
+from frigate.config import DetectorTypeEnum, InputTensorEnum, ModelConfig
 import frigate.object_detection
 
 
@@ -66,7 +66,7 @@ class TestLocalObjectDetector(unittest.TestCase):
         TEST_DETECT_RESULT = np.ndarray([1, 2, 4, 8, 16, 32])
 
         test_cfg = ModelConfig()
-        test_cfg.input_tensor = ["B", "C", "H", "W"]
+        test_cfg.input_tensor = InputTensorEnum.nchw
 
         test_obj_detect = frigate.object_detection.LocalObjectDetector(
             det_device=DetectorTypeEnum.cpu, model_config=test_cfg