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check for valid model type

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This commit is contained in:
Dan Brown 2025-12-02 20:02:40 +01:00
parent 6e288839be
commit 6ee36d6ffc
1 changed files with 12 additions and 7 deletions
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19
frigate/detectors/plugins/edgetpu_tfl.py
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@ -145,11 +145,7 @@ class EdgeTpuTfl(DetectionApi):
self.boxes_tensor_index = boxes_details["index"] self.boxes_tensor_index = boxes_details["index"]
self.boxes_scale, self.boxes_zero_point = boxes_details["quantization"] self.boxes_scale, self.boxes_zero_point = boxes_details["quantization"]
else: elif self.model_type == ModelTypeEnum.ssd:
if self.model_type not in [ModelTypeEnum.ssd, None]:
logger.warning(
f"Unsupported model_type '{self.model_type}' for EdgeTPU detector, falling back to SSD"
)
logger.debug("Using SSD preprocessing/postprocessing") logger.debug("Using SSD preprocessing/postprocessing")
# SSD model indices (4 outputs: boxes, class_ids, scores, count) # SSD model indices (4 outputs: boxes, class_ids, scores, count)
@ -162,6 +158,11 @@ class EdgeTpuTfl(DetectionApi):
self.output_class_ids_index = None self.output_class_ids_index = None
self.output_class_scores_index = None self.output_class_scores_index = None
else:
raise Exception(
f"{self.model_type} is currently not supported for edgetpu. See the docs for more info on supported models."
)
def _generate_anchors_and_strides(self): def _generate_anchors_and_strides(self):
# for decoding the bounding box DFL information into xy coordinates # for decoding the bounding box DFL information into xy coordinates
all_anchors = [] all_anchors = []
@ -334,8 +335,7 @@ class EdgeTpuTfl(DetectionApi):
detections[:num_detections, 5] = final_boxes[:, 2] / self.model_width detections[:num_detections, 5] = final_boxes[:, 2] / self.model_width
return detections return detections
else: elif self.model_type == ModelTypeEnum.ssd:
# Default SSD model
self.determine_indexes_for_non_yolo_models() self.determine_indexes_for_non_yolo_models()
boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0] boxes = self.interpreter.tensor(self.tensor_output_details[0]["index"])()[0]
class_ids = self.interpreter.tensor( class_ids = self.interpreter.tensor(
@ -366,3 +366,8 @@ class EdgeTpuTfl(DetectionApi):
] ]
return detections return detections
else:
raise Exception(
f"{self.model_type} is currently not supported for edgetpu. See the docs for more info on supported models."
)