Issues running model on raspberrypi5 + edgetpu

[han-so1omon]

I have some issues running the degirum models on my raspberrypi5 + edgetpu environment with raspberrypi os 12 bookworm. Moving from this issue https://github.com/ultralytics/ultralytics/issues/1185. @shashichilappagari Can you provide some assistance?

First step

# Download the models
degirum download-zoo --path /home/errc/v --device EDGETPU --runtime TFLITE --precision QUANT --token dg_4JRLnVvtfNdKLzj4oL816wNtL9gQBT5dfqmi3 --url https://cs.degirum.com/degirum/edgetpu

Try to run with degirum pysdk

import cv2
import degirum as dg

image = cv2.imread("./test-posenet.jpg")

zoo = dg.connect(dg.LOCAL, "/home/errc/v/yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1/yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1.json")
model = zoo.load_model("yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1")
print(model)

result = model.predict(image)
result_image = result.image_overlay
cv2.imwrite("./test-posenet-degirum.jpg", result_image)

# Result
> python pose-tracking-debug-degirum.py 
<degirum.model._ClientModel object at 0x7f73d62ed0>
terminate called without an active exception
terminate called without an active exception
Aborted

Try to run with base yolo ultralytics library

from ultralytics import YOLO

# Load model
model = YOLO('/home/errc/v/yolov8n_full_integer_quant_edgetpu.tflite') 
yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1.tflite') 

# Track with the model
results = model.track(source="/home/errc/e/ai/test-infrared.mp4", save=True)

# Result
>  python pose-tracking-debug-yolo.py 
WARNING ⚠️ Unable to automatically guess model task, assuming 'task=detect'. Explicitly define task for your model, i.e. 'task=detect', 'segment', 'classify','pose' or 'obb'.
Loading /home/errc/v/yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1/yolov8n_relu6_coco_pose--640x640_quant_tflite_edgetpu_1.tflite for TensorFlow Lite inference...
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
Traceback (most recent call last):
  File "/home/errc/e/ai/pose-tracking-debug-yolo.py", line 8, in <module>
    results = model.track(source="/home/errc/e/ai/test-infrared.mp4", save=True)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/ultralytics/engine/model.py", line 492, in track
    return self.predict(source=source, stream=stream, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/ultralytics/engine/model.py", line 445, in predict
    self.predictor.setup_model(model=self.model, verbose=is_cli)
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/ultralytics/engine/predictor.py", line 297, in setup_model
    self.model = AutoBackend(
                 ^^^^^^^^^^^^
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/ultralytics/nn/autobackend.py", line 341, in __init__
    interpreter.allocate_tensors()  # allocate
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/errc/e/ai/venv/lib/python3.11/site-packages/tflite_runtime/interpreter.py", line 531, in allocate_tensors
    return self._interpreter.AllocateTensors()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Encountered unresolved custom op: edgetpu-custom-op.
See instructions: https://www.tensorflow.org/lite/guide/ops_custom Node number 0 (edgetpu-custom-op) failed to prepare.Encountered unresolved custom op: edgetpu-custom-op.
See instructions: https://www.tensorflow.org/lite/guide/ops_custom Node number 0 (edgetpu-custom-op) failed to prepare.

I can see that the edgetpu is connected. Although I am not sure that it is being used

> lspci
0000:00:00.0 PCI bridge: Broadcom Inc. and subsidiaries Device 2712 (rev 21)
0000:01:00.0 System peripheral: Global Unichip Corp. Coral Edge TPU
0001:00:00.0 PCI bridge: Broadcom Inc. and subsidiaries Device 2712 (rev 21)
0001:01:00.0 Ethernet controller: Device 1de4:0001
> ls /dev/apex_0 
/dev/apex_0