Can not export to edgetpu model

[walterwangimagr]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Export

Bug

I was following the tutorial to train a model and export to edgetpu model.
When I use the coco128.yaml as dataset, it was fine I can train and export
But when I use a custom dataset with only one class, it fail on the step tflite -> edgetpu
I also try with the similar dataset GlobalWheat2020.yaml, same issue. Is there an extra step I need to do or it is a bug?
I use the docker image provided and install tensorflow 2.9.1 for export
Training script
python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --name coco128
Export script
python export.py --img 640 --data data/coco128.yaml --weights runs/train/coco128/weights/best.pt --include edgetpu
And this work fine

Training script
python train.py --img 640 --batch 16 --epochs 3 --data GlobalWheat2020.yaml --weights yolov5s.pt
Export script
python export.py --img 640 --data data/GlobalWheat2020.yaml --weights runs/train/exp19/weights/best.pt --include edgetpu
Run into error

Environment

-Docker images provided
-install tensorflow 2.9.1

Minimal Reproducible Example

# start docker 
sudo docker run -it --gpus '"device=1,2,3"' -v `pwd`:/workspace --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 ultralytics/yolov5 /bin/bash

# install tensorflow 
pip install tensorflow 

# train 
python train.py --img 640 --batch 16 --epochs 3 --data data/GlobalWheat2020.yaml --weights yolov5s.pt

# export 
python export.py --img 640 --data data/GlobalWheat2020.yaml --weights runs/train/exp17/weights/best.pt --include edgetpu

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[github-actions]

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[glenn-jocher]

@walterwangimagr thanks for the bug report! I'll try to reproduce using your commands.

[glenn-jocher]

@walterwangimagr yes I'm able to reproduce. Very strange, not sure what the problem might be.

!python train.py --img 640 --batch 16 --epochs 3 --data GlobalWheat2020.yaml --weights yolov5s.pt
!python export.py --img 640 --weights runs/train/exp2/weights/best.pt --include edgetpu

VOC (20cls) also fails. Edge TPU seems to be failing for all non-80 class datasets. Perhaps 80 is hardcoded somewhere in the conversion process. I'll investigate tomorrow.

!python train.py --img 640 --batch 16 --epochs 3 --data VOC.yaml --weights yolov5s.pt
!python export.py --img 640 --data VOC.yaml --weights runs/train/exp3/weights/best.pt --include edgetpu

[glenn-jocher]

@zldrobit I might need some advice here. Edge TPU export is failing for non-COCO models. I'm not sure what the cause is. Export works for coco128 but fails for VOC and GlobalWheat2020 trained models (see above).

TFLite int-8 export works correctly for both prior to Edge TPU failure. What do you think?

[zldrobit]

@glenn-jocher I could confirm that using the default setting (edgetpu_compiler -s -o) cannot export an EdgeTPU model for VOC (20 classes) or globalwheat (1 class). I searched through the export architecture with edgetpu_compiler -s -d (as suggested in google-coral/edgetpu#450 (comment)), and the best model I could get has almost 130 ops running on CPU:

You could reproduce this result immediately with edgetpu_compiler -s -i "model/tf_detect/Reshape_1,model/tf_detect/Reshape_3,model/tf_conv_33/mul_1,model/tf_conv_51/mul_1" globalwheat-int8.tflite or search the export architecture from scratch with edgetpu_compiler -s -d globalwheat-int8.tflite. The VOC model could be exported to EdgeTPU format in the same way.

[walterwangimagr]

I had look at some posts on
google-coral/edgetpu#449 and google-coral/edgetpu#405 looks like it could because the resolution limitation. But the weird thing is coco128 is able to use img 640 with 80 classes and any other num of classes will fail. I will try to train some model with lower img size to confirm

[walterwangimagr]

I had experience another weird behaviour, If I change the class name in the coco128.yaml

After training and export to edgetpu.tflite, when I use the edgetpu model to run detect.py, it will still predict as 'person'

[glenn-jocher]

@walterwangimagr class names are embedded as model attributes after training finishes, i.e. model.names.

@zldrobit ok thanks for the results! Do you think we should update Edge TPU export to edgetpu_compiler -s -i or edgetpu_compiler -s -d? Let me know what the best option is and I will create a PR to resolve this.

[glenn-jocher]

@zldrobit I don't think we can use the -i argument as we don't know the output layer names ahead of time for the different sized models. I tested edgetpu_compiler -s -d with COCO128 and VOC. The COCO128 results are the same (in the same time), the VOC results work while taking a lot longer, but the important thing is they work, so I'll create a PR to add the -d argument to all Edge TPU exports.

[glenn-jocher]

@walterwangimagr good news 😃! Your original issue may now be fixed ✅ in PR #8902 by adding a --search-delegate argument to Edge TPU model compilation per @zldrobit's solution above.

To receive this update:

• Git – git pull from within your yolov5/ directory or git clone https://github.com/ultralytics/yolov5 again
• PyTorch Hub – Force-reload model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)
• Notebooks – View updated notebooks
• Docker – sudo docker pull ultralytics/yolov5:latest to update your image

Thank you for spotting this issue and informing us of the problem. Please let us know if this update resolves the issue for you, and feel free to inform us of any other issues you discover or feature requests that come to mind. Happy trainings with YOLOv5 🚀!

[walterwangimagr]

Thank you very much

[glenn-jocher]

@walterwangimagr you're welcome! If you need further assistance or have other questions, feel free to ask. Happy to help!