EdgeTPU unassigned variable y running a detection

[RaffaeleGalliera]

Search before asking

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

YOLOv5 Component

Detection

Bug

Detection on edgetpu model returns the following error:
File "yolov5/models/common.py", line 449, in forward y[..., :4] *= [w, h, w, h] # xywh normalized to pixels UnboundLocalError: local variable 'y' referenced before assignment

I believe that the error from is caused by self.tflite not being set in the if/else branches in the forward method when edgetpu is true (common.py line 438). That should be cause by tflite &= not edgetpu # *.tflite in the model_type method.

@staticmethod
    def model_type(p='path/to/model.pt'):
        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
        from export import export_formats
        suffixes = list(export_formats().Suffix) + ['.xml']  # export suffixes
        check_suffix(p, suffixes)  # checks
        p = Path(p).name  # eliminate trailing separators
        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes)
        xml |= xml2  # *_openvino_model or *.xml
        tflite &= not edgetpu  # *.tflite
        return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs

tflite &= not edgetpu sets tflite to true only if tflite is true and edgetpu is false, it is set to false otherwise.

Environment

• OS: Ubuntu 20.04
• Python 3.8.10

Minimal Reproducible Example

I have exported the YoloV5s model to edgetpu using:
python export.py --weights yolov5s.pt --include edgetpu --img 320 --data data/coco128.yaml

Then run the detection with python detect.py --weights yolov5s-int8_edgetpu.tflite --img 320 --data data/coco128.yaml

Additional

No response

Are you willing to submit a PR?

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

[github-actions]

👋 Hello @RaffaeleGalliera, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.

Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Google Colab and Kaggle notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@RaffaeleGalliera good news 😃! Your original issue may now be fixed ✅ in PR #6686. I updated your original fix with a few changes and improvements!

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 🚀!