export.py - TensorFlow Lite export failure due to EndVector()

[HripsimeS]

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Question

@glenn-jocher Hello. I need to convert yolov5s.pt weight to yolov5s.tflite and save tflite in some folder. I saw that export.py is normally doing that action, but I did not receive the outcome model as tflite format. I run this command:

python export.py --weights yolov5s.pt --include tflite

In weights folder it saved best_saved_model folder with

1. assets folder which was empty,
2. variables folder with variables.data-00000-of-00001 and variables.index files;
3. saved_model.pb file.

Tha last line after running the command above are these
TensorFlow Lite: export failure 58.0s: EndVector() takes 1 positional argument but 2 were given

So the export was failed due to EndVector(), which I could not find in export.py file. Could you please help me to figure out what is the issue and how to fix it in order to get an outcome yolov5s.tflite file. Look forward to hearing soon!

Additional

No response

[glenn-jocher]

@HripsimeS 👋 Hello! Thanks for asking about YOLOv5 🚀 benchmarks. YOLOv5 inference is officially supported in 11 formats, and all formats are benchmarked for identical accuracy and to compare speed every 24 hours by the YOLOv5 CI.

Due to these daily benchmarks we can tell that TFLite export is operating correctly and there are no errors there.

💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks.
💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See GPU Benchmarks.

Format	export.py --include	Model
PyTorch	-	yolov5s.pt
TorchScript	torchscript	yolov5s.torchscript
ONNX	onnx	yolov5s.onnx
OpenVINO	openvino	yolov5s_openvino_model/
TensorRT	engine	yolov5s.engine
CoreML	coreml	yolov5s.mlmodel
TensorFlow SavedModel	saved_model	yolov5s_saved_model/
TensorFlow GraphDef	pb	yolov5s.pb
TensorFlow Lite	tflite	yolov5s.tflite
TensorFlow Edge TPU	edgetpu	yolov5s_edgetpu.tflite
TensorFlow.js	tfjs	yolov5s_web_model/

Benchmarks

Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook . To reproduce:

python utils/benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0

Colab Pro V100 GPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)

Benchmarks complete (458.07s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623                10.19
1             TorchScript        0.4623                 6.85
2                    ONNX        0.4623                14.63
3                OpenVINO           NaN                  NaN
4                TensorRT        0.4617                 1.89
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623                21.28
7     TensorFlow GraphDef        0.4623                21.22
8         TensorFlow Lite           NaN                  NaN
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Colab Pro CPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)

Benchmarks complete (241.20s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623               127.61
1             TorchScript        0.4623               131.23
2                    ONNX        0.4623                69.34
3                OpenVINO        0.4623                66.52
4                TensorRT           NaN                  NaN
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623               123.79
7     TensorFlow GraphDef        0.4623               121.57
8         TensorFlow Lite        0.4623               316.61
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Good luck 🍀 and let us know if you have any other questions!

[HripsimeS]

@glenn-jocher thanks for your quick reply, I could fix it with this command line, I will close this issue!
pip install --upgrade flatbuffers==1.12 # downgrade from v2 to v1.12

[glenn-jocher]

Oh that's strange. We used to install a fixed version of flatbuffers like this but it caused problems with newer versions of TF I think.