Cannot export model with cuda device on Jetson TX2

JNaranjo-Alcazar commented 3 years ago

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YOLOv5 Component

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Bug

Fusing layers... 
Model Summary: 213 layers, 7225885 parameters, 0 gradients

PyTorch: starting from yolov5s.pt (14.7 MB)
2021-11-04 11:55:46.365279: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.10.2

TensorFlow saved_model: starting export with tensorflow 2.5.0...

                 from  n    params  module                                  arguments                     

TensorFlow saved_model: export failure: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

TensorFlow Lite: starting export with tensorflow 2.5.0...

TensorFlow Lite: export failure: 'NoneType' object has no attribute 'call'

Environment

YOLOv5 \U0001f680 v6.0-23-ga18b0c3 torch 1.9.0 CUDA:0 (NVIDIA Tegra X2, 7850.375MB) OS: Ubuntu 18.04 on Jetson TX2 Python 3.6.9

Minimal Reproducible Example

python3 export.py --weights yolov5s.pt --include tflite --device 0

Additional

When converting models to tflite, the inference on the Jetson is slower. I thinks that is because I do not export with cuda device. When trying, I get the error pasted above

Are you willing to submit a PR?

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

glenn-jocher commented 3 years ago

@JNaranjo-Alcazar TFLite export should be done on CPU:

!python export.py --weights yolov5s.pt --include tflite
!python export.py --weights yolov5s.pt --include tflite --int8

TFlite models are intended for Android and EdgeTPU backends, they can not exploit CUDA devices and will be slower on CPU than simple PyTorch models.

JNaranjo-Alcazar commented 3 years ago

Thanks for the quick reply @glenn-jocher. Just to make it clear, the fastest inference on GPU (Jetson GPU) is using the pb model? It does not make sense to run a tflite model inference on Jetson (using the GPU)?

glenn-jocher commented 3 years ago

@JNaranjo-Alcazar well I've never used Jetson myself, but I don't believe TFLite has CUDA capability, or perhaps I'm just not aware of it.

In general the simplest CUDA inference will be with PyTorch, and the fastest is likely TensorRT.

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