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Noisy inference results with TF2 int8 tflite models #637
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Hi @Rahn80643, thank you very much for your comment. which backend are you using when you run ArmNN, CpuAcc, GpuAcc or CpuRef? |
Hi @TeresaARM, I've tested with CpuAcc and GpuAcc backend, and the inference results are similar |
Hi @Rahn80643, could you give it a go to CpuRef? and see if the results are closer to TensorFlow? The performance will be worse than CpuAcc and GpuAcc, as CpuRef is just a "reference", but this information will help us to find out where the issue is. Thank you! |
Hi @TeresaARM, The followings are the inference results using CpuRef, and the results are not closer to those from TensorFlow Thank you |
Hi, I compared two int8 tflite models trained in TensorFlow1 and TensorFlow2 respectively, and I found the input zero point values are different. Is this the main reason why inferring TensorFlow2 int8 tflite model generates noisy results? If so, how to include the Rahn |
yes, this could be the problem. In your application code, when you convert the input image into a tensor for Arm NN, are you taking the zero-point into account? |
If this is the problem, there is some good explanation of it here: #161 |
Hi, I've tried the following modifications but the inference results still contain noisy results:
Apart from modifying the mean values in preprocess stage, are there other approaches to take the zero-point into account in ArmNN settings? |
I'm closing this due to inactivity, if you require support please reopen. |
Hi,
I'm migrating our segmentation model from TensorFlow1 to TensorFlow2, and the model is retrained with TensorFlow quantization aware training technique; however, the inference results of TF2 tflite model from ArmNN are not ideal, the comparisons and environment settings are shown below:
Based on the inference results from TensorFlow tflite API, the weights in tflite models seems normal, but the inference results from ArmNN contain many noisy pixels, I want to ask:
Best Regards,
Rahn
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