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jiaxiang-wu avatar jiaxiang-wu commented on July 20, 2024 1

All the model compression methods requires the model to be re-trained, based on some training data, so that the performance degradation can be much smaller.

If you want to compress a model without any training operation, you may try to use:

$ python tools/conversion/export_quant_tflite_model.py --enbl_post_quant

to quantize a model with 32-bit floating-point weights into 8-bit fixed-point weights, using post-training quantization.

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JSnobody avatar JSnobody commented on July 20, 2024

OK, I got it.
But, another question, does the quantization method of PocketFlow use post-training quantization by Tensorflow?

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jiaxiang-wu avatar jiaxiang-wu commented on July 20, 2024

No, we are using quantization-aware training to produce quantized models, which takes longer but the accuracy will be higher. If you don't want to use quantization-aware training, then just use post-training quantization with the above command.

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JSnobody avatar JSnobody commented on July 20, 2024

I see.
Thank you!

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