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文章目录Quantization aware training 简介ReferenceAPI兼容性使用示例实际遇到问题Quantization aware training 简介Quantization aware training 即 量化感知训练,主要是使用tfmot.quantization.keras.quantize_model。

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Training-aware Quantization. 在训练时,是使用float精度模拟量化模型,并使用float更新梯度。在inference的时候,直接在支持INT8的硬件上跑inference。这时候就是原生的量化模型在前向计算了。如下图所示: 这里作者首先分析了量化模型相对于原始精度模型可能的掉点原因:

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nnieqat-pytorch. Nnieqat is a quantize aware training package for Neural Network Inference Engine(NNIE) on pytorch, it uses hisilicon quantization library to quantize module's weight and activation as fake fp32 format.

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Training-aware Quantization. 在训练时,是使用float精度模拟量化模型,并使用float更新梯度。在inference的时候,直接在支持INT8的硬件上跑inference。这时候就是原生的量化模型在前向计算了。如下图所示: 这里作者首先分析了量化模型相对于原始精度模型可能的掉点原因: handong1587's blog. Papers. Deep Joint Task Learning for Generic Object Extraction. intro: NIPS 2014

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Nov 13, 2020 · I created a colab notebook demonstrating quantization-aware training of an image classification model. If you want to get an in-depth look of quantization aware training in TensorFlow, I recommend reading the Google paper Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference .

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and gradient mismatch. Besides, the gradients generated by the meta quantizer are loss-aware, contributing to better performance of the quantization training. Compared with commonly-used STE and manually designed gradient propagation in quantization training, MetaQuant learns to generate proper gradients without any manually designed knowledge. quantization-aware fine-tuning [46,34,5,42,45,18] to re-duce the performance gap between the original model and the quantized model. Basically, this is a retraining proce-dure that is performed for a few epochs to adjust the NN pa-rameters to reduce accuracy drop. However, quantization-aware fine-tuning can be computationally expensive and

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