使用 TF-lite 将 MobileFacenet 转换为量化感知模型时，create_training_graph() 失败 [英] create_training_graph() failed when converted MobileFacenet to quantize-aware model with TF-lite

问题描述

我正在尝试量化 MobileFacenet (来自 Sirius-ai 的代码)根据 建议我想我遇到了与这个

I am trying to quantize MobileFacenet (code from sirius-ai) according to the suggestion and I think I met the same issue as this one

当我将 tf.contrib.quantize.create_training_graph() 添加到训练图中时
(train_nets.py ln.187: train_op = train(...) 之前或 train() utils/common.py ln.38 渐变之前)

When I add tf.contrib.quantize.create_training_graph() into training graph
(train_nets.py ln.187: before train_op = train(...) or in train() utils/common.py ln.38 before gradients)

它没有在图中添加量化感知操作来收集动态范围 max\min.

It did not add quantize-aware ops into the graph to collect dynamic range max\min.

我假设我应该在 tensorboard 中看到一些额外的节点，但我没有，因此我认为我没有在训练图中成功添加量化感知操作.我尝试跟踪 tensorflow，发现我对 _FindLayersToQuantize() 一无所获.

I assume that I should see some additional nodes in tensorboard, but I did not, thus I think I did not successfully add quantize-aware ops in training graph. And I try to trace tensorflow, found that I got nothing with _FindLayersToQuantize().

但是，当我添加 tf.contrib.quantize.create_eval_graph() 来优化训练图时.我可以看到一些量化感知操作作为 act_quant ...由于我没有成功地在训练图中添加操作，我没有权重加载到评估图中.因此我收到了一些错误消息

However when I add tf.contrib.quantize.create_eval_graph() to refine the training graph. I can see some quantize-aware ops as act_quant... Since I did not add ops in training graph successfully, I have no weights to load in eval graph. Thus I got some error message as

Key MobileFaceNet/Logits/LinearConv1x1/act_quant/max not found in checkpoint

或

tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value MobileFaceNet/Logits/LinearConv1x1/act_quant/max

有谁知道如何解决这个错误?或者如何以良好的精度获得量化的 MobileFacenet?

Does anyone know how to fix this error? or how to get quantized MobileFacenet with good accuracy?

谢谢！

推荐答案

H,

不幸的是，contrib/quantize 工具现已弃用.它将无法支持更新的模型，我们也不再致力于此.

Unfortunately, the contrib/quantize tool is now deprecated. It won't be able to support newer models, and we are not working on it anymore.

如果您对 QAT 感兴趣，我建议您尝试使用新的 TF/Keras QAT API.我们正在积极开发并为其提供支持.

If you are interested in QAT, I would recommend trying the new TF/Keras QAT API. We are actively developing that and providing support for it.

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