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MXNet 实现 TensorFlow 训练模拟量化方法

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MXNet 实现 TensorFlow 训练模拟量化方法
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MXNET-Scala TrainQuantization

Simpfly implementation of Quantization Aware Training[1][2] with MXNet-scala module.

Setup

Tested on Ubuntu 14.04

Requirements

Build steps

1, compile MXNet with CUDA, then compile the scala-pkg,doc: https://github.com/dmlc/mxnet/tree/master/scala-package

2, under the Mxnet-Scala/TrainQuantization folder:

mkdir lib;
 ln -s $MXNET_HOME/scala-package/assembly/linux-x86_64-gpu/target/mxnet-full_2.11-linux-x86_64-gpu-1.5.0-SNAPSHOT.jar lib

3, run sbt
and then compile the project

Train vgg on Cifar10

Using the script train_vgg16_cifar10.sh
under the scripts folder to train vgg from scratch on Cifar10:

FINETUNE_MODEL_EPOCH=-1
FINETUNE_MODEL_PREFIX=$ROOT/models/

Or you can finetune with the provided pretrain model:

FINETUNE_MODEL_EPOCH=46
FINETUNE_MODEL_PREFIX=$ROOT/models/cifar10_vgg16_acc_0.8772035

I did not use any data augmentation and carefully tune the hyper-parameters during training, the best accuracy I got was 0.877, worse than the best accracy 0.93 reported on Cifar10.

Train vgg with fake quantization on Cifar10

Using the script train_quantize_vgg16_cifar10.sh
under the scripts folder to train vgg with fake quantization on Cifar10,
you must provide the pretrained model:

FINETUNE_MODEL_EPOCH=46
FINETUNE_MODEL_PREFIX=$ROOT/models/cifar10_vgg16_acc_0.8772035

If everything goes right, you should get almost the same accuray with pretrained model after serveral epoch.

Test vgg with simulated quantization on Cifar10

Using the script test_quantize_vgg16_cifar10.sh
under the scripts folder to test pretrained fake quantization vgg with simulated quantization on Cifar10, you must provide the pretrained model:

FINETUNE_MODEL_EPOCH=57
FINETUNE_MODEL_PREFIX=$ROOT/models/cifar10_quantize_vgg16_acc_0.877504

Warning

Currently there is memory leak some where in the code, but I can’t figure out the reason. You will see the memory usage keep increasing when you run the tranining script. So remenber to stop the traning script when memory usage is too high, and you can resume the training process with saved model previously.

Reference

[1] Quantizing deep convolutional networks for efficient inference: A whitepaper. https://arxiv.org/pdf/1806.08342.pdf

[2] Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. https://arxiv.org/pdf/1712.05877.pdf

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