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Model Quantization Tutorial

1. Introduction

Model quantization uses low bit values to replace high bit values and it is an amazing compression method.

For example, if float values is repleaced by int8 values, the size of the model can be reduced by 4 time and the inference speed can be accelerated.

Based on PaddleSlim, PaddleSeg supports quantization aware training method (QAT). The features of QAT are as follows:

  • Use the train dataset to minimize the quantization error.
  • Pros: The accuracy of the quantized model and the original model are similar.
  • Cons: It takes a long time to train a quantized model.

2. Compare Accuracy and Performance

Requirements:

  • GPU: V100 32G
  • CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
  • CUDA: 10.1
  • cuDNN: 7.6
  • TensorRT: 6.0.1.5
  • Paddle: 2.1.1

Details:

  • Run the original model and quantized model on Nvidia GPU and enable TensorRT.
  • Use one Nvidia GPU and the batch size is 1.
  • Use the test dataset of Cityscapes with the size of 1024*2048.
  • Only count the cost time of running predictor.

The next table shows the accuracy and performance of the original model and quantized model.

Model Dtype mIoU Time(s/img） Ratio
ANN_ResNet50_OS8 FP32 0.7909 0.281 -
ANN_ResNet50_OS8 INT8 0.7906 0.195 30.6%
DANet_ResNet50_OS8 FP32 0.8027 0.330 -
DANet_ResNet50_OS8 INT8 0.8039 0.266 19.4%
DeepLabV3P_ResNet50_OS8 FP32 0.8036 0.206 -
DeepLabV3P_ResNet50_OS8 INT8 0.8044 0.083 59.7%
DNLNet_ResNet50_OS8 FP32 0.7995 0.360 -
DNLNet_ResNet50_OS8 INT8 0.7989 0.236 52.5%
EMANet_ResNet50_OS8 FP32 0.7905 0.186 -
EMANet_ResNet50_OS8 INT8 0.7939 0.106 43.0%
GCNet_ResNet50_OS8 FP32 0.7950 0.228 -
GCNet_ResNet50_OS8 INT8 0.7959 0.144 36.8%
PSPNet_ResNet50_OS8 FP32 0.7883 0.324 -
PSPNet_ResNet50_OS8 INT8 0.7915 0.223 32.1%

3. Model Quantization Demo

We use a demo to explain how to generate and deploy a quantized model.

3.1 Preparation

Please refer to the installation document and prepare the requirements of PaddleSeg. Note that, the quantization module requires the version of PaddlePaddle is at least 2.2.

Run the following instructions to install PaddleSlim.

git clone https://github.com/PaddlePaddle/PaddleSlim.git

# checkout to special commit
git reset --hard 15ef0c7dcee5a622787b7445f21ad9d1dea0a933

# install
python setup.py install

3.2 Generate Quantized Model

3.2.1 Training for the Original Model

Before generating the quantized model, we have to prepare the original model with the data type of FP32.

In this demo, we choose the PP-LiteSeg model and the optic disc segmentation dataset, and use train.py for training from scratch. The usage of train.py can be found in this document.

Specifically, run the following instructions in the root directory of PaddleSeg.

export CUDA_VISIBLE_DEVICES=0  # Set GPU for Linux
# set CUDA_VISIBLE_DEVICES=0   # Seg GPU for Windows

python tools/train.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --do_eval \
       --use_vdl \
       --save_interval 250 \
       --save_dir output_fp32

After the training, the original model with the highest accuracy will be saved in output_fp32/best_model.

3.2.2 Generate the Quantized Model

1) Generate the Quantized Model

Based on the original model, we use deploy/slim/quant/qat_train.py to generate the quantized model.

The usage of qat_train.py and train.py is basically the same, and the former uses model_path to set the weight path of the original model (as follows). Besides, the learning rate of the quantization training is usually smaller than the normal training.

Input Params Usage Optional Default Value
config The config path of the original model No -
model_path The path of weight of the original model No -
iters Iterations Yes The iters in config
batch_size Batch size for single GPU Yes The batch_size in config
learning_rate Learning rate Yes The learning_rate in config
save_dir The directory for saving model and logs Yes output
num_workers The nums of threads to processs images Yes 0
use_vdl Whether to enable visualdl Yes False
save_interval_iters The interval interations for saving Yes 1000
do_eval Enable evaluation in training stage Yes False
log_iters The interval interations for outputing log Yes 10
resume_model The resume path, such as：output/iter_1000 Yes None

Run the following instructions in the root directory of PaddleSeg to start the quantization training. After the quantization training, the quantized model with the highest accuracy will be saved in output_quant/best_model.

python deploy/slim/quant/qat_train.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output_fp32/best_model/model.pdparams \
       --learning_rate 0.001 \
       --do_eval \
       --use_vdl \
       --save_interval 250 \
       --save_dir output_quant

2）Test the Quantized Model (Optional)

We use deploy/slim/quant/qat_val.py to load the weights of the quantized model and test the accuracy.

python deploy/slim/quant/qat_val.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output_quant/best_model/model.pdparams

3）Export the Quantized Model

Before deploying the quantized model, we have to convert the dygraph model to the inference model.

With the weights of the quantized model, we utilize deploy/slim/quant/qat_export.py to export the inference model. The input params of the script are as follows.

Input params Usage Optional Default Value
config The path of config file Yes -
model_path The path of trained weight No -
save_dir The save dir for the inference model No output/inference_model
output_op Set the op that is appended to the inference model, should in [argmax, softmax, none]. PaddleSeg models outputs logits (N*C*H*W) by default. Adding argmax operation, we get the label for every pixel, the dimension of output is N*H*W. Adding softmax operation, we get the probability of different classes for every pixel. No argmax
with_softmax Deprecated params, please use --output_op. Add softmax operator at the end of the network. Since PaddleSeg networking returns Logits by default, you can set it to True if you want the deployment model to get the probability value No False
without_argmax Deprecated params, please use --output_op. Whether or not to add argmax operator at the end of the network. Since PaddleSeg networking returns Logits by default, we add argmax operator at the end of the network by default in order to directly obtain the prediction results for the deployment model No False

Run the following instructions in the root directory of PaddleSeg. Then, the quantized inference model will be saved in output_quant_infer.

python deploy/slim/quant/qat_export.py \
       --config configs/quick_start/pp_liteseg_optic_disc_512x512_1k.yml \
       --model_path output_quant/best_model/model.pdparams \
       --save_dir output_quant_infer

3.3 Deploy the Quantized Model

We deploy the quantized inference model on Nvidia GPU and X86 CPU with Paddle Inference. Besides, Paddle Lite support deploying the quantized model on ARM CPU.

Please refer to the documents for detail information:

4. Reference