Add readme of quantization (#624)

* Add readme of quantization

* Fix readme of quantization, test=document_fix

deploy/slim/quant/README.md

@@ -79,7 +79,6 @@ python3.7 -m paddle.distributed.launch \
         -o LEARNING_RATE.params.lr=0.13 \
         -o epochs=100
 ```
-如果要训练识别模型的量化，修改配置文件和加载的模型参数即可。
 
 ### 4. 导出模型
 

deploy/slim/quant/README_en.md

+
+## Introduction
+
+Generally, a more complex model would achive better performance in the task, but it also leads to some redundancy in the model.
+Quantization is a technique that reduces this redundancy by reducing the full precision data to a fixed number,
+so as to reduce model calculation complexity and improve model inference performance.
+
+This example uses PaddleSlim provided [APIs of Quantization](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/) to compress the PaddleClas models.
+
+It is recommended that you could understand following pages before reading this example：
+- [The training strategy of PaddleClas models](../../../docs/en/tutorials/quick_start_en.md)
+- [PaddleSlim Document](https://paddlepaddle.github.io/PaddleSlim/api/quantization_api/)
+
+## Quick Start
+Quantization is mostly suitable for the deployment of lightweight models on mobile terminals.
+After training, if you want to further compress the model size and accelerate the prediction, you can use quantization methods to compress the model according to the following steps.
+
+1. Install PaddleSlim
+2. Prepare trained model
+3. Quantization-Aware Training
+4. Export inference model
+5. Deploy quantization inference model
+
+
+### 1. Install PaddleSlim
+
+* Install by pip.
+
+```bash
+pip3.7 install paddleslim==2.0.0
+```
+
+* Install from source code to get the lastest features.
+
+```bash
+git clone https://github.com/PaddlePaddle/PaddleSlim.git
+cd Paddleslim
+python setup.py install
+```
+
+
+### 2. Download Pretrain Model
+PaddleClas provides a series of trained [models](../../../docs/en/models/models_intro_en.md).
+If the model to be quantified is not in the list, you need to follow the [Regular Training](../../../docs/en/tutorials/getting_started_en.md) method to get the trained model.
+
+
+### 3. Quant-Aware Training
+Quantization training includes offline quantization training and online quantization training.
+Online quantization training is more effective. It is necessary to load the pre-trained model.
+After the quantization strategy is defined, the model can be quantified.
+
+The code for quantization training is located in `deploy/slim/quant/quant.py`. The training command is as follow:
+
+* CPU/Single GPU training
+
+```bash
+python3.7 deploy/slim/quant/quant.py \
+    -c configs/MobileNetV3/MobileNetV3_large_x1_0.yaml \
+    -o pretrained_model="./MobileNetV3_large_x1_0_pretrained"
+```
+
+* Distributed training
+
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+python3.7 -m paddle.distributed.launch \
+    --gpus="0,1,2,3,4,5,6,7" \
+    deploy/slim/quant/quant.py \
+        -c configs/MobileNetV3/MobileNetV3_large_x1_0.yaml \
+        -o pretrained_model="./MobileNetV3_large_x1_0_pretrained"
+```
+
+* The command of quantizing `MobileNetV3_large_x1_0` model is as follow:
+
+```bash
+# download pre-trained model
+wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams
+
+# run training
+python3.7 -m paddle.distributed.launch \
+    --gpus="0,1,2,3,4,5,6,7" \
+    deploy/slim/quant/quant.py \
+        -c configs/MobileNetV3/MobileNetV3_large_x1_0.yaml \
+        -o pretrained_model="./MobileNetV3_large_x1_0_pretrained"
+        -o LEARNING_RATE.params.lr=0.13 \
+        -o epochs=100
+```
+
+
+### 4. Export inference model
+
+After getting the model quantization aware trained, we can export it as inference model for predictive deployment:
+
+```bash
+python3.7 deploy/slim/quant/export_model.py \
+    -m MobileNetV3_large_x1_0 \
+    -p output/MobileNetV3_large_x1_0/best_model/ppcls \
+    -o ./MobileNetV3_large_x1_0_infer/ \
+    --img_size=224 \
+    --class_dim=1000
+```
+
+### 5. Deploy
+The type of quantized model's parameters derived from the above steps is still FP32, but the numerical range of the parameters is int8.
+The derived model can be converted through the `opt tool` of PaddleLite.
+
+For quantitative model deployment, please refer to [Mobile terminal model deployment](../../lite/readme_en.md)
+
+## Notes:
+
+* In quantitative training, it is suggested to load the pre-trained model obtained from conventional training to accelerate the convergence of quantitative training.
+* In quantitative training, it is suggested that the initial learning rate should be changed to `1 / 20 ~ 1 / 10` of the conventional training, and the training epoch number should be changed to `1 / 5 ~ 1 / 2` of the conventional training. In terms of learning rate strategy, it's better to train with warmup, other configuration information is not recommended to be changed.