[Quantization] Update auto compression configs files. (#846)

* Fix links in readme

* Fix links in readme

* Update PPOCRv2/v3 examples

* Update auto compression configs

tools/README.md

@@ -11,15 +11,14 @@ FastDeploy提供了一系列高效易用的工具优化部署体验, 提升推
 FastDeploy基于PaddleSlim的Auto Compression Toolkit(ACT), 给用户提供了一键模型自动化压缩的工具, 用户可以轻松地通过一行命令对模型进行自动化压缩, 并在FastDeploy上部署压缩后的模型, 提升推理速度. 本文档将以FastDeploy一键模型自动化压缩工具为例, 介绍如何安装此工具, 并提供相应的使用文档.
 
 ### 环境准备
-1.用户参考PaddlePaddle官网, 安装develop版本
+1.用户参考PaddlePaddle官网, 安装Paddle 2.4 版本
 ```
 https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
 ```
 
-2.安装PaddleSlim develop版本
+2.安装PaddleSlim 2.4 版本
 ```bash
-git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
+pip install paddleslim==2.4.0
-python setup.py install
 ```
 
 3.安装fastdeploy-tools工具包

tools/README_EN.md

@@ -11,15 +11,14 @@ FastDeploy provides a series of efficient and easy-to-use tools to optimize the
 Based on PaddleSlim's Auto Compression Toolkit (ACT), FastDeploy provides users with a one-click model automation compression tool that allows users to easily compress the model with a single command. This document will take FastDeploy's one-click model automation compression tool as an example, introduce how to install the tool, and provide the corresponding documentation for usage.
 
 ### Environmental Preparation
-1.Install PaddlePaddle develop version
+1.Install PaddlePaddle 2.4 version
 ```
 https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
 ```
 
-2.Install PaddleSlim dev version
+2.Install PaddleSlim 2.4 version
 ```bash
-git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
+pip install paddleslim==2.4.0
-python setup.py install
 ```
 
 3.Install fastdeploy-tools package

tools/common_tools/auto_compression/README.md

@@ -6,15 +6,14 @@ FastDeploy基于PaddleSlim的Auto Compression Toolkit(ACT), 给用户提供了
 
 ### 环境依赖
 
-1.用户参考PaddlePaddle官网, 安装develop版本
+1.用户参考PaddlePaddle官网, 安装Paddle 2.4 版本
 ```
 https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
 ```
 
-2.安装paddleslim-develop版本
+2.安装PaddleSlim 2.4 版本
 ```bash
-git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
+pip install paddleslim==2.4.0
-python setup.py install
 ```
 
 ### 一键模型自动化压缩工具安装方式

tools/common_tools/auto_compression/README_EN.md

@@ -7,17 +7,14 @@ We take the Yolov5 series as an example to demonstrate how to install and execut
 
 ### Environment Dependencies
 
-1. Install the develop version downloaded from PaddlePaddle official website.
+1.Install PaddlePaddle 2.4 version
-
 ```
 https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/develop/install/pip/linux-pip.html
 ```
 
-2.Install PaddleSlim-develop
+2.Install PaddleSlim 2.4 version
-
 ```bash
-git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
+pip install paddleslim==2.4.0
-python setup.py install
 ```
 
 ### Install Fastdeploy Auto Compression Toolkit

tools/common_tools/auto_compression/configs/README.md

@@ -24,7 +24,7 @@ Distillation:
   alpha: 1.0                                  #蒸馏loss所占权重
   loss: soft_label                            #蒸馏loss算法
 
-Quantization:
+QuantAware:
   onnx_format: true                           #是否采用ONNX量化标准格式, 要在FastDeploy上部署, 必须选true
   use_pact: true                              #量化训练是否使用PACT方法
   activation_quantize_type: 'moving_average_abs_max'     #激活量化方式

tools/common_tools/auto_compression/configs/README_EN.md

@@ -26,7 +26,7 @@ Distillation:
   alpha: 1.0                                  #Distillation loss weight
   loss: soft_label                            #Distillation loss algorithm
 
-Quantization:
+QuantAware:
   onnx_format: true                           #Whether to use ONNX quantization standard format or not, must be true to deploy on FastDeploy
   use_pact: true                              #Whether to use the PACT method for training
   activation_quantize_type: 'moving_average_abs_max'     #Activations quantization methods

tools/common_tools/auto_compression/configs/classification/mobilenetv1_ssld_quant.yaml

@@ -17,7 +17,7 @@ Distillation:
   - softmax_0.tmp_0
 
 
-Quantization:
+QuantAware:
   use_pact: true
   activation_bits: 8
   is_full_quantize: false

tools/common_tools/auto_compression/configs/classification/resnet50_vd_quant.yaml

@@ -16,7 +16,7 @@ Distillation:
   node:
   - softmax_0.tmp_0
 
-Quantization:
+QuantAware:
   use_pact: true
   activation_bits: 8
   is_full_quantize: false

tools/common_tools/auto_compression/configs/detection/ppyoloe_plus_withNMS_quant.yaml

@@ -14,7 +14,7 @@ Distillation:
   alpha: 1.0
   loss: soft_label
 
-Quantization:
+QuantAware:
   onnx_format: true
   use_pact: true
   activation_quantize_type: 'moving_average_abs_max'

tools/common_tools/auto_compression/configs/detection/ppyoloe_withNMS_quant.yaml

@@ -14,7 +14,7 @@ Distillation:
   alpha: 1.0
   loss: soft_label
 
-Quantization:
+QuantAware:
   onnx_format: true
   use_pact: true
   activation_quantize_type: 'moving_average_abs_max'

tools/common_tools/auto_compression/configs/detection/yolov5s_quant.yaml

@@ -14,7 +14,7 @@ Distillation:
   alpha: 1.0
   loss: soft_label
 
-Quantization:
+QuantAware:
   onnx_format: true
   use_pact: true
   activation_quantize_type: 'moving_average_abs_max'

tools/common_tools/auto_compression/configs/detection/yolov6s_quant.yaml

@@ -14,12 +14,13 @@ Distillation:
   alpha: 1.0
   loss: soft_label
 
-Quantization:
+QuantAware:
   onnx_format: true
   activation_quantize_type: 'moving_average_abs_max'
   quantize_op_types:
   - conv2d
   - depthwise_conv2d
+  - conv2d_transpose
 
 
 PTQ:

tools/common_tools/auto_compression/configs/detection/yolov7_quant.yaml

@@ -14,7 +14,7 @@ Distillation:
   alpha: 1.0
   loss: soft_label
 
-Quantization:
+QuantAware:
   onnx_format: true
   activation_quantize_type: 'moving_average_abs_max'
   quantize_op_types:

tools/common_tools/auto_compression/configs/segmentation/pp_liteseg_quant.yaml

@@ -17,7 +17,7 @@ Distillation:
   node:
   - conv2d_94.tmp_0
 
-Quantization:
+QuantAware:
   onnx_format: True
   quantize_op_types:
   - conv2d