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

* Fix links in readme * Fix links in readme * Update PPOCRv2/v3 examples * Update auto compression configs
2025-10-05 16:48:03 +08:00 · 2022-12-11 14:16:13 +08:00
parent e877f0fd07
commit 29f034cf93
14 changed files with 23 additions and 28 deletions
--- a/tools/README.md
+++ b/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
-python setup.py install
+pip install paddleslim==2.4.0
 ```

 3.安装fastdeploy-tools工具包
--- a/tools/README_EN.md
+++ b/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
-python setup.py install
+pip install paddleslim==2.4.0
 ```

 3.Install fastdeploy-tools package
--- a/tools/common_tools/auto_compression/README.md
+++ b/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
-python setup.py install
+pip install paddleslim==2.4.0
 ```

 ### 一键模型自动化压缩工具安装方式
--- a/tools/common_tools/auto_compression/README_EN.md
+++ b/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
-python setup.py install
+pip install paddleslim==2.4.0
 ```

 ### Install Fastdeploy Auto Compression Toolkit
--- a/tools/common_tools/auto_compression/configs/README.md
+++ b/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
--- a/tools/common_tools/auto_compression/configs/README_EN.md
+++ b/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
--- a/tools/common_tools/auto_compression/configs/classification/mobilenetv1_ssld_quant.yaml
+++ b/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
--- a/tools/common_tools/auto_compression/configs/classification/resnet50_vd_quant.yaml
+++ b/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
--- a/tools/common_tools/auto_compression/configs/detection/ppyoloe_plus_withNMS_quant.yaml
+++ b/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'
--- a/tools/common_tools/auto_compression/configs/detection/ppyoloe_withNMS_quant.yaml
+++ b/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'
--- a/tools/common_tools/auto_compression/configs/detection/yolov5s_quant.yaml
+++ b/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'
--- a/tools/common_tools/auto_compression/configs/detection/yolov6s_quant.yaml
+++ b/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:
--- a/tools/common_tools/auto_compression/configs/detection/yolov7_quant.yaml
+++ b/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:
--- a/tools/common_tools/auto_compression/configs/segmentation/pp_liteseg_quant.yaml
+++ b/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