[Quantization] Improve the usage of FastDeploy tools. (#660)

* Add PaddleOCR Support

* Add PaddleOCR Support

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Support

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Supports

* Add PaddleOCRv3 Suport

* Fix Rec diff

* Remove useless functions

* Remove useless comments

* Add PaddleOCRv2 Support

* Add PaddleOCRv3 & PaddleOCRv2 Support

* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

* Fix code naming convention

* Fix code naming convention

* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

* Fix diff in PPOCRv2

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Fix conflict

* Add readme for OCRResult

* Improve OCR readme

* Add OCRResult readme

* Improve OCR readme

* Improve OCR readme

* Add Model Quantization Demo

* Fix Model Quantization Readme

* Fix Model Quantization Readme

* Add the function to do PTQ quantization

* Improve quant tools readme

* Improve quant tool readme

* Improve quant tool readme

* Add PaddleInference-GPU for OCR Rec model

* Add QAT method to fastdeploy-quantization tool

* Remove examples/slim for now

* Move configs folder

* Add Quantization Support for Classification Model

* Imporve ways of importing preprocess

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Improve Quantization configs and readme

* Add support for multi-inputs model

* Add backends and params file for YOLOv7

* Add quantized model deployment support for YOLO series

* Fix YOLOv5 quantize readme

* Fix YOLO quantize readme

* Fix YOLO quantize readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Fix bug, change Fronted to ModelFormat

* Change Fronted to ModelFormat

* Add examples to deploy quantized paddleclas models

* Fix readme

* Add quantize Readme

* Add quantize Readme

* Add quantize Readme

* Modify readme of quantization tools

* Modify readme of quantization tools

* Improve quantization tools readme

* Improve quantization readme

* Improve PaddleClas quantized model deployment  readme

* Add PPYOLOE-l quantized deployment examples

* Improve quantization tools readme

* Improve Quantize Readme

* Fix conflicts

* Fix conflicts

* improve readme

* Improve quantization tools and readme

* Improve quantization tools and readme

* Add quantized deployment examples for PaddleSeg model

* Fix cpp readme

* Fix memory leak of reader_wrapper function

* Fix model file name in PaddleClas quantization examples

* Update Runtime and E2E benchmark

* Update Runtime and E2E benchmark

* Rename quantization tools to auto compression tools

* Remove PPYOLOE data when deployed on MKLDNN

* Fix readme

* Support PPYOLOE with OR without NMS and update readme

* Update Readme

* Update configs and readme

* Update configs and readme

* Add Paddle-TensorRT backend in quantized model deploy examples

* Support PPYOLOE+ series

* Add reused_input_tensors for PPYOLOE

* Improve fastdeploy tools usage

* improve fastdeploy tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* remove modify

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Improve fastdeploy auto compression tool

* Remove extra requirements for fd-auto-compress package

* Imporve fastdeploy-tools package

* Install fastdeploy-tools package when build fastdeploy-python

* Imporve quantization readme

python/requirements.txt

@@ -3,5 +3,5 @@ requests
 tqdm
 numpy
 opencv-python
-fd-auto-compress>=0.0.1
+fastdeploy-tools
 pyyaml

tools/README.md

@@ -22,14 +22,11 @@ git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
 python setup.py install
 ```
 
-3.安装fd-auto-compress一键模型自动化压缩工具
+3.安装fastdeploy-tools工具包
 ```bash
-# 通过pip安装fd-auto-compress.
+# 通过pip安装fastdeploy-tools. 此工具包目前支持模型一键自动化压缩和模型转换的功能.
 # FastDeploy的python包已包含此工具, 不需重复安装.
-pip install fd-auto-compress==0.0.1
+pip install fastdeploy-tools==0.0.0
-
-# 在当前目录执行以下命令
-python setup.py install
 ```
 
 ### 一键模型自动化压缩工具的使用
@@ -38,7 +35,7 @@ python setup.py install
 ```bash
 fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
 ```
-详细使用文档请参考[FastDeploy一键模型自动化压缩工具](./auto_compression/README.md)
+详细使用文档请参考[FastDeploy一键模型自动化压缩工具](./common_tools/auto_compression/README.md)
 
 <p id="2"></p>
 

tools/README_EN.md

@@ -22,14 +22,12 @@ git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
 python setup.py install
 ```
 
-3.Install fd-auto-compress package
+3.Install fastdeploy-tools package
 ```bash
-# Installing fd-auto-compress via pip
+# Installing fastdeploy-tools via pip
 # This tool is included in the python installer of FastDeploy, so you don't need to install it again.
-pip install fd-auto-compress==0.0.1
+pip install fastdeploy-tools==0.0.0
 
-# Execute in the current directory
-python setup.py install
 ```
 
 ### The Usage of One-Click Model Auto Compression Tool
@@ -37,7 +35,7 @@ After the above steps are successfully installed, you can use FastDeploy one-cli
 ```bash
 fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
 ```
-For detailed documentation, please refer to [FastDeploy One-Click Model Auto Compression Tool](./auto_compression/README.md)
+For detailed documentation, please refer to [FastDeploy One-Click Model Auto Compression Tool](./common_tools/auto_compression/README_EN.md)
 
 <p id="2"></p>
 

tools/auto_compression/setup.py

@@ -1,22 +0,0 @@
-import setuptools
-import fd_auto_compress
-
-long_description = "fd_auto_compress is a toolkit for model auto compression of FastDeploy.\n\n"
-long_description += "Usage: fastdeploy --auto_compress --config_path=./yolov7_tiny_qat_dis.yaml --method='QAT' --save_dir='../v7_qat_outmodel/' \n"
-
-setuptools.setup(
-    name="fd_auto_compress",
-    version="0.0.1",
-    description="A toolkit for model auto compression of FastDeploy.",
-    long_description=long_description,
-    long_description_content_type="text/plain",
-    packages=setuptools.find_packages(),
-    author='fastdeploy',
-    author_email='fastdeploy@baidu.com',
-    url='https://github.com/PaddlePaddle/FastDeploy.git',
-    classifiers=[
-        "Programming Language :: Python :: 3",
-        "License :: OSI Approved :: Apache Software License",
-        "Operating System :: OS Independent",
-    ],
-    license='Apache 2.0', )

tools/common_tools/auto_compression/README.md

@@ -17,14 +17,8 @@ git clone https://github.com/PaddlePaddle/PaddleSlim.git & cd PaddleSlim
 python setup.py install
 ```
 
-### fastdeploy-auto-compression 一键模型自动化压缩工具安装方式
+### 一键模型自动化压缩工具安装方式
-```
+FastDeploy一键模型自动化压缩不需要单独的安装, 用户只需要正确安装好[FastDeploy工具包](../../README.md)即可.
-# 通过pip安装fd-auto-compress包
-pip install fd-auto-compress
-
-# 并在上一层目录(非本级目录)执行如下命令
-python setup.py install
-```
 
 ## 2.使用方式
 

tools/common_tools/auto_compression/README_EN.md

@@ -21,16 +21,8 @@ python setup.py install
 ```
 
 ### Install Fastdeploy Auto Compression Toolkit
+FastDeploy One-Click Model Automation compression does not require a separate installation, users only need to properly install the [FastDeploy Toolkit](../../README.md)
 
-Run the following command to install
-
-```
-# Install fd-auto-compress package using pip
-pip install fd-auto-compress
-
-# Execute the following command in the previous directory (not in the current directory)
-python setup.py install
-```
 
 ## 2. How to Use
 

tools/common_tools/auto_compression/configs/README_EN.md

@@ -5,37 +5,41 @@ In addition to using the configuration files provided by FastDeploy directly in
 
 ## Demo
 
+
 ```
 # Global config
 Global:
-  model_dir: ./yolov5s.onnx                   #Path to input model
+  model_dir: ./ppyoloe_plus_crn_s_80e_coco    #Path to input model
-  format: 'onnx'                              #Input model format, please select 'paddle' for paddle model
+  format: paddle                              #Input model format, please select 'paddle' for paddle model
   model_filename: model.pdmodel               #Quantized model name in Paddle format
-  params_filename: model.pdiparams            #Parameter name for quantized model name in Paddle format
+  params_filename: model.pdiparams            #Parameter name for quantized paddle model
-  image_path: ./COCO_val_320                  #Data set paths for post-training quantization or quantized distillation
+  qat_image_path: ./COCO_train_320            #Data set paths for quantization distillation training
-  arch: YOLOv5                                #Model Architecture
+  ptq_image_path: ./COCO_val_320              #Data set paths for PTQ
-  input_list: ['x2paddle_images']             #Input name of the model to be quantified
+  input_list: ['image','scale_factor']        #Input name of the model to be quanzitzed
-  preprocess: yolo_image_preprocess           #The preprocessing functions for the data when quantizing the model. Developers can modify or write a new one in . /fdquant/dataset.py
+  qat_preprocess: ppyoloe_plus_withNMS_image_preprocess # The preprocessing function for Quantization distillation training
+  ptq_preprocess: ppyoloe_plus_withNMS_image_preprocess # The preprocessing function for PTQ
+  qat_batch_size: 4                           #Batch size
 
-#uantization distillation training configuration
+
+# Quantization distillation training configuration
 Distillation:
-  alpha: 1.0                                  # Distillation loss weight
+  alpha: 1.0                                  #Distillation loss weight
   loss: soft_label                            #Distillation loss algorithm
 
 Quantization:
-  onnx_format: true                           #Whether to use ONNX quantization standard format or not, must be true to deploy on FastDeploye
+  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'     #Activate quantization methods
+  activation_quantize_type: 'moving_average_abs_max'     #Activations quantization methods
   quantize_op_types:                          #OPs that need to be quantized
   - conv2d
   - depthwise_conv2d
 
-#Post-Training Quantization
+# Post-Training Quantization
 PTQ:
-  calibration_method: 'avg'                   #Activate calibration algorithm of post-training quantization , Options: avg, abs_max, hist, KL, mse, emd
+  calibration_method: 'avg'                   #Activations calibration algorithm of post-training quantization , Options: avg, abs_max, hist, KL, mse, emd
   skip_tensor_list: None                      #Developers can skip some conv layers‘ quantization
 
-#Traning
+# Training Config
 TrainConfig:
   train_iter: 3000
   learning_rate: 0.00001
@@ -44,8 +48,9 @@ TrainConfig:
       type: SGD
     weight_decay: 4.0e-05
   target_metric: 0.365
+
 ```
 
 ## More details
 
-FastDeploy one-click quantization tool is powered by PaddeSlim, please refer to [Automated Compression of Hyperparameter Tutorial](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/hyperparameter_tutorial.md) for more details.
+FastDeploy one-click quantization tool is powered by PaddeSlim, please refer to [Auto Compression Hyperparameter Tutorial](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/hyperparameter_tutorial.md) for more details.

tools/common_tools/common_tools.py

@@ -78,7 +78,7 @@ def main():
     args = argsparser().parse_args()
     if args.auto_compress == True:
         try:
-            from fd_auto_compress.fd_auto_compress import auto_compress
+            from .auto_compression.fd_auto_compress.fd_auto_compress import auto_compress
             print("Welcome to use FastDeploy Auto Compression Toolkit!")
             auto_compress(args)
         except ImportError: