[Quantization] Improve the usage of fastdeploy tools (PaddlePaddle#722)

Improve the usage of fastdeploy tools

python/requirements.txt

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

tools/README.md

@@ -26,14 +26,14 @@ python setup.py install
 ```bash
 # 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 fastdeploy-tools==0.0.0
+pip install fastdeploy-tools==0.0.1
 ```
 
 ### 一键模型自动化压缩工具的使用
 按照以上步骤成功安装后,即可使用FastDeploy一键模型自动化压缩工具, 示例如下.
 
 ```bash
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
+fastdeploy 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](./common_tools/auto_compression/README.md)
 
@@ -69,7 +69,7 @@ python setup.py install
 按照以上步骤成功安装后,即可使用 FastDeploy 一键转换工具, 示例如下:
 
 ```bash
-fastdeploy --convert --framework onnx --model yolov5s.onnx --save_dir pd_model
+fastdeploy convert --framework onnx --model yolov5s.onnx --save_dir pd_model
 ```
 
 更多详细内容可参考[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)

tools/README_EN.md

@@ -26,14 +26,14 @@ python setup.py install
 ```bash
 # 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 fastdeploy-tools==0.0.0
+pip install fastdeploy-tools==0.0.1
 
 ```
 
 ### The Usage of One-Click Model Auto Compression Tool
 After the above steps are successfully installed, you can use FastDeploy one-click model automation compression tool, as shown in the following example.
 ```bash
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
+fastdeploy 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](./common_tools/auto_compression/README_EN.md)
 
@@ -69,7 +69,7 @@ python setup.py install
 After successful installation according to the above steps, you can use the FastDeploy one-click conversion tool. The example is as follows:
 
 ```bash
-fastdeploy --convert --framework onnx --model yolov5s.onnx --save_dir pd_model
+fastdeploy convert --framework onnx --model yolov5s.onnx --save_dir pd_model
 ```
 
 For more details, please refer to[X2Paddle](https://github.com/PaddlePaddle/X2Paddle)

tools/common_tools/auto_compression/README.md

@@ -40,10 +40,10 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_val_320.tar.gz
 tar -xvf COCO_val_320.tar.gz
 ```
 
-##### 2.使用fastdeploy --auto_compress命令，执行一键模型自动化压缩:
+##### 2.使用fastdeploy compress命令，执行一键模型自动化压缩:
 以下命令是对yolov5s模型进行量化, 用户若想量化其他模型, 替换config_path为configs文件夹下的其他模型配置文件即可.
 ```shell
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
+fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
 ```
 
 ##### 3.参数说明
@@ -74,12 +74,12 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_train_320.tar
 tar -xvf COCO_train_320.tar
 ```
 
-##### 2.使用fastdeploy --auto_compress命令，执行一键模型自动化压缩:
+##### 2.使用fastdeploy compress命令，执行一键模型自动化压缩:
 以下命令是对yolov5s模型进行量化, 用户若想量化其他模型, 替换config_path为configs文件夹下的其他模型配置文件即可.
 ```shell
 # 执行命令默认为单卡训练，训练前请指定单卡GPU, 否则在训练过程中可能会卡住.
 export CUDA_VISIBLE_DEVICES=0
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='QAT' --save_dir='./yolov5s_qat_model/'
+fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='QAT' --save_dir='./yolov5s_qat_model/'
 ```
 
 ##### 3.参数说明

tools/common_tools/auto_compression/README_EN.md

@@ -46,12 +46,12 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_val_320.tar.gz
 tar -xvf COCO_val_320.tar.gz
 ```
 
-##### 2. Run fastdeploy --auto_compress command to compress the model
+##### 2. Run fastdeploy compress command to compress the model
 
 The following command is to quantize the yolov5s model, if developers want to quantize other models, replace the config_path with other model configuration files in the configs folder.
 
 ```shell
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
+fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='PTQ' --save_dir='./yolov5s_ptq_model/'
 ```
 
 [notice] PTQ is short for post-training quantization
@@ -83,14 +83,14 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/COCO_val_320.tar.gz
 tar -xvf COCO_val_320.tar.gz
 ```
 
-##### 2.Use fastdeploy --auto_compress command to compress models
+##### 2.Use fastdeploy compress command to compress models
 
 The following command is to quantize the yolov5s model, if developers want to quantize other models, replace the config_path with other model configuration files in the configs folder.
 
 ```shell
 # Please specify the single card GPU before training, otherwise it may get stuck during the training process.
 export CUDA_VISIBLE_DEVICES=0
-fastdeploy --auto_compress --config_path=./configs/detection/yolov5s_quant.yaml --method='QAT' --save_dir='./yolov5s_qat_model/'
+fastdeploy compress --config_path=./configs/detection/yolov5s_quant.yaml --method='QAT' --save_dir='./yolov5s_qat_model/'
 ```
 
 ##### 3.Parameters

tools/common_tools/auto_compression/fd_auto_compress/__init__.py

@@ -1 +0,0 @@
-import fd_auto_compress

tools/common_tools/auto_compression/fd_auto_compress/fd_auto_compress.py

@@ -22,7 +22,7 @@
 from paddleslim.common import load_config, load_onnx_model
 from paddleslim.auto_compression import AutoCompression
 from paddleslim.quant import quant_post_static
-from fd_auto_compress.dataset import *
+from .dataset import *
 
 
 def argsparser():

tools/common_tools/common_tools.py

@@ -4,8 +4,8 @@
 
 def argsparser():
     parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument('tools', choices=['compress', 'convert'])
     ## argumentments for auto compression
-    parser.add_argument('--auto_compress', default=False, action='store_true')
     parser.add_argument(
         '--config_path',
         type=str,
@@ -27,7 +27,6 @@ def argsparser():
         default='gpu',
         help="which device used to compress.")
     ## arguments for other x2paddle
-    parser.add_argument('--convert', default=False, action='store_true')
     parser.add_argument(
         '--framework',
         type=str,
@@ -76,16 +75,11 @@ def argsparser():
 
 def main():
     args = argsparser().parse_args()
-    if args.auto_compress == True:
-        try:
-            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:
-            print(
-                "Can not start auto compresssion successfully! Please check if you have installed it!"
-            )
-    if args.convert == True:
+    if args.tools == "compress":
+        from .auto_compression.fd_auto_compress.fd_auto_compress import auto_compress
+        print("Welcome to use FastDeploy Auto Compression Toolkit!")
+        auto_compress(args)
+    if args.tools == "convert":
         try:
             import platform
             import logging

tools/setup.py

@@ -1,10 +1,11 @@
 import setuptools
 
 long_description = "fastdeploy-tools is a toolkit for FastDeploy, including auto compression .etc.\n\n"
-long_description += "Usage of auto compression: fastdeploy --auto_compress --config_path=./yolov7_tiny_qat_dis.yaml --method='QAT' --save_dir='./v7_qat_outmodel/' \n"
+long_description += "Usage of auto compression: fastdeploy compress --config_path=./yolov7_tiny_qat_dis.yaml --method='QAT' --save_dir='./v7_qat_outmodel/' \n"
 
 setuptools.setup(
     name="fastdeploy-tools",  # name of package
+    version="0.0.1",  #version of package
     description="A toolkit for FastDeploy.",
     long_description=long_description,
     long_description_content_type="text/plain",