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Update paddleseg doc
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felixhjh
@@ -40,7 +40,7 @@ The visualized result after running is as follows
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fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleSeg model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/model_export_cn.md) for more information
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PaddleSeg model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/model_export_cn.md) for more information
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**Parameter**
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@@ -1,35 +1,25 @@
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[English](README.md) | 简体中文
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# PaddleSeg Python部署示例
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在部署前，需确认以下两个步骤
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本目录下提供`infer.py`快速完成PP-LiteSeg在华为昇腾上部署的示例。
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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在部署前，需自行编译基于昆仑芯XPU的FastDeploy wheel 包，参考文档[昆仑芯XPU部署环境编译安装](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/kunlunxin.md)，编译python wheel包并安装
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【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
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>>**注意** **PP-Matting**、**PP-HumanMatting**的模型，请从[Matting模型部署](../../../matting)下载
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本目录下提供`infer.py`快速完成Unet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python
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cd FastDeploy/examples/vision/segmentation/paddleseg/ascend/cpp
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# 下载Unet模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
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tar -xvf Unet_cityscapes_without_argmax_infer.tgz
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# 下载PP-LiteSeg模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# CPU推理
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
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# GPU推理
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
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# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
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# 昆仑芯XPU推理
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
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# 华为昇腾推理
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python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
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python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png
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```
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运行完成可视化结果如下图所示
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@@ -43,7 +33,7 @@ python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_
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fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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PaddleSeg模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/docs/model_export_cn.md)
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PaddleSeg模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/model_export_cn.md)
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**参数**
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@@ -67,7 +57,7 @@ PaddleSeg模型加载和初始化，其中model_file, params_file以及config_fi
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> **返回**
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>
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> > 返回`fastdeploy.vision.SegmentationResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > 返回`fastdeploy.vision.SegmentationResult`结构体，SegmentationResult结构体说明参考[SegmentationResult结构体介绍](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)
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### 类成员属性
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#### 预处理参数
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@@ -78,9 +68,12 @@ PaddleSeg模型加载和初始化，其中model_file, params_file以及config_fi
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#### 后处理参数
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> > * **apply_softmax**(bool): 当模型导出时，并未指定`apply_softmax`参数，可通过此设置此参数为`true`，将预测的输出分割标签（label_map）对应的概率结果(score_map)做softmax归一化处理
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## 其它文档
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## 快速链接
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- [PaddleSeg 模型介绍](..)
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- [PaddleSeg C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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## 常见问题
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- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/api/vision_results/segmentation_result_CN.md)
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- [如何切换模型推理后端引擎](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/how_to_change_backend.md)
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- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
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@@ -11,42 +11,13 @@ def parse_arguments():
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"--model", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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if args.device.lower() == "kunlunxin":
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option.use_kunlunxin()
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if args.device.lower() == "ascend":
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option.use_ascend()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
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[1, 3, 2048, 2048])
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return option
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args = parse_arguments()
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runtime_option = fd.RuntimeOption()
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runtime_option.use_kunlunxin()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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@@ -1,36 +0,0 @@
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English | [简体中文](README_CN.md)
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# PaddleSegmentation Python Simple Serving Demo
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## Environment
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- 1. Prepare environment and install FastDeploy Python whl, refer to [download_prebuilt_libraries](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Server:
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```bash
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# Download demo code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
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# Download PP_LiteSeg model
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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# Launch server, change the configurations in server.py to select hardware, backend, etc.
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# and use --host, --port to specify IP and port
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fastdeploy simple_serving --app server:app
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```
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Client:
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```bash
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# Download demo code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
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# Download test image
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wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
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# Send request and get inference result (Please adapt the IP and port if necessary)
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python client.py
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```
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@@ -1,36 +0,0 @@
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简体中文 | [English](README.md)
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# PaddleSegmentation Python轻量服务化部署示例
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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服务端：
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/segmentation/paddleseg/python/serving
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# 下载PP_LiteSeg模型文件
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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tar -xvf PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz
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# 启动服务，可修改server.py中的配置项来指定硬件、后端等
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# 可通过--host、--port指定IP和端口号
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fastdeploy simple_serving --app server:app
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```
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客户端：
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```bash
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# 下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/detection/paddledetection/python/serving
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# 下载测试图片
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 请求服务，获取推理结果（如有必要，请修改脚本中的IP和端口号）
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python client.py
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```
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@@ -1,23 +0,0 @@
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import requests
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import json
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import cv2
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import fastdeploy as fd
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from fastdeploy.serving.utils import cv2_to_base64
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if __name__ == '__main__':
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url = "http://127.0.0.1:8000/fd/ppliteseg"
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headers = {"Content-Type": "application/json"}
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im = cv2.imread("cityscapes_demo.png")
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data = {"data": {"image": cv2_to_base64(im)}, "parameters": {}}
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resp = requests.post(url=url, headers=headers, data=json.dumps(data))
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if resp.status_code == 200:
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r_json = json.loads(resp.json()["result"])
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result = fd.vision.utils.json_to_segmentation(r_json)
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vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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else:
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print("Error code:", resp.status_code)
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print(resp.text)
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@@ -1,38 +0,0 @@
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import fastdeploy as fd
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from fastdeploy.serving.server import SimpleServer
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import os
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import logging
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logging.getLogger().setLevel(logging.INFO)
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# Configurations
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model_dir = 'PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer'
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device = 'cpu'
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use_trt = False
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# Prepare model
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model_file = os.path.join(model_dir, "model.pdmodel")
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params_file = os.path.join(model_dir, "model.pdiparams")
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config_file = os.path.join(model_dir, "deploy.yaml")
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# Setup runtime option to select hardware, backend, etc.
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option = fd.RuntimeOption()
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if device.lower() == 'gpu':
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option.use_gpu()
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if use_trt:
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option.use_trt_backend()
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option.set_trt_cache_file('pp_lite_seg.trt')
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# Create model instance
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model_instance = fd.vision.segmentation.PaddleSegModel(
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model_file=model_file,
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params_file=params_file,
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config_file=config_file,
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runtime_option=option)
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# Create server, setup REST API
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app = SimpleServer()
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app.register(
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task_name="fd/ppliteseg",
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model_handler=fd.serving.handler.VisionModelHandler,
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predictor=model_instance)
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