[Docs] Pick seg fastdeploy docs from PaddleSeg (#1482)

* [Docs] Pick seg fastdeploy docs from PaddleSeg * [Docs] update seg docs * [Docs] Add c&csharp examples for seg * [Docs] Add c&csharp examples for seg * [Doc] Update paddleseg README.md * Update README.md
2025-10-06 00:57:33 +08:00 · 2023-03-17 11:22:46 +08:00
parent 3b1343c726
commit 5b143219ce
177 changed files with 1019 additions and 815 deletions
--- a/examples/vision/segmentation/paddleseg/semantic_segmentation/cpu-gpu/python/README.md
+++ b/examples/vision/segmentation/paddleseg/semantic_segmentation/cpu-gpu/python/README.md
@@ -0,0 +1,70 @@
+[English](README.md) | 简体中文  
+# PaddleSeg CPU-GPU Python部署示例
+本目录下提供`infer.py`快速完成PP-LiteSeg在CPU/GPU，以及GPU上通过Paddle-TensorRT加速部署的示例。执行如下脚本即可完成
+
+## 1. 说明  
+PaddleSeg支持利用FastDeploy在NVIDIA GPU、X86 CPU、飞腾CPU、ARM CPU、Intel GPU(独立显卡/集成显卡)硬件上快速部署Segmentation模型。
+
+## 2. 部署环境准备  
+在部署前，需确认软硬件环境，同时下载预编译部署库，参考[FastDeploy安装文档](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install#FastDeploy预编译库安装)安装FastDeploy预编译库。
+
+## 3. 部署模型准备
+在部署前，请准备好您所需要运行的推理模型，你可以选择使用[预导出的推理模型](../README.md)或者[自行导出PaddleSeg部署模型](../README.md)，如果你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)。  
+
+## 4. 运行部署示例
+```bash
+# 安装FastDpeloy python包（详细文档请参考`部署环境准备`）
+pip install fastdeploy-gpu-python -f https://www.paddlepaddle.org.cn/whl/fastdeploy.html
+conda config --add channels conda-forge && conda install cudatoolkit=11.2 cudnn=8.2
+
+# 下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/examples/vision/segmentation/semantic_segmentation/cpp-gpu/python
+# 如果您希望从PaddleSeg下载示例代码，请运行
+# git clone https://github.com/PaddlePaddle/PaddleSeg.git
+# # 注意：如果当前分支找不到下面的fastdeploy测试代码，请切换到develop分支
+# # git checkout develop
+# cd PaddleSeg/deploy/fastdeploy/semantic_segmentation/cpp-gpu/python
+
+# 下载Unet模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
+tar -xvf PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz
+wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
+
+# 运行部署示例
+# CPU推理
+python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
+# GPU推理
+python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
+# GPU上使用Paddle-TensorRT推理 （注意：Paddle-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）
+python infer.py --model PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
+```
+
+运行完成可视化结果如下图所示
+<div  align="center">  
+<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
+</div>
+
+## 5. 部署示例选项说明  
+
+|参数|含义|默认值
+|---|---|---|  
+|--model|指定模型文件夹所在的路径|None|
+|--image|指定测试图片所在的路径|None|  
+|--device|指定即将运行的硬件类型，支持的值为`[cpu, gpu]`，当设置为cpu时，可运行在x86 cpu/arm cpu等cpu上|cpu|
+|--use_trt|是否使用trt，该项只在device为gpu时有效|False|  
+
+关于如何通过FastDeploy使用更多不同的推理后端，以及如何使用不同的硬件，请参考文档：[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)
+
+## 6. 更多指南
+- [PaddleSeg python API文档](https://www.paddlepaddle.org.cn/fastdeploy-api-doc/python/html/semantic_segmentation.html)
+- [FastDeploy部署PaddleSeg模型概览](..)
+- [PaddleSeg C++部署](../cpp)
+
+## 7. 常见问题
+- [如何将模型预测结果SegmentationResult转为numpy格式](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/faq/vision_result_related_problems.md)
+- [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)
+- [Intel GPU(独立显卡/集成显卡)的使用](https://github.com/PaddlePaddle/FastDeploy/blob/develop/tutorials/intel_gpu/README.md)
+- [编译CPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/cpu.md)
+- [编译GPU部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/gpu.md)
+- [编译Jetson部署库](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/cn/build_and_install/jetson.md)
--- a/examples/vision/segmentation/paddleseg/semantic_segmentation/cpu-gpu/python/infer.py
+++ b/examples/vision/segmentation/paddleseg/semantic_segmentation/cpu-gpu/python/infer.py
@@ -0,0 +1,61 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleSeg model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'kunlunxin', 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        # If use original Tensorrt, not Paddle-TensorRT,
+        # comment the following two lines
+        option.enable_paddle_to_trt()
+        option.enable_paddle_trt_collect_shape()
+        option.set_trt_input_shape("x", [1, 3, 256, 256], [1, 3, 1024, 1024],
+                                   [1, 3, 2048, 2048])
+    return option
+
+
+args = parse_arguments()
+
+# settting for runtime
+runtime_option = build_option(args)
+model_file = os.path.join(args.model, "model.pdmodel")
+params_file = os.path.join(args.model, "model.pdiparams")
+config_file = os.path.join(args.model, "deploy.yaml")
+model = fd.vision.segmentation.PaddleSegModel(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# predict
+im = cv2.imread(args.image)
+result = model.predict(im)
+print(result)
+
+# visualize
+vis_im = fd.vision.vis_segmentation(im, result, weight=0.5)
+cv2.imwrite("vis_img.png", vis_im)