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[Doc]Add English version of documents in examples (#1070)
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Hu Chuqi
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# PaddleSeg 模型部署
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English | [简体中文](README_CN.md)
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# PaddleSeg Model Deployment
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## 模型版本说明
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## Model Description
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- [PaddleSeg develop](https://github.com/PaddlePaddle/PaddleSeg/tree/develop)
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目前FastDeploy支持如下模型的部署
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FastDeploy currently supports the deployment of the following models
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- [U-Net系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/unet/README.md)
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- [PP-LiteSeg系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/pp_liteseg/README.md)
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- [PP-HumanSeg系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/README.md)
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- [FCN系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/fcn/README.md)
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- [DeepLabV3系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/deeplabv3/README.md)
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- [U-Net models](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/unet/README.md)
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- [PP-LiteSeg models](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/pp_liteseg/README.md)
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- [PP-HumanSeg models](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/README.md)
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- [FCN models](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/fcn/README.md)
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- [DeepLabV3 models](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/deeplabv3/README.md)
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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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【Attention】For **PP-Matting**、**PP-HumanMatting** and **ModNet** deployment, please refer to [Matting Model Deployment](../../matting)
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## 准备PaddleSeg部署模型
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## Prepare PaddleSeg Deployment Model
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PaddleSeg模型导出，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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For the export of the PaddleSeg model, refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/blob/develop/docs/model_export_cn.md) for more information
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**注意**
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- PaddleSeg导出的模型包含`model.pdmodel`、`model.pdiparams`和`deploy.yaml`三个文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息
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**Attention**
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- The exported PaddleSeg model contains three files, including `model.pdmodel`、`model.pdiparams` and `deploy.yaml`. FastDeploy will get the pre-processing information for inference from yaml files.
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## 下载预训练模型
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## Download Pre-trained Model
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为了方便开发者的测试，下面提供了PaddleSeg导出的部分模型
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- without-argmax导出方式为：**不指定**`--input_shape`，**指定**`--output_op none`
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- with-argmax导出方式为：**不指定**`--input_shape`，**指定**`--output_op argmax`
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For developers' testing, part of the PaddleSeg exported models are provided below.
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- without-argmax export mode: **Not specified**`--input_shape`，**specified**`--output_op none`
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- with-argmax export mode：**Not specified**`--input_shape`，**specified**`--output_op argmax`
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开发者可直接下载使用。
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Developers can download directly.
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| 模型 | 参数文件大小 |输入Shape | mIoU | mIoU (flip) | mIoU (ms+flip) |
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| Model | Parameter Size | Input Shape | mIoU | mIoU (flip) | mIoU (ms+flip) |
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|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- | :----- |
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| [Unet-cityscapes-with-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_with_argmax_infer.tgz) \| [Unet-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz) | 52MB | 1024x512 | 65.00% | 66.02% | 66.89% |
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| [PP-LiteSeg-B(STDC2)-cityscapes-with-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_with_argmax_infer.tgz) \| [PP-LiteSeg-B(STDC2)-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_B_STDC2_cityscapes_without_argmax_infer.tgz) | 31MB | 1024x512 | 79.04% | 79.52% | 79.85% |
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|[PP-HumanSegV1-Lite-with-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV1_Lite_with_argmax_infer.tgz) \| [PP-HumanSegV1-Lite-without-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Lite_infer.tgz) | 543KB | 192x192 | 86.2% | - | - |
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|[PP-HumanSegV2-Lite-with-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Lite_192x192_with_argmax_infer.tgz) \| [PP-HumanSegV2-Lite-without-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Lite_192x192_infer.tgz) | 12MB | 192x192 | 92.52% | - | - |
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| [PP-HumanSegV2-Mobile-with-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Mobile_192x192_with_argmax_infer.tgz) \| [PP-HumanSegV2-Mobile-without-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Mobile_192x192_infer.tgz) | 29MB | 192x192 | 93.13% | - | - |
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|[PP-HumanSegV1-Server-with-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Server_with_argmax_infer.tgz) \| [PP-HumanSegV1-Server-without-argmax(通用人像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Server_infer.tgz) | 103MB | 512x512 | 96.47% | - | - |
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| [Portait-PP-HumanSegV2-Lite-with-argmax(肖像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV2_Lite_256x144_with_argmax_infer.tgz) \| [Portait-PP-HumanSegV2-Lite-without-argmax(肖像分割模型)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV2_Lite_256x144_infer.tgz) | 3.6M | 256x144 | 96.63% | - | - |
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| [FCN-HRNet-W18-cityscapes-with-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/FCN_HRNet_W18_cityscapes_with_argmax_infer.tgz) \| [FCN-HRNet-W18-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/FCN_HRNet_W18_cityscapes_without_argmax_infer.tgz)(暂时不支持ONNXRuntime的GPU推理) | 37MB | 1024x512 | 78.97% | 79.49% | 79.74% |
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|[PP-HumanSegV1-Lite-with-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV1_Lite_with_argmax_infer.tgz) \| [PP-HumanSegV1-Lite-without-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Lite_infer.tgz) | 543KB | 192x192 | 86.2% | - | - |
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|[PP-HumanSegV2-Lite-with-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Lite_192x192_with_argmax_infer.tgz) \| [PP-HumanSegV2-Lite-without-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Lite_192x192_infer.tgz) | 12MB | 192x192 | 92.52% | - | - |
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| [PP-HumanSegV2-Mobile-with-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Mobile_192x192_with_argmax_infer.tgz) \| [PP-HumanSegV2-Mobile-without-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV2_Mobile_192x192_infer.tgz) | 29MB | 192x192 | 93.13% | - | - |
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|[PP-HumanSegV1-Server-with-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Server_with_argmax_infer.tgz) \| [PP-HumanSegV1-Server-without-argmax(General Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Server_infer.tgz) | 103MB | 512x512 | 96.47% | - | - |
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| [Portait-PP-HumanSegV2-Lite-with-argmax(Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV2_Lite_256x144_with_argmax_infer.tgz) \| [Portait-PP-HumanSegV2-Lite-without-argmax(Portrait Segmentation Model)](https://bj.bcebos.com/paddlehub/fastdeploy/Portrait_PP_HumanSegV2_Lite_256x144_infer.tgz) | 3.6M | 256x144 | 96.63% | - | - |
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| [FCN-HRNet-W18-cityscapes-with-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/FCN_HRNet_W18_cityscapes_with_argmax_infer.tgz) \| [FCN-HRNet-W18-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/FCN_HRNet_W18_cityscapes_without_argmax_infer.tgz)(GPU inference for ONNXRuntime is not supported now) | 37MB | 1024x512 | 78.97% | 79.49% | 79.74% |
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| [Deeplabv3-ResNet101-OS8-cityscapes-with-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/Deeplabv3_ResNet101_OS8_cityscapes_with_argmax_infer.tgz) \| [Deeplabv3-ResNet101-OS8-cityscapes-without-argmax](https://bj.bcebos.com/paddlehub/fastdeploy/Deeplabv3_ResNet101_OS8_cityscapes_without_argmax_infer.tgz) | 150MB | 1024x512 | 79.90% | 80.22% | 80.47% |
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## 详细部署文档
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## Detailed Deployment Tutorials
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- [Python部署](python)
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- [C++部署](cpp)
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- [Python Deployment](python)
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- [C++ Deployment](cpp)
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[English](README.md) | 简体中文
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# 视觉模型部署
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本目录下提供了各类视觉模型的部署，主要涵盖以下任务类型
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| 任务类型 | 说明 | 预测结果结构体 |
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|:-------------- |:----------------------------------- |:-------------------------------------------------------------------------------- |
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| Detection | 目标检测，输入图像，检测图像中物体位置，并返回检测框坐标及类别和置信度 | [DetectionResult](../../docs/api/vision_results/detection_result.md) |
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| Segmentation | 语义分割，输入图像，给出图像中每个像素的分类及置信度 | [SegmentationResult](../../docs/api/vision_results/segmentation_result.md) |
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| Classification | 图像分类，输入图像，给出图像的分类结果和置信度 | [ClassifyResult](../../docs/api/vision_results/classification_result.md) |
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| FaceDetection | 人脸检测，输入图像，检测图像中人脸位置，并返回检测框坐标及人脸关键点 | [FaceDetectionResult](../../docs/api/vision_results/face_detection_result.md) |
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| FaceAlignment | 人脸对齐(人脸关键点检测)，输入图像，返回人脸关键点 | [FaceAlignmentResult](../../docs/api/vision_results/face_alignment_result.md) |
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| KeypointDetection | 关键点检测，输入图像，返回图像中人物行为的各个关键点坐标和置信度 | [KeyPointDetectionResult](../../docs/api/vision_results/keypointdetection_result.md) |
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| FaceRecognition | 人脸识别，输入图像，返回可用于相似度计算的人脸特征的embedding | [FaceRecognitionResult](../../docs/api/vision_results/face_recognition_result.md) |
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| Matting | 抠图，输入图像，返回图片的前景每个像素点的Alpha值 | [MattingResult](../../docs/api/vision_results/matting_result.md) |
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| OCR | 文本框检测，分类，文本框内容识别，输入图像，返回文本框坐标，文本框的方向类别以及框内的文本内容 | [OCRResult](../../docs/api/vision_results/ocr_result.md) |
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| MOT | 多目标跟踪，输入图像，检测图像中物体位置，并返回检测框坐标，对象id及类别置信度 | [MOTResult](../../docs/api/vision_results/mot_result.md) |
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| HeadPose | 头部姿态估计，返回头部欧拉角 | [HeadPoseResult](../../docs/api/vision_results/headpose_result.md) |
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## FastDeploy API设计
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视觉模型具有较有统一任务范式，在设计API时（包括C++/Python），FastDeploy将视觉模型的部署拆分为四个步骤
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- 模型加载
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- 图像预处理
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- 模型推理
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- 推理结果后处理
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FastDeploy针对飞桨的视觉套件，以及外部热门模型，提供端到端的部署服务，用户只需准备模型，按以下步骤即可完成整个模型的部署
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- 加载模型
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- 调用`predict`接口
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|
||||
FastDeploy在各视觉模型部署时，也支持一键切换后端推理引擎，详情参阅[如何切换模型推理引擎](../../docs/cn/faq/how_to_change_backend.md)。
|
||||
@@ -1,11 +1,12 @@
|
||||
# PP-LiteSeg 量化模型在 A311D 上的部署
|
||||
目前 FastDeploy 已经支持基于 Paddle Lite 部署 PP-LiteSeg 量化模型到 A311D 上。
|
||||
English | [简体中文](README_CN.md)
|
||||
# Deployment of PP-LiteSeg Quantification Model on A311D
|
||||
Now FastDeploy allows deploying PP-LiteSeg quantization model to A311D based on Paddle Lite.
|
||||
|
||||
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||
For model quantization and download of quantized models, refer to [Model Quantization](../quantize/README.md)
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
## Detailed Deployment Tutorials
|
||||
|
||||
在 A311D 上只支持 C++ 的部署。
|
||||
Only C++ deployment is supported on A311D.
|
||||
|
||||
- [C++部署](cpp)
|
||||
- [C++ deployment](cpp)
|
||||
|
||||
12
examples/vision/segmentation/paddleseg/a311d/README_CN.md
Normal file
12
examples/vision/segmentation/paddleseg/a311d/README_CN.md
Normal file
@@ -0,0 +1,12 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PP-LiteSeg 量化模型在 A311D 上的部署
|
||||
目前 FastDeploy 已经支持基于 Paddle Lite 部署 PP-LiteSeg 量化模型到 A311D 上。
|
||||
|
||||
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
|
||||
在 A311D 上只支持 C++ 的部署。
|
||||
|
||||
- [C++部署](cpp)
|
||||
@@ -1,57 +1,53 @@
|
||||
# PaddleSeg C++部署示例
|
||||
English | [简体中文](README_CN.md)
|
||||
# PaddleSeg C++ Deployment Example
|
||||
|
||||
本目录下提供`infer.cc`快速完成Unet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
||||
This directory provides examples that `infer.cc` fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT.
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 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)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
|
||||
【Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
|
||||
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
|
||||
Taking the inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 1.0.0 or above (x.x.x>=1.0.0) is required to support this model.
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
||||
make -j
|
||||
|
||||
# 下载Unet模型文件和测试图片
|
||||
# Download Unet model files and test images
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
|
||||
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
|
||||
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||
|
||||
|
||||
# CPU推理
|
||||
# CPU inference
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
|
||||
# GPU推理
|
||||
# GPU inference
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
|
||||
# GPU上TensorRT推理
|
||||
# TensorRT inference on GPU
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
|
||||
# 昆仑芯XPU推理
|
||||
# kunlunxin XPU inference
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
|
||||
# 华为昇腾推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 4
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
The visualized result after running is as follows
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
|
||||
</div>
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
|
||||
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
## PaddleSeg C++ Interface
|
||||
|
||||
## PaddleSeg C++接口
|
||||
|
||||
### PaddleSeg类
|
||||
### PaddleSeg Class
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::segmentation::PaddleSegModel(
|
||||
@@ -62,39 +58,39 @@ fastdeploy::vision::segmentation::PaddleSegModel(
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||
```
|
||||
|
||||
PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模型格式。
|
||||
PaddleSegModel model loading and initialization, among which model_file is the exported Paddle model format.
|
||||
|
||||
**参数**
|
||||
**Parameter**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
> * **model_file**(str): Model file path
|
||||
> * **params_file**(str): Parameter file path
|
||||
> * **config_file**(str): Inference deployment configuration file
|
||||
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
|
||||
> * **model_format**(ModelFormat): Model format. Paddle format by default
|
||||
|
||||
#### Predict函数
|
||||
#### Predict Function
|
||||
|
||||
> ```c++
|
||||
> PaddleSegModel::Predict(cv::Mat* im, DetectionResult* result)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
> Model prediction interface. Input images and output detection results.
|
||||
>
|
||||
> **参数**
|
||||
> **Parameter**
|
||||
>
|
||||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||||
> > * **result**: 分割结果，包括分割预测的标签以及标签对应的概率值, SegmentationResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
> > * **im**: Input images in HWC or BGR format
|
||||
> > * **result**: The segmentation result, including the predicted label of the segmentation and the corresponding probability of the label. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of SegmentationResult
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
### Class Member Variable
|
||||
#### Pre-processing Parameter
|
||||
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
|
||||
|
||||
> > * **is_vertical_screen**(bool): PP-HumanSeg系列模型通过设置此参数为`true`表明输入图片是竖屏，即height大于width的图片
|
||||
> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait, height greater than a width, by setting this parameter to`true`
|
||||
|
||||
#### 后处理参数
|
||||
> > * **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
|
||||
#### Post-processing Parameter
|
||||
> > * **apply_softmax**(bool): The `apply_softmax` parameter is not specified when the model is exported. Set this parameter to `true` to normalize the probability result (score_map) of the predicted output segmentation label (label_map)
|
||||
|
||||
- [模型介绍](../../)
|
||||
- [Python部署](../python)
|
||||
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
- [Model Description](../../)
|
||||
- [Python Deployment](../python)
|
||||
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
|
||||
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
101
examples/vision/segmentation/paddleseg/cpp/README_CN.md
Normal file
101
examples/vision/segmentation/paddleseg/cpp/README_CN.md
Normal file
@@ -0,0 +1,101 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PaddleSeg C++部署示例
|
||||
|
||||
本目录下提供`infer.cc`快速完成Unet在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 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)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
|
||||
|
||||
以Linux上推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.0以上(x.x.x>=1.0.0)
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
||||
make -j
|
||||
|
||||
# 下载Unet模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
|
||||
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
|
||||
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||
|
||||
|
||||
# CPU推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 0
|
||||
# GPU推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 1
|
||||
# GPU上TensorRT推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 2
|
||||
# 昆仑芯XPU推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 3
|
||||
# 华为昇腾推理
|
||||
./infer_demo Unet_cityscapes_without_argmax_infer cityscapes_demo.png 4
|
||||
```
|
||||
|
||||
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>
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境:
|
||||
- [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md)
|
||||
|
||||
## PaddleSeg C++接口
|
||||
|
||||
### PaddleSeg类
|
||||
|
||||
```c++
|
||||
fastdeploy::vision::segmentation::PaddleSegModel(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const string& config_file,
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||
```
|
||||
|
||||
PaddleSegModel模型加载和初始化，其中model_file为导出的Paddle模型格式。
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
|
||||
#### Predict函数
|
||||
|
||||
> ```c++
|
||||
> PaddleSegModel::Predict(cv::Mat* im, DetectionResult* result)
|
||||
> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||||
> > * **result**: 分割结果，包括分割预测的标签以及标签对应的概率值, SegmentationResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
> > * **is_vertical_screen**(bool): PP-HumanSeg系列模型通过设置此参数为`true`表明输入图片是竖屏，即height大于width的图片
|
||||
|
||||
#### 后处理参数
|
||||
> > * **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
|
||||
|
||||
- [模型介绍](../../)
|
||||
- [Python部署](../python)
|
||||
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
@@ -1,85 +1,82 @@
|
||||
# PaddleSeg Python部署示例
|
||||
English | [简体中文](README_CN.md)
|
||||
# PaddleSeg Python Deployment Example
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 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)
|
||||
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
|
||||
|
||||
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
|
||||
【Attention】For the deployment of **PP-Matting**、**PP-HumanMatting** and **ModNet**, refer to [Matting Model Deployment](../../../matting)
|
||||
|
||||
This directory provides examples that `infer.py` fast finishes the deployment of Unet on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
|
||||
```bash
|
||||
#下载部署示例代码
|
||||
# Download the deployment example code
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/segmentation/paddleseg/python
|
||||
|
||||
# 下载Unet模型文件和测试图片
|
||||
# Download Unet model files and test images
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
|
||||
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
|
||||
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||
|
||||
# CPU推理
|
||||
# CPU inference
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
|
||||
# GPU推理
|
||||
# GPU inference
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
|
||||
# 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）
|
||||
# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
|
||||
# 昆仑芯XPU推理
|
||||
# kunlunxin XPU inference
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
|
||||
# 华为昇腾推理
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
The visualized result after running is as follows
|
||||
<div align="center">
|
||||
<img src="https://user-images.githubusercontent.com/16222477/191712880-91ae128d-247a-43e0-b1e3-cafae78431e0.jpg", width=512px, height=256px />
|
||||
</div>
|
||||
|
||||
## PaddleSegModel Python接口
|
||||
## PaddleSegModel Python Interface
|
||||
|
||||
```python
|
||||
fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
|
||||
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)
|
||||
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
|
||||
|
||||
**参数**
|
||||
**Parameter**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
> * **model_file**(str): Model file path
|
||||
> * **params_file**(str): Parameter file path
|
||||
> * **config_file**(str): Inference deployment configuration file
|
||||
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
|
||||
> * **model_format**(ModelFormat): Model format. Paddle format by default
|
||||
|
||||
### predict函数
|
||||
### predict function
|
||||
|
||||
> ```python
|
||||
> PaddleSegModel.predict(input_image)
|
||||
> ```
|
||||
>
|
||||
> 模型预测结口，输入图像直接输出检测结果。
|
||||
> Model prediction interface. Input images and output detection results.
|
||||
>
|
||||
> **参数**
|
||||
> **Parameter**
|
||||
>
|
||||
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
|
||||
|
||||
> **返回**
|
||||
> **Return**
|
||||
>
|
||||
> > 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/)
|
||||
> > Return `fastdeploy.vision.SegmentationResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
### Class Member Variable
|
||||
#### Pre-processing Parameter
|
||||
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
|
||||
|
||||
> > * **is_vertical_screen**(bool): PP-HumanSeg系列模型通过设置此参数为`true`表明输入图片是竖屏，即height大于width的图片
|
||||
> > * **is_vertical_screen**(bool): For PP-HumanSeg models, the input image is portrait with height greater than width by setting this parameter to `true`
|
||||
#### Post-processing Parameter
|
||||
> > * **apply_softmax**(bool): The `apply_softmax` parameter is not specified when the model is exported. Set this parameter to `true` to normalize the probability result (score_map) of the predicted output segmentation label (label_map) in softmax
|
||||
|
||||
#### 后处理参数
|
||||
> > * **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
|
||||
## Other Documents
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [PaddleSeg 模型介绍](..)
|
||||
- [PaddleSeg C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
- [PaddleSeg Model Description](..)
|
||||
- [PaddleSeg C++ Deployment](../cpp)
|
||||
- [Model Prediction Results](../../../../../docs/api/vision_results/)
|
||||
- [How to switch the model inference backend engine](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
|
||||
86
examples/vision/segmentation/paddleseg/python/README_CN.md
Normal file
86
examples/vision/segmentation/paddleseg/python/README_CN.md
Normal file
@@ -0,0 +1,86 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PaddleSeg Python部署示例
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 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)
|
||||
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
【注意】如你部署的为**PP-Matting**、**PP-HumanMatting**以及**ModNet**请参考[Matting模型部署](../../../matting)
|
||||
|
||||
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
|
||||
|
||||
```bash
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/segmentation/paddleseg/python
|
||||
|
||||
# 下载Unet模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz
|
||||
tar -xvf Unet_cityscapes_without_argmax_infer.tgz
|
||||
wget https://paddleseg.bj.bcebos.com/dygraph/demo/cityscapes_demo.png
|
||||
|
||||
# CPU推理
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
|
||||
# GPU推理
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu
|
||||
# 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）
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
|
||||
# 昆仑芯XPU推理
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device kunlunxin
|
||||
# 华为昇腾推理
|
||||
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device ascend
|
||||
```
|
||||
|
||||
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>
|
||||
|
||||
## PaddleSegModel Python接口
|
||||
|
||||
```python
|
||||
fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
|
||||
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)
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
|
||||
### predict函数
|
||||
|
||||
> ```python
|
||||
> PaddleSegModel.predict(input_image)
|
||||
> ```
|
||||
>
|
||||
> 模型预测结口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
|
||||
> **返回**
|
||||
>
|
||||
> > 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/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
> > * **is_vertical_screen**(bool): PP-HumanSeg系列模型通过设置此参数为`true`表明输入图片是竖屏，即height大于width的图片
|
||||
|
||||
#### 后处理参数
|
||||
> > * **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
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [PaddleSeg 模型介绍](..)
|
||||
- [PaddleSeg C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
@@ -1,11 +1,12 @@
|
||||
# PP-LiteSeg 量化模型在 RV1126 上的部署
|
||||
目前 FastDeploy 已经支持基于 Paddle Lite 部署 PP-LiteSeg 量化模型到 RV1126 上。
|
||||
English | [简体中文](README_CN.md)
|
||||
# Deployment of PP-LiteSeg Quantification Model on RV1126
|
||||
Now FastDeploy allows deploying PP-LiteSeg quantization model to RV1126 based on Paddle Lite.
|
||||
|
||||
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||
For model quantization and download of quantized models, refer to [Model Quantization](../quantize/README.md)
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
## Detailed Deployment Tutorials
|
||||
|
||||
在 RV1126 上只支持 C++ 的部署。
|
||||
Only C++ deployment is supported on RV1126.
|
||||
|
||||
- [C++部署](cpp)
|
||||
- [C++ Deployment](cpp)
|
||||
|
||||
12
examples/vision/segmentation/paddleseg/rv1126/README_CN.md
Normal file
12
examples/vision/segmentation/paddleseg/rv1126/README_CN.md
Normal file
@@ -0,0 +1,12 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PP-LiteSeg 量化模型在 RV1126 上的部署
|
||||
目前 FastDeploy 已经支持基于 Paddle Lite 部署 PP-LiteSeg 量化模型到 RV1126 上。
|
||||
|
||||
模型的量化和量化模型的下载请参考：[模型量化](../quantize/README.md)
|
||||
|
||||
|
||||
## 详细部署文档
|
||||
|
||||
在 RV1126 上只支持 C++ 的部署。
|
||||
|
||||
- [C++部署](cpp)
|
||||
Reference in New Issue
Block a user