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Add PaddleSeg doc and infer.cc demo (#114)

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Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
2022-08-15 15:24:38 +08:00
committed by GitHub
parent 773d6bb938
commit a016ef99ce
10 changed files with 159 additions and 150 deletions
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2
csrc/fastdeploy/vision/segmentation/ppseg/model.cc
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@@ -14,7 +14,7 @@ PaddleSegModel::PaddleSegModel(const std::string& model_file,
const Frontend& model_format) {
config_file_ = config_file;
valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
valid_gpu_backends = {Backend::PDINFER, Backend::ORT};
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
runtime_option = custom_option;
runtime_option.model_format = model_format;
runtime_option.model_file = model_file;

2
examples/vision/classification/paddleclas/README.md
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@@ -21,7 +21,7 @@
PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)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，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文件，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)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，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
## 下载预训练模型

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examples/vision/classification/paddleclas/python/README.md
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@@ -8,22 +8,21 @@
本目录下提供`infer.py`快速完成ResNet50_vd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
```
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/classification/paddleclas/python
# 下载ResNet50_vd模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/classification/paddleclas/python
# CPU推理
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
# GPU推理
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
# 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 ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
```
运行完成后返回结果如下所示

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examples/vision/detection/paddledetection/cpp/README.md
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@@ -15,7 +15,7 @@ wget https://bj.bcebos.com/paddlehub/fastdeploy/libs/0.2.0/fastdeploy-linux-x64-
tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz
cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/detection/paddledetection
mkdir build && cd build
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../fastdeploy-linux-x64-gpu-0.2.0
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0
make -j
# 下载PPYOLOE模型文件和测试图片

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examples/vision/segmentation/paddleseg/README.md
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@@ -1,54 +1,36 @@
# PaddleClas 模型部署
# PaddleSeg 模型部署
## 模型版本说明
- [PaddleClas Release/2.4](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4)
- [PaddleSeg Release/2.6](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6)
目前FastDeploy支持如下模型的部署
- [PP-LCNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNet.md)
- [PP-LCNetV2系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-LCNetV2.md)
- [EfficientNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md)
- [GhostNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
- [MobileNet系列模型(包含v1,v2,v3)](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
- [ShuffleNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Mobile.md)
- [SqueezeNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Others.md)
- [Inception系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/Inception.md)
- [PP-HGNet系列模型](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/models/PP-HGNet.md)
- [ResNet系列模型（包含vd系列）](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/models/ResNet_and_vd.md)
- [U-Net系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/unet/README.md)
- [PP-LiteSeg系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/pp_liteseg/README.md)
- [PP-HumanSeg系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/contrib/PP-HumanSeg/README.md)
- [FCN系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/fcn/README.md)
- [DeepLabV3系列模型](https://github.com/PaddlePaddle/PaddleSeg/blob/release/2.6/configs/deeplabv3/README.md)
## 准备PaddleClas部署模型
## 准备PaddleSeg部署模型
PaddleClas模型导出，请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
PaddleSeg模型导出，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)
注意：PaddleClas导出的模型仅包含`inference.pdmodel`和`inference.pdiparams`两个文档，但为了满足部署的需求，同时也需准备其提供的通用[inference_cls.yaml](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/deploy/configs/inference_cls.yaml)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，开发者可直接下载此文件使用。但需根据自己的需求修改yaml文件中的配置参数，具体可比照PaddleClas模型训练[config](https://github.com/PaddlePaddle/PaddleClas/tree/release/2.4/ppcls/configs/ImageNet)中的infer部分的配置信息进行修改。
注意：在使用PaddleSeg模型导出时，可指定`--input_shape`参数，若预测输入图片尺寸并不固定，建议使用默认值即不指定该参数。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。
## 下载预训练模型
为了方便开发者的测试，下面提供了PaddleClas导出的部分模型（含inference_cls.yaml文件），开发者可直接下载使用。
为了方便开发者的测试，下面提供了PaddleSeg导出的部分模型（导出方式为：**不指定**`input_shape`和`with_softmax`，**指定**`without_argmax`），开发者可直接下载使用。
| 模型 | 参数文件大小 |输入Shape | Top1 | Top5 |
|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- |
| [PPLCNet_x1_0](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNet_x1_0_infer.tgz) | 12MB | 224x224 |71.32% | 90.03% |
| [PPLCNetV2_base](https://bj.bcebos.com/paddlehub/fastdeploy/PPLCNetV2_base_infer.tgz) | 26MB | 224x224 |77.04% | 93.27% |
| [EfficientNetB7](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB7_infer.tgz) | 255MB | 600x600 | 84.3% | 96.9% |
| [EfficientNetB0_small](https://bj.bcebos.com/paddlehub/fastdeploy/EfficientNetB0_small_infer.tgz)| 18MB | 224x224 | 75.8% | 75.8% |
| [GhostNet_x1_3_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x1_3_ssld_infer.tgz) | 29MB | 224x224 | 75.7% | 92.5% |
| [GhostNet_x0_5_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/GhostNet_x0_5_infer.tgz) | 10MB | 224x224 | 66.8% | 86.9% |
| [MobileNetV1_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_x0_25_infer.tgz) | 1.9MB | 224x224 | 51.4% | 75.5% |
| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV1_ssld_infer.tgz) | 17MB | 224x224 | 77.9% | 93.9% |
| [MobileNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_x0_25_infer.tgz) | 5.9MB | 224x224 | 53.2% | 76.5% |
| [MobileNetV2_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV2_ssld_infer.tgz) | 14MB | 224x224 | 76.74% | 93.39% |
| [MobileNetV3_small_x0_35_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_small_x0_35_ssld_infer.tgz) | 6.4MB | 224x224 | 55.55% | 77.71% |
| [MobileNetV3_large_x1_0_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/MobileNetV3_large_x1_0_ssld_infer.tgz) | 22MB | 224x224 | 78.96% | 94.48% |
| [ShuffleNetV2_x0_25](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x0_25_infer.tgz) | 2.4MB | 224x224 | 49.9% | 73.79% |
| [ShuffleNetV2_x2_0](https://bj.bcebos.com/paddlehub/fastdeploy/ShuffleNetV2_x2_0_infer.tgz) | 29MB | 224x224 | 73.15% | 91.2% |
| [SqueezeNet1_1](https://bj.bcebos.com/paddlehub/fastdeploy/SqueezeNet1_1_infer.tgz) | 4.8MB | 224x224 | 60.1% | 81.9% |
| [InceptionV3](https://bj.bcebos.com/paddlehub/fastdeploy/InceptionV3_infer.tgz) | 92MB | 299x299 | 79.14% | 94.59% |
| [PPHGNet_tiny_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_tiny_ssld_infer.tgz) | 57MB | 224x224 | 81.95% | 96.12% |
| [PPHGNet_base_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/PPHGNet_base_ssld_infer.tgz) | 274MB | 224x224 | 85.0% | 97.35% |
| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz) | 98MB | 224x224 | 79.12% | 94.44% |
| 模型 | 参数文件大小 |输入Shape | mIoU | mIoU (flip) | mIoU (ms+flip) |
|:---------------------------------------------------------------- |:----- |:----- | :----- | :----- | :----- |
| [Unet-cityscapes](https://bj.bcebos.com/paddlehub/fastdeploy/Unet_cityscapes_without_argmax_infer.tgz) | 52MB | 1024x512 | 65.00% | 66.02% | 66.89% |
| [PP-LiteSeg-T(STDC1)-cityscapes](https://bj.bcebos.com/paddlehub/fastdeploy/PP_LiteSeg_T_STDC1_cityscapes_without_argmax_infer.tgz) | 31MB | 1024x512 |73.10% | 73.89% | - |
| [PP-HumanSegV1-Lite](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Lite_infer.tgz) | 543KB | 192x192 | 86.2% | - | - |
| [PP-HumanSegV1-Server](https://bj.bcebos.com/paddlehub/fastdeploy/PP_HumanSegV1_Server_infer.tgz) | 103MB | 512x512 | 96.47% | - | - |
| [FCN-HRNet-W18-cityscapes](https://bj.bcebos.com/paddlehub/fastdeploy/FCN_HRNet_W18_cityscapes_without_argmax_infer.tgz) | 37MB | 1024x512 | 78.97% | 79.49% | 79.74% |
| [Deeplabv3-ResNet50-OS8-cityscapes](https://bj.bcebos.com/paddlehub/fastdeploy/Deeplabv3_ResNet50_OS8_cityscapes_without_argmax_infer.tgz) | 150MB | 1024x512 | 79.90% | 80.22% | 80.47% |
## 详细部署文档

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examples/vision/segmentation/paddleseg/cpp/README.md
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@@ -1,6 +1,6 @@
# YOLOv7 C++部署示例
# PaddleSeg C++部署示例
本目录下提供`infer.cc`快速完成YOLOv7在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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
@@ -12,51 +12,58 @@
```
mkdir build
cd build
wget https://xxx.tgz
tar xvf fastdeploy-linux-x64-0.2.0.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-0.2.0
wget https://bj.bcebos.com/paddlehub/fastdeploy/libs/0.2.0/fastdeploy-linux-x64-gpu-0.2.0.tgz
tar xvf fastdeploy-linux-x64-gpu-0.2.0.tgz
cd fastdeploy-linux-x64-gpu-0.2.0/examples/vision/segmentation/paddleseg/cpp/build
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.0
make -j
#下载官方转换好的yolov7模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000087038.jpg
# 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 yolov7.onnx 000000087038.jpg 0
./infer_demo Unet_cityscapes_without_argmax_infer infer.cc cityscapes_demo.png 0
# GPU推理
./infer_demo yolov7.onnx 000000087038.jpg 1
./infer_demo Unet_cityscapes_without_argmax_infer infer.cc cityscapes_demo.png 1
# GPU上TensorRT推理
./infer_demo yolov7.onnx 000000087038.jpg 2
./infer_demo Unet_cityscapes_without_argmax_infer infer.cc cityscapes_demo.png 2
```
## YOLOv7 C++接口
运行完成可视化结果如下图所示
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/184588768-45ee673b-ef1f-40f4-9fbd-6b1a9ce17c59.png", width=512px, height=256px />
</div>
### YOLOv7类
## PaddleSeg C++接口
### PaddleSeg类
```
fastdeploy::vision::detection::YOLOv7(
fastdeploy::vision::segmentation::PaddleSegModel(
const string& model_file,
const string& params_file = "",
const string& config_file,
const RuntimeOption& runtime_option = RuntimeOption(),
const Frontend& model_format = Frontend::ONNX)
const Frontend& model_format = Frontend::PADDLE)
```
YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。
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，当模型格式为ONNX时，此参数传入空字符串即可
> * **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**(Frontend): 模型格式，默认为ONNX格式
> * **model_format**(Frontend): 模型格式，默认为Paddle格式
#### Predict函数
> ```
> YOLOv7::Predict(cv::Mat* im, DetectionResult* result,
> float conf_threshold = 0.25,
> float nms_iou_threshold = 0.5)
> 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。
@@ -64,13 +71,16 @@ YOLOv7模型加载和初始化，其中model_file为导出的ONNX模型格式。
> **参数**
>
> > * **im**: 输入图像，注意需为HWC，BGR格式
> > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > * **conf_threshold**: 检测框置信度过滤阈值
> > * **nms_iou_threshold**: NMS处理过程中iou阈值
> > * **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/)
### 类成员变量
### 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
> > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
> > * **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
> > * **with_softmax**(bool): 当模型导出时，并未指定`with_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)

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examples/vision/segmentation/paddleseg/cpp/infer.cc
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@@ -14,34 +14,45 @@
#include "fastdeploy/vision.h"
void CpuInfer(const std::string& model_file, const std::string& params_file,
const std::string& config_file, const std::string& image_file) {
auto option = fastdeploy::RuntimeOption();
option.UseCpu() auto model =
fastdeploy::vision::classification::PaddleClasModel(
model_file, params_file, config_file, option);
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
void CpuInfer(const std::string& model_dir, const std::string& image_file) {
auto model_file = model_dir + sep + "model.pdmodel";
auto params_file = model_dir + sep + "model.pdiparams";
auto config_file = model_dir + sep + "deploy.yaml";
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
model_file, params_file, config_file);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
return;
}
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::ClassifyResult res;
fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
// print res
res.Str();
auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void GpuInfer(const std::string& model_file, const std::string& params_file,
const std::string& config_file, const std::string& image_file) {
void GpuInfer(const std::string& model_dir, const std::string& image_file) {
auto model_file = model_dir + sep + "model.pdmodel";
auto params_file = model_dir + sep + "model.pdiparams";
auto config_file = model_dir + sep + "deploy.yaml";
auto option = fastdeploy::RuntimeOption();
option.UseGpu();
auto model = fastdeploy::vision::classification::PaddleClasModel(
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
model_file, params_file, config_file, option);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
@@ -49,25 +60,30 @@ void GpuInfer(const std::string& model_file, const std::string& params_file,
}
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::ClassifyResult res;
fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
// print res
res.Str();
auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
void TrtInfer(const std::string& model_file, const std::string& params_file,
const std::string& config_file, const std::string& image_file) {
void TrtInfer(const std::string& model_dir, const std::string& image_file) {
auto model_file = model_dir + sep + "model.pdmodel";
auto params_file = model_dir + sep + "model.pdiparams";
auto config_file = model_dir + sep + "deploy.yaml";
auto option = fastdeploy::RuntimeOption();
option.UseGpu();
option.UseTrtBackend();
option.SetTrtInputShape("inputs", [ 1, 3, 224, 224 ], [ 1, 3, 224, 224 ],
[ 1, 3, 224, 224 ]);
auto model = fastdeploy::vision::classification::PaddleClasModel(
option.SetTrtInputShape("x", {1, 3, 256, 256}, {1, 3, 1024, 1024},
{1, 3, 2048, 2048});
auto model = fastdeploy::vision::segmentation::PaddleSegModel(
model_file, params_file, config_file, option);
if (!model.Initialized()) {
std::cerr << "Failed to initialize." << std::endl;
@@ -75,40 +91,37 @@ void TrtInfer(const std::string& model_file, const std::string& params_file,
}
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::ClassifyResult res;
fastdeploy::vision::SegmentationResult res;
if (!model.Predict(&im, &res)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
// print res
res.Str();
auto vis_im = fastdeploy::vision::Visualize::VisSegmentation(im_bak, res);
cv::imwrite("vis_result.jpg", vis_im);
std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
}
int main(int argc, char* argv[]) {
if (argc < 4) {
std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
"e.g ./infer_demo ./ResNet50_vd ./test.jpeg 0"
<< std::endl;
std::cout
<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
"e.g ./infer_model ./ppseg_model_dir ./test.jpeg 0"
<< std::endl;
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
"with gpu; 2: run with gpu and use tensorrt backend."
<< std::endl;
return -1;
}
std::string model_file =
argv[1] + "/" + "model.pdmodel" std::string params_file =
argv[1] + "/" + "model.pdiparams" std::string config_file =
argv[1] + "/" + "inference_cls.yaml" std::string image_file =
argv[2] if (std::atoi(argv[3]) == 0) {
CpuInfer(model_file, params_file, config_file, image_file);
}
else if (std::atoi(argv[3]) == 1) {
GpuInfer(model_file, params_file, config_file, image_file);
}
else if (std::atoi(argv[3]) == 2) {
TrtInfer(model_file, params_file, config_file, image_file);
if (std::atoi(argv[3]) == 0) {
CpuInfer(argv[1], argv[2]);
} else if (std::atoi(argv[3]) == 1) {
GpuInfer(argv[1], argv[2]);
} else if (std::atoi(argv[3]) == 2) {
TrtInfer(argv[1], argv[2]);
}
return 0;
}

58
examples/vision/segmentation/paddleseg/python/README.md
View File

@@ -1,46 +1,43 @@
# PaddleClas模型 Python部署示例
# 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/quick_start/requirements.md)
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start/install.md)
本目录下提供`infer.py`快速完成ResNet50_vd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
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
```
# 下载ResNet50_vd模型文件和测试图片
wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
tar -xvf ResNet50_vd_infer.tgz
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
#下载部署示例代码
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/classification/paddleclas/python
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 ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device cpu
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device cpu
# GPU推理
python infer.py --model ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu
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 ResNet50_vd_infer --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True
python infer.py --model Unet_cityscapes_without_argmax_infer --image cityscapes_demo.png --device gpu --use_trt True
```
运行完成后返回结果如下所示
```
ClassifyResult(
label_ids: 153,
scores: 0.686229,
)
```
运行完成可视化结果如下图所示
<div align="center">
<img src="https://user-images.githubusercontent.com/16222477/184588768-45ee673b-ef1f-40f4-9fbd-6b1a9ce17c59.png", width=512px, height=256px />
</div>
## PaddleClasModel Python接口
## PaddleSegModel Python接口
```
fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
fd.vision.segmentation.PaddleSegModel(model_file, params_file, config_file, runtime_option=None, model_format=Frontend.PADDLE)
```
PaddleClas模型加载和初始化，其中model_file, params_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/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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**
@@ -53,7 +50,7 @@ PaddleClas模型加载和初始化，其中model_file, params_file为训练模
### predict函数
> ```
> PaddleClasModel.predict(input_image, topk=1)
> PaddleSegModel.predict(input_image)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
@@ -61,15 +58,22 @@ PaddleClas模型加载和初始化，其中model_file, params_file为训练模
> **参数**
>
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **topk**(int):返回预测概率最高的topk个分类结果
> **返回**
>
> > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > 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
> > * **with_softmax**(bool): 当模型导出时，并未指定`with_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
- [PaddleClas 模型介绍](..)
- [PaddleClas C++部署](../cpp)
- [PaddleSeg 模型介绍](..)
- [PaddleSeg C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)

19
examples/vision/segmentation/paddleseg/python/infer.py
View File

@@ -8,11 +8,9 @@ def parse_arguments():
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--model", required=True, help="Path of PaddleClas model.")
"--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(
"--topk", type=int, default=1, help="Return topk results.")
parser.add_argument(
"--device",
type=str,
@@ -43,14 +41,17 @@ args = parse_arguments()
# 配置runtime，加载模型
runtime_option = build_option(args)
model_file = os.path.join(args.model, "inference.pdmodel")
params_file = os.path.join(args.model, "inference.pdiparams")
config_file = os.path.join(args.model, "inference_cls.yaml")
#model = fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=runtime_option)
model = fd.vision.classification.ResNet50vd(
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)
# 预测图片分类结果
im = cv2.imread(args.image)
result = model.predict(im, args.topk)
result = model.predict(im)
print(result)
# 可视化结果
vis_im = fd.vision.visualize.vis_segmentation(im, result)
cv2.imwrite("vis_img.png", vis_im)

4
fastdeploy/vision/segmentation/ppseg/__init__.py
View File

@@ -23,9 +23,9 @@ class PaddleSegModel(FastDeployModel):
model_file,
params_file,
config_file,
backend_option=None,
runtime_option=None,
model_format=Frontend.PADDLE):
super(Model, self).__init__(backend_option)
super(PaddleSegModel, self).__init__(runtime_option)
assert model_format == Frontend.PADDLE, "PaddleSeg only support model format of Frontend.Paddle now."
self._model = C.vision.segmentation.PaddleSegModel(