diff --git a/csrc/fastdeploy/vision/segmentation/ppseg/model.cc b/csrc/fastdeploy/vision/segmentation/ppseg/model.cc index be5c241f7..9604d665c 100644 --- a/csrc/fastdeploy/vision/segmentation/ppseg/model.cc +++ b/csrc/fastdeploy/vision/segmentation/ppseg/model.cc @@ -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; diff --git a/examples/vision/classification/paddleclas/README.md b/examples/vision/classification/paddleclas/README.md index 1f8377616..9a62f07f0 100644 --- a/examples/vision/classification/paddleclas/README.md +++ b/examples/vision/classification/paddleclas/README.md @@ -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 diff --git a/examples/vision/classification/paddleclas/python/README.md b/examples/vision/classification/paddleclas/python/README.md index c6bb35d2e..77ad78797 100644 --- a/examples/vision/classification/paddleclas/python/README.md +++ b/examples/vision/classification/paddleclas/python/README.md @@ -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 diff --git a/examples/vision/detection/paddledetection/cpp/README.md b/examples/vision/detection/paddledetection/cpp/README.md index 4ed39929b..64e2dc997 100644 --- a/examples/vision/detection/paddledetection/cpp/README.md +++ b/examples/vision/detection/paddledetection/cpp/README.md @@ -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 diff --git a/examples/vision/segmentation/paddleseg/README.md b/examples/vision/segmentation/paddleseg/README.md index 1f8377616..413e10b8a 100644 --- a/examples/vision/segmentation/paddleseg/README.md +++ b/examples/vision/segmentation/paddleseg/README.md @@ -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 diff --git a/examples/vision/segmentation/paddleseg/cpp/README.md b/examples/vision/segmentation/paddleseg/cpp/README.md index 302f3f74f..8e5a9627b 100644 --- a/examples/vision/segmentation/paddleseg/cpp/README.md +++ b/examples/vision/segmentation/paddleseg/cpp/README.md @@ -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 +
+ +
-### 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): 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) diff --git a/examples/vision/segmentation/paddleseg/cpp/infer.cc b/examples/vision/segmentation/paddleseg/cpp/infer.cc index bd07ba988..1f20ea9c8 100644 --- a/examples/vision/segmentation/paddleseg/cpp/infer.cc +++ b/examples/vision/segmentation/paddleseg/cpp/infer.cc @@ -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; } diff --git a/examples/vision/segmentation/paddleseg/python/README.md b/examples/vision/segmentation/paddleseg/python/README.md index 1259c1509..a44564884 100644 --- a/examples/vision/segmentation/paddleseg/python/README.md +++ b/examples/vision/segmentation/paddleseg/python/README.md @@ -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 +
+ +
-## 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/) diff --git a/examples/vision/segmentation/paddleseg/python/infer.py b/examples/vision/segmentation/paddleseg/python/infer.py index 88c272bca..28903e929 100644 --- a/examples/vision/segmentation/paddleseg/python/infer.py +++ b/examples/vision/segmentation/paddleseg/python/infer.py @@ -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) diff --git a/fastdeploy/vision/segmentation/ppseg/__init__.py b/fastdeploy/vision/segmentation/ppseg/__init__.py index 9d3cfaf4d..bc516351d 100644 --- a/fastdeploy/vision/segmentation/ppseg/__init__.py +++ b/fastdeploy/vision/segmentation/ppseg/__init__.py @@ -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(