[Model] Add YOLOv5-seg (PaddlePaddle#988)

* add onnx_ort_runtime demo * rm in requirements * support batch eval * fixed MattingResults bug * move assignment for DetectionResult * integrated x2paddle * add model convert readme * update readme * re-lint * add processor api * Add MattingResult Free * change valid_cpu_backends order * add ppocr benchmark * mv bs from 64 to 32 * fixed quantize.md * fixed quantize bugs * Add Monitor for benchmark * update mem monitor * Set trt_max_batch_size default 1 * fixed ocr benchmark bug * support yolov5 in serving * Fixed yolov5 serving * Fixed postprocess * update yolov5 to 7.0 * add poros runtime demos * update readme * Support poros abi=1 * rm useless note * deal with comments * support pp_trt for ppseg * fixed symlink problem * Add is_mini_pad and stride for yolov5 * Add yolo series for paddle format * fixed bugs * fixed bug * support yolov5seg * fixed bug * refactor yolov5seg * fixed bug * mv Mask int32 to uint8 * add yolov5seg example * rm log info * fixed code style * add yolov5seg example in python * fixed dtype bug * update note * deal with comments * get sorted index * add yolov5seg test case * Add GPL-3.0 License * add round func * deal with comments * deal with commens Co-authored-by: Jason <[email protected]>
Venonary · Jan 11, 2023 · aa6931b · aa6931b
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diff --git a/cmake/paddle_inference.cmake b/cmake/paddle_inference.cmake
diff --git a/examples/vision/detection/yolov5seg/README.md b/examples/vision/detection/yolov5seg/README.md
@@ -0,0 +1,27 @@
+# YOLOv5Seg准备部署模型
+
+- YOLOv5Seg v7.0部署模型实现来自[YOLOv5](https://github.com/ultralytics/yolov5/tree/v7.0),和[基于COCO的预训练模型](https://github.com/ultralytics/yolov5/releases/tag/v7.0)
+  - （1）[官方库](https://github.com/ultralytics/yolov5/releases/tag/v7.0)提供的*.onnx可直接进行部署；
+  - （2）开发者基于自己数据训练的YOLOv5Seg v7.0模型，可使用[YOLOv5](https://github.com/ultralytics/yolov5)中的`export.py`导出ONNX文件后，完成部署。
+
+
+## 下载预训练ONNX模型
+
+为了方便开发者的测试，下面提供了YOLOv5Seg导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
+| 模型                                                               | 大小    | 精度    | 备注 |
+|:---------------------------------------------------------------- |:----- |:----- |:----- |
+| [YOLOv5n-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-seg.onnx) | 7.7MB | 27.6% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
+| [YOLOv5s-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx) | 30MB | 37.6% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
+| [YOLOv5m-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5m-seg.onnx) | 84MB | 45.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
+| [YOLOv5l-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5l-seg.onnx) | 183MB | 49.0% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
+| [YOLOv5x-seg](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5x-seg.onnx) | 339MB | 50.7% | 此模型文件来源于[YOLOv5](https://github.com/ultralytics/yolov5)，GPL-3.0 License |
+
+
+## 详细部署文档
+
+- [Python部署](python)
+- [C++部署](cpp)
+
+## 版本说明
+
+- 本版本文档和代码基于[YOLOv5 v7.0](https://github.com/ultralytics/yolov5/tree/v7.0) 编写
diff --git a/examples/vision/detection/yolov5seg/cpp/CMakeLists.txt b/examples/vision/detection/yolov5seg/cpp/CMakeLists.txt
@@ -0,0 +1,14 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# Specify the fastdeploy library path after downloading and decompression
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# Add FastDeploy dependent header files
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# Add FastDeploy library dependencies
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
diff --git a/examples/vision/detection/yolov5seg/cpp/README.md b/examples/vision/detection/yolov5seg/cpp/README.md
@@ -0,0 +1,74 @@
+# YOLOv5Seg C++部署示例
+
+本目录下提供`infer.cc`快速完成YOLOv5Seg在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.3以上(x.x.x>=1.0.3)
+
+```bash
+mkdir build
+cd build
+# 下载 FastDeploy 预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# 1. 下载官方转换好的 YOLOv5Seg ONNX 模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# CPU推理
+./infer_demo yolov5s-seg.onnx 000000014439.jpg 0
+# GPU推理
+./infer_demo yolov5s-seg.onnx 000000014439.jpg 1
+# GPU上TensorRT推理
+./infer_demo yolov5s-seg.onnx 000000014439.jpg 2
+```
+运行完成可视化结果如下图所示
+
+<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
+
+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)
+
+## YOLOv5Seg C++接口
+
+### YOLOv5Seg类
+
+```c++
+fastdeploy::vision::detection::YOLOv5Seg(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const ModelFormat& model_format = ModelFormat::ONNX)
+```
+
+YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX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
+> * **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ONNX格式
+
+#### Predict函数
+
+```c++
+YOLOv5Seg::Predict(const cv::Mat& img, DetectionResult* result)
+```
+
+**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/)
+
+- [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)
diff --git a/examples/vision/detection/yolov5seg/cpp/infer.cc b/examples/vision/detection/yolov5seg/cpp/infer.cc
@@ -0,0 +1,105 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "fastdeploy/vision.h"
+
+void CpuInfer(const std::string& model_file, const std::string& image_file) {
+  auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+
+  auto vis_im = fastdeploy::vision::VisDetection(im, 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& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+
+  auto vis_im = fastdeploy::vision::VisDetection(im, 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& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  option.SetTrtInputShape("images", {1, 3, 640, 640});
+  auto model = fastdeploy::vision::detection::YOLOv5Seg(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+
+  auto vis_im = fastdeploy::vision::VisDetection(im, 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_model ./yolov5.onnx ./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;
+  }
+
+  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/detection/yolov5seg/python/README.md b/examples/vision/detection/yolov5seg/python/README.md
@@ -0,0 +1,67 @@
+# YOLOv5Seg 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)
+
+本目录下提供`infer.py`快速完成YOLOv5Seg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 examples/vision/detection/yolov5seg/python/
+
+#下载yolov5seg模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-seg.onnx
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+
+# CPU推理
+python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device cpu
+# GPU推理
+python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu
+# GPU上使用TensorRT推理
+python infer.py --model yolov5s-seg.onnx --image 000000014439.jpg --device gpu --use_trt True
+```
+
+运行完成可视化结果如下图所示
+
+<img width="640" src="https://user-images.githubusercontent.com/19977378/209955620-657bdd1d-574c-40a2-b05d-42b9e5a15ae8.png">
+
+## YOLOv5Seg Python接口
+
+```python
+fastdeploy.vision.detection.YOLOv5Seg(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
+```
+
+YOLOv5Seg模型加载和初始化，其中model_file为导出的ONNX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格式时，此参数无需设定
+> * **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ONNX
+
+### predict函数
+
+```python
+YOLOv5Seg.predict(image_data)
+```
+
+模型预测结口，输入图像直接输出检测结果。
+
+**参数**
+
+> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+
+**返回**
+
+> > 返回`fastdeploy.vision.DetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+## 其它文档
+
+- [YOLOv5Seg 模型介绍](..)
+- [YOLOv5Seg 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)
diff --git a/examples/vision/detection/yolov5seg/python/infer.py b/examples/vision/detection/yolov5seg/python/infer.py
@@ -0,0 +1,56 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", default=None, help="Path of yolov5seg model.")
+    parser.add_argument(
+        "--image", default=None, help="Path of test image file.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+        option.set_trt_input_shape("images", [1, 3, 640, 640])
+    return option
+
+
+args = parse_arguments()
+
+# Configure runtime, load model
+runtime_option = build_option(args)
+model = fd.vision.detection.YOLOv5Seg(
+    args.model, runtime_option=runtime_option)
+
+# Predicting image
+if args.image is None:
+    image = fd.utils.get_detection_test_image()
+else:
+    image = args.image
+im = cv2.imread(image)
+result = model.predict(im)
+
+# Visualization
+vis_im = fd.vision.vis_detection(im, result)
+cv2.imwrite("visualized_result.jpg", vis_im)
+print("Visualized result save in ./visualized_result.jpg")
diff --git a/fastdeploy/runtime/backends/paddle/paddle_backend.h b/fastdeploy/runtime/backends/paddle/paddle_backend.h
diff --git a/fastdeploy/vision.h b/fastdeploy/vision.h
@@ -22,6 +22,7 @@
 #include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
 #include "fastdeploy/vision/detection/contrib/yolor.h"
 #include "fastdeploy/vision/detection/contrib/yolov5/yolov5.h"
+#include "fastdeploy/vision/detection/contrib/yolov5seg/yolov5seg.h"
 #include "fastdeploy/vision/detection/contrib/fastestdet/fastestdet.h"
 #include "fastdeploy/vision/detection/contrib/yolov5lite.h"
 #include "fastdeploy/vision/detection/contrib/yolov6.h"

diff --git a/fastdeploy/vision/common/result.cc b/fastdeploy/vision/common/result.cc
@@ -48,7 +48,7 @@ void Mask::Reserve(int size) { data.reserve(size); }
 void Mask::Resize(int size) { data.resize(size); }
 
 void Mask::Clear() {
-  std::vector<int32_t>().swap(data);
+  std::vector<uint8_t>().swap(data);
   std::vector<int64_t>().swap(shape);
 }
 

diff --git a/fastdeploy/vision/common/result.h b/fastdeploy/vision/common/result.h
@@ -67,7 +67,7 @@ struct FASTDEPLOY_DECL ClassifyResult : public BaseResult {
  */
 struct FASTDEPLOY_DECL Mask : public BaseResult {
   /// Mask data buffer
-  std::vector<int32_t> data;
+  std::vector<uint8_t> data;
   /// Shape of mask
   std::vector<int64_t> shape;  // (H,W) ...
   ResultType type = ResultType::MASK;
@@ -107,7 +107,7 @@ struct FASTDEPLOY_DECL DetectionResult : public BaseResult {
   /** \brief For instance segmentation model, `masks` is the predict mask for all the deteced objects
    */
   std::vector<Mask> masks;
-  //// Shows if the DetectionResult has mask
+  /// Shows if the DetectionResult has mask
   bool contain_masks = false;
 
   ResultType type = ResultType::DETECTION;