[Turorials] Add tutorials for intel gpu (#860)

* Add tutorials for intel gpu * fix gflags dependency * Update README_CN.md * Update README.md * Update README.md
2025-10-06 00:57:33 +08:00 · 2022-12-13 10:21:56 +08:00
parent 5fc6cf30df
commit 534d5b8c8b
20 changed files with 650 additions and 39 deletions
--- a/tutorials/intel_gpu/cpp/CMakeLists.txt
+++ b/tutorials/intel_gpu/cpp/CMakeLists.txt
@@ -0,0 +1,20 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# specify the decompress directory of FastDeploy SDK
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+include(${FASTDEPLOY_INSTALL_DIR}/utils/gflags.cmake)
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_resnet50 ${PROJECT_SOURCE_DIR}/infer_resnet50.cc)
+add_executable(infer_ppyoloe ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cc)
+
+if(UNIX AND (NOT APPLE) AND (NOT ANDROID))
+  target_link_libraries(infer_resnet50 ${FASTDEPLOY_LIBS} gflags pthread)
+  target_link_libraries(infer_ppyoloe ${FASTDEPLOY_LIBS} gflags pthread)
+else()
+  target_link_libraries(infer_resnet50 ${FASTDEPLOY_LIBS} gflags)
+  target_link_libraries(infer_ppyoloe ${FASTDEPLOY_LIBS} gflags)
+endif()
--- a/tutorials/intel_gpu/cpp/README.md
+++ b/tutorials/intel_gpu/cpp/README.md
@@ -0,0 +1,52 @@
+English | [中文](README_CN.md)
+
+# PaddleClas Python Example
+
+Before deployment, confirm the following two steps
+
+- 1. The software and hardware environment meet the requirements. Refer to [FastDeploy Environment Requirements](../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+- 2. Install FastDeploy Python wheel package. Refer to [Install FastDeploy](../../../docs/en/build_and_install/download_prebuilt_libraries.md)
+
+**Notice** This doc require FastDeploy version >= 1.0.2, or just use nightly built version.
+
+```bash
+# Get FastDeploy codes
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/tutorials/intel_gpu/cpu
+
+mkdir build && cd build
+
+# Please the preparation step to get the download link
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+
+# Download PaddleClas model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+tar -xvf ResNet50_vd_infer.tgz
+
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# Inference with CPU
+./infer_resnet50 -model ResNet50_vd_infer -image ILSVRC2012_val_00000010.jpeg -device cpu -topk 3
+
+# Inference with Intel GPU
+./infer_resnet50 -model ResNet50_vd_infer -image ILSVRC2012_val_00000010.jpeg -device intel_gpu -topk 3
+
+
+# Download PaddleDetection/PP-YOLOE model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+
+# Inference with CPU
+./infer_ppyoloe -model ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device cpu
+
+# Inference with Intel GPU
+./infer_ppyoloe -model ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device intel_gpu
+```
+
+This documents only shows how to compile on Linux/Mac, if you are using Windows, please refer the following documents
+
+- [How to use FastDeploy C++ SDK on Windows](../../../docs/en/faq/use_sdk_on_windows.md)
--- a/tutorials/intel_gpu/cpp/README_CN.md
+++ b/tutorials/intel_gpu/cpp/README_CN.md
@@ -0,0 +1,52 @@
+English | [中文](README_CN.md)
+
+# PaddleClas Python Example
+
+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** 本文档依赖FastDeploy>=1.0.2版本，或nightly built版本。
+
+```bash
+# Get FastDeploy codes
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd FastDeploy/tutorials/intel_gpu/cpu
+
+mkdir build && cd build
+
+# Please the preparation step to get the download link
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+
+# Download PaddleClas model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+tar -xvf ResNet50_vd_infer.tgz
+
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# Inference with CPU
+./infer_resnet50 -model ResNet50_vd_infer -image ILSVRC2012_val_00000010.jpeg -device cpu -topk 3
+
+# Inference with Intel GPU
+./infer_resnet50 -model ResNet50_vd_infer -image ILSVRC2012_val_00000010.jpeg -device intel_gpu -topk 3
+
+
+# Download PaddleDetection/PP-YOLOE model and test image
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz
+wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
+tar xvf ppyoloe_crn_l_300e_coco.tgz
+
+# Inference with CPU
+./infer_ppyoloe -model ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device cpu
+
+# Inference with Intel GPU
+./infer_ppyoloe -model ppyoloe_crn_l_300e_coco -image 000000014439.jpg -device intel_gpu
+```
+
+这篇文档展示的是如何在Linux/Mac上编译和运行，如果你是使用Windows系统，请参考下面的文档进行使用
+
+- [Windows上使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
--- a/tutorials/intel_gpu/cpp/infer_ppyoloe.cc
+++ b/tutorials/intel_gpu/cpp/infer_ppyoloe.cc
@@ -0,0 +1,100 @@
+// 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"
+#include "gflags/gflags.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+DEFINE_string(model, "", "Directory of the inference model");
+DEFINE_string(image, "", "Path of the image file.");
+
+DEFINE_string(device, "cpu", "Type of openvino device, 'cpu' or 'intel_gpu'");
+
+void InitAndInfer(const std::string& model_dir, const std::string& image_file, const fastdeploy::RuntimeOption& option) {
+  auto model_file = model_dir + sep + "model.pdmodel";
+  auto params_file = model_dir + sep + "model.pdiparams";
+  auto config_file = model_dir + sep + "infer_cfg.yml";
+
+  auto model = fastdeploy::vision::detection::PPYOLOE(
+      model_file, params_file, config_file, option);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  std::cout << "Warmup 20 times..." << std::endl;
+  for (int i = 0; i < 20; ++i) {
+    fastdeploy::vision::DetectionResult res;
+    if (!model.Predict(im, &res)) {
+      std::cerr << "Failed to predict." << std::endl;
+      return;
+    }
+  }
+
+  std::cout << "Counting time..." << std::endl;
+  fastdeploy::TimeCounter tc;
+  tc.Start();
+  for (int i = 0; i < 50; ++i) {
+    fastdeploy::vision::DetectionResult res;
+    if (!model.Predict(im, &res)) {
+      std::cerr << "Failed to predict." << std::endl;
+      return;
+    }
+  }
+  tc.End();
+  std::cout << "Elapsed time: " << tc.Duration() * 1000 << "ms." << std::endl;
+
+  fastdeploy::vision::DetectionResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+
+  cv::Mat vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
+  cv::imwrite("vis_result.jpg", vis_im);
+  std::cout << "Visualized result saved in ./vis_result.jpg" << std::endl;
+}
+
+fastdeploy::RuntimeOption BuildOption(const std::string& device) {
+  if (device != "cpu" && device != "intel_gpu") {
+    std::cerr << "The flag device only can be 'cpu' or 'intel_gpu'" << std::endl;
+    std::abort();
+  }
+  fastdeploy::RuntimeOption option;
+  option.UseOpenVINOBackend();
+  if (device == "intel_gpu") {
+    option.SetOpenVINODevice("HETERO:GPU,CPU");
+    std::map<std::string, std::vector<int64_t>> shape_info;
+    shape_info["image"] = {1, 3, 640, 640};
+    shape_info["scale_factor"] = {1, 2};
+    option.SetOpenVINOShapeInfo(shape_info);
+    option.SetOpenVINOCpuOperators({"MulticlassNms"});
+  }
+  return option;
+}
+
+int main(int argc, char* argv[]) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  auto option = BuildOption(FLAGS_device);
+  InitAndInfer(FLAGS_model, FLAGS_image, option);
+  return 0;
+}
--- a/tutorials/intel_gpu/cpp/infer_resnet50.cc
+++ b/tutorials/intel_gpu/cpp/infer_resnet50.cc
@@ -0,0 +1,100 @@
+// 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"
+#include "gflags/gflags.h"
+
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+DEFINE_string(model, "", "Directory of the inference model");
+DEFINE_string(image, "", "Path of the image file.");
+DEFINE_int64(topk, 1, "Topk classify result of the image file");
+
+DEFINE_string(device, "cpu", "Type of openvino device, 'cpu' or 'intel_gpu'");
+
+void InitAndInfer(const std::string& model_dir, const std::string& image_file, int topk, const fastdeploy::RuntimeOption& option) {
+  auto model_file = model_dir + sep + "inference.pdmodel";
+  auto params_file = model_dir + sep + "inference.pdiparams";
+  auto config_file = model_dir + sep + "inference_cls.yaml";
+
+  auto model = fastdeploy::vision::classification::PaddleClasModel(
+      model_file, params_file, config_file, option);
+
+  model.GetPostprocessor().SetTopk(topk);
+
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  std::cout << "Warmup 20 times..." << std::endl;
+  for (int i = 0; i < 20; ++i) {
+    fastdeploy::vision::ClassifyResult res;
+    if (!model.Predict(im, &res)) {
+      std::cerr << "Failed to predict." << std::endl;
+      return;
+    }
+  }
+
+  std::cout << "Counting time..." << std::endl;
+  fastdeploy::TimeCounter tc;
+  tc.Start();
+  for (int i = 0; i < 50; ++i) {
+    fastdeploy::vision::ClassifyResult res;
+    if (!model.Predict(im, &res)) {
+      std::cerr << "Failed to predict." << std::endl;
+      return;
+    }
+  }
+  tc.End();
+  std::cout << "Elapsed time: " << tc.Duration() * 1000 << "ms." << std::endl;
+
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  // print res
+  std::cout << res.Str() << std::endl;
+}
+
+fastdeploy::RuntimeOption BuildOption(const std::string& device) {
+  if (device != "cpu" && device != "intel_gpu") {
+    std::cerr << "The flag device only can be 'cpu' or 'intel_gpu'" << std::endl;
+    std::abort();
+  }
+  fastdeploy::RuntimeOption option;
+  option.UseOpenVINOBackend();
+  if (device == "intel_gpu") {
+    option.SetOpenVINODevice("GPU");
+    std::map<std::string, std::vector<int64_t>> shape_info;
+    shape_info["inputs"] = {1, 3, 224, 224};
+    option.SetOpenVINOShapeInfo(shape_info);
+  }
+  return option;
+}
+
+int main(int argc, char* argv[]) {
+  google::ParseCommandLineFlags(&argc, &argv, true);
+  auto option = BuildOption(FLAGS_device);
+  InitAndInfer(FLAGS_model, FLAGS_image, FLAGS_topk, option);
+  return 0;
+}