Fix failed to build TensorRT Engine with Custom Plugins on Windows

CMakeLists.txt

@@ -2,7 +2,7 @@
 cmake_minimum_required(VERSION 3.15.0)                  # 设置CMake的最低版本要求
 cmake_policy(SET CMP0091 NEW)                           # 允许在CMake 3.10+中自动设置项目名作为二进制目录名
 cmake_policy(SET CMP0146 OLD)                           # 忽略对find_package的过时警告
-project(TensorRT-YOLO VERSION 5.0.0 LANGUAGES CXX CUDA) # 定义项目名称、版本和使用的编程语言（C++和CUDA）
+project(TensorRT-YOLO VERSION 5.0.2 LANGUAGES CXX CUDA) # 定义项目名称、版本和使用的编程语言（C++和CUDA）
 
 # 设置 C++ 标准
 set(CMAKE_CXX_STANDARD 17)                              # 设置C++标准为17

docs/cn/model_export.md

@@ -49,11 +49,8 @@ trtexec --onnx=model.onnx --saveEngine=model.engine --minShapes=images:1x3x640x6
 trtexec --onnx=yolov8n-obb.onnx --saveEngine=yolov8n-obb.engine --fp16 --staticPlugins=./lib/plugin/libcustom_plugins.so --setPluginsToSerialize=./lib/plugin/libcustom_plugins.so
 
 # YOLO11-OBB 动态 batch
-trtexec --onnx=yolov11n-obb.onnx --saveEngine=yolov11n-obb.engine --fp16 --minShapes=images:1x3x640x640 --optShapes=images:4x3x640x640 --maxShapes=images:8x3x640x640 --dynamicPlugins=./lib/plugin/custom_plugins.dll --setPluginsToSerialize=./lib/plugin/custom_plugins.dll
+trtexec --onnx=yolov11n-obb.onnx --saveEngine=yolov11n-obb.engine --fp16 --minShapes=images:1x3x640x640 --optShapes=images:4x3x640x640 --maxShapes=images:8x3x640x640 --staticPlugins=./lib/plugin/custom_plugins.dll --setPluginsToSerialize=./lib/plugin/custom_plugins.dll
 ```
 
 > [!NOTE]  
-> 在使用 `--dynamicPlugins` 和 `--setPluginsToSerialize` 参数构建包含自定义插件的动态模型时，如果遇到错误 `[E] Error[4]: IRuntime::deserializeCudaEngine: Error Code 4: API Usage Error (Cannot register the library as plugin creator of EfficientRotatedNMS_TRT exists already.)`，这通常意味着引擎构建已成功，但加载并反序列化引擎时检测到插件重复注册。这种情况下，可以忽略该错误。
-
-> [!IMPORTANT]
-> 强烈推荐您使用Linux操作系统。经过测试，在Windows和Linux系统中使用NVIDIA 20、30、40系列显卡时发现，对于经过显存修改的2080Ti 22G显卡，在Windows环境下构建包含自定义插件的动态模型时，可能会遇到“`IPluginRegistry::loadLibrary: Error Code 3: API Usage Error (SymbolAddress for getCreators could not be loaded, check function name against library symbol)`”的错误。然而，在Linux系统中，该问题并未出现。目前尚不清楚这一问题是由于Windows系统本身的缺陷，还是由于显卡显存的修改所导致。因此，为了避免潜在的兼容性问题，强烈建议您在Linux系统下进行相关操作。
+> 在使用 `--staticPlugins` 和 `--setPluginsToSerialize` 参数构建包含自定义插件的动态模型时，如果遇到错误 `[E] Error[4]: IRuntime::deserializeCudaEngine: Error Code 4: API Usage Error (Cannot register the library as plugin creator of EfficientRotatedNMS_TRT exists already.)`，这通常意味着引擎构建已成功，但加载并反序列化引擎时检测到插件重复注册。这种情况下，可以忽略该错误。

docs/en/model_export.md

@@ -48,11 +48,8 @@ trtexec --onnx=model.onnx --saveEngine=model.engine --minShapes=images:1x3x640x6
 trtexec --onnx=yolov8n-obb.onnx --saveEngine=yolov8n-obb.engine --fp16 --staticPlugins=./lib/plugin/libcustom_plugins.so --setPluginsToSerialize=./lib/plugin/libcustom_plugins.so
 
 # YOLO11-OBB dynamic batch
-trtexec --onnx=yolov11n-obb.onnx --saveEngine=yolov11n-obb.engine --fp16 --minShapes=images:1x3x640x640 --optShapes=images:4x3x640x640 --maxShapes=images:8x3x640x640 --dynamicPlugins=./lib/plugin/custom_plugins.dll --setPluginsToSerialize=./lib/plugin/custom_plugins.dll
+trtexec --onnx=yolov11n-obb.onnx --saveEngine=yolov11n-obb.engine --fp16 --minShapes=images:1x3x640x640 --optShapes=images:4x3x640x640 --maxShapes=images:8x3x640x640 --staticPlugins=./lib/plugin/custom_plugins.dll --setPluginsToSerialize=./lib/plugin/custom_plugins.dll
 ```
 
 > [!NOTE]  
-> When using the `--dynamicPlugins` and `--setPluginsToSerialize` parameters to build a dynamic model with custom plugins, encountering the error `[E] Error[4]: IRuntime::deserializeCudaEngine: Error Code 4: API Usage Error (Cannot register the library as plugin creator of EfficientRotatedNMS_TRT exists already.)` typically indicates that the engine has been successfully built, but an attempt to load and deserialize the engine detected a duplicate plugin registration. This error can be safely ignored.
-
-> [!IMPORTANT]
-> We strongly recommend using the Linux operating system. After testing, it has been found that when using NVIDIA 20, 30, and 40 series graphics cards in both Windows and Linux systems, a modified 2080Ti 22G graphics card may encounter the error `IPluginRegistry::loadLibrary: Error Code 3: API Usage Error (SymbolAddress for getCreators could not be loaded, check function name against library symbol)` when building dynamic models with custom plugins in the Windows environment. However, this issue does not occur in the Linux system. It is currently unclear whether this problem is due to defects in the Windows system itself or due to the modification of the graphics card's memory. Therefore, to avoid potential compatibility issues, it is strongly advised that you perform related operations in the Linux system.
+> When using the `--staticPlugins` and `--setPluginsToSerialize` parameters to build a dynamic model with custom plugins, encountering the error `[E] Error[4]: IRuntime::deserializeCudaEngine: Error Code 4: API Usage Error (Cannot register the library as plugin creator of EfficientRotatedNMS_TRT exists already.)` typically indicates that the engine has been successfully built, but an attempt to load and deserialize the engine detected a duplicate plugin registration. This error can be safely ignored.

plugin/common/vfcCommon.cpp

@@ -73,13 +73,13 @@ ILogger* getPluginLogger()
 } // namespace plugin
 } // namespace 
 
-extern "C" TENSORRTAPI void setLoggerFinder(nvinfer1::ILoggerFinder* finder)
+extern "C" DEPLOYAPI void setLoggerFinder(nvinfer1::ILoggerFinder* finder)
 {
     nvinfer1::plugin::gLoggerFinder.setLoggerFinder(finder);
 }
 
 #if (TENSORRT_VERSION >= 100000)
-extern "C" TENSORRTAPI IPluginCreatorInterface* const* getCreators(int32_t& nbCreators)
+extern "C" DEPLOYAPI IPluginCreatorInterface* const* getCreators(int32_t& nbCreators)
 {
     nbCreators = 2;
     static EfficientRotatedNMSPluginCreator efficientRotatedNMSPluginCreator;
@@ -91,7 +91,7 @@ extern "C" TENSORRTAPI IPluginCreatorInterface* const* getCreators(int32_t& nbCr
 #endif
 
 #if (TENSORRT_VERSION < 100100)
-extern "C" TENSORRTAPI IPluginCreator* const* getPluginCreators(int32_t& nbCreators)
+extern "C" DEPLOYAPI IPluginCreator* const* getPluginCreators(int32_t& nbCreators)
 {
     nbCreators = 2;
     static EfficientRotatedNMSPluginCreator efficientRotatedNMSPluginCreator;

plugin/common/vfcCommon.h

@@ -22,6 +22,12 @@
 #include "NvInferRuntimeCommon.h"
 #include "NvInferVersion.h"
 
+#ifdef _MSC_VER
+#define DEPLOYAPI __declspec(dllexport)
+#else
+#define DEPLOYAPI __attribute__((visibility("default")))
+#endif
+
 #define TENSORRT_VERSION (NV_TENSORRT_MAJOR * 10000L + NV_TENSORRT_MINOR * 100L  + NV_TENSORRT_PATCH * 1L)
 
 using namespace nvinfer1;
@@ -36,14 +42,14 @@ ILogger* getPluginLogger();
 } // namespace plugin
 } // namespace nvinfer1
 
-extern "C" TENSORRTAPI void setLoggerFinder(nvinfer1::ILoggerFinder* finder);
+extern "C" DEPLOYAPI void setLoggerFinder(nvinfer1::ILoggerFinder* finder);
 
 #if (TENSORRT_VERSION >= 100000)
-extern "C" TENSORRTAPI IPluginCreatorInterface* const* getCreators(int32_t& nbCreators);
+extern "C" DEPLOYAPI IPluginCreatorInterface* const* getCreators(int32_t& nbCreators);
 #endif
 
 #if (TENSORRT_VERSION < 100100)
-extern "C" TENSORRTAPI IPluginCreator* const* getPluginCreators(int32_t& nbCreators);
+extern "C" DEPLOYAPI IPluginCreator* const* getPluginCreators(int32_t& nbCreators);
 #endif
 
 #endif // TRT_PLUGIN_VFC_COMMON_H

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 # Project settings -----------------------------------------------------------------------------------------------------
 [project]
-version = "5.0.0"
+version = "5.0.2"
 readme = "README.md"
 name = "tensorrt_yolo"
 requires-python = ">=3.8"

xmake.lua

@@ -1,6 +1,6 @@
 -- 设置项目信息
 set_project("TensorRT-YOLO")
-set_version("5.0.0")
+set_version("5.0.2")
 set_languages("cxx17")
 set_allowedplats("windows", "linux")