Add DeepStream 5.1 support

2023-05-31 15:58:17 -03:00
parent b2c4bee8dc
commit 3f14b0d95d
14 changed files with 236 additions and 4 deletions
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # DeepStream-Yolo

-NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO models
+NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 / 5.1  configuration for YOLO models

 --------------------------------------------------------------------------------------------------
 ### Important: please generate the ONNX model and the TensorRT engine again with the updated files
@@ -21,8 +21,9 @@ NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO mod
 * Models benchmarks
 * **Support for Darknet YOLO models (YOLOv4, etc) using cfg and weights conversion with GPU post-processing**
 * **Support for YOLO-NAS, PPYOLOE+, PPYOLOE, DAMO-YOLO, YOLOX, YOLOR, YOLOv8, YOLOv7, YOLOv6 and YOLOv5 using ONNX conversion with GPU post-processing**
-* **Add GPU bbox parser (it is slightly slower than CPU bbox parser on V100 GPU tests)**
+* **GPU bbox parser (it is slightly slower than CPU bbox parser on V100 GPU tests)**
 * **Dynamic batch-size for ONNX exported models (YOLO-NAS, PPYOLOE+, PPYOLOE, DAMO-YOLO, YOLOX, YOLOR, YOLOv8, YOLOv7, YOLOv6 and YOLOv5)**
+* **Support for DeepStream 5.1**

 ##

@@ -92,6 +93,16 @@ NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO mod
 * [GStreamer 1.14.5](https://gstreamer.freedesktop.org/)
 * [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)

+#### DeepStream 5.1 on x86 platform
+
+* [Ubuntu 18.04](https://releases.ubuntu.com/18.04.6/)
+* [CUDA 11.1](https://developer.nvidia.com/cuda-11.1.0-download-archive?target_os=Linux&target_arch=x86_64&target_distro=Ubuntu&target_version=1804&target_type=runfilelocal)
+* [TensorRT 7.2.2](https://developer.nvidia.com/nvidia-tensorrt-7x-download)
+* [NVIDIA Driver 460.32.03](https://www.nvidia.com.br/Download/index.aspx)
+* [NVIDIA DeepStream SDK 5.1](https://developer.nvidia.com/deepstream-sdk-download-tesla-archived)
+* [GStreamer 1.14.5](https://gstreamer.freedesktop.org/)
+* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
+
 #### DeepStream 6.2 on Jetson platform

 * [JetPack 5.1.1 / 5.1](https://developer.nvidia.com/embedded/jetpack)
@@ -116,6 +127,12 @@ NVIDIA DeepStream SDK 6.2 / 6.1.1 / 6.1 / 6.0.1 / 6.0 configuration for YOLO mod
 * [NVIDIA DeepStream SDK 6.0.1 / 6.0](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived)
 * [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)

+#### DeepStream 5.1 on Jetson platform
+
+* [JetPack 4.5.1](https://developer.nvidia.com/embedded/jetpack-sdk-451-archive)
+* [NVIDIA DeepStream SDK 5.1](https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived)
+* [DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo)
+
 ##

 ### Suported models
@@ -950,6 +967,12 @@ cd DeepStream-Yolo
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -962,6 +985,12 @@ cd DeepStream-Yolo
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 #### 4. Edit the `config_infer_primary.txt` file according to your model (example for YOLOv4)

 ```
@@ -1073,6 +1102,12 @@ sudo apt-get install libopencv-dev
  CUDA_VER=11.4 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 OPENCV=1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -1085,6 +1120,12 @@ sudo apt-get install libopencv-dev
  CUDA_VER=10.2 OPENCV=1 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 OPENCV=1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 #### 3. For COCO dataset, download the [val2017](https://drive.google.com/file/d/1gbvfn7mcsGDRZ_luJwtITL-ru2kK99aK/view?usp=sharing), extract, and move to DeepStream-Yolo folder

 * Select 1000 random images from COCO dataset to run calibration
--- a/docs/DAMOYOLO.md
+++ b/docs/DAMOYOLO.md
@@ -43,6 +43,12 @@ Generate the ONNX model file (example for DAMO-YOLO-S*)
 python3 export_damoyolo.py -w damoyolo_tinynasL25_S_477.pth -c configs/damoyolo_tinynasL25_S.py --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 11 or lower.
+
+```
+--opset 11
+```
+
 **NOTE**: To change the inference size (defaut: 640)

 ```
@@ -98,6 +104,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -110,6 +122,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_damoyolo file
--- a/docs/PPYOLOE.md
+++ b/docs/PPYOLOE.md
@@ -41,6 +41,12 @@ pip3 install onnx onnxsim onnxruntime
 python3 export_ppyoloe.py -w ppyoloe_plus_crn_s_80e_coco.pdparams -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml --simplify
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 11.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated files

 Copy the generated ONNX model file and labels.txt file (if generated) to the `DeepStream-Yolo` folder.
@@ -75,6 +81,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -87,6 +99,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_ppyoloe_plus file
--- a/docs/YOLONAS.md
+++ b/docs/YOLONAS.md
@@ -46,6 +46,12 @@ Generate the ONNX model file (example for YOLO-NAS S)
 python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 14.
+
+```
+--opset 12
+```
+
 **NOTE**: Model names

 ```
@@ -119,6 +125,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -131,6 +143,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yolonas file
--- a/docs/YOLOR.md
+++ b/docs/YOLOR.md
@@ -55,6 +55,12 @@ Generate the ONNX model file
  python3 export_yolor.py -w yolor-p6.pt --simplify --dynamic
  ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 12.
+
+```
+--opset 12
+```
+
 **NOTE**: To convert a P6 model

 ```
@@ -116,6 +122,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -128,6 +140,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yolor file
--- a/docs/YOLOX.md
+++ b/docs/YOLOX.md
@@ -46,6 +46,12 @@ Generate the ONNX model file (example for YOLOX-s)
 python3 export_yolox.py -w yolox_s.pth -c exps/default/yolox_s.py --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 11.
+
+```
+--opset 12
+```
+
 #### 5. Copy generated file

 Copy the generated ONNX model file to the `DeepStream-Yolo` folder.
@@ -80,6 +86,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -92,6 +104,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yolox file
--- a/docs/YOLOv5.md
+++ b/docs/YOLOv5.md
@@ -47,6 +47,12 @@ Generate the ONNX model file (example for YOLOv5s)
 python3 export_yoloV5.py -w yolov5s.pt --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 17.
+
+```
+--opset 12
+```
+
 **NOTE**: To convert a P6 model

 ```
@@ -108,6 +114,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -120,6 +132,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yoloV5 file
--- a/docs/YOLOv6.md
+++ b/docs/YOLOv6.md
@@ -47,6 +47,12 @@ Generate the ONNX model file (example for YOLOv6-S 4.0)
 python3 export_yoloV6.py -w yolov6s.pt --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 13.
+
+```
+--opset 12
+```
+
 **NOTE**: To convert a P6 model

 ```
@@ -108,6 +114,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -120,6 +132,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yoloV6 file
--- a/docs/YOLOv7.md
+++ b/docs/YOLOv7.md
@@ -49,6 +49,12 @@ Generate the ONNX model file (example for YOLOv7)
 python3 export_yoloV7.py -w yolov7.pt --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 12.
+
+```
+--opset 12
+```
+
 **NOTE**: To convert a P6 model

 ```
@@ -110,6 +116,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -122,6 +134,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yoloV7 file
--- a/docs/YOLOv8.md
+++ b/docs/YOLOv8.md
@@ -46,6 +46,12 @@ Generate the ONNX model file (example for YOLOv8s)
 python3 export_yoloV8.py -w yolov8s.pt --simplify --dynamic
 ```

+**NOTE**: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 16.
+
+```
+--opset 12
+```
+
 **NOTE**: To change the inference size (defaut: 640)

 ```
@@ -101,6 +107,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on x86 platform
+
+  ```
+  CUDA_VER=11.1 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 * DeepStream 6.2 / 6.1.1 / 6.1 on Jetson platform

  ```
@@ -113,6 +125,12 @@ Open the `DeepStream-Yolo` folder and compile the lib
  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  ```

+* DeepStream 5.1 on Jetson platform
+
+  ```
+  CUDA_VER=10.2 LEGACY=1 make -C nvdsinfer_custom_impl_Yolo
+  ```
+
 ##

 ### Edit the config_infer_primary_yoloV8 file
--- a/nvdsinfer_custom_impl_Yolo/Makefile
+++ b/nvdsinfer_custom_impl_Yolo/Makefile
@@ -33,6 +33,11 @@ ifeq ($(OPENCV),)
 	OPENCV=0
 endif

+LEGACY?=
+ifeq ($(LEGACY),)
+	LEGACY=0
+endif
+
 CC:= g++
 NVCC:=/usr/local/cuda-$(CUDA_VER)/bin/nvcc

@@ -40,11 +45,15 @@ CFLAGS:= -Wall -std=c++11 -shared -fPIC -Wno-error=deprecated-declarations
 CFLAGS+= -I/opt/nvidia/deepstream/deepstream/sources/includes -I/usr/local/cuda-$(CUDA_VER)/include

 ifeq ($(OPENCV), 1)
-	COMMON= -DOPENCV
+	COMMON+= -DOPENCV
 	CFLAGS+= $(shell pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv)
 	LIBS+= $(shell pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv)
 endif

+ifeq ($(LEGACY), 1)
+	COMMON+= -DLEGACY
+endif
+
 CUFLAGS:= -I/opt/nvidia/deepstream/deepstream/sources/includes -I/usr/local/cuda-$(CUDA_VER)/include

 LIBS+= -lnvinfer_plugin -lnvinfer -lnvparsers -L/usr/local/cuda-$(CUDA_VER)/lib64 -lcudart -lcublas -lstdc++fs
--- a/nvdsinfer_custom_impl_Yolo/yolo.cpp
+++ b/nvdsinfer_custom_impl_Yolo/yolo.cpp
@@ -54,7 +54,13 @@ Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config

  nvinfer1::INetworkDefinition *network = builder->createNetworkV2(0);
  if (parseModel(*network) != NVDSINFER_SUCCESS) {
+
+#ifdef LEGACY
+    network->destroy();
+#else
    delete network;
+#endif
+
    return nullptr;
  }

@@ -105,7 +111,12 @@ Yolo::createEngine(nvinfer1::IBuilder* builder, nvinfer1::IBuilderConfig* config
  else
    std::cerr << "Building engine failed\n" << std::endl;

+#ifdef LEGACY
+    network->destroy();
+#else
    delete network;
+#endif
+
  return engine;
 }

--- a/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
+++ b/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
@@ -120,9 +120,14 @@ YoloLayer::configureWithFormat(const nvinfer1::Dims* inputDims, int nbInputs, co
  assert(inputDims != nullptr);
 }

+#ifdef LEGACY
+int
+YoloLayer::enqueue(int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream)
+#else
 int32_t
 YoloLayer::enqueue(int batchSize, void const* const* inputs, void* const* outputs, void* workspace,	cudaStream_t stream)
    noexcept
+#endif
 {
  void* output = outputs[0];
  CUDA_CHECK(cudaMemsetAsync((float*) output, 0, sizeof(float) * m_OutputSize * 6 * batchSize, stream));
--- a/nvdsinfer_custom_impl_Yolo/yoloPlugins.h
+++ b/nvdsinfer_custom_impl_Yolo/yoloPlugins.h
@@ -71,8 +71,12 @@ class YoloLayer : public nvinfer1::IPluginV2 {
      return maxBatchSize * sizeof(int);
    }

+#ifdef LEGACY
+    int enqueue(int batchSize, const void* const* inputs, void** outputs, void* workspace, cudaStream_t stream) override;
+#else
    int32_t enqueue(int batchSize, void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
        noexcept override;
+#endif

    size_t getSerializationSize() const noexcept override;