Add Ghostnet && Fix object destruction order in APIToModel function to avoid undefined behavior

ghostnet/README.md

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+# GhostNet
+
+GhostNetv1 architecture is from the paper "GhostNet: More Features from Cheap Operations" [(https://arxiv.org/abs/1911.11907)](https://arxiv.org/abs/1911.11907).
+GhostNetv2 architecture is from the paper "GhostNetV2: Enhance Cheap Operation with Long-Range Attention" [(https://arxiv.org/abs/2211.12905)](https://arxiv.org/abs/2211.12905).
+
+For the PyTorch implementations, you can refer to [huawei-noah/ghostnet](https://github.com/huawei-noah/ghostnet).
+
+Both versions use the following techniques in their TensorRT implementations:
+
+- **BatchNorm** layer is implemented by TensorRT's **Scale** layer.
+- **Ghost Modules** are used to generate more features from cheap operations, as described in the paper.
+- Replacing `IPoolingLayer` with `IReduceLayer` in TensorRT for Global Average Pooling. The `IReduceLayer` allows you to perform reduction operations (such as sum, average, max) over specified dimensions without being constrained by the kernel size limitations of pooling layers.
+
+## Project Structure
+
+```plaintext
+ghostnet
+│
+├── ghostnetv1
+│   ├── CMakeLists.txt
+│   ├── gen_wts.py
+│   ├── ghostnetv1.cpp
+│   └── logging.h
+│
+├── ghostnetv2
+│   ├── CMakeLists.txt
+│   ├── gen_wts.py
+│   ├── ghostnetv2.cpp
+│   └── logging.h
+│
+└── README.md
+```
+
+## Steps to use GhostNet in TensorRT
+
+### 1. Generate `.wts` files for both GhostNetv1 and GhostNetv2
+
+```bash
+# For ghostnetv1
+python ghostnetv1/gen_wts.py
+
+# For ghostnetv2
+python ghostnetv2/gen_wts.py
+```
+
+### 2. Build the project
+
+```bash
+cd tensorrtx/ghostnet
+mkdir build
+cd build
+cmake ..
+make
+```
+
+### 3. Serialize the models to engine files
+
+Use the following commands to serialize the PyTorch models into TensorRT engine files (`ghostnetv1.engine` and `ghostnetv2.engine`):
+
+```bash
+# For ghostnetv1
+sudo ./ghostnetv1 -s
+
+# For ghostnetv2
+sudo ./ghostnetv2 -s
+```
+
+### 4. Run inference using the engine files
+
+Once the engine files are generated, you can run inference with the following commands:
+
+```bash
+# For ghostnetv1
+sudo ./ghostnetv1 -d
+
+# For ghostnetv2
+sudo ./ghostnetv2 -d
+```
+
+### 5. Verify output
+
+Compare the output with the PyTorch implementation from [huawei-noah/ghostnet](https://github.com/huawei-noah/ghostnet) to ensure that the TensorRT results are consistent with the PyTorch model.

ghostnet/ghostnetv1/CMakeLists.txt

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+cmake_minimum_required(VERSION 2.6)
+
+project(ghostnetv1)
+
+add_definitions(-std=c++11)
+
+option(CUDA_USE_STATIC_CUDA_RUNTIME OFF)
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_BUILD_TYPE Debug)
+
+include_directories(${PROJECT_SOURCE_DIR}/include)
+# include and link dirs of cuda and tensorrt, you need adapt them if yours are different
+# cuda
+include_directories(/usr/local/cuda/include)
+link_directories(/usr/local/cuda/lib64)
+# tensorrt
+include_directories(/usr/include/x86_64-linux-gnu/)
+link_directories(/usr/lib/x86_64-linux-gnu/)
+
+add_executable(ghostnetv1 ${PROJECT_SOURCE_DIR}/ghostnetv1.cpp)
+target_link_libraries(ghostnetv1 nvinfer)
+target_link_libraries(ghostnetv1 cudart)
+
+add_definitions(-O2 -pthread)