GhostNet

GhostNetv1 architecture is from the paper "GhostNet: More Features from Cheap Operations" (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).

For the PyTorch implementations, you can refer to 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

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

# For ghostnetv1
python ghostnetv1/gen_wts.py

# For ghostnetv2
python ghostnetv2/gen_wts.py

2. Build the project

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):

# 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:

# For ghostnetv1
sudo ./ghostnetv1 -d

# For ghostnetv2
sudo ./ghostnetv2 -d

5. Verify output

Compare the output with the PyTorch implementation from huawei-noah/ghostnet to ensure that the TensorRT results are consistent with the PyTorch model.