b671024a27
* add ghostnet * add ghostnet * add ghostnetV1&ghostnetV2 * Fix object destruction order in APIToModel function to avoid undefined behavior * Fix pre-commit errors in ghostnet/README.md * Fix pre-commit errors * Fix pre-commit errors in mobilenetV3 * Add a noqa marker in mobilenet py files
83 lines
2.4 KiB
Markdown
83 lines
2.4 KiB
Markdown
# GhostNet
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GhostNetv1 architecture is from the paper "GhostNet: More Features from Cheap Operations" [(https://arxiv.org/abs/1911.11907)](https://arxiv.org/abs/1911.11907).
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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).
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For the PyTorch implementations, you can refer to [huawei-noah/ghostnet](https://github.com/huawei-noah/ghostnet).
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Both versions use the following techniques in their TensorRT implementations:
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- **BatchNorm** layer is implemented by TensorRT's **Scale** layer.
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- **Ghost Modules** are used to generate more features from cheap operations, as described in the paper.
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- 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.
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## Project Structure
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```plaintext
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ghostnet
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│
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├── ghostnetv1
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│ ├── CMakeLists.txt
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│ ├── gen_wts.py
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│ ├── ghostnetv1.cpp
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│ └── logging.h
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│
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├── ghostnetv2
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│ ├── CMakeLists.txt
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│ ├── gen_wts.py
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│ ├── ghostnetv2.cpp
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│ └── logging.h
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│
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└── README.md
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```
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## Steps to use GhostNet in TensorRT
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### 1. Generate `.wts` files for both GhostNetv1 and GhostNetv2
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```bash
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# For ghostnetv1
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python ghostnetv1/gen_wts.py
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# For ghostnetv2
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python ghostnetv2/gen_wts.py
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```
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### 2. Build the project
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```bash
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cd tensorrtx/ghostnet
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mkdir build
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cd build
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cmake ..
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make
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```
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### 3. Serialize the models to engine files
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Use the following commands to serialize the PyTorch models into TensorRT engine files (`ghostnetv1.engine` and `ghostnetv2.engine`):
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```bash
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# For ghostnetv1
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sudo ./ghostnetv1 -s
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# For ghostnetv2
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sudo ./ghostnetv2 -s
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```
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### 4. Run inference using the engine files
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Once the engine files are generated, you can run inference with the following commands:
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```bash
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# For ghostnetv1
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sudo ./ghostnetv1 -d
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# For ghostnetv2
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sudo ./ghostnetv2 -d
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```
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### 5. Verify output
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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.
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