https://github.com/virxec/elementv5
Example port of Element from RLBot v4 to v5 using NCNN instead of PyTorch
https://github.com/virxec/elementv5
rlbot
Last synced: 2 months ago
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Example port of Element from RLBot v4 to v5 using NCNN instead of PyTorch
- Host: GitHub
- URL: https://github.com/virxec/elementv5
- Owner: VirxEC
- Created: 2025-01-19T05:15:56.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-03-08T22:25:37.000Z (2 months ago)
- Last Synced: 2025-03-08T23:21:15.689Z (2 months ago)
- Topics: rlbot
- Language: Python
- Homepage: https://rlgym.org/
- Size: 1.56 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Element in NCNN
This project is a fork of the RLBot v4 verison of Element.
It's aim is to port the bot to RLBot v5 and to port the PyTorch model to NCNN so the resulting exe is smaller and faster.
The ported model is already in `src/`.
## Porting from PyTorch to NCNN
The following steps were taken to port the PyTorch model to NCNN. It is assumed your bot has already been ported to RLBot v5 and runs it's PyTorch model.
### Prerequisites
Install the normal bot requirements via `pip install -r requirements.txt`.
Install [PyTorch](https://pytorch.org/) and `pip install pnnx`. These dependencies are needed to export to NCNN.
### Step 1: Export the PyTorch model to TorchScript
This is done in the `torch/` directory. `agent.py` and `model.p` were files taken almost directly from the original bot.
At the end of the file, you will find this snippet added:
```python
if __name__ == "__main__":
os.makedirs("../ncnn", exist_ok=True)
# load the original model
obs_size = 107
agent = Agent(obs_size, action_categoricals=5, action_bernoullis=3)
# generate random input tensor
x = torch.rand(1, obs_size)
# export model to torchscript
mod = torch.jit.trace(agent.actor, x)
mod.save("../ncnn/model.pt")
```
This creates the `ncnn/` directory and saves the TorchScript model there.
### Step 2: Exporting the TorchScript model to NCNN
This is done in the `ncnn/` directory. Inside the directory, run `pnnx model.pt inputshape=[1,107]`.
### Step 3: Using the NCNN model
The resulting `ncnn/model.ncnn.bin` and `ncnn/model.ncnn.param` files can be used in the bot. Move them into the `src/` directory.
Now, `src/agent.py` must be remade to use the NCNN model. Example usage can be seen in the generated `ncnn/model_ncnn.py`.
This file includes tenors, but those tensors are immediately converted into numpy arrays.
### Common runtime errors
```
terminate called after throwing an instance of 'std::runtime_error'
what(): Convert ncnn.Mat to numpy.ndarray. Support only elemsize 1, 2, 4; but given 8
```
This is caused by the inputted numpy array having a dtype of `float64`. Convert it to `float32` before inputting it into the NCNN model.
## Configuring for the v5 botpack
1. `pip install pyinstaller`
1. `pyinstaller --onefile src/bot.py --paths src --add-data src/model.ncnn.bin:. --add-data src/model.ncnn.param:.`
This will create a file called `bot.spec` - you may have to remove `*.spec` from `.gitignore` to commit it.
1. Create `bob.toml` in the same directory as the spec file with the following content:
```toml
[[config]]
project_name = "PythonExample"
bot_configs = ["src/bot.toml"]
[config.builder_config]
builder_type = "pyinstaller"
entry_file = "bot.spec"
```
- `project_name` will be the name of your bot's folder in the botpack
- `bot_configs` is a list of bot configs that will be included in the botpack
- `builder_type` should always be `pyinstaller`
- `entry_file` is the name of the spec file
1. Commit both `bot.spec` and `bob.toml` to your bot's repository.
Note that `bob.toml` CANNOT be renamed, but `bot.spec` can be anything as long as `entry_file` is also renamed to reflect the change.