Adaptive_detection/windows_setup.txt at master · ApeliDev/Adaptive_detection · GitHub

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Windows Setup Guide for Adaptive CARLA RL Project

1. Python Setup
# Download and install Python 3.7.9 from:
https://www.python.org/ftp/python/3.7.9/python-3.7.9-amd64.exe


# During installation:
- Check "Add Python 3.7 to PATH"  ====incase you are running 3.8 contact me
- Choose "Customize installation"
- Make sure "pip" is selected
- Install for all users

2. Create Virtual Environment
# Open Command Prompt as Administrator and run:
python -m venv venv
.\venv\Scripts\activate

3. Install PyTorch with CUDA
# Install PyTorch (adjust CUDA version based on your GPU):
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

4. Install Project Dependencies
# Create requirements.txt with these packages:
torch>=1.7.0
torchvision>=0.8.0
numpy>=1.19.0
opencv-python>=4.5.0
gymnasium>=0.29.0
stable-baselines3>=2.2.0
matplotlib>=3.3.0
seaborn>=0.11.0
pyyaml>=5.4.0
transformers>=4.36.0

# Install requirements:
pip install -r requirements.txt

5. CARLA Setup
# Add CARLA to PYTHONPATH (adjust path to your CARLA installation):
set CARLA_ROOT=C:\path\to\CARLA_0.9.15
set PYTHONPATH=%PYTHONPATH%;%CARLA_ROOT%\PythonAPI\carla\dist\carla-0.9.15-py3.7-win-amd64.egg

6. Running the Project

Start CARLA Server:
# In one terminal:
cd C:\path\to\CARLA_0.9.15
CarlaUE4.exe -quality-level=Low

Training:
# In another terminal:
.\venv\Scripts\activate
python scripts/train.py

Evaluation:
python scripts/evaluate.py --model path/to/model --config path/to/config

Visualization:
python scripts/visualize_comparison.py

Important Notes:
1. Make sure CARLA server is running before starting training/evaluation
2. Keep the CARLA server terminal window open
3. If you get CUDA errors, verify your NVIDIA drivers are up to date
4. If you get import errors, verify your PYTHONPATH includes CARLA

Troubleshooting:
- If Python package installation fails: pip install --upgrade pip
- If CARLA import fails: Verify CARLA path in PYTHONPATH
- If CUDA errors occur: Update NVIDIA drivers
- If memory errors: Reduce batch size in config files