Add CPU/GPU compatibility and automatic device selection #73
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JimmyOhn
commented
Jul 30, 2025
JimmyOhn
commented
Resolves memory issues and improves accessibility on CPU-only systems. - Add device parameter to SyncNetInstance with CPU as default - Implement automatic CUDA detection and fallback to CPU - Update all scripts (demo_syncnet.py, demo_feature.py, run_syncnet.py, run_pipeline.py) to use automatic device selection - Add device_utils.py for device management utilities - Modify S3FD face detector to support CPU/GPU selection - Ensure backward compatibility with existing CUDA setups - Enable SyncNet to run on systems without NVIDIA GPU
These changes maintain functionality while supporting modern NumPy/PyTorch versions. - Replace np.int with int in detectors/s3fd/box_utils.py - Replace tensor.view() with tensor.reshape() in SyncNetModel.py for PyTorch compatibility - Fix AttributeError: module 'numpy' has no attribute 'int' in newer NumPy versions - Ensure compatibility with NumPy 1.20+ where deprecated aliases were removed
Improves user guidance for hardware compatibility and troubleshooting. - Add comprehensive Device Support section to README.md - Document automatic CUDA detection and CPU fallback behavior - Provide instructions for forcing CPU-only execution - Explain device selection mechanism for different hardware setups - Add examples for both GPU-accelerated and CPU-only usage
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@joonson |
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