| # TODO: Implement Fine-Tuning Logic | |
| """ | |
| DeepDream Training / Fine-Tuning Script (Placeholder) | |
| Goal: | |
| Allow users to fine-tune these base models (VGG, GoogLeNet, etc.) on their own datasets | |
| to create custom Dream styles. | |
| Steps to Implement: | |
| 1. Load Dataset: Use `torchvision.datasets.ImageFolder` or custom loader for user images. | |
| 2. Load Model: Use our MLX models (need to add `train()` mode with dropout/grad support if missing, | |
| or simpler: use PyTorch for training -> export to MLX). | |
| *Easier path:* Train in PyTorch using standard scripts, then use `export_*.py` to bring it here. | |
| 3. Training Loop: Standard classification training or style transfer fine-tuning. | |
| 4. Export: Save the fine-tuned weights to `.pth`, then run export script. | |
| Usage: | |
| python train_dream.py --data /path/to/images --epochs 10 --model vgg16 | |
| """ | |
| import argparse | |
| def main(): | |
| print("--- DeepDream-MLX Training Stub ---") | |
| print("Feature coming soon.") | |
| print("Current Workflow: Train in PyTorch -> Use export_*.py -> Dream in MLX") | |
| if __name__ == "__main__": | |
| main() | |