Download scripts/train.py from ryali93/hf_segm_test: direct link, hf CLI and curl.
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- Download file 764 Bytes
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https://huggingface.co/ryali93/hf_segm_test/resolve/main/scripts/train.py
- Command line
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hf download hf://ryali93/hf_segm_test/scripts/train.py
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curl -L -o train.py https://huggingface.co/ryali93/hf_segm_test/resolve/main/scripts/train.py
764 Bytes
| # scripts/train.py | |
| import sys | |
| sys.path.append("/home/ryali93/Desktop/projects/hf_segm") | |
| import pytorch_lightning as pl | |
| from model.lightning_unet import LightningUNet | |
| from model.config import UNetConfig | |
| from data.datamodule import SegmentationDataModule | |
| import torch | |
| if __name__ == '__main__': | |
| # Configuraciones | |
| config = UNetConfig() | |
| data_dir = "data" | |
| # Inicializa el modelo y el DataModule | |
| model = LightningUNet(config) | |
| datamodule = SegmentationDataModule(data_dir, config) | |
| # Entrena el modelo | |
| trainer = pl.Trainer(max_epochs=10) # Puedes añadir más configuraciones al Trainer si lo necesitas | |
| trainer.fit(model, datamodule) | |
| # Guarda los pesos del modelo | |
| torch.save(model.state_dict(), 'weights/unet_weights.pth') | |