53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
#!/usr/bin/env python
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from __future__ import annotations
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import argparse
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from torch.utils.data import DataLoader, random_split
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from flux2_resolution_guard.config import TrainConfig
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from flux2_resolution_guard.data import Flux2TripletDataset
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from flux2_resolution_guard.models import SMICConfig, SMICCorrectionModel
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from flux2_resolution_guard.training import Trainer
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--manifest", required=True)
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parser.add_argument("--output-dir", required=True)
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parser.add_argument("--image-size", type=int, default=256)
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parser.add_argument("--epochs", type=int, default=10)
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parser.add_argument("--batch-size", type=int, default=2)
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parser.add_argument("--device", default="cuda")
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args = parser.parse_args()
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dataset = Flux2TripletDataset(
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manifest_path=args.manifest,
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image_size=args.image_size,
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)
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n_val = max(1, int(len(dataset) * 0.1))
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n_train = max(1, len(dataset) - n_val)
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train_ds, val_ds = random_split(dataset, [n_train, n_val])
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train_loader = DataLoader(train_ds, batch_size=args.batch_size, shuffle=True, num_workers=0)
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val_loader = DataLoader(val_ds, batch_size=args.batch_size, shuffle=False, num_workers=0)
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model = SMICCorrectionModel(SMICConfig())
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trainer = Trainer(
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model=model,
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train_loader=train_loader,
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val_loader=val_loader,
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output_dir=args.output_dir,
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config=TrainConfig(
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image_size=args.image_size,
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epochs=args.epochs,
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batch_size=args.batch_size,
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device=args.device,
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),
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)
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trainer.fit()
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if __name__ == "__main__":
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main()
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