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67 lines
2.6 KiB
67 lines
2.6 KiB
import subprocess
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import time
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from pathlib import Path
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import hydra
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import torch
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from ultralytics.yolo import v8
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from ultralytics.yolo.data import build_classification_dataloader
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from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
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from ultralytics.yolo.utils.downloads import download
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from ultralytics.yolo.utils.files import WorkingDirectory
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from ultralytics.yolo.utils.loggers import colorstr
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from ultralytics.yolo.utils.torch_utils import LOCAL_RANK, torch_distributed_zero_first
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# BaseTrainer python usage
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class ClassificationTrainer(BaseTrainer):
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def get_dataset(self, dataset):
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# temporary solution. Replace with new ultralytics.yolo.ClassificationDataset module
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data = Path("datasets") / dataset
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with torch_distributed_zero_first(LOCAL_RANK), WorkingDirectory(Path.cwd()):
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data_dir = data if data.is_dir() else (Path.cwd() / data)
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if not data_dir.is_dir():
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self.console.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
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t = time.time()
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if str(data) == 'imagenet':
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subprocess.run(f"bash {v8.ROOT / 'data/scripts/get_imagenet.sh'}", shell=True, check=True)
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else:
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url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
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download(url, dir=data_dir.parent)
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s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
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self.console.info(s)
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train_set = data_dir / "train"
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test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val' # data/test or data/val
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return train_set, test_set
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def get_dataloader(self, dataset_path, batch_size=None, rank=0):
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return build_classification_dataloader(path=dataset_path, batch_size=self.args.batch_size, rank=rank)
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def get_validator(self):
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return v8.classify.ClassificationValidator(self.test_loader, self.device, logger=self.console)
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def criterion(self, preds, targets):
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return torch.nn.functional.cross_entropy(preds, targets)
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@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
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def train(cfg):
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cfg.model = cfg.model or "resnet18"
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cfg.data = cfg.data or "imagenette160" # or yolo.ClassificationDataset("mnist")
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trainer = ClassificationTrainer(cfg)
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trainer.train()
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if __name__ == "__main__":
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"""
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CLI usage:
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python path/to/train.py epochs=10 project=PROJECT lr0=0.1
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TODO:
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Direct cli support, i.e, yolov8 classify_train args.epochs 10
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"""
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train()
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