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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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import torchvision |
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from val import ClassificationValidator |
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from ultralytics.yolo import BaseTrainer, utils, v8 |
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from ultralytics.yolo.data import build_classification_dataloader |
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from ultralytics.yolo.engine.trainer import CONFIG_PATH_ABS, DEFAULT_CONFIG |
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# BaseTrainer python usage |
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class ClassificationTrainer(BaseTrainer): |
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def get_dataset(self): |
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# temporary solution. Replace with new ultralytics.yolo.ClassificationDataset module |
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data = Path("datasets") / self.data |
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with utils.torch_distributed_zero_first(utils.LOCAL_RANK), utils.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/{self.data}.zip' |
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utils.download(url, dir=data_dir.parent) |
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# TODO: add colorstr |
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s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {'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, batch_size=None, rank=0): |
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return build_classification_dataloader(path=dataset, batch_size=self.train.batch_size, rank=rank) |
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def get_model(self): |
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# temp. minimal. only supports torchvision models |
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if self.model in torchvision.models.__dict__: # TorchVision models i.e. resnet50, efficientnet_b0 |
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model = torchvision.models.__dict__[self.model](weights='IMAGENET1K_V1' if self.train.pretrained else None) |
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else: |
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raise ModuleNotFoundError(f'--model {self.model} not found.') |
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for m in model.modules(): |
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if not self.train.pretrained and hasattr(m, 'reset_parameters'): |
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m.reset_parameters() |
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for p in model.parameters(): |
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p.requires_grad = True # for training |
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return model |
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def get_validator(self): |
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return ClassificationValidator(self.test_loader, self.device, logger=self.console) # validator |
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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=CONFIG_PATH_ABS, config_name=str(DEFAULT_CONFIG).split(".")[0]) |
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def train(cfg): |
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cfg.model = cfg.model or "squeezenet1_0" |
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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.run() |
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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 train.epochs=10 train.project="name" hyps.lr0=0.1 |
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TODO: |
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Direct cli support, i.e, yolov8 classify_train train.epochs 10 |
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""" |
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train()
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