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56 lines
2.0 KiB
56 lines
2.0 KiB
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.modeling.tasks import ClassificationModel |
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class ClassificationTrainer(BaseTrainer): |
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def load_model(self, model_cfg, weights, data): |
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# TODO: why treat clf models as unique. We should have clf yamls? |
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if weights and not weights.__class__.__name__.startswith("yolo"): # torchvision |
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model = weights |
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else: |
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model = ClassificationModel(model_cfg, weights, data["nc"]) |
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ClassificationModel.reshape_outputs(model, data["nc"]) |
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return model |
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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, |
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imgsz=self.args.img_size, |
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batch_size=batch_size, |
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rank=rank) |
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def preprocess_batch(self, batch): |
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batch["img"] = batch["img"].to(self.device) |
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batch["cls"] = batch["cls"].to(self.device) |
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return batch |
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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, batch): |
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loss = torch.nn.functional.cross_entropy(preds, batch["cls"]) |
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return loss, loss |
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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 ultralytics/yolo/v8/classify/train.py model=resnet18 data=imagenette160 epochs=1 img_size=224 |
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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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