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103 lines
3.3 KiB
103 lines
3.3 KiB
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 ultralytics.nn.tasks import ClassificationModel, attempt_load_weights
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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 BaseTrainer
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from ultralytics.yolo.utils import DEFAULT_CONFIG
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class ClassificationTrainer(BaseTrainer):
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def __init__(self, config=DEFAULT_CONFIG, overrides={}):
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overrides["task"] = "classify"
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super().__init__(config, overrides)
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def set_model_attributes(self):
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self.model.names = self.data["names"]
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def get_model(self, cfg=None, weights=None):
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model = ClassificationModel(cfg, nc=self.data["nc"])
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if weights:
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model.load(weights)
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return model
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def setup_model(self):
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"""
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load/create/download model for any task
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"""
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# classification models require special handling
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if isinstance(self.model, torch.nn.Module): # if model is loaded beforehand. No setup needed
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return
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model = self.model
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pretrained = False
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# Load a YOLO model locally, from torchvision, or from Ultralytics assets
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if model.endswith(".pt"):
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model = model.split(".")[0]
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pretrained = True
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else:
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self.model = self.get_model(cfg=model)
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# order: check local file -> torchvision assets -> ultralytics asset
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if Path(f"{model}.pt").is_file(): # local file
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self.model = attempt_load_weights(f"{model}.pt", device='cpu')
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elif model in torchvision.models.__dict__:
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self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
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else:
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self.model = attempt_load_weights(f"{model}.pt", device='cpu')
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return # dont return ckpt. Classification doesn't support resume
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def get_dataloader(self, dataset_path, batch_size, rank=0, mode="train"):
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return build_classification_dataloader(path=dataset_path,
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imgsz=self.args.imgsz,
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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.save_dir, 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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def check_resume(self):
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pass
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def resume_training(self, ckpt):
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pass
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def final_eval(self):
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pass
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@hydra.main(version_base=None, config_path=str(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 imgsz=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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