# Ultralytics YOLO 🚀, AGPL-3.0 license import torch import torchvision from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight from ultralytics.yolo import v8 from ultralytics.yolo.data import ClassificationDataset, build_dataloader from ultralytics.yolo.engine.trainer import BaseTrainer from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr from ultralytics.yolo.utils.plotting import plot_images, plot_results from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first class ClassificationTrainer(BaseTrainer): def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): """Initialize a ClassificationTrainer object with optional configuration overrides and callbacks.""" if overrides is None: overrides = {} overrides['task'] = 'classify' super().__init__(cfg, overrides, _callbacks) def set_model_attributes(self): """Set the YOLO model's class names from the loaded dataset.""" self.model.names = self.data['names'] def get_model(self, cfg=None, weights=None, verbose=True): """Returns a modified PyTorch model configured for training YOLO.""" model = ClassificationModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1) if weights: model.load(weights) pretrained = self.args.pretrained for m in model.modules(): if not pretrained and hasattr(m, 'reset_parameters'): m.reset_parameters() if isinstance(m, torch.nn.Dropout) and self.args.dropout: m.p = self.args.dropout # set dropout for p in model.parameters(): p.requires_grad = True # for training # Update defaults if self.args.imgsz == 640: self.args.imgsz = 224 return model def setup_model(self): """ load/create/download model for any task """ # Classification models require special handling if isinstance(self.model, torch.nn.Module): # if model is loaded beforehand. No setup needed return model = str(self.model) # Load a YOLO model locally, from torchvision, or from Ultralytics assets if model.endswith('.pt'): self.model, _ = attempt_load_one_weight(model, device='cpu') for p in self.model.parameters(): p.requires_grad = True # for training elif model.endswith('.yaml'): self.model = self.get_model(cfg=model) elif model in torchvision.models.__dict__: pretrained = True self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None) else: FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.') ClassificationModel.reshape_outputs(self.model, self.data['nc']) return # dont return ckpt. Classification doesn't support resume def build_dataset(self, img_path, mode='train'): dataset = ClassificationDataset(root=img_path, imgsz=self.args.imgsz, augment=mode == 'train') return dataset def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'): """Returns PyTorch DataLoader with transforms to preprocess images for inference.""" with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = self.build_dataset(dataset_path, mode) loader = build_dataloader(dataset, batch_size, self.args.workers, rank=rank) # Attach inference transforms if mode != 'train': if is_parallel(self.model): self.model.module.transforms = loader.dataset.torch_transforms else: self.model.transforms = loader.dataset.torch_transforms return loader def preprocess_batch(self, batch): """Preprocesses a batch of images and classes.""" batch['img'] = batch['img'].to(self.device) batch['cls'] = batch['cls'].to(self.device) return batch def progress_string(self): """Returns a formatted string showing training progress.""" return ('\n' + '%11s' * (4 + len(self.loss_names))) % \ ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size') def get_validator(self): """Returns an instance of ClassificationValidator for validation.""" self.loss_names = ['loss'] return v8.classify.ClassificationValidator(self.test_loader, self.save_dir) def criterion(self, preds, batch): """Compute the classification loss between predictions and true labels.""" loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / self.args.nbs loss_items = loss.detach() return loss, loss_items def label_loss_items(self, loss_items=None, prefix='train'): """ Returns a loss dict with labelled training loss items tensor """ # Not needed for classification but necessary for segmentation & detection keys = [f'{prefix}/{x}' for x in self.loss_names] if loss_items is None: return keys loss_items = [round(float(loss_items), 5)] return dict(zip(keys, loss_items)) def resume_training(self, ckpt): """Resumes training from a given checkpoint.""" pass def plot_metrics(self): """Plots metrics from a CSV file.""" plot_results(file=self.csv, classify=True) # save results.png def final_eval(self): """Evaluate trained model and save validation results.""" for f in self.last, self.best: if f.exists(): strip_optimizer(f) # strip optimizers # TODO: validate best.pt after training completes # if f is self.best: # LOGGER.info(f'\nValidating {f}...') # self.validator.args.save_json = True # self.metrics = self.validator(model=f) # self.metrics.pop('fitness', None) # self.run_callbacks('on_fit_epoch_end') LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}") def plot_training_samples(self, batch, ni): """Plots training samples with their annotations.""" plot_images(images=batch['img'], batch_idx=torch.arange(len(batch['img'])), cls=batch['cls'].squeeze(-1), fname=self.save_dir / f'train_batch{ni}.jpg') def train(cfg=DEFAULT_CFG, use_python=False): """Train the YOLO classification model.""" model = cfg.model or 'yolov8n-cls.pt' # or "resnet18" data = cfg.data or 'mnist160' # or yolo.ClassificationDataset("mnist") device = cfg.device if cfg.device is not None else '' args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) else: trainer = ClassificationTrainer(overrides=args) trainer.train() if __name__ == '__main__': train()