# Ultralytics YOLO 🚀, AGPL-3.0 license import torch import torchvision from ultralytics.data import ClassificationDataset, build_dataloader from ultralytics.engine.trainer import BaseTrainer from ultralytics.models import yolo from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK, colorstr from ultralytics.utils.plotting import plot_images, plot_results from ultralytics.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first class ClassificationTrainer(BaseTrainer): """ A class extending the BaseTrainer class for training based on a classification model. Notes: - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'. Example: ```python from ultralytics.models.yolo.classify import ClassificationTrainer args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3) trainer = ClassificationTrainer(overrides=args) trainer.train() ``` """ 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" if overrides.get("imgsz") is None: overrides["imgsz"] = 224 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) for m in model.modules(): if not self.args.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 return model def setup_model(self): """Load, create or download model for any task.""" if isinstance(self.model, torch.nn.Module): # if model is loaded beforehand. No setup needed return model, ckpt = str(self.model), None # Load a YOLO model locally, from torchvision, or from Ultralytics assets if model.endswith(".pt"): self.model, ckpt = attempt_load_one_weight(model, device="cpu") for p in self.model.parameters(): p.requires_grad = True # for training elif model.split(".")[-1] in ("yaml", "yml"): self.model = self.get_model(cfg=model) elif model in torchvision.models.__dict__: self.model = torchvision.models.__dict__[model](weights="IMAGENET1K_V1" if self.args.pretrained else None) else: raise FileNotFoundError(f"ERROR: model={model} not found locally or online. Please check model name.") ClassificationModel.reshape_outputs(self.model, self.data["nc"]) return ckpt def build_dataset(self, img_path, mode="train", batch=None): """Creates a ClassificationDataset instance given an image path, and mode (train/test etc.).""" return ClassificationDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode) 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 yolo.classify.ClassificationValidator(self.test_loader, self.save_dir, _callbacks=self.callbacks) 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 plot_metrics(self): """Plots metrics from a CSV file.""" plot_results(file=self.csv, classify=True, on_plot=self.on_plot) # 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 if f is self.best: LOGGER.info(f"\nValidating {f}...") self.validator.args.data = self.args.data self.validator.args.plots = self.args.plots 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"].view(-1), # warning: use .view(), not .squeeze() for Classify models fname=self.save_dir / f"train_batch{ni}.jpg", on_plot=self.on_plot, )