# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license from copy import copy from typing import Any, Dict, Optional import torch from ultralytics.data import ClassificationDataset, build_dataloader from ultralytics.engine.trainer import BaseTrainer from ultralytics.models import yolo from ultralytics.nn.tasks import ClassificationModel from ultralytics.utils import DEFAULT_CFG, LOGGER, RANK 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 trainer class extending BaseTrainer for training image classification models. This trainer handles the training process for image classification tasks, supporting both YOLO classification models and torchvision models with comprehensive dataset handling and validation. Attributes: model (ClassificationModel): The classification model to be trained. data (Dict[str, Any]): Dictionary containing dataset information including class names and number of classes. loss_names (List[str]): Names of the loss functions used during training. validator (ClassificationValidator): Validator instance for model evaluation. Methods: set_model_attributes: Set the model's class names from the loaded dataset. get_model: Return a modified PyTorch model configured for training. setup_model: Load, create or download model for classification. build_dataset: Create a ClassificationDataset instance. get_dataloader: Return PyTorch DataLoader with transforms for image preprocessing. preprocess_batch: Preprocess a batch of images and classes. progress_string: Return a formatted string showing training progress. get_validator: Return an instance of ClassificationValidator. label_loss_items: Return a loss dict with labelled training loss items. plot_metrics: Plot metrics from a CSV file. final_eval: Evaluate trained model and save validation results. plot_training_samples: Plot training samples with their annotations. Examples: Initialize and train a classification model >>> from ultralytics.models.yolo.classify import ClassificationTrainer >>> args = dict(model="yolo11n-cls.pt", data="imagenet10", epochs=3) >>> trainer = ClassificationTrainer(overrides=args) >>> trainer.train() """ def __init__(self, cfg=DEFAULT_CFG, overrides: Optional[Dict[str, Any]] = None, _callbacks=None): """ Initialize a ClassificationTrainer object. This constructor sets up a trainer for image classification tasks, configuring the task type and default image size if not specified. Args: cfg (Dict[str, Any], optional): Default configuration dictionary containing training parameters. overrides (Dict[str, Any], optional): Dictionary of parameter overrides for the default configuration. _callbacks (List[Any], optional): List of callback functions to be executed during training. Examples: Create a trainer with custom configuration >>> from ultralytics.models.yolo.classify import ClassificationTrainer >>> args = dict(model="yolo11n-cls.pt", data="imagenet10", epochs=3) >>> trainer = ClassificationTrainer(overrides=args) >>> trainer.train() """ 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: bool = True): """ Return a modified PyTorch model configured for training YOLO classification. Args: cfg (Any, optional): Model configuration. weights (Any, optional): Pre-trained model weights. verbose (bool, optional): Whether to display model information. Returns: (ClassificationModel): Configured PyTorch model for classification. """ model = ClassificationModel(cfg, nc=self.data["nc"], ch=self.data["channels"], 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 classification tasks. Returns: (Any): Model checkpoint if applicable, otherwise None. """ import torchvision # scope for faster 'import ultralytics' if str(self.model) in torchvision.models.__dict__: self.model = torchvision.models.__dict__[self.model]( weights="IMAGENET1K_V1" if self.args.pretrained else None ) ckpt = None else: ckpt = super().setup_model() ClassificationModel.reshape_outputs(self.model, self.data["nc"]) return ckpt def build_dataset(self, img_path: str, mode: str = "train", batch=None): """ Create a ClassificationDataset instance given an image path and mode. Args: img_path (str): Path to the dataset images. mode (str, optional): Dataset mode ('train', 'val', or 'test'). batch (Any, optional): Batch information (unused in this implementation). Returns: (ClassificationDataset): Dataset for the specified mode. """ return ClassificationDataset(root=img_path, args=self.args, augment=mode == "train", prefix=mode) def get_dataloader(self, dataset_path: str, batch_size: int = 16, rank: int = 0, mode: str = "train"): """ Return PyTorch DataLoader with transforms to preprocess images. Args: dataset_path (str): Path to the dataset. batch_size (int, optional): Number of images per batch. rank (int, optional): Process rank for distributed training. mode (str, optional): 'train', 'val', or 'test' mode. Returns: (torch.utils.data.DataLoader): DataLoader for the specified dataset and mode. """ 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: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]: """Preprocess 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) -> str: """Return 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): """Return an instance of ClassificationValidator for validation.""" self.loss_names = ["loss"] return yolo.classify.ClassificationValidator( self.test_loader, self.save_dir, args=copy(self.args), _callbacks=self.callbacks ) def label_loss_items(self, loss_items: Optional[torch.Tensor] = None, prefix: str = "train"): """ Return a loss dict with labelled training loss items tensor. Args: loss_items (torch.Tensor, optional): Loss tensor items. prefix (str, optional): Prefix to prepend to loss names. Returns: keys (List[str]): List of loss keys if loss_items is None. loss_dict (Dict[str, float]): Dictionary of loss items if loss_items is provided. """ 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): """Plot 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") def plot_training_samples(self, batch: Dict[str, torch.Tensor], ni: int): """ Plot training samples with their annotations. Args: batch (Dict[str, torch.Tensor]): Batch containing images and class labels. ni (int): Number of iterations. """ batch["batch_idx"] = torch.arange(len(batch["img"])) # add batch index for plotting plot_images( labels=batch, fname=self.save_dir / f"train_batch{ni}.jpg", on_plot=self.on_plot, )