Reference for ultralytics/models/yolo/classify/val.py#

class ClassificationValidator(BaseValidator):
    """A class extending the BaseValidator class for validation based on a classification model.

    This validator handles the validation process for classification models, including metrics calculation, confusion
    matrix generation, and visualization of results.

    Attributes:
        targets (list[torch.Tensor]): Ground truth class labels.
        pred (list[torch.Tensor]): Model predictions.
        metrics (ClassifyMetrics): Object to calculate and store classification metrics.
        names (dict): Mapping of class indices to class names.
        nc (int): Number of classes.
        confusion_matrix (ConfusionMatrix): Matrix to evaluate model performance across classes.

    Methods:
        get_desc: Return a formatted string summarizing classification metrics.
        init_metrics: Initialize confusion matrix, class names, and tracking containers.
        preprocess: Preprocess input batch by moving data to device.
        update_metrics: Update running metrics with model predictions and batch targets.
        finalize_metrics: Finalize metrics including confusion matrix and processing speed.
        postprocess: Extract the primary prediction from model output.
        get_stats: Calculate and return a dictionary of metrics.
        build_dataset: Create a ClassificationDataset instance for validation.
        get_dataloader: Build and return a data loader for classification validation.
        print_results: Print evaluation metrics for the classification model.
        plot_val_samples: Plot validation image samples with their ground truth labels.
        plot_predictions: Plot images with their predicted class labels.

    Examples:
        >>> from ultralytics.models.yolo.classify import ClassificationValidator
        >>> args = dict(model="yolo26n-cls.pt", data="imagenet10")
        >>> validator = ClassificationValidator(args=args)
        >>> validator()

    Notes:
        Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
    """

    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks: dict | None = None) -> None:
        """Initialize ClassificationValidator with dataloader, save directory, and other parameters.

        Args:
            dataloader (torch.utils.data.DataLoader, optional): DataLoader to use for validation.
            save_dir (str | Path, optional): Directory to save results.
            args (dict, optional): Arguments containing model and validation configuration.
            _callbacks (dict, optional): Dictionary of callback functions to be called during validation.
        """
        super().__init__(dataloader, save_dir, args, _callbacks)
        self.targets = None
        self.pred = None
        self.args.task = "classify"
        self.metrics = ClassifyMetrics()

def build_dataset(self, img_path: str) -> ClassificationDataset:
    """Create a ClassificationDataset instance for validation."""
    return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)

def finalize_metrics(self) -> None:
    """Finalize metrics including confusion matrix and processing speed.

    Examples:
        >>> validator = ClassificationValidator()
        >>> validator.pred = [torch.tensor([[0, 1, 2]])]  # Top-3 predictions for one sample
        >>> validator.targets = [torch.tensor([0])]  # Ground truth class
        >>> validator.finalize_metrics()
        >>> print(validator.metrics.confusion_matrix)  # Access the confusion matrix

    Notes:
        This method processes the accumulated predictions and targets to generate the confusion matrix,
        optionally plots it, and updates the metrics object with speed information.
    """
    self.confusion_matrix.process_cls_preds(self.pred, self.targets)
    if self.args.plots:
        for normalize in True, False:
            self.confusion_matrix.plot(save_dir=self.save_dir, normalize=normalize, on_plot=self.on_plot)
    self.metrics.speed = self.speed
    self.metrics.save_dir = self.save_dir
    self.metrics.confusion_matrix = self.confusion_matrix

def gather_stats(self) -> None:
    """Gather stats from all GPUs."""
    if RANK == 0:
        gathered_preds = [None] * dist.get_world_size()
        gathered_targets = [None] * dist.get_world_size()
        dist.gather_object(self.pred, gathered_preds, dst=0)
        dist.gather_object(self.targets, gathered_targets, dst=0)
        self.pred = [pred for rank in gathered_preds for pred in rank]
        self.targets = [targets for rank in gathered_targets for targets in rank]
    elif RANK > 0:
        dist.gather_object(self.pred, None, dst=0)
        dist.gather_object(self.targets, None, dst=0)

def get_dataloader(self, dataset_path: Path | str, batch_size: int) -> torch.utils.data.DataLoader:
    """Build and return a data loader for classification validation.

    Args:
        dataset_path (str | Path): Path to the dataset directory.
        batch_size (int): Number of samples per batch.

    Returns:
        (torch.utils.data.DataLoader): DataLoader object for the classification validation dataset.
    """
    dataset = self.build_dataset(dataset_path)
    return build_dataloader(dataset, batch_size, self.args.workers, rank=-1, device=self.device)

def get_desc(self) -> str:
    """Return a formatted string summarizing classification metrics."""
    return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")

def get_stats(self) -> dict[str, float]:
    """Calculate and return a dictionary of metrics by processing targets and predictions."""
    self.metrics.process(self.targets, self.pred)
    return self.metrics.results_dict

def init_metrics(self, model: torch.nn.Module) -> None:
    """Initialize confusion matrix, class names, and tracking containers for predictions and targets."""
    self.names = model.names
    self.nc = len(model.names)
    self.pred = []
    self.targets = []
    self.confusion_matrix = ConfusionMatrix(names=model.names, task="classify")

def plot_predictions(self, batch: dict[str, Any], preds: torch.Tensor, ni: int) -> None:
    """Plot images with their predicted class labels and save the visualization.

    Args:
        batch (dict[str, Any]): Batch data containing images and other information.
        preds (torch.Tensor): Model predictions with shape (batch_size, num_classes).
        ni (int): Batch index used for naming the output file.

    Examples:
        >>> validator = ClassificationValidator()
        >>> batch = {"img": torch.rand(16, 3, 224, 224)}
        >>> preds = torch.rand(16, 10)  # 16 images, 10 classes
        >>> validator.plot_predictions(batch, preds, 0)
    """
    batched_preds = {
        "img": batch["img"],
        "batch_idx": torch.arange(batch["img"].shape[0]),
        "cls": torch.argmax(preds, dim=1),
        "conf": torch.amax(preds, dim=1),
    }
    plot_images(
        batched_preds,
        fname=self.save_dir / f"val_batch{ni}_pred.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )  # pred

def plot_val_samples(self, batch: dict[str, Any], ni: int) -> None:
    """Plot validation image samples with their ground truth labels.

    Args:
        batch (dict[str, Any]): Dictionary containing batch data with 'img' (images) and 'cls' (class labels).
        ni (int): Batch index used for naming the output file.

    Examples:
        >>> validator = ClassificationValidator()
        >>> batch = {"img": torch.rand(16, 3, 224, 224), "cls": torch.randint(0, 10, (16,))}
        >>> validator.plot_val_samples(batch, 0)
    """
    batch["batch_idx"] = torch.arange(batch["img"].shape[0])  # add batch index for plotting
    plot_images(
        labels=batch,
        fname=self.save_dir / f"val_batch{ni}_labels.jpg",
        names=self.names,
        on_plot=self.on_plot,
    )

def postprocess(self, preds: torch.Tensor | list[torch.Tensor] | tuple[torch.Tensor]) -> torch.Tensor:
    """Extract the primary prediction from model output if it's in a list or tuple format."""
    return preds[0] if isinstance(preds, (list, tuple)) else preds

def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
    """Preprocess input batch by moving data to device and converting to appropriate dtype."""
    batch["img"] = batch["img"].to(self.device, non_blocking=self.device.type not in {"cpu", "mps"})
    batch["img"] = batch["img"].half() if self.args.quantize == 16 else batch["img"].float()
    batch["cls"] = batch["cls"].to(self.device, non_blocking=self.device.type not in {"cpu", "mps"})
    return batch

def print_results(self) -> None:
    """Print evaluation metrics for the classification model."""
    pf = "%22s" + "%11.3g" * len(self.metrics.keys)  # print format
    LOGGER.info(pf % ("all", self.metrics.top1, self.metrics.top5))

def update_metrics(self, preds: torch.Tensor, batch: dict[str, Any]) -> None:
    """Update running metrics with model predictions and batch targets.

    Args:
        preds (torch.Tensor): Model predictions, typically logits or probabilities for each class.
        batch (dict): Batch data containing images and class labels.

    Notes:
        This method appends the top-N predictions (sorted by confidence in descending order) to the
        prediction list for later evaluation. N is limited to the minimum of 5 and the number of classes.
    """
    n5 = min(len(self.names), 5)
    self.pred.append(preds.argsort(1, descending=True)[:, :n5].type(torch.int32).cpu())
    self.targets.append(batch["cls"].type(torch.int32).cpu())