init commit

ultralytics/models/yolo/classify/__init__.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.models.yolo.classify.predict import ClassificationPredictor
+from ultralytics.models.yolo.classify.train import ClassificationTrainer
+from ultralytics.models.yolo.classify.val import ClassificationValidator
+
+__all__ = "ClassificationPredictor", "ClassificationTrainer", "ClassificationValidator"

ultralytics/models/yolo/classify/predict.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import cv2
+import torch
+from PIL import Image
+
+from ultralytics.data.augment import classify_transforms
+from ultralytics.engine.predictor import BasePredictor
+from ultralytics.engine.results import Results
+from ultralytics.utils import DEFAULT_CFG, ops
+
+
+class ClassificationPredictor(BasePredictor):
+    """
+    A class extending the BasePredictor class for prediction based on a classification model.
+
+    This predictor handles the specific requirements of classification models, including preprocessing images
+    and postprocessing predictions to generate classification results.
+
+    Attributes:
+        args (dict): Configuration arguments for the predictor.
+
+    Methods:
+        preprocess: Convert input images to model-compatible format.
+        postprocess: Process model predictions into Results objects.
+
+    Notes:
+        - Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
+
+    Examples:
+        >>> from ultralytics.utils import ASSETS
+        >>> from ultralytics.models.yolo.classify import ClassificationPredictor
+        >>> args = dict(model="yolo11n-cls.pt", source=ASSETS)
+        >>> predictor = ClassificationPredictor(overrides=args)
+        >>> predictor.predict_cli()
+    """
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """
+        Initialize the ClassificationPredictor with the specified configuration and set task to 'classify'.
+
+        This constructor initializes a ClassificationPredictor instance, which extends BasePredictor for classification
+        tasks. It ensures the task is set to 'classify' regardless of input configuration.
+
+        Args:
+            cfg (dict): Default configuration dictionary containing prediction settings.
+            overrides (dict, optional): Configuration overrides that take precedence over cfg.
+            _callbacks (list, optional): List of callback functions to be executed during prediction.
+        """
+        super().__init__(cfg, overrides, _callbacks)
+        self.args.task = "classify"
+
+    def setup_source(self, source):
+        """Set up source and inference mode and classify transforms."""
+        super().setup_source(source)
+        updated = (
+            self.model.model.transforms.transforms[0].size != max(self.imgsz)
+            if hasattr(self.model.model, "transforms") and hasattr(self.model.model.transforms.transforms[0], "size")
+            else False
+        )
+        self.transforms = (
+            classify_transforms(self.imgsz) if updated or not self.model.pt else self.model.model.transforms
+        )
+
+    def preprocess(self, img):
+        """Convert input images to model-compatible tensor format with appropriate normalization."""
+        if not isinstance(img, torch.Tensor):
+            img = torch.stack(
+                [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
+            )
+        img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
+        return img.half() if self.model.fp16 else img.float()  # Convert uint8 to fp16/32
+
+    def postprocess(self, preds, img, orig_imgs):
+        """
+        Process predictions to return Results objects with classification probabilities.
+
+        Args:
+            preds (torch.Tensor): Raw predictions from the model.
+            img (torch.Tensor): Input images after preprocessing.
+            orig_imgs (list[np.ndarray] | torch.Tensor): Original images before preprocessing.
+
+        Returns:
+            (list[Results]): List of Results objects containing classification results for each image.
+        """
+        if not isinstance(orig_imgs, list):  # Input images are a torch.Tensor, not a list
+            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)
+
+        preds = preds[0] if isinstance(preds, (list, tuple)) else preds
+        return [
+            Results(orig_img, path=img_path, names=self.model.names, probs=pred)
+            for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0])
+        ]

ultralytics/models/yolo/classify/train.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from copy import copy
+from typing import Any
+
+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
+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.
+        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: dict[str, Any] | None = None, _callbacks=None):
+        """
+        Initialize a ClassificationTrainer object.
+
+        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.
+        """
+        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, drop_last=self.args.compile)
+        # 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, non_blocking=self.device.type == "cuda")
+        batch["cls"] = batch["cls"].to(self.device, non_blocking=self.device.type == "cuda")
+        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: torch.Tensor | None = 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 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(batch["img"].shape[0])  # add batch index for plotting
+        plot_images(
+            labels=batch,
+            fname=self.save_dir / f"train_batch{ni}.jpg",
+            on_plot=self.on_plot,
+        )

ultralytics/models/yolo/classify/val.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+
+from ultralytics.data import ClassificationDataset, build_dataloader
+from ultralytics.engine.validator import BaseValidator
+from ultralytics.utils import LOGGER
+from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
+from ultralytics.utils.plotting import plot_images
+
+
+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="yolo11n-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=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 (list, optional): List of callback functions to be called during validation.
+
+        Examples:
+            >>> from ultralytics.models.yolo.classify import ClassificationValidator
+            >>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
+            >>> validator = ClassificationValidator(args=args)
+            >>> validator()
+        """
+        super().__init__(dataloader, save_dir, args, _callbacks)
+        self.targets = None
+        self.pred = None
+        self.args.task = "classify"
+        self.metrics = ClassifyMetrics()
+
+    def get_desc(self) -> str:
+        """Return a formatted string summarizing classification metrics."""
+        return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")
+
+    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)
+
+    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 == "cuda")
+        batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
+        batch["cls"] = batch["cls"].to(self.device, non_blocking=self.device.type == "cuda")
+        return batch
+
+    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())
+
+    def finalize_metrics(self) -> None:
+        """
+        Finalize metrics including confusion matrix and processing speed.
+
+        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.
+
+        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
+        """
+        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 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 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 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 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)
+
+    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 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 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 = dict(
+            img=batch["img"],
+            batch_idx=torch.arange(batch["img"].shape[0]),
+            cls=torch.argmax(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