单目3D初始代码

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, 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 labeled 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="yolo26n-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)
+
+        # Filter out samples with class indices >= nc (prevents CUDA assertion errors)
+        nc = self.data.get("nc", 0)
+        dataset_nc = len(dataset.base.classes)
+        if nc and dataset_nc > nc:
+            extra_classes = dataset.base.classes[nc:]
+            original_count = len(dataset.samples)
+            dataset.samples = [s for s in dataset.samples if s[1] < nc]
+            skipped = original_count - len(dataset.samples)
+            LOGGER.warning(
+                f"{mode} split has {dataset_nc} classes but model expects {nc}. "
+                f"Skipping {skipped} samples from extra classes: {extra_classes}"
+            )
+
+        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 labeled training loss items tensor.
+
+        Args:
+            loss_items (torch.Tensor, optional): Loss tensor items.
+            prefix (str, optional): Prefix to prepend to loss names.
+
+        Returns:
+            (dict | list): Dictionary of labeled loss items if loss_items is provided, otherwise list of keys.
+        """
+        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_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): Batch index used for naming the output file.
+        """
+        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,
+        )