Vehicle-cpp/hkpc/ultralytics-main/ultralytics/models/yolo/classify/train.py

# 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/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:
            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):
        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)

    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
                # TODO: validate best.pt after training completes
                # if f is self.best:
                #     LOGGER.info(f'\nValidating {f}...')
                #     self.validator.args.save_json = True
                #     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)