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Reference for ultralytics/models/yolo/classify/train.py

Note

Full source code for this file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/models/yolo/classify/train.py. Help us fix any issues you see by submitting a Pull Request 🛠️. Thank you 🙏!


ultralytics.models.yolo.classify.train.ClassificationTrainer

Bases: 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
from ultralytics.models.yolo.classify import ClassificationTrainer

args = dict(model='yolov8n-cls.pt', data='imagenet10', epochs=3)
trainer = ClassificationTrainer(overrides=args)
trainer.train()
Source code in ultralytics/models/yolo/classify/train.py
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 or 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
                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')
        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)

__init__(cfg=DEFAULT_CFG, overrides=None, _callbacks=None)

Initialize a ClassificationTrainer object with optional configuration overrides and callbacks.

Source code in ultralytics/models/yolo/classify/train.py
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)

final_eval()

Evaluate trained model and save validation results.

Source code in ultralytics/models/yolo/classify/train.py
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')
    LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")

get_dataloader(dataset_path, batch_size=16, rank=0, mode='train')

Returns PyTorch DataLoader with transforms to preprocess images for inference.

Source code in ultralytics/models/yolo/classify/train.py
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

get_model(cfg=None, weights=None, verbose=True)

Returns a modified PyTorch model configured for training YOLO.

Source code in ultralytics/models/yolo/classify/train.py
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

get_validator()

Returns an instance of ClassificationValidator for validation.

Source code in ultralytics/models/yolo/classify/train.py
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)

label_loss_items(loss_items=None, prefix='train')

Returns a loss dict with labelled training loss items tensor. Not needed for classification but necessary for segmentation & detection

Source code in ultralytics/models/yolo/classify/train.py
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))

plot_metrics()

Plots metrics from a CSV file.

Source code in ultralytics/models/yolo/classify/train.py
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

plot_training_samples(batch, ni)

Plots training samples with their annotations.

Source code in ultralytics/models/yolo/classify/train.py
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)

preprocess_batch(batch)

Preprocesses a batch of images and classes.

Source code in ultralytics/models/yolo/classify/train.py
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

progress_string()

Returns a formatted string showing training progress.

Source code in ultralytics/models/yolo/classify/train.py
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')

set_model_attributes()

Set the YOLO model's class names from the loaded dataset.

Source code in ultralytics/models/yolo/classify/train.py
def set_model_attributes(self):
    """Set the YOLO model's class names from the loaded dataset."""
    self.model.names = self.data['names']

setup_model()

Load, create or download model for any task.

Source code in ultralytics/models/yolo/classify/train.py
def setup_model(self):
    """Load, create or 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




Created 2023-07-16, Updated 2023-08-20
Authors: glenn-jocher (6), Laughing-q (1)