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# Reference for `ultralytics/utils/callbacks/base.py`

Note

This file is available at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/callbacks/base.py. If you spot a problem please help fix it by contributing a Pull Request 🛠️. Thank you 🙏!

## `` ultralytics.utils.callbacks.base.on_pretrain_routine_start

    on_pretrain_routine_start(trainer)

Called before the pretraining routine starts.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_pretrain_routine_start(trainer):
        """Called before the pretraining routine starts."""
        pass

## `` ultralytics.utils.callbacks.base.on_pretrain_routine_end

    on_pretrain_routine_end(trainer)

Called after the pretraining routine ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_pretrain_routine_end(trainer):
        """Called after the pretraining routine ends."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_start

    on_train_start(trainer)

Called when the training starts.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_start(trainer):
        """Called when the training starts."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_epoch_start

    on_train_epoch_start(trainer)

Called at the start of each training epoch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_epoch_start(trainer):
        """Called at the start of each training epoch."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_batch_start

    on_train_batch_start(trainer)

Called at the start of each training batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_batch_start(trainer):
        """Called at the start of each training batch."""
        pass

## `` ultralytics.utils.callbacks.base.optimizer_step

    optimizer_step(trainer)

Called when the optimizer takes a step.

Source code in `ultralytics/utils/callbacks/base.py`

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    def optimizer_step(trainer):
        """Called when the optimizer takes a step."""
        pass

## `` ultralytics.utils.callbacks.base.on_before_zero_grad

    on_before_zero_grad(trainer)

Called before the gradients are set to zero.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_before_zero_grad(trainer):
        """Called before the gradients are set to zero."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_batch_end

    on_train_batch_end(trainer)

Called at the end of each training batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_batch_end(trainer):
        """Called at the end of each training batch."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_epoch_end

    on_train_epoch_end(trainer)

Called at the end of each training epoch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_epoch_end(trainer):
        """Called at the end of each training epoch."""
        pass

## `` ultralytics.utils.callbacks.base.on_fit_epoch_end

    on_fit_epoch_end(trainer)

Called at the end of each fit epoch (train + val).

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_fit_epoch_end(trainer):
        """Called at the end of each fit epoch (train + val)."""
        pass

## `` ultralytics.utils.callbacks.base.on_model_save

    on_model_save(trainer)

Called when the model is saved.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_model_save(trainer):
        """Called when the model is saved."""
        pass

## `` ultralytics.utils.callbacks.base.on_train_end

    on_train_end(trainer)

Called when the training ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_train_end(trainer):
        """Called when the training ends."""
        pass

## `` ultralytics.utils.callbacks.base.on_params_update

    on_params_update(trainer)

Called when the model parameters are updated.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_params_update(trainer):
        """Called when the model parameters are updated."""
        pass

## `` ultralytics.utils.callbacks.base.teardown

    teardown(trainer)

Called during the teardown of the training process.

Source code in `ultralytics/utils/callbacks/base.py`

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    def teardown(trainer):
        """Called during the teardown of the training process."""
        pass

## `` ultralytics.utils.callbacks.base.on_val_start

    on_val_start(validator)

Called when the validation starts.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_val_start(validator):
        """Called when the validation starts."""
        pass

## `` ultralytics.utils.callbacks.base.on_val_batch_start

    on_val_batch_start(validator)

Called at the start of each validation batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_val_batch_start(validator):
        """Called at the start of each validation batch."""
        pass

## `` ultralytics.utils.callbacks.base.on_val_batch_end

    on_val_batch_end(validator)

Called at the end of each validation batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_val_batch_end(validator):
        """Called at the end of each validation batch."""
        pass

## `` ultralytics.utils.callbacks.base.on_val_end

    on_val_end(validator)

Called when the validation ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_val_end(validator):
        """Called when the validation ends."""
        pass

## `` ultralytics.utils.callbacks.base.on_predict_start

    on_predict_start(predictor)

Called when the prediction starts.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_predict_start(predictor):
        """Called when the prediction starts."""
        pass

## `` ultralytics.utils.callbacks.base.on_predict_batch_start

    on_predict_batch_start(predictor)

Called at the start of each prediction batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_predict_batch_start(predictor):
        """Called at the start of each prediction batch."""
        pass

## `` ultralytics.utils.callbacks.base.on_predict_batch_end

    on_predict_batch_end(predictor)

Called at the end of each prediction batch.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_predict_batch_end(predictor):
        """Called at the end of each prediction batch."""
        pass

## `` ultralytics.utils.callbacks.base.on_predict_postprocess_end

    on_predict_postprocess_end(predictor)

Called after the post-processing of the prediction ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_predict_postprocess_end(predictor):
        """Called after the post-processing of the prediction ends."""
        pass

## `` ultralytics.utils.callbacks.base.on_predict_end

    on_predict_end(predictor)

Called when the prediction ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_predict_end(predictor):
        """Called when the prediction ends."""
        pass

## `` ultralytics.utils.callbacks.base.on_export_start

    on_export_start(exporter)

Called when the model export starts.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_export_start(exporter):
        """Called when the model export starts."""
        pass

## `` ultralytics.utils.callbacks.base.on_export_end

    on_export_end(exporter)

Called when the model export ends.

Source code in `ultralytics/utils/callbacks/base.py`

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    def on_export_end(exporter):
        """Called when the model export ends."""
        pass

## `` ultralytics.utils.callbacks.base.get_default_callbacks

    get_default_callbacks()

Get the default callbacks for Ultralytics training, validation, prediction, and export processes.

Returns:

Type | Description
--- | ---
`dict` | Dictionary of default callbacks for various training events. Each key represents an event during the training process, and the corresponding value is a list of callback functions executed when that event occurs.

Examples:

    >>> callbacks = get_default_callbacks()
    >>> print(list(callbacks.keys()))  # show all available callback events
    ['on_pretrain_routine_start', 'on_pretrain_routine_end', ...]

Source code in `ultralytics/utils/callbacks/base.py`

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    def get_default_callbacks():
        """
        Get the default callbacks for Ultralytics training, validation, prediction, and export processes.

        Returns:
            (dict): Dictionary of default callbacks for various training events. Each key represents an event during the
                training process, and the corresponding value is a list of callback functions executed when that event
                occurs.

        Examples:
            >>> callbacks = get_default_callbacks()
            >>> print(list(callbacks.keys()))  # show all available callback events
            ['on_pretrain_routine_start', 'on_pretrain_routine_end', ...]
        """
        return defaultdict(list, deepcopy(default_callbacks))

## `` ultralytics.utils.callbacks.base.add_integration_callbacks

    add_integration_callbacks(instance)

Add integration callbacks to the instance's callbacks dictionary.

This function loads and adds various integration callbacks to the provided instance. The specific callbacks added depend on the type of instance provided. All instances receive HUB callbacks, while Trainer instances also receive additional callbacks for various integrations like ClearML, Comet, DVC, MLflow, Neptune, Ray Tune, TensorBoard, and Weights & Biases.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`instance` | `Trainer | Predictor | Validator | Exporter` | The object instance to which callbacks will be added. The type of instance determines which callbacks are loaded. | required

Examples:

    >>> from ultralytics.engine.trainer import BaseTrainer
    >>> trainer = BaseTrainer()
    >>> add_integration_callbacks(trainer)

Source code in `ultralytics/utils/callbacks/base.py`

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    def add_integration_callbacks(instance):
        """
        Add integration callbacks to the instance's callbacks dictionary.

        This function loads and adds various integration callbacks to the provided instance. The specific callbacks added
        depend on the type of instance provided. All instances receive HUB callbacks, while Trainer instances also receive
        additional callbacks for various integrations like ClearML, Comet, DVC, MLflow, Neptune, Ray Tune, TensorBoard,
        and Weights & Biases.

        Args:
            instance (Trainer | Predictor | Validator | Exporter): The object instance to which callbacks will be added.
                The type of instance determines which callbacks are loaded.

        Examples:
            >>> from ultralytics.engine.trainer import BaseTrainer
            >>> trainer = BaseTrainer()
            >>> add_integration_callbacks(trainer)
        """
        from .hub import callbacks as hub_cb
        from .platform import callbacks as platform_cb

        # Load Ultralytics callbacks
        callbacks_list = [hub_cb, platform_cb]

        # Load training callbacks
        if "Trainer" in instance.__class__.__name__:
            from .clearml import callbacks as clear_cb
            from .comet import callbacks as comet_cb
            from .dvc import callbacks as dvc_cb
            from .mlflow import callbacks as mlflow_cb
            from .neptune import callbacks as neptune_cb
            from .raytune import callbacks as tune_cb
            from .tensorboard import callbacks as tb_cb
            from .wb import callbacks as wb_cb

            callbacks_list.extend([clear_cb, comet_cb, dvc_cb, mlflow_cb, neptune_cb, tune_cb, tb_cb, wb_cb])

        # Add the callbacks to the callbacks dictionary
        for callbacks in callbacks_list:
            for k, v in callbacks.items():
                if v not in instance.callbacks[k]:
                    instance.callbacks[k].append(v)

📅 Created 1 year ago ✏️ Updated 1 year ago