更新 detacttracking
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detecttracking/ultralytics/utils/callbacks/base.py
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detecttracking/ultralytics/utils/callbacks/base.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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"""
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Base callbacks
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"""
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from collections import defaultdict
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from copy import deepcopy
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# Trainer callbacks ----------------------------------------------------------------------------------------------------
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def on_pretrain_routine_start(trainer):
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"""Called before the pretraining routine starts."""
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pass
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def on_pretrain_routine_end(trainer):
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"""Called after the pretraining routine ends."""
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pass
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def on_train_start(trainer):
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"""Called when the training starts."""
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pass
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def on_train_epoch_start(trainer):
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"""Called at the start of each training epoch."""
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pass
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def on_train_batch_start(trainer):
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"""Called at the start of each training batch."""
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pass
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def optimizer_step(trainer):
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"""Called when the optimizer takes a step."""
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pass
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def on_before_zero_grad(trainer):
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"""Called before the gradients are set to zero."""
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pass
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def on_train_batch_end(trainer):
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"""Called at the end of each training batch."""
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pass
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def on_train_epoch_end(trainer):
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"""Called at the end of each training epoch."""
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pass
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def on_fit_epoch_end(trainer):
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"""Called at the end of each fit epoch (train + val)."""
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pass
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def on_model_save(trainer):
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"""Called when the model is saved."""
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pass
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def on_train_end(trainer):
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"""Called when the training ends."""
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pass
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def on_params_update(trainer):
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"""Called when the model parameters are updated."""
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pass
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def teardown(trainer):
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"""Called during the teardown of the training process."""
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pass
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# Validator callbacks --------------------------------------------------------------------------------------------------
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def on_val_start(validator):
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"""Called when the validation starts."""
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pass
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def on_val_batch_start(validator):
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"""Called at the start of each validation batch."""
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pass
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def on_val_batch_end(validator):
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"""Called at the end of each validation batch."""
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pass
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def on_val_end(validator):
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"""Called when the validation ends."""
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pass
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# Predictor callbacks --------------------------------------------------------------------------------------------------
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def on_predict_start(predictor):
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"""Called when the prediction starts."""
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pass
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def on_predict_batch_start(predictor):
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"""Called at the start of each prediction batch."""
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pass
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def on_predict_batch_end(predictor):
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"""Called at the end of each prediction batch."""
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pass
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def on_predict_postprocess_end(predictor):
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"""Called after the post-processing of the prediction ends."""
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pass
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def on_predict_end(predictor):
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"""Called when the prediction ends."""
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pass
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# Exporter callbacks ---------------------------------------------------------------------------------------------------
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def on_export_start(exporter):
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"""Called when the model export starts."""
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pass
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def on_export_end(exporter):
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"""Called when the model export ends."""
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pass
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default_callbacks = {
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# Run in trainer
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'on_pretrain_routine_start': [on_pretrain_routine_start],
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'on_pretrain_routine_end': [on_pretrain_routine_end],
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'on_train_start': [on_train_start],
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'on_train_epoch_start': [on_train_epoch_start],
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'on_train_batch_start': [on_train_batch_start],
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'optimizer_step': [optimizer_step],
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'on_before_zero_grad': [on_before_zero_grad],
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'on_train_batch_end': [on_train_batch_end],
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'on_train_epoch_end': [on_train_epoch_end],
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'on_fit_epoch_end': [on_fit_epoch_end], # fit = train + val
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'on_model_save': [on_model_save],
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'on_train_end': [on_train_end],
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'on_params_update': [on_params_update],
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'teardown': [teardown],
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# Run in validator
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'on_val_start': [on_val_start],
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'on_val_batch_start': [on_val_batch_start],
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'on_val_batch_end': [on_val_batch_end],
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'on_val_end': [on_val_end],
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# Run in predictor
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'on_predict_start': [on_predict_start],
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'on_predict_batch_start': [on_predict_batch_start],
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'on_predict_postprocess_end': [on_predict_postprocess_end],
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'on_predict_batch_end': [on_predict_batch_end],
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'on_predict_end': [on_predict_end],
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# Run in exporter
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'on_export_start': [on_export_start],
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'on_export_end': [on_export_end]}
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def get_default_callbacks():
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"""
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Return a copy of the default_callbacks dictionary with lists as default values.
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Returns:
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(defaultdict): A defaultdict with keys from default_callbacks and empty lists as default values.
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"""
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return defaultdict(list, deepcopy(default_callbacks))
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def add_integration_callbacks(instance):
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"""
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Add integration callbacks from various sources to the instance's callbacks.
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Args:
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instance (Trainer, Predictor, Validator, Exporter): An object with a 'callbacks' attribute that is a dictionary
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of callback lists.
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"""
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# Load HUB callbacks
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from .hub import callbacks as hub_cb
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callbacks_list = [hub_cb]
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# Load training callbacks
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if 'Trainer' in instance.__class__.__name__:
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from .clearml import callbacks as clear_cb
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from .comet import callbacks as comet_cb
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from .dvc import callbacks as dvc_cb
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from .mlflow import callbacks as mlflow_cb
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from .neptune import callbacks as neptune_cb
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from .raytune import callbacks as tune_cb
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from .tensorboard import callbacks as tb_cb
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from .wb import callbacks as wb_cb
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callbacks_list.extend([clear_cb, comet_cb, dvc_cb, mlflow_cb, neptune_cb, tune_cb, tb_cb, wb_cb])
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# Load export callbacks (patch to avoid CoreML protobuf error)
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if 'Exporter' in instance.__class__.__name__:
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from .tensorboard import callbacks as tb_cb
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callbacks_list.append(tb_cb)
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# Add the callbacks to the callbacks dictionary
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for callbacks in callbacks_list:
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for k, v in callbacks.items():
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if v not in instance.callbacks[k]:
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instance.callbacks[k].append(v)
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