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