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# Ultralytics YOLO 🚀, GPL-3.0 license
"""
Base callbacks
"""
# Trainer callbacks ----------------------------------------------------------------------------------------------------
def on_pretrain_routine_start(trainer):
pass
def on_pretrain_routine_end(trainer):
pass
def on_train_start(trainer):
pass
def on_train_epoch_start(trainer):
pass
def on_train_batch_start(trainer):
pass
def optimizer_step(trainer):
pass
def on_before_zero_grad(trainer):
pass
def on_train_batch_end(trainer):
pass
def on_train_epoch_end(trainer):
pass
def on_fit_epoch_end(trainer):
pass
def on_model_save(trainer):
pass
def on_train_end(trainer):
pass
def on_params_update(trainer):
pass
def teardown(trainer):
pass
# Validator callbacks --------------------------------------------------------------------------------------------------
def on_val_start(validator):
pass
def on_val_batch_start(validator):
pass
def on_val_batch_end(validator):
pass
def on_val_end(validator):
pass
# Predictor callbacks --------------------------------------------------------------------------------------------------
def on_predict_start(predictor):
pass
def on_predict_batch_start(predictor):
pass
def on_predict_batch_end(predictor):
pass
def on_predict_postprocess_end(predictor):
pass
def on_predict_end(predictor):
pass
# Exporter callbacks ---------------------------------------------------------------------------------------------------
def on_export_start(exporter):
pass
def on_export_end(exporter):
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 add_integration_callbacks(instance):
from .clearml import callbacks as clearml_callbacks
from .comet import callbacks as comet_callbacks
from .hub import callbacks as hub_callbacks
from .tensorboard import callbacks as tb_callbacks
for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks:
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)