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157 lines
3.4 KiB
157 lines
3.4 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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"""
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Base callbacks
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"""
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# Trainer callbacks ----------------------------------------------------------------------------------------------------
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def on_pretrain_routine_start(trainer):
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pass
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def on_pretrain_routine_end(trainer):
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pass
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def on_train_start(trainer):
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pass
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def on_train_epoch_start(trainer):
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pass
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def on_train_batch_start(trainer):
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pass
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def optimizer_step(trainer):
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pass
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def on_before_zero_grad(trainer):
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pass
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def on_train_batch_end(trainer):
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pass
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def on_train_epoch_end(trainer):
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pass
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def on_fit_epoch_end(trainer):
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pass
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def on_model_save(trainer):
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pass
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def on_train_end(trainer):
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pass
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def on_params_update(trainer):
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pass
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def teardown(trainer):
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pass
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# Validator callbacks --------------------------------------------------------------------------------------------------
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def on_val_start(validator):
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pass
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def on_val_batch_start(validator):
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pass
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def on_val_batch_end(validator):
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pass
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def on_val_end(validator):
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pass
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# Predictor callbacks --------------------------------------------------------------------------------------------------
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def on_predict_start(predictor):
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pass
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def on_predict_batch_start(predictor):
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pass
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def on_predict_batch_end(predictor):
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pass
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def on_predict_postprocess_end(predictor):
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pass
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def on_predict_end(predictor):
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pass
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# Exporter callbacks ---------------------------------------------------------------------------------------------------
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def on_export_start(exporter):
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pass
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def on_export_end(exporter):
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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 add_integration_callbacks(instance):
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from .clearml import callbacks as clearml_callbacks
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from .comet import callbacks as comet_callbacks
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from .hub import callbacks as hub_callbacks
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from .mlflow import callbacks as mf_callbacks
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from .tensorboard import callbacks as tb_callbacks
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for x in clearml_callbacks, comet_callbacks, hub_callbacks, tb_callbacks, mf_callbacks:
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for k, v in x.items():
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if v not in instance.callbacks[k]: # prevent duplicate callbacks addition
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instance.callbacks[k].append(v) # callback[name].append(func)
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