def test_trainer_callback_hook_system_validate(tmpdir):
"""Test the callback hook system for validate."""
model = BoringModel()
callback_mock = MagicMock()
trainer = Trainer(
default_root_dir=tmpdir,
callbacks=[callback_mock],
max_epochs=1,
limit_val_batches=2,
progress_bar_refresh_rate=0,
)
trainer.validate(model)
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
call.on_before_accelerator_backend_setup(trainer, model),
call.setup(trainer, model, 'validate'),
call.on_configure_sharded_model(trainer, model),
call.on_validation_start(trainer, model),
call.on_epoch_start(trainer, model),
call.on_validation_epoch_start(trainer, model),
call.on_validation_batch_start(trainer, model, ANY, 0, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_validation_batch_start(trainer, model, ANY, 1, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 1, 0),
call.on_validation_epoch_end(trainer, model),
call.on_epoch_end(trainer, model),
call.on_validation_end(trainer, model),
call.teardown(trainer, model, 'validate'),
]
def assert_expected_calls(_trainer, model_callback, trainer_callback):
# some methods in callbacks configured through model won't get called
uncalled_methods = [call.on_init_start(_trainer), call.on_init_end(_trainer)]
for uncalled in uncalled_methods:
assert uncalled not in model_callback.method_calls
# assert that the rest of calls are the same as for trainer callbacks
expected_calls = [m for m in trainer_callback.method_calls if m not in uncalled_methods]
assert expected_calls
assert model_callback.method_calls == expected_calls
def test_trainer_callback_system(torch_save):
"""Test the callback system."""
model = BoringModel()
callback_mock = MagicMock()
trainer_options = dict(
callbacks=[callback_mock],
max_epochs=1,
limit_val_batches=1,
limit_train_batches=3,
limit_test_batches=2,
progress_bar_refresh_rate=0,
)
# no call yet
callback_mock.assert_not_called()
# fit model
trainer = Trainer(**trainer_options)
# check that only the to calls exists
assert trainer.callbacks[0] == callback_mock
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
]
trainer.fit(model)
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
call.setup(trainer, model, 'fit'),
call.on_fit_start(trainer, model),
call.on_pretrain_routine_start(trainer, model),
call.on_pretrain_routine_end(trainer, model),
call.on_sanity_check_start(trainer, model),
call.on_validation_start(trainer, model),
call.on_validation_epoch_start(trainer, model),
call.on_validation_batch_start(trainer, model, ANY, 0, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_validation_epoch_end(trainer, model),
call.on_validation_end(trainer, model),
call.on_sanity_check_end(trainer, model),
call.on_train_start(trainer, model),
call.on_epoch_start(trainer, model),
call.on_train_epoch_start(trainer, model),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 0, 0),
call.on_after_backward(trainer, model),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_batch_end(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 1, 0),
call.on_after_backward(trainer, model),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_batch_end(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 1, 0),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 2, 0),
call.on_after_backward(trainer, model),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_batch_end(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 2, 0),
call.on_validation_start(trainer, model),
call.on_validation_epoch_start(trainer, model),
call.on_validation_batch_start(trainer, model, ANY, 0, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_validation_epoch_end(trainer, model),
call.on_validation_end(trainer, model),
call.on_save_checkpoint(trainer, model),
call.on_epoch_end(trainer, model),
call.on_train_epoch_end(trainer, model, ANY),
call.on_train_end(trainer, model),
call.on_fit_end(trainer, model),
call.teardown(trainer, model, 'fit'),
]
callback_mock.reset_mock()
trainer = Trainer(**trainer_options)
trainer.test(model)
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
call.setup(trainer, model, 'test'),
call.on_fit_start(trainer, model),
call.on_test_start(trainer, model),
call.on_test_epoch_start(trainer, model),
call.on_test_batch_start(trainer, model, ANY, 0, 0),
call.on_test_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_test_batch_start(trainer, model, ANY, 1, 0),
call.on_test_batch_end(trainer, model, ANY, ANY, 1, 0),
call.on_test_epoch_end(trainer, model),
call.on_test_end(trainer, model),
call.on_fit_end(trainer, model),
call.teardown(trainer, model, 'fit'),
call.teardown(trainer, model, 'test'),
]
def test_trainer_callback_hook_system_fit(_, tmpdir):
"""Test the callback hook system for fit."""
model = BoringModel()
callback_mock = MagicMock()
trainer = Trainer(
default_root_dir=tmpdir,
callbacks=[callback_mock],
max_epochs=1,
limit_val_batches=1,
limit_train_batches=3,
progress_bar_refresh_rate=0,
)
# check that only the to calls exists
assert trainer.callbacks[0] == callback_mock
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
]
# fit model
trainer.fit(model)
assert callback_mock.method_calls == [
call.on_init_start(trainer),
call.on_init_end(trainer),
call.on_before_accelerator_backend_setup(trainer, model),
call.setup(trainer, model, 'fit'),
call.on_configure_sharded_model(trainer, model),
call.on_fit_start(trainer, model),
call.on_pretrain_routine_start(trainer, model),
call.on_pretrain_routine_end(trainer, model),
call.on_sanity_check_start(trainer, model),
call.on_validation_start(trainer, model),
call.on_epoch_start(trainer, model),
call.on_validation_epoch_start(trainer, model),
call.on_validation_batch_start(trainer, model, ANY, 0, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_validation_epoch_end(trainer, model),
call.on_epoch_end(trainer, model),
call.on_validation_end(trainer, model),
call.on_sanity_check_end(trainer, model),
call.on_train_start(trainer, model),
call.on_epoch_start(trainer, model),
call.on_train_epoch_start(trainer, model),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 0, 0),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_after_backward(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_batch_end(trainer, model),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 1, 0),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_after_backward(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 1, 0),
call.on_batch_end(trainer, model),
call.on_batch_start(trainer, model),
call.on_train_batch_start(trainer, model, ANY, 2, 0),
call.on_before_zero_grad(trainer, model, trainer.optimizers[0]),
call.on_after_backward(trainer, model),
call.on_train_batch_end(trainer, model, ANY, ANY, 2, 0),
call.on_batch_end(trainer, model),
call.on_train_epoch_end(trainer, model, ANY),
call.on_epoch_end(trainer, model),
call.on_validation_start(trainer, model),
call.on_epoch_start(trainer, model),
call.on_validation_epoch_start(trainer, model),
call.on_validation_batch_start(trainer, model, ANY, 0, 0),
call.on_validation_batch_end(trainer, model, ANY, ANY, 0, 0),
call.on_validation_epoch_end(trainer, model),
call.on_epoch_end(trainer, model),
call.on_validation_end(trainer, model),
call.on_save_checkpoint(
trainer,
model), # should take ANY but we are inspecting signature for BC
call.on_train_end(trainer, model),
call.on_fit_end(trainer, model),
call.teardown(trainer, model, 'fit'),
]