def test_loops_state_dict():
trainer = Trainer()
fit_loop = FitLoop()
fit_loop.trainer = trainer
state_dict = fit_loop.state_dict()
new_fit_loop = FitLoop()
new_fit_loop.trainer = trainer
new_fit_loop.load_state_dict(state_dict)
assert fit_loop.state_dict() == new_fit_loop.state_dict()
def test_loops_state_dict():
trainer = Trainer()
fit_loop = FitLoop()
with pytest.raises(MisconfigurationException, match="Loop FitLoop should be connected to a"):
fit_loop.trainer = object()
fit_loop.trainer = trainer
state_dict = fit_loop.state_dict()
new_fit_loop = FitLoop()
new_fit_loop.trainer = trainer
new_fit_loop.load_state_dict(state_dict)
assert fit_loop.state_dict() == new_fit_loop.state_dict()
def test_fit_loop_done_log_messages(caplog):
fit_loop = FitLoop()
trainer = Mock(spec=Trainer)
fit_loop.trainer = trainer
trainer.should_stop = False
trainer.num_training_batches = 5
assert not fit_loop.done
assert not caplog.messages
trainer.num_training_batches = 0
assert fit_loop.done
assert "No training batches" in caplog.text
caplog.clear()
trainer.num_training_batches = 5
epoch_loop = Mock()
epoch_loop.global_step = 10
fit_loop.connect(epoch_loop=epoch_loop)
fit_loop.max_steps = 10
assert fit_loop.done
assert "max_steps=10` reached" in caplog.text
caplog.clear()
fit_loop.max_steps = 20
fit_loop.epoch_progress.current.processed = 3
fit_loop.max_epochs = 3
trainer.should_stop = True
assert fit_loop.done
assert "max_epochs=3` reached" in caplog.text
caplog.clear()
fit_loop.max_epochs = 5
fit_loop.epoch_loop.min_steps = 0
with caplog.at_level(level=logging.DEBUG, logger="pytorch_lightning.utilities.rank_zero"):
assert fit_loop.done
assert "should_stop` was set" in caplog.text
fit_loop.epoch_loop.min_steps = 100
assert not fit_loop.done
assert "was signaled to stop but" in caplog.text