lightning/tests/callbacks/test_early_stopping.py

import pickle

import cloudpickle
import pytest
import torch

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from tests.base import EvalModelTemplate


def test_resume_early_stopping_from_checkpoint(tmpdir):
    """
    Prevent regressions to bugs:
    https://github.com/PyTorchLightning/pytorch-lightning/issues/1464
    https://github.com/PyTorchLightning/pytorch-lightning/issues/1463
    """

    class EarlyStoppingTestStore(EarlyStopping):
        def __init__(self, *args, **kwargs):
            super().__init__(*args, **kwargs)
            # cache the state for each epoch
            self.saved_states = []

        def on_validation_end(self, trainer, pl_module):
            super().on_validation_end(trainer, pl_module)
            self.saved_states.append(self.state_dict().copy())

    class EarlyStoppingTestRestore(EarlyStopping):
        def __init__(self, expected_state):
            super().__init__()
            self.expected_state = expected_state

        def on_train_start(self, trainer, pl_module):
            assert self.state_dict() == self.expected_state

    model = EvalModelTemplate()
    checkpoint_callback = ModelCheckpoint(save_top_k=1)
    early_stop_callback = EarlyStoppingTestStore()
    trainer = Trainer(
        default_root_dir=tmpdir,
        checkpoint_callback=checkpoint_callback,
        early_stop_callback=early_stop_callback,
        max_epochs=4,
    )
    trainer.fit(model)

    checkpoint_filepath = checkpoint_callback.kth_best_model
    # ensure state is persisted properly
    checkpoint = torch.load(checkpoint_filepath)
    # the checkpoint saves "epoch + 1"
    early_stop_callback_state = early_stop_callback.saved_states[checkpoint['epoch'] - 1]
    assert 4 == len(early_stop_callback.saved_states)
    assert checkpoint['early_stop_callback_state_dict'] == early_stop_callback_state

    # ensure state is reloaded properly (assertion in the callback)
    early_stop_callback = EarlyStoppingTestRestore(early_stop_callback_state)
    new_trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=2,
        resume_from_checkpoint=checkpoint_filepath,
        early_stop_callback=early_stop_callback,
    )
    new_trainer.fit(model)


def test_early_stopping_no_extraneous_invocations(tmpdir):
    """Test to ensure that callback methods aren't being invoked outside of the callback handler."""
    class EarlyStoppingTestInvocations(EarlyStopping):
        def __init__(self, expected_count):
            super().__init__()
            self.count = 0
            self.expected_count = expected_count

        def on_validation_end(self, trainer, pl_module):
            self.count += 1

        def on_train_end(self, trainer, pl_module):
            assert self.count == self.expected_count

    model = EvalModelTemplate()
    expected_count = 4
    early_stop_callback = EarlyStoppingTestInvocations(expected_count)
    trainer = Trainer(
        default_root_dir=tmpdir,
        early_stop_callback=early_stop_callback,
        val_check_interval=1.0,
        max_epochs=expected_count,
    )
    trainer.fit(model)


@pytest.mark.parametrize('loss_values, patience, expected_stop_epoch', [
    ([6, 5, 5, 5, 5, 5], 3, 4),
    ([6, 5, 4, 4, 3, 3], 1, 3),
    ([6, 5, 6, 5, 5, 5], 3, 4),
])
def test_early_stopping_patience(tmpdir, loss_values, patience, expected_stop_epoch):
    """Test to ensure that early stopping is not triggered before patience is exhausted."""

    class ModelOverrideValidationReturn(EvalModelTemplate):
        validation_return_values = torch.Tensor(loss_values)
        count = 0

        def validation_epoch_end(self, outputs):
            loss = self.validation_return_values[self.count]
            self.count += 1
            return {"test_val_loss": loss}

    model = ModelOverrideValidationReturn()
    early_stop_callback = EarlyStopping(monitor="test_val_loss", patience=patience, verbose=True)
    trainer = Trainer(
        default_root_dir=tmpdir,
        early_stop_callback=early_stop_callback,
        val_check_interval=1.0,
        num_sanity_val_steps=0,
        max_epochs=10,
    )
    trainer.fit(model)
    assert trainer.current_epoch == expected_stop_epoch


def test_pickling(tmpdir):
    early_stopping = EarlyStopping()

    early_stopping_pickled = pickle.dumps(early_stopping)
    early_stopping_loaded = pickle.loads(early_stopping_pickled)
    assert vars(early_stopping) == vars(early_stopping_loaded)

    early_stopping_pickled = cloudpickle.dumps(early_stopping)
    early_stopping_loaded = cloudpickle.loads(early_stopping_pickled)
    assert vars(early_stopping) == vars(early_stopping_loaded)


def test_early_stopping_no_val_step(tmpdir):
    """Test that early stopping callback falls back to training metrics when no validation defined."""

    class CurrentModel(EvalModelTemplate):
        def training_step(self, *args, **kwargs):
            output = super().training_step(*args, **kwargs)
            output.update({'my_train_metric': output['loss']})  # could be anything else
            return output

    model = CurrentModel()
    model.validation_step = None
    model.val_dataloader = None

    stopping = EarlyStopping(monitor='my_train_metric', min_delta=0.1)
    trainer = Trainer(
        default_root_dir=tmpdir,
        early_stop_callback=stopping,
        overfit_batches=0.20,
        max_epochs=2,
    )
    result = trainer.fit(model)

    assert result == 1, 'training failed to complete'
    assert trainer.current_epoch < trainer.max_epochs


def test_early_stopping_functionality(tmpdir):

    class CurrentModel(EvalModelTemplate):
        def validation_epoch_end(self, outputs):
            losses = [8, 4, 2, 3, 4, 5, 8, 10]
            val_loss = losses[self.current_epoch]
            return {'val_loss': torch.tensor(val_loss)}

    model = CurrentModel()

    trainer = Trainer(
        default_root_dir=tmpdir,
        early_stop_callback=True,
        overfit_batches=0.20,
        max_epochs=20,
    )
    trainer.fit(model)
    assert trainer.current_epoch == 5, 'early_stopping failed'