lightning/tests/loggers/test_base.py

import pickle
from typing import Optional
from unittest.mock import MagicMock

import numpy as np

from pytorch_lightning import Trainer
from pytorch_lightning.loggers import LightningLoggerBase, LoggerCollection
from pytorch_lightning.utilities import rank_zero_only
from tests.base import EvalModelTemplate


def test_logger_collection():
    mock1 = MagicMock()
    mock2 = MagicMock()

    logger = LoggerCollection([mock1, mock2])

    assert logger[0] == mock1
    assert logger[1] == mock2

    assert logger.experiment[0] == mock1.experiment
    assert logger.experiment[1] == mock2.experiment

    assert logger.save_dir is None

    logger.update_agg_funcs({'test': np.mean}, np.sum)
    mock1.update_agg_funcs.assert_called_once_with({'test': np.mean}, np.sum)
    mock2.update_agg_funcs.assert_called_once_with({'test': np.mean}, np.sum)

    logger.agg_and_log_metrics({'test': 2.0}, 4)
    mock1.agg_and_log_metrics.assert_called_once_with({'test': 2.0}, 4)
    mock2.agg_and_log_metrics.assert_called_once_with({'test': 2.0}, 4)

    logger.close()
    mock1.close.assert_called_once()
    mock2.close.assert_called_once()


class CustomLogger(LightningLoggerBase):
    def __init__(self):
        super().__init__()
        self.hparams_logged = None
        self.metrics_logged = None
        self.finalized = False

    @property
    def experiment(self):
        return 'test'

    @rank_zero_only
    def log_hyperparams(self, params):
        self.hparams_logged = params

    @rank_zero_only
    def log_metrics(self, metrics, step):
        self.metrics_logged = metrics

    @rank_zero_only
    def finalize(self, status):
        self.finalized_status = status

    @property
    def save_dir(self) -> Optional[str]:
        """
        Return the root directory where experiment logs get saved, or `None` if the logger does not
        save data locally.
        """
        return None

    @property
    def name(self):
        return "name"

    @property
    def version(self):
        return "1"


def test_custom_logger(tmpdir):
    hparams = EvalModelTemplate.get_default_hparams()
    model = EvalModelTemplate(**hparams)

    logger = CustomLogger()

    trainer = Trainer(
        max_epochs=1,
        limit_train_batches=0.05,
        logger=logger,
        default_root_dir=tmpdir,
    )
    result = trainer.fit(model)
    assert result == 1, "Training failed"
    assert logger.hparams_logged == hparams
    assert logger.metrics_logged != {}
    assert logger.finalized_status == "success"


def test_multiple_loggers(tmpdir):
    hparams = EvalModelTemplate.get_default_hparams()
    model = EvalModelTemplate(**hparams)

    logger1 = CustomLogger()
    logger2 = CustomLogger()

    trainer = Trainer(
        max_epochs=1,
        limit_train_batches=0.05,
        logger=[logger1, logger2],
        default_root_dir=tmpdir,
    )
    result = trainer.fit(model)
    assert result == 1, "Training failed"

    assert logger1.hparams_logged == hparams
    assert logger1.metrics_logged != {}
    assert logger1.finalized_status == "success"

    assert logger2.hparams_logged == hparams
    assert logger2.metrics_logged != {}
    assert logger2.finalized_status == "success"


def test_multiple_loggers_pickle(tmpdir):
    """Verify that pickling trainer with multiple loggers works."""

    logger1 = CustomLogger()
    logger2 = CustomLogger()

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=1,
        logger=[logger1, logger2],
    )
    pkl_bytes = pickle.dumps(trainer)
    trainer2 = pickle.loads(pkl_bytes)
    trainer2.logger.log_metrics({"acc": 1.0}, 0)

    assert logger1.metrics_logged != {}
    assert logger2.metrics_logged != {}


def test_adding_step_key(tmpdir):
    logged_step = 0

    def _validation_epoch_end(outputs):
        nonlocal logged_step
        logged_step += 1
        return {"log": {"step": logged_step, "val_acc": logged_step / 10}}

    def _training_epoch_end(outputs):
        nonlocal logged_step
        logged_step += 1
        return {"log": {"step": logged_step, "train_acc": logged_step / 10}}

    def _log_metrics_decorator(log_metrics_fn):
        def decorated(metrics, step):
            if "val_acc" in metrics:
                assert step == logged_step
            return log_metrics_fn(metrics, step)

        return decorated

    model = EvalModelTemplate()
    model.validation_epoch_end = _validation_epoch_end
    model.training_epoch_end = _training_epoch_end
    trainer = Trainer(
        max_epochs=3,
        default_root_dir=tmpdir,
        limit_train_batches=0.1,
        limit_val_batches=0.1,
        num_sanity_val_steps=0,
    )
    trainer.logger.log_metrics = _log_metrics_decorator(
        trainer.logger.log_metrics)
    trainer.fit(model)


def test_with_accumulate_grad_batches():
    """Checks if the logging is performed once for `accumulate_grad_batches` steps."""

    class StoreHistoryLogger(CustomLogger):
        def __init__(self):
            super().__init__()
            self.history = {}

        @rank_zero_only
        def log_metrics(self, metrics, step):
            if step not in self.history:
                self.history[step] = {}
            self.history[step].update(metrics)

    logger = StoreHistoryLogger()

    np.random.seed(42)
    for i, loss in enumerate(np.random.random(10)):
        logger.agg_and_log_metrics({'loss': loss}, step=int(i / 5))

    assert logger.history == {0: {'loss': 0.5623850983416314}}
    logger.close()
    assert logger.history == {0: {'loss': 0.5623850983416314}, 1: {'loss': 0.4778883735637184}}