lightning/tests/accelerators/test_common.py

import pytest
import torch

import tests.helpers.utils as tutils
from pytorch_lightning import Trainer
from tests.accelerators.test_dp import CustomClassificationModelDP
from tests.helpers.datamodules import ClassifDataModule
from tests.helpers.runif import RunIf


@pytest.mark.parametrize(
    "trainer_kwargs", (
        pytest.param(dict(gpus=1), marks=RunIf(min_gpus=1)),
        pytest.param(dict(accelerator="dp", gpus=2), marks=RunIf(min_gpus=2)),
        pytest.param(dict(accelerator="ddp_spawn", gpus=2), marks=RunIf(min_gpus=2)),
    )
)
def test_evaluate(tmpdir, trainer_kwargs):
    tutils.set_random_master_port()

    dm = ClassifDataModule()
    model = CustomClassificationModelDP()
    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=2,
        limit_train_batches=10,
        limit_val_batches=10,
        deterministic=True,
        **trainer_kwargs
    )

    result = trainer.fit(model, datamodule=dm)
    assert result
    assert 'ckpt' in trainer.checkpoint_callback.best_model_path

    old_weights = model.layer_0.weight.clone().detach().cpu()

    result = trainer.validate(datamodule=dm)
    assert result[0]['val_acc'] > 0.55

    result = trainer.test(datamodule=dm)
    assert result[0]['test_acc'] > 0.55

    # make sure weights didn't change
    new_weights = model.layer_0.weight.clone().detach().cpu()
    torch.testing.assert_allclose(old_weights, new_weights)