72 lines
2.5 KiB
Python
72 lines
2.5 KiB
Python
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# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import pytest
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import torch
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import tests.helpers.utils as tutils
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from pytorch_lightning import Trainer
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from pytorch_lightning.utilities.seed import seed_everything
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.datamodules import ClassifDataModule
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from tests.helpers.runif import RunIf
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from tests.strategies.test_dp import CustomClassificationModelDP
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@pytest.mark.parametrize(
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"trainer_kwargs",
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(
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pytest.param(dict(gpus=1), marks=RunIf(min_gpus=1)),
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pytest.param(dict(strategy="dp", gpus=2), marks=RunIf(min_gpus=2)),
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pytest.param(dict(strategy="ddp_spawn", gpus=2), marks=RunIf(min_gpus=2)),
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),
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)
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def test_evaluate(tmpdir, trainer_kwargs):
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tutils.set_random_main_port()
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seed_everything(1)
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dm = ClassifDataModule()
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model = CustomClassificationModelDP()
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trainer = Trainer(
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default_root_dir=tmpdir, max_epochs=2, limit_train_batches=10, limit_val_batches=10, **trainer_kwargs
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)
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trainer.fit(model, datamodule=dm)
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assert "ckpt" in trainer.checkpoint_callback.best_model_path
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old_weights = model.layer_0.weight.clone().detach().cpu()
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trainer.validate(datamodule=dm)
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trainer.test(datamodule=dm)
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# make sure weights didn't change
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new_weights = model.layer_0.weight.clone().detach().cpu()
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torch.testing.assert_allclose(old_weights, new_weights)
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def test_model_parallel_setup_called(tmpdir):
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class TestModel(BoringModel):
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def __init__(self):
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super().__init__()
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self.configure_sharded_model_called = False
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self.layer = None
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def configure_sharded_model(self):
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self.configure_sharded_model_called = True
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self.layer = torch.nn.Linear(32, 2)
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model = TestModel()
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trainer = Trainer(default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, max_epochs=1)
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trainer.fit(model)
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assert model.configure_sharded_model_called
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