50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
# 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 os
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import pytest
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from pytorch_lightning import Trainer
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from tests.helpers.advanced_models import BasicGAN, ParityModuleMNIST, ParityModuleRNN
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from tests.helpers.boring_model import BoringModel
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from tests.helpers.datamodules import ClassifDataModule, RegressDataModule
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from tests.helpers.simple_models import ClassificationModel, RegressionModel
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@pytest.mark.parametrize(
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"data_class,model_class",
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[
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(None, BoringModel),
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(None, BasicGAN),
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(None, ParityModuleRNN),
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(None, ParityModuleMNIST),
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(ClassifDataModule, ClassificationModel),
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(RegressDataModule, RegressionModel),
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],
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)
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def test_models(tmpdir, data_class, model_class):
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"""Test simple models."""
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dm = data_class() if data_class else data_class
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model = model_class()
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trainer = Trainer(default_root_dir=tmpdir, max_epochs=1)
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trainer.fit(model, datamodule=dm)
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if dm is not None:
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trainer.test(model, datamodule=dm)
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model.to_torchscript()
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if data_class:
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model.to_onnx(os.path.join(tmpdir, "my-model.onnx"), input_sample=dm.sample)
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