94 lines
2.8 KiB
Python
94 lines
2.8 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 importlib
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import platform
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from unittest import mock
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import pytest
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import torch
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from pl_examples import _DALI_AVAILABLE
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ARGS_DEFAULT = """
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--trainer.default_root_dir %(tmpdir)s \
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--trainer.max_epochs 1 \
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--trainer.limit_train_batches 2 \
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--trainer.limit_val_batches 2 \
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--data.batch_size 32 \
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"""
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ARGS_GPU = ARGS_DEFAULT + """
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--trainer.gpus 1 \
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"""
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ARGS_DP = ARGS_DEFAULT + """
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--trainer.gpus 2 \
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--trainer.accelerator dp \
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"""
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ARGS_AMP = """
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--trainer.precision 16 \
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"""
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@pytest.mark.parametrize(
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'import_cli', [
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'pl_examples.basic_examples.simple_image_classifier',
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'pl_examples.basic_examples.backbone_image_classifier',
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'pl_examples.basic_examples.autoencoder',
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]
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)
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@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
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@pytest.mark.parametrize('cli_args', [ARGS_DP, ARGS_DP + ARGS_AMP])
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def test_examples_dp(tmpdir, import_cli, cli_args):
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module = importlib.import_module(import_cli)
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# update the temp dir
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cli_args = cli_args % {'tmpdir': tmpdir}
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
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module.cli_main()
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@pytest.mark.parametrize(
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'import_cli', [
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'pl_examples.basic_examples.simple_image_classifier',
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'pl_examples.basic_examples.backbone_image_classifier',
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'pl_examples.basic_examples.autoencoder',
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]
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)
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@pytest.mark.parametrize('cli_args', [ARGS_DEFAULT])
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def test_examples_cpu(tmpdir, import_cli, cli_args):
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module = importlib.import_module(import_cli)
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# update the temp dir
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cli_args = cli_args % {'tmpdir': tmpdir}
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
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module.cli_main()
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@pytest.mark.skipif(not _DALI_AVAILABLE, reason="Nvidia DALI required")
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.skipif(platform.system() != 'Linux', reason='Only applies to Linux platform.')
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@pytest.mark.parametrize('cli_args', [ARGS_GPU])
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def test_examples_mnist_dali(tmpdir, cli_args):
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from pl_examples.basic_examples.dali_image_classifier import cli_main
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# update the temp dir
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cli_args = cli_args % {'tmpdir': tmpdir}
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args.strip().split()):
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cli_main()
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