161 lines
6.8 KiB
Python
161 lines
6.8 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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"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import sys
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import pytest
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import torch
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from pytorch_lightning import Trainer
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from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
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from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
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from tests.base import BoringModel
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from tests.deprecated_api import _soft_unimport_module
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def test_v1_4_0_deprecated_imports():
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_soft_unimport_module('pytorch_lightning.utilities.argparse_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.argparse_utils import from_argparse_args # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.model_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.model_utils import is_overridden # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.warning_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.warning_utils import WarningCache # noqa: F811 F401
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_soft_unimport_module('pytorch_lightning.utilities.xla_device_utils')
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with pytest.deprecated_call(match='will be removed in v1.4'):
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from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils # noqa: F811 F401
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def test_v1_4_0_deprecated_trainer_device_distrib():
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"""Test that Trainer attributes works fine."""
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trainer = Trainer()
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trainer._distrib_type = None
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trainer._device_type = None
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_cpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_cpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_gpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_gpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.on_tpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.on_tpu
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trainer._device_type = None
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_tpu = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_tpu
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_dp = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_dp
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_ddp = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_ddp
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_ddp2 = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_ddp2
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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trainer.use_horovod = True
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with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
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assert trainer.use_horovod
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def test_v1_4_0_deprecated_metrics():
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from pytorch_lightning.metrics.functional.classification import stat_scores_multiple_classes
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with pytest.deprecated_call(match='will be removed in v1.4'):
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stat_scores_multiple_classes(pred=torch.tensor([0, 1]), target=torch.tensor([0, 1]))
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from pytorch_lightning.metrics.functional.classification import iou
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with pytest.deprecated_call(match='will be removed in v1.4'):
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iou(torch.randint(0, 2, (10, 3, 3)),
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torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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recall(torch.randint(0, 2, (10, 3, 3)),
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torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import precision
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision(torch.randint(0, 2, (10, 3, 3)),
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torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional.classification import precision_recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision_recall(torch.randint(0, 2, (10, 3, 3)),
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torch.randint(0, 2, (10, 3, 3)))
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from pytorch_lightning.metrics.functional import precision
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# Testing deprecation of class_reduction arg in the *new* precision
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with pytest.deprecated_call(match='will be removed in v1.4'):
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precision(torch.randint(0, 2, (10,)),
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torch.randint(0, 2, (10,)),
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class_reduction='micro')
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from pytorch_lightning.metrics.functional import recall
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# Testing deprecation of class_reduction arg in the *new* recall
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with pytest.deprecated_call(match='will be removed in v1.4'):
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recall(torch.randint(0, 2, (10,)),
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torch.randint(0, 2, (10,)),
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class_reduction='micro')
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class CustomDDPPlugin(DDPPlugin):
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def configure_ddp(self, model, device_ids):
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# old, deprecated implementation
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with pytest.deprecated_call(
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match='`LightningDistributedDataParallel` is deprecated since v1.2 and will be removed in v1.4.'
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):
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model = LightningDistributedDataParallel(
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module=model,
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device_ids=device_ids,
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**self._ddp_kwargs,
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)
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return model
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
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def test_v1_4_0_deprecated_lightning_distributed_data_parallel(tmpdir):
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model = BoringModel()
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trainer = Trainer(
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default_root_dir=tmpdir,
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fast_dev_run=True,
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gpus=2,
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accelerator="ddp_spawn",
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plugins=[CustomDDPPlugin()]
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)
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trainer.fit(model)
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