lightning/tests/deprecated_api/test_remove_1-4.py

161 lines
6.8 KiB
Python

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Test deprecated functionality which will be removed in vX.Y.Z"""
import sys
import pytest
import torch
from pytorch_lightning import Trainer
from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
from tests.base import BoringModel
from tests.deprecated_api import _soft_unimport_module
def test_v1_4_0_deprecated_imports():
_soft_unimport_module('pytorch_lightning.utilities.argparse_utils')
with pytest.deprecated_call(match='will be removed in v1.4'):
from pytorch_lightning.utilities.argparse_utils import from_argparse_args # noqa: F811 F401
_soft_unimport_module('pytorch_lightning.utilities.model_utils')
with pytest.deprecated_call(match='will be removed in v1.4'):
from pytorch_lightning.utilities.model_utils import is_overridden # noqa: F811 F401
_soft_unimport_module('pytorch_lightning.utilities.warning_utils')
with pytest.deprecated_call(match='will be removed in v1.4'):
from pytorch_lightning.utilities.warning_utils import WarningCache # noqa: F811 F401
_soft_unimport_module('pytorch_lightning.utilities.xla_device_utils')
with pytest.deprecated_call(match='will be removed in v1.4'):
from pytorch_lightning.utilities.xla_device_utils import XLADeviceUtils # noqa: F811 F401
def test_v1_4_0_deprecated_trainer_device_distrib():
"""Test that Trainer attributes works fine."""
trainer = Trainer()
trainer._distrib_type = None
trainer._device_type = None
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.on_cpu = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.on_cpu
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.on_gpu = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.on_gpu
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.on_tpu = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.on_tpu
trainer._device_type = None
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.use_tpu = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.use_tpu
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.use_dp = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.use_dp
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.use_ddp = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.use_ddp
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.use_ddp2 = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.use_ddp2
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
trainer.use_horovod = True
with pytest.deprecated_call(match='deprecated in v1.2 and will be removed in v1.4'):
assert trainer.use_horovod
def test_v1_4_0_deprecated_metrics():
from pytorch_lightning.metrics.functional.classification import stat_scores_multiple_classes
with pytest.deprecated_call(match='will be removed in v1.4'):
stat_scores_multiple_classes(pred=torch.tensor([0, 1]), target=torch.tensor([0, 1]))
from pytorch_lightning.metrics.functional.classification import iou
with pytest.deprecated_call(match='will be removed in v1.4'):
iou(torch.randint(0, 2, (10, 3, 3)),
torch.randint(0, 2, (10, 3, 3)))
from pytorch_lightning.metrics.functional.classification import recall
with pytest.deprecated_call(match='will be removed in v1.4'):
recall(torch.randint(0, 2, (10, 3, 3)),
torch.randint(0, 2, (10, 3, 3)))
from pytorch_lightning.metrics.functional.classification import precision
with pytest.deprecated_call(match='will be removed in v1.4'):
precision(torch.randint(0, 2, (10, 3, 3)),
torch.randint(0, 2, (10, 3, 3)))
from pytorch_lightning.metrics.functional.classification import precision_recall
with pytest.deprecated_call(match='will be removed in v1.4'):
precision_recall(torch.randint(0, 2, (10, 3, 3)),
torch.randint(0, 2, (10, 3, 3)))
from pytorch_lightning.metrics.functional import precision
# Testing deprecation of class_reduction arg in the *new* precision
with pytest.deprecated_call(match='will be removed in v1.4'):
precision(torch.randint(0, 2, (10,)),
torch.randint(0, 2, (10,)),
class_reduction='micro')
from pytorch_lightning.metrics.functional import recall
# Testing deprecation of class_reduction arg in the *new* recall
with pytest.deprecated_call(match='will be removed in v1.4'):
recall(torch.randint(0, 2, (10,)),
torch.randint(0, 2, (10,)),
class_reduction='micro')
class CustomDDPPlugin(DDPPlugin):
def configure_ddp(self, model, device_ids):
# old, deprecated implementation
with pytest.deprecated_call(
match='`LightningDistributedDataParallel` is deprecated since v1.2 and will be removed in v1.4.'
):
model = LightningDistributedDataParallel(
module=model,
device_ids=device_ids,
**self._ddp_kwargs,
)
return model
@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
def test_v1_4_0_deprecated_lightning_distributed_data_parallel(tmpdir):
model = BoringModel()
trainer = Trainer(
default_root_dir=tmpdir,
fast_dev_run=True,
gpus=2,
accelerator="ddp_spawn",
plugins=[CustomDDPPlugin()]
)
trainer.fit(model)