138 lines
4.9 KiB
Python
138 lines
4.9 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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from argparse import ArgumentParser
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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 pytorch_lightning import LightningModule, Trainer
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from pytorch_lightning.metrics.functional.classification import auc
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from pytorch_lightning.profiler.profilers import PassThroughProfiler, SimpleProfiler
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from tests.base import EvalModelTemplate
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def test_tbd_remove_in_v1_3_0(tmpdir):
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with pytest.deprecated_call(match='will no longer be supported in v1.3'):
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callback = ModelCheckpoint()
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Trainer(checkpoint_callback=callback, callbacks=[], default_root_dir=tmpdir)
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# Deprecate prefix
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with pytest.deprecated_call(match='will be removed in v1.3'):
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ModelCheckpoint(prefix='temp')
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# Deprecate auto mode
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with pytest.deprecated_call(match='will be removed in v1.3'):
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ModelCheckpoint(mode='auto')
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with pytest.deprecated_call(match='will be removed in v1.3'):
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EarlyStopping(mode='auto')
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with pytest.deprecated_call(match="The setter for self.hparams in LightningModule is deprecated"):
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class DeprecatedHparamsModel(LightningModule):
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def __init__(self, hparams):
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super().__init__()
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self.hparams = hparams
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DeprecatedHparamsModel({})
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def test_tbd_remove_in_v1_2_0():
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with pytest.deprecated_call(match='will be removed in v1.2'):
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checkpoint_cb = ModelCheckpoint(filepath='.')
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with pytest.deprecated_call(match='will be removed in v1.2'):
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checkpoint_cb = ModelCheckpoint('.')
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with pytest.raises(MisconfigurationException, match='inputs which are not feasible'):
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checkpoint_cb = ModelCheckpoint(filepath='.', dirpath='.')
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# TODO: remove bool from Trainer.profiler param in v1.3.0, update profiler_connector.py
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@pytest.mark.parametrize(['profiler', 'expected'], [
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(True, SimpleProfiler),
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(False, PassThroughProfiler),
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])
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def test_trainer_profiler_remove_in_v1_3_0(profiler, expected):
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with pytest.deprecated_call(match='will be removed in v1.3'):
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trainer = Trainer(profiler=profiler)
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assert isinstance(trainer.profiler, expected)
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@pytest.mark.parametrize(
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['cli_args', 'expected_parsed_arg', 'expected_profiler'],
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[
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('--profiler', True, SimpleProfiler),
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('--profiler True', True, SimpleProfiler),
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('--profiler False', False, PassThroughProfiler),
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],
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)
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def test_trainer_cli_profiler_remove_in_v1_3_0(cli_args, expected_parsed_arg, expected_profiler):
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cli_args = cli_args.split(' ')
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with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
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parser = ArgumentParser(add_help=False)
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parser = Trainer.add_argparse_args(parent_parser=parser)
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args = Trainer.parse_argparser(parser)
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assert getattr(args, "profiler") == expected_parsed_arg
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trainer = Trainer.from_argparse_args(args)
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assert isinstance(trainer.profiler, expected_profiler)
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def _soft_unimport_module(str_module):
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# once the module is imported e.g with parsing with pytest it lives in memory
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if str_module in sys.modules:
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del sys.modules[str_module]
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class ModelVer0_6(EvalModelTemplate):
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# todo: this shall not be needed while evaluate asks for dataloader explicitly
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def val_dataloader(self):
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return self.dataloader(train=False)
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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return {'val_loss': torch.tensor(0.6)}
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def validation_end(self, outputs):
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return {'val_loss': torch.tensor(0.6)}
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def test_dataloader(self):
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return self.dataloader(train=False)
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def test_end(self, outputs):
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return {'test_loss': torch.tensor(0.6)}
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class ModelVer0_7(EvalModelTemplate):
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# todo: this shall not be needed while evaluate asks for dataloader explicitly
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def val_dataloader(self):
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return self.dataloader(train=False)
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def validation_step(self, batch, batch_idx, *args, **kwargs):
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return {'val_loss': torch.tensor(0.7)}
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def validation_end(self, outputs):
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return {'val_loss': torch.tensor(0.7)}
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def test_dataloader(self):
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return self.dataloader(train=False)
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def test_end(self, outputs):
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return {'test_loss': torch.tensor(0.7)}
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