107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
"""Test deprecated functionality which will be removed in vX.Y.Z"""
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import random
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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.callbacks import GpuUsageLogger, LearningRateLogger
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from tests.base import EvalModelTemplate
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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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def test_tbd_remove_in_v0_11_0_trainer():
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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LearningRateLogger()
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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trainer = Trainer(row_log_interval=8)
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assert trainer.log_every_n_steps == 8
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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assert trainer.row_log_interval == 8
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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trainer = Trainer(log_save_interval=9)
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assert trainer.flush_logs_every_n_steps == 9
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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assert trainer.log_save_interval == 9
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
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def test_tbd_remove_in_v0_11_0_trainer_gpu():
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with pytest.deprecated_call(match='will be removed in v0.11.0'):
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GpuUsageLogger()
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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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# def test_tbd_remove_in_v1_0_0_model_hooks():
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#
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# model = ModelVer0_6()
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#
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# with pytest.deprecated_call(match='will be removed in v1.0. Use `test_epoch_end` instead'):
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# trainer = Trainer(logger=False)
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# trainer.test(model)
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# assert trainer.logger_connector.callback_metrics == {'test_loss': torch.tensor(0.6)}
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#
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# with pytest.deprecated_call(match='will be removed in v1.0. Use `validation_epoch_end` instead'):
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# trainer = Trainer(logger=False)
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# # TODO: why `dataloder` is required if it is not used
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# result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
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# assert result[0] == {'val_loss': torch.tensor(0.6)}
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#
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# model = ModelVer0_7()
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#
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# with pytest.deprecated_call(match='will be removed in v1.0. Use `test_epoch_end` instead'):
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# trainer = Trainer(logger=False)
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# trainer.test(model)
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# assert trainer.logger_connector.callback_metrics == {'test_loss': torch.tensor(0.7)}
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#
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# with pytest.deprecated_call(match='will be removed in v1.0. Use `validation_epoch_end` instead'):
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# trainer = Trainer(logger=False)
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# # TODO: why `dataloder` is required if it is not used
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# result = trainer._evaluate(model, dataloaders=[[None]], max_batches=1)
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# assert result[0] == {'val_loss': torch.tensor(0.7)}
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