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* remove the need for hparams * remove the need for hparams * remove the need for hparams * remove the need for hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * replace self.hparams * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * fixed * finished moco * basic * testing * todo * recurse * hparams * persist * hparams * chlog * tests * tests * tests * tests * tests * tests * review * saving * tests * tests * tests * docs * finished moco * hparams * review * Apply suggestions from code review Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * hparams * overwrite * transform * transform * transform * transform * cleaning * cleaning * tests * examples * examples * examples * Apply suggestions from code review Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> * chp key * tests * Apply suggestions from code review * class * updated docs * updated docs * updated docs * updated docs * save * wip * fix * flake8 Co-authored-by: Jirka <jirka@pytorchlightning.ai> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import pytest
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import torch
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from pytorch_lightning import Trainer
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from tests.base import EvalModelTemplate
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@pytest.mark.parametrize('max_steps', [1, 2, 3])
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def test_on_before_zero_grad_called(max_steps):
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class CurrentTestModel(EvalModelTemplate):
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on_before_zero_grad_called = 0
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def on_before_zero_grad(self, optimizer):
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self.on_before_zero_grad_called += 1
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model = CurrentTestModel()
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trainer = Trainer(
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max_steps=max_steps,
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num_sanity_val_steps=5,
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)
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assert 0 == model.on_before_zero_grad_called
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trainer.fit(model)
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assert max_steps == model.on_before_zero_grad_called
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model.on_before_zero_grad_called = 0
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trainer.test(model)
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assert 0 == model.on_before_zero_grad_called
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def test_training_epoch_end_metrics_collection(tmpdir):
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""" Test that progress bar metrics also get collected at the end of an epoch. """
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num_epochs = 3
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class CurrentModel(EvalModelTemplate):
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def training_step(self, *args, **kwargs):
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output = super().training_step(*args, **kwargs)
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output['progress_bar'].update({'step_metric': torch.tensor(-1)})
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output['progress_bar'].update({'shared_metric': 100})
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return output
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def training_epoch_end(self, outputs):
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epoch = self.current_epoch
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# both scalar tensors and Python numbers are accepted
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return {
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'progress_bar': {
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f'epoch_metric_{epoch}': torch.tensor(epoch), # add a new metric key every epoch
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'shared_metric': 111,
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}
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}
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model = CurrentModel()
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trainer = Trainer(
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max_epochs=num_epochs,
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default_root_dir=tmpdir,
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overfit_pct=0.1,
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)
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result = trainer.fit(model)
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assert result == 1
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metrics = trainer.progress_bar_dict
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# metrics added in training step should be unchanged by epoch end method
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assert metrics['step_metric'] == -1
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# a metric shared in both methods gets overwritten by epoch_end
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assert metrics['shared_metric'] == 111
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# metrics are kept after each epoch
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for i in range(num_epochs):
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assert metrics[f'epoch_metric_{i}'] == i
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