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prune unused methods (#5860)

This commit is contained in:
Jirka Borovec 2021-02-08 02:31:44 +01:00 committed by GitHub
parent 7f8fdda9a2
commit 26bc754cc1
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7 changed files with 2 additions and 118 deletions
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1
Makefile
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@ -9,6 +9,7 @@ clean:
# clean all temp runs
rm -rf $(shell find . -name "mlruns")
rm -rf $(shell find . -name "lightning_log")
rm -rf $(shell find . -name "lightning_logs")
rm -rf _ckpt_*
rm -rf .mypy_cache
rm -rf .pytest_cache

15
tests/base/model_optimizers.py
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@ -41,16 +41,6 @@ class ConfigureOptimizersPool(ABC):
optimizer = optim.Adagrad(self.parameters(), lr=self.learning_rate)
return optimizer
def configure_optimizers__multiple_optimizers(self):
"""
return whatever optimizers we want here.
:return: list of optimizers
"""
# try no scheduler for this model (testing purposes)
optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
return optimizer1, optimizer2
def configure_optimizers__multiple_optimizers_frequency(self):
optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
@ -70,11 +60,6 @@ class ConfigureOptimizersPool(ABC):
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
return [optimizer], [lr_scheduler]
def configure_optimizers__onecycle_scheduler(self):
optimizer = optim.SGD(self.parameters(), lr=self.learning_rate, momentum=0.9)
lr_scheduler = optim.lr_scheduler.OneCycleLR(optimizer, max_lr=self.learning_rate, total_steps=10_000)
return [optimizer], [lr_scheduler]
def configure_optimizers__multiple_schedulers(self):
optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)

6
tests/base/model_test_dataloaders.py
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@ -35,9 +35,3 @@ class TestDataloaderVariations(ABC):
lengths = [50, 30, 40]
dataloaders = [self.dataloader(train=False, num_samples=n) for n in lengths]
return dataloaders
def test_dataloader__empty(self):
return None
def test_dataloader__multiple(self):
return [self.dataloader(train=False), self.dataloader(train=False)]

6
tests/base/model_train_dataloaders.py
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@ -43,9 +43,3 @@ class TrainDataloaderVariations(ABC):
'a': self.dataloader(train=True, num_samples=100),
'b': self.dataloader(train=True, num_samples=50),
}
def train_dataloader__multiple_sequence(self):
return [
self.dataloader(train=True, num_samples=100),
self.dataloader(train=True, num_samples=50),
]

66
tests/base/model_train_steps.py
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@ -62,72 +62,6 @@ class TrainingStepVariations(ABC):
output /= 0
return output
def training_step_end_full_loop_result_obj_dp(self, result):
"""
Full loop flow train step (result obj + dp)
"""
result.minimize = result.minimize.mean()
result.checkpoint_on = result.checkpoint_on.mean()
result.train_step_metric = result.train_step_metric.mean()
result.log('train_step_end_metric', 1)
self.training_step_end_called = True
return result
def training_epoch_end_full_loop_result_obj_dp(self, result):
"""
Full loop flow train step (result obj + dp)
"""
result.log('train_epoch_end_metric', 1, on_epoch=True)
self.training_epoch_end_called = True
return result
def eval_step_end_full_loop_result_obj_dp(self, result):
"""
Full loop flow train step (result obj + dp)
"""
eval_name = 'validation' if not self.trainer.testing else 'test'
reduced = getattr(result, f'{eval_name}_step_metric_step').mean()
setattr(result, f'{eval_name}_step_metric_step', reduced)
reduced = getattr(result, f'{eval_name}_step_metric_epoch').mean()
setattr(result, f'{eval_name}_step_metric_epoch', reduced)
reduced = getattr(result, f'{eval_name}_step_metric').mean()
setattr(result, f'{eval_name}_step_metric', reduced)
result.checkpoint_on = result.checkpoint_on.mean()
result.early_stop_on = result.early_stop_on.mean()
result.log(f'{eval_name}_step_end_metric', torch.tensor(1).type_as(result.checkpoint_on))
setattr(self, f'{eval_name}_step_end_called', True)
return result
def eval_epoch_end_full_loop_result_obj_dp(self, result):
"""
Full loop flow train step (result obj + dp)
"""
eval_name = 'validation' if not self.trainer.testing else 'test'
result.log(f'{eval_name}_epoch_end_metric', torch.tensor(1).type_as(result.checkpoint_on), on_epoch=True)
result.checkpoint_on = result.checkpoint_on.mean()
result.early_stop_on = result.early_stop_on.mean()
setattr(self, f'{eval_name}_epoch_end_called', True)
# reduce the parametrized values
reduced = getattr(result, f'{eval_name}_step_metric_step').mean()
setattr(result, f'{eval_name}_step_metric_step', reduced)
reduced = getattr(result, f'{eval_name}_step_metric_epoch').mean()
setattr(result, f'{eval_name}_step_metric_epoch', reduced)
reduced = getattr(result, f'{eval_name}_step_end_metric').mean()
setattr(result, f'{eval_name}_step_end_metric', reduced)
reduced = getattr(result, f'{eval_name}_step_metric').mean()
setattr(result, f'{eval_name}_step_metric', reduced)
return result
def training_step__multiple_dataloaders(self, batch, batch_idx, optimizer_idx=None):
"""Training step for multiple train loaders"""

24
tests/base/model_valid_epoch_ends.py
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@ -21,30 +21,6 @@ class ValidationEpochEndVariations(ABC):
Houses all variations of validation_epoch_end steps
"""
def validation_epoch_end_no_monitor(self, outputs):
"""
Called at the end of validation to aggregate outputs
Args:
outputs: list of individual outputs of each validation step
"""
# if returned a scalar from validation_step, outputs is a list of tensor scalars
# we return just the average in this case (if we want)
def _mean(res, key):
# recursive mean for multilevel dicts
return torch.stack([x[key] if isinstance(x, dict) else _mean(x, key) for x in res]).mean()
val_acc_mean = _mean(outputs, 'val_acc')
# alternate between tensor and scalar
if self.current_epoch % 2 == 0:
val_acc_mean = val_acc_mean.item()
metrics_dict = {'val_acc': val_acc_mean}
results = {'progress_bar': metrics_dict, 'log': metrics_dict}
return results
def validation_epoch_end(self, outputs):
"""
Called at the end of validation to aggregate outputs

2
tests/trainer/test_dataloaders.py
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@ -143,7 +143,7 @@ def test_multiple_test_dataloader(tmpdir, ckpt_path):
class MultipleTestDataloaderModel(EvalModelTemplate):
def test_dataloader(self):
return model_template.test_dataloader__multiple()
return [self.dataloader(train=False), self.dataloader(train=False)]
def test_step(self, batch, batch_idx, *args, **kwargs):
return model_template.test_step__multiple_dataloaders(batch, batch_idx, *args, **kwargs)