def test_lightning_setattr(tmpdir, model_cases):
"""Test that the lightning_setattr works in all cases."""
models = model_cases
for m in models[:3]:
lightning_setattr(m, "learning_rate", 10)
assert lightning_getattr(
m, "learning_rate") == 10, "attribute not correctly set"
model5, model6, model7 = models[4:]
lightning_setattr(model5, "batch_size", 128)
lightning_setattr(model6, "batch_size", 128)
lightning_setattr(model7, "batch_size", 128)
assert lightning_getattr(
model5, "batch_size") == 128, "batch_size not correctly set"
assert lightning_getattr(
model6, "batch_size") == 128, "batch_size not correctly set"
assert lightning_getattr(
model7, "batch_size") == 128, "batch_size not correctly set"
for m in models:
with pytest.raises(
AttributeError,
match=
"is neither stored in the model namespace nor the `hparams` namespace/dict, nor the datamodule.",
):
lightning_setattr(m, "this_attr_not_exist", None)
def test_lightning_setattr(tmpdir):
"""Test that the lightning_setattr works in all cases"""
models = _get_test_cases()
for m in models[:3]:
lightning_setattr(m, 'learning_rate', 10)
assert lightning_getattr(m, 'learning_rate') == 10, \
'attribute not correctly set'
model5, model6, model7 = models[4:]
lightning_setattr(model5, 'batch_size', 128)
lightning_setattr(model6, 'batch_size', 128)
lightning_setattr(model7, 'batch_size', 128)
assert lightning_getattr(model5, 'batch_size') == 128, \
'batch_size not correctly set'
assert lightning_getattr(model6, 'batch_size') == 128, \
'batch_size not correctly set'
assert lightning_getattr(model7, 'batch_size') == 128, \
'batch_size not correctly set'
for m in models:
with pytest.raises(
AttributeError,
match=
"is neither stored in the model namespace nor the `hparams` namespace/dict, nor the datamodule."
):
lightning_setattr(m, "this_attr_not_exist", None)
def test_lightning_getattr(tmpdir):
""" Test that the lightning_getattr works in all cases"""
models = _get_test_cases()
for i, m in enumerate(models[:3]):
value = lightning_getattr(m, 'learning_rate')
assert value == i, 'attribute not correctly extracted'
model5 = models[4]
assert lightning_getattr(model5, 'batch_size') == 8, \
'batch_size not correctly extracted'
def test_lightning_setattr(tmpdir):
""" Test that the lightning_setattr works in all cases"""
models = _get_test_cases()
for m in models[:3]:
lightning_setattr(m, 'learning_rate', 10)
assert lightning_getattr(m, 'learning_rate') == 10, \
'attribute not correctly set'
model5 = models[4]
lightning_setattr(model5, 'batch_size', 128)
assert lightning_getattr(model5, 'batch_size') == 128, \
'batch_size not correctly set'
def _adjust_batch_size(trainer,
batch_arg_name: str = 'batch_size',
factor: float = 1.0,
value: Optional[int] = None,
desc: str = None):
""" Function for adjusting the batch size. It is expected that the user
has provided a model that has a hparam field called `batch_size` i.e.
`model.hparams.batch_size` should exist. Additionally there can be a
datamodule attached to either Trainer or model, in that case the attribute
also gets updated when present.
Args:
trainer: instance of pytorch_lightning.Trainer
batch_arg_name: field where batch_size is stored in `model.hparams`
factor: value which the old batch size is multiplied by to get the
new batch size
value: if a value is given, will override the batch size with this value.
Note that the value of `factor` will not have an effect in this case
desc: either `succeeded` or `failed`. Used purely for logging
"""
model = trainer.get_model()
batch_size = lightning_getattr(model, batch_arg_name)
new_size = value if value is not None else int(batch_size * factor)
if desc:
log.info(
f'Batch size {batch_size} {desc}, trying batch size {new_size}')
lightning_setattr(model, batch_arg_name, new_size)
return new_size
def _adjust_batch_size(
trainer: "pl.Trainer",
batch_arg_name: str = "batch_size",
factor: float = 1.0,
value: Optional[int] = None,
desc: Optional[str] = None,
) -> Tuple[int, bool]:
"""Helper function for adjusting the batch size.
Args:
trainer: instance of pytorch_lightning.Trainer
batch_arg_name: name of the field where batch_size is stored.
factor: value which the old batch size is multiplied by to get the
new batch size
value: if a value is given, will override the batch size with this value.
Note that the value of `factor` will not have an effect in this case
desc: either `succeeded` or `failed`. Used purely for logging
Returns:
The new batch size for the next trial and a bool that signals whether the
new value is different than the previous batch size.
"""
model = trainer.lightning_module
batch_size = lightning_getattr(model, batch_arg_name)
new_size = value if value is not None else int(batch_size * factor)
if desc:
log.info(
f"Batch size {batch_size} {desc}, trying batch size {new_size}")
if not _is_valid_batch_size(new_size, trainer.train_dataloader):
new_size = min(new_size, len(trainer.train_dataloader.dataset))
changed = new_size != batch_size
lightning_setattr(model, batch_arg_name, new_size)
return new_size, changed
def test_lightning_getattr():
"""Test that the lightning_getattr works in all cases."""
models = model_cases()
for i, m in enumerate(models[:3]):
value = lightning_getattr(m, "learning_rate")
assert value == i, "attribute not correctly extracted"
model5, model6, model7 = models[4:]
assert lightning_getattr(model5, "batch_size") == 8, "batch_size not correctly extracted"
assert lightning_getattr(model6, "batch_size") == 8, "batch_size not correctly extracted"
assert lightning_getattr(model7, "batch_size") == 8, "batch_size not correctly extracted"
for m in models:
with pytest.raises(
AttributeError,
match="is neither stored in the model namespace nor the `hparams` namespace/dict, nor the datamodule.",
):
lightning_getattr(m, "this_attr_not_exist")
def test_lightning_getattr(tmpdir, model_cases):
"""Test that the lightning_getattr works in all cases"""
models = model_cases
for i, m in enumerate(models[:3]):
value = lightning_getattr(m, 'learning_rate')
assert value == i, 'attribute not correctly extracted'
model5, model6, model7 = models[4:]
assert lightning_getattr(model5, 'batch_size') == 8, \
'batch_size not correctly extracted'
assert lightning_getattr(model6, 'batch_size') == 8, \
'batch_size not correctly extracted'
assert lightning_getattr(model7, 'batch_size') == 8, \
'batch_size not correctly extracted'
for m in models:
with pytest.raises(
AttributeError,
match="is neither stored in the model namespace nor the `hparams` namespace/dict, nor the datamodule."
):
lightning_getattr(m, "this_attr_not_exist")
