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Fix type hints of callbacks/finetuning.py #13516

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Merged
merged 2 commits into from
Jul 4, 2022
Merged

Fix type hints of callbacks/finetuning.py #13516

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ef6a4e7
Fix type hints
ar90n Jul 3, 2022
504992c
Fix incompatible assignment
ar90n Jul 3, 2022
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1 change: 0 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,6 @@ warn_no_return = "False"
# the list can be generated with:
# mypy | tr ':' ' ' | awk '{print $1}' | sort | uniq | sed 's/\.py//g' | sed 's|\/|\.|g' | xargs -I {} echo '"{}",'
module = [
"pytorch_lightning.callbacks.finetuning",
"pytorch_lightning.callbacks.model_checkpoint",
"pytorch_lightning.callbacks.progress.rich_progress",
"pytorch_lightning.callbacks.quantization",
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13 changes: 7 additions & 6 deletions src/pytorch_lightning/callbacks/finetuning.py
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Expand Up @@ -32,8 +32,8 @@
log = logging.getLogger(__name__)


def multiplicative(epoch):
return 2
def multiplicative(epoch: int) -> float:
return 2.0


class BaseFinetuning(Callback):
Expand Down Expand Up @@ -79,7 +79,7 @@ class BaseFinetuning(Callback):
... )
"""

def __init__(self):
def __init__(self) -> None:
self._internal_optimizer_metadata: Dict[int, List[Dict[str, Any]]] = {}
self._restarting = False

Expand All @@ -94,7 +94,7 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self._internal_optimizer_metadata = state_dict["internal_optimizer_metadata"]
else:
# compatibility to load from old checkpoints before PR #11887
self._internal_optimizer_metadata = state_dict
self._internal_optimizer_metadata = state_dict # type: ignore[assignment]

def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
# restore the param_groups created during the previous training.
Expand Down Expand Up @@ -122,10 +122,11 @@ def flatten_modules(modules: Union[Module, Iterable[Union[Module, Iterable]]]) -
modules = modules.values()

if isinstance(modules, Iterable):
_modules = []
_flatten_modules = []
for m in modules:
_modules.extend(BaseFinetuning.flatten_modules(m))
_flatten_modules.extend(BaseFinetuning.flatten_modules(m))

_modules = iter(_flatten_modules)
else:
_modules = modules.modules()

Expand Down