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Change Logic._forward() to public #775

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Change Logic._forward() to public #775

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17 changes: 13 additions & 4 deletions pytorch_pfn_extras/handler/_logic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -206,7 +206,16 @@ def consume_options(self, options: Dict[str, Any]) -> None:
"torch.cuda.amp.GradScaler object"
)

def _forward(self, model: torch.nn.Module, batch: Any) -> Any:
def forward(self, model: torch.nn.Module, batch: Any) -> Any:
"""Get the result of inputting the sampled data batch into the model.

Args:
model (torch.nn.Module): Model to input data.
batch (Any): Mini-batch sampled from data loader running in Trainer

Returns:
Any: Output of the model. loss is assumed to be output.
"""
if isinstance(batch, tuple) and hasattr(batch, "_fields"):
# namedtuple
return model(batch)
Expand Down Expand Up @@ -309,7 +318,7 @@ def train_step(
"""
with self._autocast.autocast():
optimizers[self.model_name].zero_grad()
outs = self._forward(models[self.model_name], batch)
outs = self.forward(models[self.model_name], batch)
to_back_outs = _normalize_outputs(outs)
self._backward(to_back_outs)
return outs
Expand Down Expand Up @@ -369,7 +378,7 @@ def eval_step(
"""
model = models[self.model_name]
with self._autocast.autocast():
outs = self._forward(model, batch)
outs = self.forward(model, batch)
return outs


Expand Down Expand Up @@ -530,7 +539,7 @@ def train_step(
def clousure() -> ClousureModelOutput:
with self._autocast.autocast():
optimizers[self.model_name].zero_grad()
outs = self._forward(models[self.model_name], batch)
outs = self.forward(models[self.model_name], batch)
to_back_outs = _normalize_outputs(outs)
if len(to_back_outs) > 1:
raise RuntimeError(
Expand Down