def load_checkpoint(self, path: str):
"""Load checkpoint from path.
Args:
path: checkpoint file to load
Returns:
loaded checkpoint
"""
return load_checkpoint(path=path)
def predict_loader(
self,
*,
loader: DataLoader,
model: TorchModel = None,
engine: Union["Engine", str] = None,
seed: int = 42,
# extra info
resume: str = None,
# engine extra params,
cpu: bool = False,
fp16: bool = False,
) -> Generator:
"""
Runs model inference on PyTorch DataLoader and returns
python generator with model predictions from `runner.predict_batch`.
Args:
loader: loader to predict
model: model to use for prediction
engine: engine to use for prediction
seed: random seed to use before prediction
resume: path to checkpoint for model
cpu: boolean flag to force CPU usage
fp16: boolean flag to use half-precision
Yields:
bathes with model predictions
.. note::
Please follow the `minimal examples`_ sections for use cases.
.. _`minimal examples`: https://github.com/catalyst-team/catalyst#minimal-examples # noqa: E501, W505
"""
self.engine = engine or get_available_engine(cpu=cpu, fp16=fp16)
if model is not None:
self.model = model
assert self.model is not None
if resume is not None:
self.engine.wait_for_everyone()
unwrapped_model = self.engine.unwrap_model(self.model)
unwrapped_model.load_state_dict(load_checkpoint(resume))
self.model = self.engine.prepare(self.model)
maybe_recursive_call(self.model, "train", mode=False)
loader = self.engine.prepare(loader)
set_global_seed(seed)
for batch in loader:
yield self.predict_batch(batch)