def deserialize(
cls,
path: Path,
ctx: Optional[mx.Context] = None) -> "RepresentableBlockPredictor":
ctx = ctx if ctx is not None else get_mxnet_context()
with mx.Context(ctx):
# deserialize constructor parameters
with (path / "parameters.json").open("r") as fp:
parameters = load_json(fp.read())
# deserialize transformation chain
with (path / "input_transform.json").open("r") as fp:
transform = load_json(fp.read())
# deserialize prediction network
prediction_net = import_repr_block(path, "prediction_net")
# input_names is derived from the prediction_net
if "input_names" in parameters:
del parameters["input_names"]
parameters["ctx"] = ctx
return RepresentableBlockPredictor(
input_transform=transform,
prediction_net=prediction_net,
**parameters,
)
def deserialize(cls, path: Path):
try:
# deserialize constructor parameters
with (path / 'parameters.json').open('r') as fp:
parameters = load_json(fp.read())
# deserialize transformation chain
with (path / 'input_transform.json').open('r') as fp:
transform = load_json(fp.read())
# deserialize prediction network
prediction_net = import_repr_block(path, 'prediction_net')
# input_names is derived from the prediction_net
if 'input_names' in parameters:
del parameters['input_names']
return RepresentableBlockPredictor(
input_transform=transform,
prediction_net=prediction_net,
**parameters,
)
except Exception as e:
raise IOError(f'Cannot deserialize {fqname_for(cls)}') from e
