def test_symb_block_import_backward_compatible(block_type) -> None:
x1 = mx.nd.array([1, 2, 3])
x2 = [mx.nd.array([1, 5, 5]), mx.nd.array([2, 3, 3])]
my_block = block_type()
my_block.collect_params().initialize()
my_block.hybridize()
my_block(x1, x2)
with tempfile.TemporaryDirectory(
prefix="gluonts-estimator-temp-"
) as temp_dir:
temp_path = Path(temp_dir)
export_symb_block(my_block, temp_path, "gluonts-model")
format_json_path = temp_path / "gluonts-model-in_out_format.json"
assert format_json_path.exists()
try:
format_json_path.unlink()
import_symb_block(3, temp_path, "gluonts-model")
except FileNotFoundError:
pytest.fail(
"Symbol block import fails when format json is not in path"
)
def deserialize(
cls, path: Path, ctx: Optional[mx.Context] = None
) -> "SymbolBlockPredictor":
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())
parameters["ctx"] = ctx
# deserialize transformation chain
with (path / "input_transform.json").open("r") as fp:
transform = load_json(fp.read())
# deserialize prediction network
num_inputs = len(parameters["input_names"])
prediction_net = import_symb_block(
num_inputs, path, "prediction_net"
)
return SymbolBlockPredictor(
input_transform=transform,
prediction_net=prediction_net,
**parameters,
)