def as_symbol_block_predictor(
self,
batch: Optional[DataBatch] = None,
dataset: Optional[Dataset] = None) -> SymbolBlockPredictor:
if batch is None:
data_loader = InferenceDataLoader(dataset,
transform=self.input_transform,
batch_size=self.batch_size,
stack_fn=partial(
batchify,
ctx=self.ctx,
dtype=self.dtype))
batch = next(iter(data_loader))
with self.ctx:
symbol_block_net = hybrid_block_to_symbol_block(
hb=self.prediction_net,
data_batch=[batch[k] for k in self.input_names],
)
return SymbolBlockPredictor(
input_names=self.input_names,
prediction_net=symbol_block_net,
batch_size=self.batch_size,
prediction_length=self.prediction_length,
freq=self.freq,
ctx=self.ctx,
input_transform=self.input_transform,
lead_time=self.lead_time,
forecast_generator=self.forecast_generator,
output_transform=self.output_transform,
dtype=self.dtype,
)
def test_symb_block_export_import_nested_array(block_type, hybridize) -> 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()
if hybridize:
my_block.hybridize()
my_block(x1, x2)
sb = util.hybrid_block_to_symbol_block(my_block, [x1, x2])
assert np.allclose(sb(x1, x2).asnumpy(), my_block(x1, x2).asnumpy())
def as_symbol_block_predictor(self,
batch: DataBatch) -> SymbolBlockPredictor:
symbol_block_net = hybrid_block_to_symbol_block(
hb=self.prediction_net,
data_batch=[batch[k] for k in self.input_names],
)
return SymbolBlockPredictor(
input_names=self.input_names,
prediction_net=symbol_block_net,
batch_size=self.batch_size,
prediction_length=self.prediction_length,
freq=self.freq,
ctx=self.ctx,
input_transform=self.input_transform,
forecast_generator=self.forecast_generator,
output_transform=self.output_transform,
float_type=self.float_type,
)
