def create_predictor(
self,
transformation: transform.Transformation,
trained_network: mx.gluon.HybridBlock,
bin_values: np.ndarray,
) -> Predictor:
prediction_network = WaveNetSampler(
num_samples=self.num_parallel_samples,
temperature=self.temperature,
**self._get_wavenet_args(bin_values),
)
# The lookup layer is specific to the sampling network here
# we make sure it is initialized.
prediction_network.initialize()
copy_parameters(
net_source=trained_network,
net_dest=prediction_network,
allow_missing=True,
)
return RepresentableBlockPredictor(
input_transform=transformation,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
)
def create_predictor(
self,
transformation: transform.Transformation,
trained_network: mx.gluon.HybridBlock,
bin_values: np.ndarray,
) -> Predictor:
prediction_network = WaveNetSampler(
num_samples=self.num_eval_samples,
temperature=self.temperature,
**self._get_wavenet_args(bin_values),
)
# copy_parameters(net_source=trained_network, net_dest=prediction_network, ignore_extra=True)
copy_parameters(net_source=trained_network,
net_dest=prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
)