def build_ranking_serving_module(
self,
actor: ModelBase,
state_normalization_data: NormalizationData,
candidate_normalization_data: NormalizationData,
num_candidates: int,
action_normalization_data: NormalizationData,
) -> torch.nn.Module:
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters,
use_gpu=False)
candidate_preprocessor = Preprocessor(
candidate_normalization_data.dense_normalization_parameters,
use_gpu=False)
postprocessor = Postprocessor(
action_normalization_data.dense_normalization_parameters,
use_gpu=False)
actor_with_preprocessor = RankingActorWithPreprocessor(
model=actor.cpu_model().eval(),
state_preprocessor=state_preprocessor,
candidate_preprocessor=candidate_preprocessor,
num_candidates=num_candidates,
action_postprocessor=postprocessor,
)
action_features = Preprocessor(
action_normalization_data.dense_normalization_parameters,
use_gpu=False).sorted_features
return RankingActorPredictorWrapper(actor_with_preprocessor,
action_features)
def build_serving_module(
self,
actor: ModelBase,
state_normalization_data: NormalizationData,
action_normalization_data: NormalizationData,
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_normalization_parameters = (
state_normalization_data.dense_normalization_parameters)
action_normalization_parameters = (
action_normalization_data.dense_normalization_parameters)
assert state_normalization_parameters is not None
assert action_normalization_parameters is not None
state_preprocessor = Preprocessor(state_normalization_parameters,
use_gpu=False)
postprocessor = Postprocessor(action_normalization_parameters,
use_gpu=False)
actor_with_preprocessor = ActorWithPreprocessor(
actor.cpu_model().eval(), state_preprocessor, postprocessor)
action_features = Preprocessor(action_normalization_parameters,
use_gpu=False).sorted_features
return ActorPredictorWrapper(actor_with_preprocessor, action_features)
def build_serving_module(
self,
actor: ModelBase,
state_feature_config: rlt.ModelFeatureConfig,
state_normalization_data: NormalizationData,
action_normalization_data: NormalizationData,
serve_mean_policy: bool = False,
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters,
use_gpu=False)
postprocessor = Postprocessor(
action_normalization_data.dense_normalization_parameters,
use_gpu=False)
actor_with_preprocessor = ActorWithPreprocessor(
actor.cpu_model().eval(),
state_preprocessor,
state_feature_config,
postprocessor,
serve_mean_policy=serve_mean_policy,
)
action_features = Preprocessor(
action_normalization_data.dense_normalization_parameters,
use_gpu=False).sorted_features
return ActorPredictorWrapper(actor_with_preprocessor,
state_feature_config, action_features)
def build_serving_module(
self,
q_network: ModelBase,
state_normalization_data: NormalizationData,
action_names: List[str],
state_feature_config: rlt.ModelFeatureConfig,
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters, False)
dqn_with_preprocessor = DiscreteDqnWithPreprocessor(
q_network.cpu_model().eval(), state_preprocessor)
return DiscreteDqnPredictorWrapper(dqn_with_preprocessor, action_names,
state_feature_config)
def build_serving_module(
self,
q_network: ModelBase,
state_normalization_parameters: Dict[int, NormalizationParameters],
action_normalization_parameters: Dict[int, NormalizationParameters],
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(state_normalization_parameters, False)
action_preprocessor = Preprocessor(action_normalization_parameters, False)
dqn_with_preprocessor = ParametricDqnWithPreprocessor(
q_network.cpu_model().eval(), state_preprocessor, action_preprocessor
)
return ParametricDqnPredictorWrapper(
dqn_with_preprocessor=dqn_with_preprocessor
)
def build_serving_module(
self,
actor: ModelBase,
state_normalization_data: NormalizationData,
action_feature_ids: List[int],
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters, use_gpu=False
)
actor_with_preprocessor = ActorWithPreprocessor(
actor.cpu_model().eval(),
state_preprocessor,
)
return ActorPredictorWrapper(actor_with_preprocessor, action_feature_ids)
def build_binary_difference_scorer(
self,
q_network: ModelBase,
state_normalization_data: NormalizationData,
action_names: List[str],
state_feature_config: rlt.ModelFeatureConfig,
) -> torch.nn.Module:
"""
Returns softmax(1) - softmax(0)
"""
assert len(action_names) == 2
state_preprocessor = Preprocessor(
state_normalization_data.dense_normalization_parameters, False)
binary_difference_scorer_with_preprocessor = (
BinaryDifferenceScorerWithPreprocessor(
q_network.cpu_model().eval(), state_preprocessor,
state_feature_config))
return BinaryDifferenceScorerPredictorWrapper(
binary_difference_scorer_with_preprocessor, state_feature_config)
def build_serving_module(
self,
q_network: ModelBase,
state_normalization_parameters: Dict[int, NormalizationParameters],
action_names: List[str],
state_feature_config: rlt.ModelFeatureConfig,
) -> torch.nn.Module:
"""
Returns a TorchScript predictor module
"""
state_preprocessor = Preprocessor(state_normalization_parameters,
False)
dqn_with_preprocessor = DiscreteDqnWithPreprocessor(
torch.nn.Sequential( # type: ignore
q_network.cpu_model().eval(), _Mean()),
state_preprocessor,
)
return DiscreteDqnPredictorWrapper(dqn_with_preprocessor, action_names,
state_feature_config)
