Source code for

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from typing import Any
from typing import Callable
from typing import Dict
from typing import Iterable
from typing import Mapping
from typing import Optional
from typing import Sequence

import numpy

import onnx
import onnxruntime as ort
from import FileSystems
from import utils
from import ModelHandler
from import PredictionResult

__all__ = ['OnnxModelHandlerNumpy']

NumpyInferenceFn = Callable[
    [Sequence[numpy.ndarray], ort.InferenceSession, Optional[Dict[str, Any]]],

def default_numpy_inference_fn(
    inference_session: ort.InferenceSession,
    batch: Sequence[numpy.ndarray],
    inference_args: Optional[Dict[str, Any]] = None) -> Any:
  ort_inputs = {
      inference_session.get_inputs()[0].name: numpy.stack(batch, axis=0)
  if inference_args:
    ort_inputs = {**ort_inputs, **inference_args}
  ort_outs =, ort_inputs)[0]
  return ort_outs

[docs]class OnnxModelHandlerNumpy(ModelHandler[numpy.ndarray, PredictionResult, ort.InferenceSession]): def __init__( #pylint: disable=dangerous-default-value self, model_uri: str, session_options=None, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'], provider_options=None, *, inference_fn: NumpyInferenceFn = default_numpy_inference_fn, large_model: bool = False, min_batch_size: Optional[int] = None, max_batch_size: Optional[int] = None, max_batch_duration_secs: Optional[int] = None, **kwargs): """ Implementation of the ModelHandler interface for onnx using numpy arrays as input. Note that inputs to ONNXModelHandler should be of the same sizes Example Usage:: pcoll | RunInference(OnnxModelHandler(model_uri="my_uri")) Args: model_uri: The URI to where the model is saved. inference_fn: The inference function to use on RunInference calls. default=default_numpy_inference_fn large_model: set to true if your model is large enough to run into memory pressure if you load multiple copies. Given a model that consumes N memory and a machine with W cores and M memory, you should set this to True if N*W > M. min_batch_size: the minimum batch size to use when batching inputs. max_batch_size: the maximum batch size to use when batching inputs. max_batch_duration_secs: the maximum amount of time to buffer a batch before emitting; used in streaming contexts. kwargs: 'env_vars' can be used to set environment variables before loading the model. """ self._model_uri = model_uri self._session_options = session_options self._providers = providers self._provider_options = provider_options self._model_inference_fn = inference_fn self._env_vars = kwargs.get('env_vars', {}) self._large_model = large_model self._batching_kwargs = {} if min_batch_size is not None: self._batching_kwargs["min_batch_size"] = min_batch_size if max_batch_size is not None: self._batching_kwargs["max_batch_size"] = max_batch_size if max_batch_duration_secs is not None: self._batching_kwargs["max_batch_duration_secs"] = max_batch_duration_secs
[docs] def load_model(self) -> ort.InferenceSession: """Loads and initializes an onnx inference session for processing.""" # when path is remote, we should first load into memory then deserialize f =, "rb") model_proto = onnx.load(f) model_proto_bytes = onnx._serialize(model_proto) ort_session = ort.InferenceSession( model_proto_bytes, sess_options=self._session_options, providers=self._providers, provider_options=self._provider_options) return ort_session
[docs] def run_inference( self, batch: Sequence[numpy.ndarray], inference_session: ort.InferenceSession, inference_args: Optional[Dict[str, Any]] = None ) -> Iterable[PredictionResult]: """Runs inferences on a batch of numpy arrays. Args: batch: A sequence of examples as numpy arrays. They should be single examples. inference_session: An onnx inference session. Must be runnable with input x where x is sequence of numpy array inference_args: Any additional arguments for an inference. Returns: An Iterable of type PredictionResult. """ predictions = self._model_inference_fn( inference_session, batch, inference_args) return utils._convert_to_result(batch, predictions)
[docs] def get_num_bytes(self, batch: Sequence[numpy.ndarray]) -> int: """ Returns: The number of bytes of data for a batch. """ return sum((np_array.itemsize for np_array in batch))
[docs] def get_metrics_namespace(self) -> str: """ Returns: A namespace for metrics collected by the RunInference transform. """ return 'BeamML_Onnx'
[docs] def share_model_across_processes(self) -> bool: return self._large_model
[docs] def batch_elements_kwargs(self) -> Mapping[str, Any]: return self._batching_kwargs