Source code for polygraphy.backend.onnxrt.runner

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import time
from collections import OrderedDict

from polygraphy import mod, util
from polygraphy.backend.base import BaseRunner
from polygraphy.common import TensorMetadata
from polygraphy.datatype import DataType

[docs] @mod.export() class OnnxrtRunner(BaseRunner): """ Runs inference using an ONNX-Runtime inference session. """ def __init__(self, sess, name=None): """ Args: sess (Union[onnxruntime.InferenceSession, Callable() -> onnxruntime.InferenceSession]): An ONNX-Runtime inference session or a callable that returns one. """ super().__init__(name=name, prefix="onnxrt-runner") self._sess = sess @util.check_called_by("activate") def activate_impl(self): self.sess, _ = util.invoke_if_callable(self._sess) @util.check_called_by("get_input_metadata") def get_input_metadata_impl(self): meta = TensorMetadata() for node in self.sess.get_inputs(): meta.add(, dtype=DataType.from_dtype(node.type, "onnxruntime"), shape=node.shape, ) return meta
[docs] @util.check_called_by("infer") def infer_impl(self, feed_dict): """ Implementation for running inference with ONNX-Runtime. Do not call this method directly - use ``infer()`` instead, which will forward unrecognized arguments to this method. Args: feed_dict (OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]): A mapping of input tensor names to corresponding input NumPy arrays or PyTorch tensors. If PyTorch tensors are provided in the feed_dict, then this function will return the outputs also as PyTorch tensors. Returns: OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]: A mapping of output tensor names to corresponding output NumPy arrays or PyTorch tensors. """ use_torch = any(util.array.is_torch(t) for t in feed_dict.values()) # `to_numpy()`` and `to_torch()` should be zero-copy whenever possible. feed_dict = {name: util.array.to_numpy(t) for name, t in feed_dict.items()} start = time.time() inference_outputs =, feed_dict) end = time.time() out_dict = OrderedDict() for node, out in zip(self.sess.get_outputs(), inference_outputs): out_dict[] = out if not use_torch else util.array.to_torch(out) self.inference_time = end - start return out_dict
@util.check_called_by("deactivate") def deactivate_impl(self): del self.sess