wehub-resource-sync
91 lines
3.4 KiB
Python
91 lines
3.4 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import time
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from collections import OrderedDict
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from polygraphy import mod, util
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from polygraphy.backend.base import BaseRunner
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from polygraphy.common import TensorMetadata
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from polygraphy.datatype import DataType
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@mod.export()
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class OnnxrtRunner(BaseRunner):
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"""
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Runs inference using an ONNX-Runtime inference session.
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"""
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def __init__(self, sess, name=None):
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"""
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Args:
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sess (Union[onnxruntime.InferenceSession, Callable() -> onnxruntime.InferenceSession]):
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An ONNX-Runtime inference session or a callable that returns one.
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"""
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super().__init__(name=name, prefix="onnxrt-runner")
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self._sess = sess
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@util.check_called_by("activate")
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def activate_impl(self):
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self.sess, _ = util.invoke_if_callable(self._sess)
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@util.check_called_by("get_input_metadata")
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def get_input_metadata_impl(self):
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meta = TensorMetadata()
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for node in self.sess.get_inputs():
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meta.add(
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node.name,
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dtype=DataType.from_dtype(node.type, "onnxruntime"),
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shape=node.shape,
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)
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return meta
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@util.check_called_by("infer")
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def infer_impl(self, feed_dict):
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"""
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Implementation for running inference with ONNX-Runtime.
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Do not call this method directly - use ``infer()`` instead,
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which will forward unrecognized arguments to this method.
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Args:
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feed_dict (OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]):
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A mapping of input tensor names to corresponding input NumPy arrays or PyTorch tensors.
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If PyTorch tensors are provided in the feed_dict, then this function
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will return the outputs also as PyTorch tensors.
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Returns:
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OrderedDict[str, Union[numpy.ndarray, torch.Tensor]]:
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A mapping of output tensor names to corresponding output NumPy arrays
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or PyTorch tensors.
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"""
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use_torch = any(util.array.is_torch(t) for t in feed_dict.values())
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# `to_numpy()`` and `to_torch()` should be zero-copy whenever possible.
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feed_dict = {name: util.array.to_numpy(t) for name, t in feed_dict.items()}
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start = time.time()
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inference_outputs = self.sess.run(None, feed_dict)
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end = time.time()
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out_dict = OrderedDict()
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for node, out in zip(self.sess.get_outputs(), inference_outputs):
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out_dict[node.name] = out if not use_torch else util.array.to_torch(out)
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self.inference_time = end - start
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return out_dict
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@util.check_called_by("deactivate")
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def deactivate_impl(self):
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del self.sess
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