#
# SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import time
from collections import OrderedDict
from polygraphy import mod, util
from polygraphy.backend.base import BaseRunner
from polygraphy.common import TensorMetadata
np = mod.lazy_import("numpy")
[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
def activate_impl(self):
self.sess, _ = util.invoke_if_callable(self._sess)
def get_input_metadata_impl(self):
ONNX_RT_TYPE_TO_NP = {
"tensor(double)": np.float64,
"tensor(float)": np.float32,
"tensor(float16)": np.float16,
"tensor(int16)": np.int16,
"tensor(int32)": np.int32,
"tensor(int64)": np.int64,
"tensor(int8)": np.int8,
"tensor(uint16)": np.uint16,
"tensor(uint32)": np.uint32,
"tensor(uint64)": np.uint64,
"tensor(uint8)": np.uint8,
"tensor(bool)": bool,
"tensor(string)": np.unicode,
}
meta = TensorMetadata()
for node in self.sess.get_inputs():
dtype = ONNX_RT_TYPE_TO_NP[node.type] if node.type in ONNX_RT_TYPE_TO_NP else None
meta.add(node.name, dtype=dtype, shape=node.shape)
return meta
def infer_impl(self, feed_dict):
start = time.time()
inference_outputs = self.sess.run(None, feed_dict)
end = time.time()
out_dict = OrderedDict()
for node, out in zip(self.sess.get_outputs(), inference_outputs):
out_dict[node.name] = out
self.inference_time = end - start
return out_dict
def deactivate_impl(self):
del self.sess