wehub-resource-sync
108 lines
3.7 KiB
Python
108 lines
3.7 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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# Sets up everything needed to perform inference in TensorFlow.
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import os
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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.backend.tf import util as tf_util
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from polygraphy.logger import G_LOGGER
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tf = mod.lazy_import("tensorflow<2.0")
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@mod.export()
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class TfRunner(BaseRunner):
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"""
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Runs inference using a TensorFlow session.
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"""
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def __init__(self, sess, timeline_dir=None, name=None):
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"""
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Args:
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sess (Union[Tuple[tf.Session, Sequence[str]], Callable() -> Tuple[tf.Session, Sequence[str]]]):
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A tuple containing a TensorFlow session and output names or a callable that returns one.
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timeline_dir (str):
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Path to write a TensorFlow timeline.
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Note that profiling may affect execution time.
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name (str):
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The human-readable name prefix to use for this runner.
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A runner count and timestamp will be appended to this prefix.
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"""
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super().__init__(name=name, prefix="tf-runner")
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self._sess = sess
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self.timeline_dir = timeline_dir
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self.num_inferences = 0
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self.run_options = None
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self.run_metadata = None
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if self.timeline_dir is not None:
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# Enable profiling
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G_LOGGER.warning("Profiling is enabled. This will impact performance")
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self.run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
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self.run_metadata = tf.RunMetadata()
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@util.check_called_by("activate")
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def activate_impl(self):
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(self.sess, self.output_names), _ = 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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return tf_util.get_input_metadata(self.sess.graph)
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@util.check_called_by("infer")
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def infer_impl(self, feed_dict):
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G_LOGGER.extra_verbose(f"Received feed_dict: {feed_dict}")
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start = time.time()
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inference_outputs = self.sess.run(
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self.output_names,
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feed_dict=feed_dict,
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options=self.run_options,
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run_metadata=self.run_metadata,
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)
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end = time.time()
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out_dict = OrderedDict()
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for name, out in zip(self.output_names, inference_outputs):
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out_dict[name] = out
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self.inference_time = end - start
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if self.timeline_dir is not None:
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from tensorflow.python.client import timeline
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t1 = timeline.Timeline(self.run_metadata.step_stats)
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util.save_file(
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contents=t1.generate_chrome_trace_format(),
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dest=os.path.join(self.timeline_dir, f"run-{self.num_inferences}"),
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mode="w",
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)
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self.num_inferences += 1
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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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self.sess.close()
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del (self.sess, self.output_names)
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self.num_inferences = 0
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