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get_engine.py
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get_engine.py
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#!/usr/bin/env python3
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# 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.
#
from __future__ import print_function
import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import sys, os
import common
TRT_LOGGER = trt.Logger()
def get_engine(onnx_file_path, engine_file_path=""):
"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""
def build_engine():
"""Takes an ONNX file and creates a TensorRT engine to run inference with"""
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(common.EXPLICIT_BATCH) as network, builder.create_builder_config() as config, trt.OnnxParser(network, TRT_LOGGER) as parser, trt.Runtime(TRT_LOGGER) as runtime:
config.max_workspace_size = 1 << 28 # 256MiB
builder.max_batch_size = 1
# Parse model file
if not os.path.exists(onnx_file_path):
print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
exit(0)
print('Loading ONNX file from path {}...'.format(onnx_file_path))
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
return None
# Modified for Tiny YOLO v2 input shape
network.get_input(0).shape = [1, 3, 416, 416]
print('Completed parsing of ONNX file')
print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
plan = builder.build_serialized_network(network, config)
engine = runtime.deserialize_cuda_engine(plan)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(plan)
return engine
if os.path.exists(engine_file_path):
# If a serialized engine exists, use it instead of building an engine.
print("Reading engine from file {}".format(engine_file_path))
with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
else:
return build_engine()