get_engine_prev.py

#!/usr/bin/env python
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from __future__ import print_function

import numpy as np
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import 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():
        # EXPLICIT_BATCH is needed for TensorRT v7
        creation_flag = 0
        if trt.__version__ >= '7.0.0.0':
            creation_flag = common.EXPLICIT_BATCH
            print('EXPLICIT_BATCH is set.')
        """Takes an ONNX file and creates a TensorRT engine to run inference with"""
        with trt.Builder(TRT_LOGGER) as builder, builder.create_network(creation_flag) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
            builder.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))
            engine = builder.build_cuda_engine(network)
            print("Completed creating Engine")
            with open(engine_file_path, "wb") as f:
                f.write(engine.serialize())
            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()