Tensorrt python版本的推理实例

model_path = "./model.engine"
import tensorrt as trt
verbose = True 
IN_NAME = 'input' #输入节点名字 
OUT_NAME = 'output' #输出节点名字
IN_H = 512
IN_W = 512
BATCH_SIZE = 1 

EXPLICIT_BATCH = 1 << (int)( 
    trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH) 
import tensorrt as trt #必须安装
import pycuda.driver as cuda #必须安装
import pycuda.autoinit
import numpy as np
import time
import cv2
import os
from tensorflow import keras
import threading
# 加载TRT引擎

class OxfordPets1(keras.utils.Sequence):

    # 在__init__方法中指定batch_size,img_size,input_img_paths,target_img_paths
    def __init__(self, batch_size, img_size, input_img_paths):
        self.batch_size = batch_size  # 批量大小
        self.img_size = img_size  # 图像大小
        self.input_img_paths = input_img_paths  # 输入图像路径
        #self.target_img_paths = target_img_paths  # 标注图像路径
        #self.on_epoch_end()

    def __len__(self):