tensorrt 推理Unet网络（python && C++）

对该博客的推理修改https://blog.youkuaiyun.com/hello_dear_you/article/details/109744627

我的输入是（1，3，720，1280），输出是（1，2，720，1280），源码GitHub - milesial/Pytorch-UNet: PyTorch implementation of the U-Net for image semantic segmentation with high quality images分类数量改为2，训练部分参考源码 ，源码中可能由于pytorch版本不一样需要改一下。可以百度到

重要推理部分如下

import tensorrt as trt
import numpy as np
import pycuda.driver as cuda
import pycuda.autoinit
import cv2
from PIL import Image


def preprocess(mask_values, pil_img, scale, is_mask):
    w, h = pil_img.size
    newW, newH = int(scale * w), int(scale * h)
    assert newW > 0 and newH > 0, 'Scale is too small, resized images would have no pixel'
    pil_img = pil_img.resize((newW, newH), resample=Image.NEAREST if is_mask else Image.BICUBIC)
    img = np.asarray(pil_img)

    if is_mask:
        mask = np.zeros((newH, newW), dtype=np.int64)
        for i, v in enumerate(mask_values):
            if img.ndim == 2:
                mask[img == v] = i
            else:
                mask[(img == v).all(-1)] = i

        return mask

    else:
        if img.ndim == 2:
            img = img[np.newaxis, ...]
        else:
            img = img.transpose((2, 0, 1))

        i