pytorch transform数据处理转c++问题

目录

• pytorch transform数据处理转c++
• 1.python代码
• 2.transforms.Resize(256)
• 3.transforms.ToTensor()
• 总结

pytorch transform数据处理转c++

python推理代码转c++ sdk过程遇到pytorch数据处理的转换

1.python代码

import torch
from PIL import Image
from torchvision import transforms

data_transform = transforms.Compose(
  [transforms.Resize(256),
   transforms.CenterCrop(224),
   transforms.ToTensor(),
   transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])

img = Image.open(img_path)
img = data_transform(img)

2.transforms.Resize(256)

Parameters

size (sequence or int) –

Desired output size. If size is a sequence like (h, w), output size will be matched to this. If size is an int, smaller edge of the image will be matched to thihttp://www.devze.coms number. i.e, if height > width, then image will be rescaled to (size * height / width, size).

3.transforms.ToTensor(开发者_自学开发)

Convert a PIL Image or numpy.ndarray to tensor. This transform does not support torchscript.

Converts a PIL Image or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1) or if the numpy.ndarray has dtype = np.uint8

cv::Mat ClsSixPrivate::processImage(cv::Mat &img) {
 int inW = img.cols;
 int inH = img.rows;
 cv::Mat croped_image;
 if (inW > inH)
 {
  int newWidth = 256 * inW / inH;
  cv::resize(img, img, cv::Size(newWidth, 256), 0, 0, cv::INTER_LINEAR);
  croped_image = img(cv::Rect((newWidth - 224) / 2, 16, 224, 224)).clone();
 }
 else {
  int newHeight= 256 * inH / pythoninW;
  cv::resize(img, img, cv::Size(2javascript56, newHeight), 0, 0, cv::INTER_LINEAR);
  croped_image = img(cv::Rect(16, (newHeight - 224) / 2, 224, 224)).clone();
 }
 
 std::vector<float> mean_value{ 0.485, 0.456,0.406 };
 std::vector<float> std_value{ 0.229, 0.224, 0.225 };
 cv::Mat dst;
 std::http://www.devze.comvector<cv::Mat> rgbChannels(3);
 cv::split(croped_image, rgbChannels);

 for (auto niivSzdi = 0; i < rgbChannels.size(); i++)
 {
  rgbChannels[i].convertTo(rgbChannels[i], CV_32FC1, 1.0 / (std_value[i] * 255.0), (0.0 - mean_value[i]) / std_value[i]);
 }

 cv::merge(rgbChannels, dst);
 return dst;
}

总结

以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。