如何将以下 TensorFlow 代码转换为 Pytorch（迁移学习）？

Question

我想知道如何将以下代码（Tensorflow）转换为 Pytorch。 我想使用 DataLoader 但我不能。是否可以使用 DataLoader 进行转换？或者你能告诉我任何其他的转换方式吗？

非常感谢:)

---

from tensorflow.keras.preprocessing import image as image_utils
from tensorflow.keras.applications.vgg16 import preprocess_input

def load_and_process_image(image_path):
# Print image's original shape, for reference
print('Original image shape: ', mpimg.imread(image_path).shape)

    # Load in the image with a target size of 224, 224
    image = image_utils.load_img(image_path, target_size=(224, 224))
    # Convert the image from a PIL format to a numpy array
    image = image_utils.img_to_array(image)
    # Add a dimension for number of images, in our case 1
    image = image.reshape(1,224,224,3)
    # Preprocess image to align with original ImageNet dataset
    image = preprocess_input(image)
    # Print image's shape after processing
    print('Processed image shape: ', image.shape)
    return image

Answer 1

import os
from PIL import Image
import torch
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms

class MyData(Dataset):
    def __init__(self, data_path):
        #path of the folder where your images are located
        self.data_path = data_path
        #transforms to perform on image. In general, these are the default normalization used. you can change std, mean values about three channels according to your requirement
        #when ToTensor() is used it automatically permutes the dimensions according to the torch layers
        self.transforms = transforms.Compose([
                              transforms.Resize((224, 224)),
                              transforms.ToTensor(),
                              transforms.Normalize((mean=[0.485, 0.456, 0.406],
                                                    std=[0.229, 0.224, 0.225])

        self.image_path_list = sorted(os.listdir(self.data_path))

    def __len__(self):
        #returns the length of your dataset
        return len(self.image_path_list)

    def __getitem__(self, idx):
        #pytorch accepts PIL images, use PIL.Image to load images
        image = Image.open(self.image_path_list[idx])
        image = self.transform(image)
        return image

以上是基于我对您帖子的假设的小型 sn-p。我假设您需要调整给定平均值的大小、置换和规范化。 DataLoader 是可迭代的。它一次生成单个图像。

例如，

#instantiate your loader, with the desired parameters. checkout the pytorch documentation for other arguments 
myloader = DataLoader(MyData, batch_size = 32, num_workers = 10)
myloader = iter(myloader)

for i in range(0, 10):
#this yields first 10 batches of your dataset 
img = next(myloader)

希望这就是您正在寻找的。请随时评论您的问题以获取进一步的说明。