如何从图像列表 Pytorch 开始加载图像数据集 [英] How to load a dataset of images starting from list of images Pytorch

问题描述

我有一个服务从另一个服务接收二进制格式的图像(我们称之为服务 B):

I have a service that receives images in a binary format from another service (let's call it service B):

from PIL import Image

img_list = []
img_bin = get_image_from_service_B()
image = Image.open(io.BytesIO(img_bin)) # Convert bytes to image using PIL

<小时>当图像通过 PIL 成功转换时，它也会附加到图像列表中.

---

When an image is successfully converted thanks to PIL it is also appended to a list of images.

img_list.append(image)    

<小时>

当我有足够的图像时，我想使用 Pytorch 加载我的图像列表，就像它是一个数据集

---

When I've enough images I want to load my list of images using Pytorch as if it was a dataset

if img_list.__len__() == 500:
     ### Load dataset and do a transform operation on the data

<小时>在以前版本的软件中，要求只是从文件夹中检索图像，因此加载所有图像非常简单

---

In a previous version of the software the requirement was simply to retrieve the images from a folder, so it was quite simple to load all the images

my_dataset = datasets.ImageFolder("path/to/images/folder/", transform=transform)
dataset_iterator = DataLoader(my_dataset, batch_size=1)

现在我的问题是如何执行转换并从列表中加载数据集.

Now my issue is how to perform the transform and load the dataset from a list.

推荐答案

您可以简单地编写自定义数据集:

You can simply write a custom dataset:

class MyDataset(torch.util.data.Dataset):
  def __init__(self, img_list, augmentations):
    super(MyDataset, self).__init__()
    self.img_list = img_list
    self.augmentations = augmentations

  def __len__(self):
    return len(self.img_list)

  def __getitem__(self, idx):
    img = self.img_list[idx]
    return self.augmentations(img)
  

您现在可以将此自定义数据集插入 DataLoader 并完成.

You can now plug this custom dataset into DataLoader and you are done.

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