如何将 numpy 数组列表加载到 pytorch 数据集加载器?

问题描述

我有一个巨大的 numpy 数组列表，其中每个数组代表一个图像，我想使用 torch.utils.data.Dataloader 对象加载它.但是 torch.utils.data.Dataloader 的文档提到它直接从文件夹加载数据.我如何根据我的原因修改它?我是 pytorch 的新手，任何帮助将不胜感激.我的单个图像的 numpy 数组看起来像这样.图片为RBG图片.

I have a huge list of numpy arrays, where each array represents an image and I want to load it using torch.utils.data.Dataloader object. But the documentation of torch.utils.data.Dataloader mentions that it loads data directly from a folder. How do I modify it for my cause? I am new to pytorch and any help would be greatly appreciated.my numpy array for a single image looks something like this. The image is RBG image.

[[[ 70  82  94]
  [ 67  81  93]
  [ 66  82  94]
  ...,
  [182 182 188]
  [183 183 189]
  [188 186 192]]

 [[ 66  80  92]
  [ 62  78  91]
  [ 64  79  95]
  ...,
  [176 176 182]
  [178 178 184]
  [180 180 186]]

 [[ 62  82  93]
  [ 62  81  96]
  [ 65  80  99]
  ...,
  [169 172 177]
  [173 173 179]
  [172 172 178]]

 ...,

推荐答案

我认为 DataLoader 实际需要的是一个子类 Dataset 的输入.您可以编写自己的数据集类，子类化 Dataset 或使用 TensorDataset ，就像我在下面所做的那样:

I think what DataLoader actually requires is an input that subclasses Dataset. You can either write your own dataset class that subclasses Datasetor use TensorDataset as I have done below:

import torch
import numpy as np
from torch.utils.data import TensorDataset, DataLoader

my_x = [np.array([[1.0,2],[3,4]]),np.array([[5.,6],[7,8]])] # a list of numpy arrays
my_y = [np.array([4.]), np.array([2.])] # another list of numpy arrays (targets)

tensor_x = torch.Tensor(my_x) # transform to torch tensor
tensor_y = torch.Tensor(my_y)

my_dataset = TensorDataset(tensor_x,tensor_y) # create your datset
my_dataloader = DataLoader(my_dataset) # create your dataloader

对我有用.希望能帮到你.

Works for me. Hope it helps you.

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