如何将numpy数组列表加载到pytorch数据集加载器? [英] How to load a list of numpy arrays to pytorch dataset loader?
问题描述
我有一个庞大的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.如何为我的原因修改它?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.该图像是RBG图像.
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实际需要的是对Dataset
进行子类化的输入.您可以编写自己的子集Dataset
的数据集类,也可以像下面所做的那样使用TensorDataset
:
I think what DataLoader actually requires is an input that subclasses Dataset
. You can either write your own dataset class that subclasses Dataset
or use TensorDataset
as I have done below:
import torch
import numpy as np
from torch.utils import data
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 = data.TensorDataset(tensor_x,tensor_y) # create your datset
my_dataloader = data.DataLoader(my_dataset) # create your dataloader
为我工作.希望对您有帮助.
Works for me. Hope it helps you.
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