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pytorch - 如何将图像数据集转换为张量？

问题描述

我有一个看起来像这样的图像数据集：

array([[[[0.35980392, 0.26078431, 0.14313725],
     [0.38137255, 0.26470588, 0.15196078],
     [0.51960784, 0.3745098 , 0.26176471],
     ...,
     [0.34313725, 0.22352941, 0.15      ],
     [0.30784314, 0.2254902 , 0.15686275],
     [0.28823529, 0.22843137, 0.16862745]],

    [[0.38627451, 0.28235294, 0.16764706],
     [0.45098039, 0.32843137, 0.21666667],
     [0.62254902, 0.47254902, 0.36470588],
     ...,
     [0.34607843, 0.22745098, 0.15490196],
     [0.30686275, 0.2245098 , 0.15588235],
     [0.27843137, 0.21960784, 0.16176471]],

    [[0.41568627, 0.30098039, 0.18431373],
     [0.51862745, 0.38529412, 0.27352941],
     [0.67745098, 0.52058824, 0.40980392],
     ...,
     [0.34901961, 0.22941176, 0.15588235],
     [0.29901961, 0.21666667, 0.14901961],
     [0.26078431, 0.20098039, 0.14313725]],

    ...,

我需要将它转换为张量，以便我可以将它传递给 CNN。我正在尝试这样做：

from torchvision import transforms as transforms
transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

如何将其transform应用于我的数据集？谢谢你的帮助。

标签： pytorchconv-neural-network

解决方案

您可能想要创建一个数据加载器。您将需要一个遍历数据集的类，您可以这样做：

import torch
import torchvision.transforms

class YourDataset(torch.utils.data.Dataset):
    def __init__(self):

       # load your dataset (how every you want, this example has the dataset stored in a json file
        with open(<dataset-path>, "r") as f:
            self.dataset = json.load(f)

    def __getitem__(self, idx):
        sample = self.dataset[idx]
        data, label = sample[0], sample[1]
        
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
        return transform(data), torch.tensor(label)

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

现在您可以创建一个数据加载器：


train_set = YourDataset()

train_dataloader = torch.utils.data.DataLoader(
            train_set,
            batch_size=64,
            num_workers=1,
            shuffle=True,
        )

现在您可以在训练循环中迭代数据加载器：

for samples, labels in self.train_set:
    . . . 
    # samples will hold N samples of your dataset where N is the batchsize

如果您需要更多解释，请查看有关此主题的 pytorchs 文档。

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