Pytorch图像数据转为Tensor张量

PyTorch的所有模型（nn.Module）都只接受Tensor格式的输入，所以我们在使用图像数据集时，必须将图像转换为Tensor格式。PyTorch提供了torchvision.transforms模块来处理图像数据集。torchvision.transforms模块提供了一些常用的图像预处理方法，如Resize、CenterCrop、RandomCrop、RandomHorizontalFlip等。
torchvision.transforms模块还提供了ToTensor()方法，可以将PIL格式的图像转换为Tensor格式。ToTensor()方法会将图像的像素值从[0, 255]范围缩放到[0, 1]范围，并将图像的通道顺序从(H, W, C)转换为(C, H, W)。
本文以Pytorch中TorchVision的FashionMNIST数据集为例，展示如何将图像数据转换为Tensor格式。

import torch
from torchvision.datasets import FashionMNIST
from torchvision.transforms import ToTensor #将图像数据转换为张量 #加载数据集
train_data = FashionMNIST(root='./fashion_data', train=True, download=True) 
test_data = FashionMNIST(root='./fashion_data', train=False, download=True)

未转换为Tensor格式的FashionMNIST数据集

train_data  #Dataset对象（输入数据的集合） 60000个样本

Dataset FashionMNISTNumber of datapoints: 60000Root location: ./fashion_dataSplit: Train

import matplotlib.pyplot as plt train_data = FashionMNIST(root='./fashion_data', train=True, download=True) img,clzz = train_data[5]  #返回一个元组，第一个元素是图片，第二个元素是标签
plt.imshow(img, cmap='gray') #显示图片,cmap='gray'表示以灰度图显示;img是一个tensor,是一个PIL.Image对象(python原始数据类型)
plt.title(clzz)
plt.show()

在这里插入图片描述

train_data[1]  #返回的是一个元组，第一个元素是图片，第二个元素是标签
#train_data[1][0].shape  #图像数据(一个颜色通道：28*28，图像高度，图像宽度)
#train_data[1][0].reshape(-1).shape  #将图像数据展平为一维张量

(<PIL.Image.Image image mode=L size=28x28>, 0)

转换为Tensor格式的FashionMNIST数据集

#加载数据集
train_data = FashionMNIST(root='./fashion_data', train=True, download=True, transform=ToTensor()) 
test_data = FashionMNIST(root='./fashion_data', train=False, download=True, transform=ToTensor())

train_data  #Dataset对象（输入数据的集合） 60000个样本

Dataset FashionMNISTNumber of datapoints: 60000Root location: ./fashion_dataSplit: TrainStandardTransform
Transform: ToTensor()

可以对比下图像数据集转换前后的效果，未转换为Tensor格式的FashionMNIST数据集是PIL格式的图像，而转换为Tensor格式的FashionMNIST数据集是Tensor格式的图像。

train_data[1]  #输出的是一个元组，第一个元素是图片，第二个元素是标签

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此时，图像数据集的每个图像都是一个Tensor对象，Tensor对象的形状为(C, H, W)，其中C表示图像的通道数，H表示图像的高度，W表示图像的宽度。对于灰度图像，C=1；对于RGB图像，C=3。Tensor对象的dtype为torch.float32。
Tensor对象的像素值范围为[0, 1]，而PIL格式的图像的像素值范围为[0, 255]。Tensor对象的像素值是浮点数，而PIL格式的图像的像素值是整数。

train_data[1][0].shape  #图像数据(一个颜色通道：28*28，图像高度，图像宽度)

torch.Size([1, 28, 28])

上面输出的是FashionMNIST数据集中第0个图像的Tensor对象。Tensor对象的形状为(1, 28, 28)，表示图像的通道数为1，高度为28，宽度为28。Tensor对象的dtype为torch.float32，表示数据类型为32位浮点数。Tensor对象的像素值范围为[0, 1]，表示像素值是浮点数。

train_data[1][0].reshape(-1).shape  #将图像数据展平为一维张量

torch.Size([784])