深度学习初学--数据增强可视化

在深度学习中，数据增强是个很重要的数据预处理过程，处理的好坏影响训练的结果，以下是一个可视化数据增强后图像的可视化代码：

import numpy as np
import torchvision.transforms as transforms
from matplotlib import pyplot as plt
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.utils import make_grid
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=(0.485,0.455,0.406),std=(0.229,0.224,0.225))
])
traindata = datasets.ImageFolder(root ='train', transform=transform)
trainloader = DataLoader(dataset=traindata, batch_size=64, shuffle=True,num_workers=8)

def image_show(image):
    plt.figure(figsize=(50,50))
    image = image.numpy().transpose((1,2,0))
    mean = np.array([0.485,0.456,0.406])
    std = np.array([0.229,0.224,0.225])
    image = std *image +mean
    image = np.clip(image,0,1)
    plt.imshow(image)
    plt.show()
datas , targets = next(iter(trainloader))
out = make_grid(datas,nrow=4,padding=10)
image_show(out)
print(targets)

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