PyTorch 如何将CIFAR100数据按类标归类保存

few-shot learning的采样

Few-shot learning 基于任务对模型进行训练，在N-way-K-shot中，一个任务中的meta-training中含有N类,每一类抽取K个样本构成support set, query set则是在刚才抽取的N类剩余的样本中sample一定数量的样本（可以是均匀采样，也可以是不均匀采样）。

对数据按类标归类

针对上述情况，我们需要使用不同类别放置在不同文件夹的数据集。但有时，数据并没有按类放置，这时就需要对数据进行处理。

下面以CIFAR100为列(不含N-way-k-shot的采样）：

import os
from skimage import io
import torchvision as tv
import numpy as np
import torch
def Cifar100(root):
    character = [[] for i in range(100)]
    train_set = tv.datasets.CIFAR100(root, train=True, download=True)
    test_set = tv.datasets.CIFAR100(root, train=False, download=True)
    dataset = []
    for (X, Y) in zip(train_set.train_data, train_set.train_labels):  # 将train_set的数据和label读入列表
        dataset.append(list((X, Y)))
    for (X, Y) in zip(test_set.test_data, test_set.test_labels):  # 将test_set的数据和label读入列表
        dataset.append(list((X, Y)))
    for X, Y in dataset:
        character[Y].append(X)  # 32*32*3
    character = np.array(character)
    character = torch.from_numpy(character)
    # 按类打乱
    np.random.seed(6)
    shuffle_class = np.arange(len(character))
    np.random.shuffle(shuffle_class)
    character = character[shuffle_class]
    # shape = self.character.shape
    # self.character = self.character.view(shape[0], shape[1], shape[4], shape[2], shape[3])  # 将数据转成channel在前
    meta_training, meta_validation, meta_testing = 
    character[:64], character[64:80], character[80:]  # meta_training : meta_validation : Meta_testing = 64类：16类：20类
    dataset = []  # 释放内存
    character = []
    os.mkdir(os.path.join(root, "meta_training"))
    for i, per_class in enumerate(meta_training):
        character_path = os.path.join(root, "meta_training", "character_" + str(i))
        os.mkdir(character_path)
        for j, img in enumerate(per_class):
            img_path = character_path + "/" + str(j) + ".jpg"
            io.imsave(img_path, img)
    os.mkdir(os.path.join(root, "meta_validation"))
    for i, per_class in enumerate(meta_validation):
        character_path = os.path.join(root, "meta_validation", "character_" + str(i))
        os.mkdir(character_path)
        for j, img in enumerate(per_class):
            img_path = character_path + "/" + str(j) + ".jpg"
            io.imsave(img_path, img)
    os.mkdir(os.path.join(root, "meta_testing"))
    for i, per_class in enumerate(meta_testing):
        character_path = os.path.join(root, "meta_testing", "character_" + str(i))
        os.mkdir(character_path)
        for j, img in enumerate(per_class):
            img_path = character_path + "/" + str(j) + ".jpg"
            io.imsave(img_path, img)
if __name__ == "__main__":
    root = "/home/xie/文档/datasets/cifar_100"
    Cifar100(root)
    print("-----------------")

补充：使用Pytorch对数据集CIFAR-10进行分类

主要是以下几个步骤：

1、下载并预处理数据集

2、定义网络结构

3、定义损失函数和优化器

4、训练网络并更新参数

5、测试网络效果

#数据加载和预处理
#使用CIFAR-10数据进行分类实验
import torch as t
import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
show = ToPILImage() # 可以把Tensor转成Image，方便可视化
 
#定义对数据的预处理
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5)),  #归一化
])
 
#训练集
trainset = tv.datasets.CIFAR10(
    root = "./data/",
    train = True,
    download = True,
    transform = transform
)
 
trainloader = t.utils.data.DataLoader(
    trainset,
    batch_size = 4,
    shuffle = True,
    num_workers = 2,
)
 
#测试集
testset = tv.datasets.CIFAR10(
    root = "./data/",
    train = False,
    download = True,
    transform = transform,
)
testloader = t.utils.data.DataLoader(
    testset,
    batch_size = 4,
    shuffle = False,
    num_workers = 2,
)
 
classes = ("plane", "car", "bird", "cat",
           "deer", "dog", "frog", "horse", "ship", "truck")

初次下载需要一些时间，运行结束后，显示如下：

import torch.nn as nn
import torch.nn.functional as F
import time
start = time.time()#计时
#定义网络结构
class Net(nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.conv1 = nn.Conv2d(3,6,5)
        self.conv2 = nn.Conv2d(6,16,5)
        self.fc1 = nn.Linear(16*5*5,120)
        self.fc2 = nn.Linear(120,84)
        self.fc3 = nn.Linear(84,10)
        
    def forward(self,x):
        x = F.max_pool2d(F.relu(self.conv1(x)),2)
        x = F.max_pool2d(F.relu(self.conv2(x)),2)
        
        x = x.view(x.size()[0],-1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
net = Net()
print(net)

显示net结构如下：

#定义优化和损失
loss_func = nn.CrossEntropyLoss()  #交叉熵损失函数
optimizer = t.optim.SGD(net.parameters(),lr = 0.001,momentum = 0.9)
 
#训练网络
for epoch in range(2):
    running_loss = 0
    for i,data in enumerate(trainloader,0):
        inputs,labels = data
       
        outputs = net(inputs)
        loss = loss_func(outputs,labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        running_loss +=loss.item()
        if i%2000 ==1999:
            print("epoch:",epoch+1,"|i:",i+1,"|loss:%.3f"%(running_loss/2000))
            running_loss = 0.0
end = time.time()
time_using = end - start
print("finish training")
print("time:",time_using)

结果如下：

下一步进行使用测试集进行网络测试：

#测试网络
correct = 0 #定义的预测正确的图片数
total = 0#总共图片个数
with t.no_grad():
    for data in testloader:
        images,labels = data
        outputs = net(images)
        _,predict = t.max(outputs,1)
        total += labels.size(0)
        correct += (predict == labels).sum()
print("测试集中的准确率为：%d%%"%(100*correct/total))

结果如下：

简单的网络训练确实要比10%的比例高一点：）

在GPU中训练：

#在GPU中训练
device = t.device("cuda:0" if t.cuda.is_available() else "cpu")
 
net.to(device)
images = images.to(device)
labels = labels.to(device)
 
output = net(images)
loss = loss_func(output,labels)
 
loss

以上为个人经验，希望能给大家一个参考，也希望大家多多支持服务器之家。如有错误或未考虑完全的地方，望不吝赐教。

原文链接：https://blog.csdn.net/Xie_learning/article/details/89365305