「深度学习一遍过」必修3：使用Pytorch进行数据读取_from createdataloader import loaddata

本专栏用于记录关于深度学习的笔记，不光方便自己复习与查阅，同时也希望能给您解决一些关于深度学习的相关问题，并提供一些微不足道的人工神经网络模型设计思路。
专栏地址：「深度学习一遍过」必修篇

目录

1 CreateDataset——生成训练集和测试集 

1.1 思路

1.2 代码

1.3 结果

2 CreateDataloader——数据加载

2.1 思路

2.2 代码

2.3 结果

2.4 补充

3 加载自己的数据——使用DataLoader读取Dataset

3.1 思路

3.2 代码

3.3 结果

---

1 CreateDataset——生成训练集和测试集 

生成训练集和测试集，保存在 txt 文件中。

相当于模型的输入。后面做数据加载器 dataload 的时候从里面读它的数据。

1.1 思路

导入库

import os   # 导入系统库
import random  # 随机数生成器（打乱数据用的）

百分之 60 用来当训练集
百分制 40 用来当测试集

train_ratio = 0.6
test_ratio = 1 - train_ratio

数据的根目录，保存到这个根目录下的 data 文件夹

rootdata = r"data" 

产生 train.txt 和 test.txt

前一部分：图片所在目录/位置；
后一部分：图片对应标签       具体如下图所示

 打乱训练数据集次序

random.shuffle(train_list)

 写入 train.txt 和 test.txt

1.2 代码

import os 
import random 
 
train_ratio = 0.6
test_ratio = 1 - train_ratio
 
rootdata = r"data" 
 
train_list, test_list = [], [] 
data_list = []
 
class_flag = -1
for a, b, c in os.walk(rootdata):
    print(a)
    for i in range(len(c)):
        data_list.append(os.path.join(a, c[i]))
    for i in range(0, int(len(c) * train_ratio)):
        train_data = os.path.join(a, c[i]) + '\t' + str(class_flag) + '\n'    # os.path.join 拼接起来，给一个从0开始逐一编号的标签
        train_list.append(train_data)
 
    for i in range(int(len(c) * train_ratio), len(c)):
        test_data = os.path.join(a, c[i]) + '\t' + str(class_flag) + '\n'
        test_list.append(test_data)
 
    class_flag += 1
 
print(train_list)
random.shuffle(train_list)
random.shuffle(test_list)
 
with open('train.txt', 'w', encoding='UTF-8') as f:   
    for train_img in train_list:    
        f.write(str(train_img))
 
with open('test.txt', 'w', encoding='UTF-8') as f:
    for test_img in test_list:
        f.write(test_img)

1.3 结果

2 CreateDataloader——数据加载

把数据传入模型中进行训练。 

2.1 思路

图片标准化

transform_BZ= transforms.Normalize(
    mean=[0.5, 0.5, 0.5],# 取决于数据集
    std=[0.5, 0.5, 0.5]
)

加载得到图片信息

def get_images(self, txt_path):
        with open(txt_path, 'r', encoding='utf-8') as f:
            imgs_info = f.readlines()
            imgs_info = list(map(lambda x:x.strip().split('\t'), imgs_info))
        return imgs_info

训练与测试数据的归一化与标准化

self.train_tf = transforms.Compose([
        transforms.Resize(224),               # 将图片压缩成224*224的大小
        transforms.RandomHorizontalFlip(),    # 对图片进行随机的水平翻转
        transforms.RandomVerticalFlip(),      # 随机的垂直翻转
        transforms.ToTensor(),                # 把图片改为Tensor格式
        transform_BZ                          # 图片标准化的步骤
    ])
self.val_tf = transforms.Compose([            # 简单把图片压缩了变成Tensor模式
        transforms.Resize(224),
        transforms.ToTensor(),
        transform_BZ                          # 标准化操作
    ])

填充黑色操作，如果尺寸太小可以扩充，填充黑色使得图片成为 224*224。

 def padding_black(self, img):
        w, h  = img.size
        scale = 224. / max(w, h)
        img_fg = img.resize([int(x) for x in [w * scale, h * scale]])
        size_fg = img_fg.size
        size_bg = 224
        img_bg = Image.new("RGB", (size_bg, size_bg))
        img_bg.paste(img_fg, ((size_bg - size_fg[0]) // 2,
                              (size_bg - size_fg[1]) // 2))
        img = img_bg
        return img

 True 为将 "train.txt" 文件数据看做训练集对待

 train_dataset = LoadData("train.txt", True)    

2.2 代码

import torch
from PIL import Image
 
import torchvision.transforms as transforms
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
 
from torch.utils.data import Dataset
 
transform_BZ= transforms.Normalize(
    mean=[0.5, 0.5, 0.5],
    std=[0.5, 0.5, 0.5]
)
 
 
class LoadData(Dataset):
    def __init__(self, txt_path, train_flag=True):
        self.imgs_info = self.get_images(txt_path)
        self.train_flag = train_flag
 
        self.train_tf = transforms.Compose([
                transforms.Resize(224),
                transforms.RandomHorizontalFlip(),
                transforms.RandomVerticalFlip(),
                transforms.ToTensor(),
                transform_BZ
            ])
        self.val_tf = transforms.Compose([
                transforms.Resize(224),
                transforms.ToTensor(),
                transform_BZ
            ])
 
    def get_images(self, txt_path):
        with open(txt_path, 'r', encoding='utf-8') as f:
            imgs_info = f.readlines()
            imgs_info = list(map(lambda x:x.strip().split('\t'), imgs_info))
        return imgs_info
 
    def padding_black(self, img):
        w, h  = img.size
        scale = 224. / max(w, h)
        img_fg = img.resize([int(x) for x in [w * scale, h * scale]])
        size_fg = img_fg.size
        size_bg = 224
        img_bg = Image.new("RGB", (size_bg, size_bg))
        img_bg.paste(img_fg, ((size_bg - size_fg[0]) // 2,
                              (size_bg - size_fg[1]) // 2))
        img = img_bg
        return img
 
    def __getitem__(self, index):          # 返回真正想返回的东西
        img_path, label = self.imgs_info[index]
        img = Image.open(img_path)
        img = img.convert('RGB')
        img = self.padding_black(img)
        if self.train_flag:
            img = self.train_tf(img)
        else:
            img = self.val_tf(img)
        label = int(label)
 
        return img, label
 
    def __len__(self):
        return len(self.imgs_info)
 
 
if __name__ == "__main__":
    train_dataset = LoadData("train.txt", True)
    print("数据个数：", len(train_dataset))
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                               batch_size=10,
                                               shuffle=True)
    for image, label in train_loader:
        print(image.shape)
        print(image)
        print(label)

2.3 结果

2.4 补充

import torchvision
# 准备的测试数据集
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
 
test_data = torchvision.datasets.CIFAR10('./dataset', train=False, download=True, transform=torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=True)
 
# 测试数据集中第一张图片及target
img, target = test_data[0]
print(img.shape)
print(target)
 
writer = SummaryWriter('dataloader')
step = 0
 
for data in test_loader:
    imgs, targets = data
    writer.add_images('test_data', imgs, step)
    step = step + 1
 
writer.close()

3 加载自己的数据——使用DataLoader读取Dataset

3.1 思路

定义网络模型

class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        # 碾平，将数据碾平为一维
        self.flatten = nn.Flatten()
        # 定义linear_relu_stack，由以下众多层构成
        self.linear_relu_stack = nn.Sequential(
            # 全连接层
            nn.Linear(3*224*224, 512),
            # ReLU激活函数
            nn.ReLU(),
            # 全连接层
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 5),     # 5个类别按照分类数来
            nn.ReLU()
        )
    # x为传入数据
    def forward(self, x):
        # x先经过碾平变为1维
        x = self.flatten(x)
        # 随后x经过linear_relu_stack
        logits = self.linear_relu_stack(x)
        # 输出logits
        return logits

 定义训练函数

def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    # 从数据加载器中读取batch（一次读取多少张，即批次数），X(图片数据)，y（图片真实标签）。
    for batch, (X, y) in enumerate(dataloader):
        # 将数据存到显卡
        X, y = X.cuda(), y.cuda()
 
        # 得到预测的结果pred
        pred = model(X)
 
        # 计算预测的误差
        # print(pred,y)
        loss = loss_fn(pred, y)
 
        # 反向传播，更新模型参数
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        # 每训练100次，输出一次当前信息
        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")

定义测试函数 

def test(dataloader, model):
    size = len(dataloader.dataset)
    print("size = ",size)
    # 将模型转为验证模式
    model.eval()
    # 初始化test_loss 和 correct， 用来统计每次的误差
    test_loss, correct = 0, 0
    # 测试时模型参数不用更新，所以no_gard()
    # 非训练， 推理期用到
    with torch.no_grad():
        # 加载数据加载器，得到里面的X（图片数据）和y(真实标签）
        for X, y in dataloader:
            # 将数据转到GPU
            X, y = X.cuda(), y.cuda()
            # 将图片传入到模型当中就，得到预测的值pred
            pred = model(X)
            # 计算预测值pred和真实值y的差距
            test_loss += loss_fn(pred, y).item()
            # 统计预测正确的个数
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= size
    correct /= size
    print("correct = ",correct)
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")

 给训练集和测试集分别创建一个数据集加载器

train_data = LoadData("train.txt", True)
valid_data = LoadData("test.txt", False)

num_workers ：CPU开多线程读取数据（数字越大读取速度越快，适合多少要看 CPU 多线程能力有多强）；
pin_memory = True ：原本先放到内存在放到显卡，现在避免放到电脑虚拟内存中，这样写使得读取速度变快；
batch_size = batch_size ：一次读多少数据；
shuffle = True ：每次读取数据进行打乱。

而测试数据集不需打乱数据。

train_dataloader = DataLoader(dataset=train_data, num_workers=4, pin_memory=True, batch_size=batch_size, shuffle=True)
test_dataloader = DataLoader(dataset=valid_data, num_workers=4, pin_memory=True, batch_size=batch_size)

如果显卡可用，则用显卡进行训练

device = "cuda" if torch.cuda.is_available() else "cpu"

调用刚定义的模型，将模型转到GPU（如果可用）

model = NeuralNetwork().to(device)

定义损失函数，计算相差多少，交叉熵，

loss_fn = nn.CrossEntropyLoss()

定义优化器，用来训练时候优化模型参数，随机梯度下降法

optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)  # lr：初始学习率

读取训练好的模型，加载训练好的参数

model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth")) 

3.2 代码

import torch
from torch import nn
from torch.utils.data import DataLoader
from transfer_learning.CreateDataloader import LoadData
 
# 定义网络模型
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(3*224*224, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 5),
            nn.ReLU()
        )
 
    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits
 
# 定义训练函数
def train(dataloader, model, loss_fn, optimizer):
    size = len(dataloader.dataset)
    for batch, (X, y) in enumerate(dataloader):
        X, y = X.cuda(), y.cuda()
        pred = model(X)
        loss = loss_fn(pred, y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if batch % 100 == 0:
            loss, current = loss.item(), batch * len(X)
            print(f"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]")
 
# 定义测试函数
def test(dataloader, model):
    size = len(dataloader.dataset)
    print("size = ",size)
    model.eval()
    test_loss, correct = 0, 0
    with torch.no_grad():
        for X, y in dataloader:
            X, y = X.cuda(), y.cuda()
            pred = model(X)
            test_loss += loss_fn(pred, y).item()
            correct += (pred.argmax(1) == y).type(torch.float).sum().item()
    test_loss /= size
    correct /= size
    print("correct = ",correct)
    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
 
if __name__=='__main__':
    batch_size = 16
 
    train_data = LoadData("train.txt", True)
    valid_data = LoadData("test.txt", False)
 
    train_dataloader = DataLoader(dataset=train_data, num_workers=4, pin_memory=True, batch_size=batch_size, shuffle=True)
    test_dataloader = DataLoader(dataset=valid_data, num_workers=4, pin_memory=True, batch_size=batch_size)
 
    for X, y in test_dataloader:
        print("Shape of X [N, C, H, W]: ", X.shape)
        print("Shape of y: ", y.shape, y.dtype)
        break
 
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print("Using {} device".format(device))
 
    model = NeuralNetwork().to(device)
    print(model)
 
    loss_fn = nn.CrossEntropyLoss()
 
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
 
    epochs = 5
    for t in range(epochs):
        print(f"Epoch {t+1}\n-------------------------------")
        train(train_dataloader, model, loss_fn, optimizer)
        test(test_dataloader, model)
    print("Done!")
 
    model = NeuralNetwork()
    model.load_state_dict(torch.load("model.pth"))

3.3 结果

欢迎大家交流评论，一起学习

希望本文能帮助您解决您在这方面遇到的问题

感谢阅读
END

版权声明：本文为CSDN博主「荣仔！最靓的仔！」的原创文章，遵循 CC 4.0 BY-SA 版权协议。
         转载请在醒目位置附上原文出处链接及本声明。