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 实战内容简介

2 数据集读取

2.1 dataset

2.2 dataloader

3 模型搭建

3.1 基准模型

3.2 与基准模型相对应的双线性模型

4 性能差异比较

4.1 tensorboard查看测试集准确率差异

4.2 耗时比较（单位：秒）

1 实战内容简介

数据集：CUB-200，共200类不同种类的鸟。

• 第  步：计算每一个通道对应的特征，， 表示第  行  列激活值最大， 表示图像个数。
• 第  步：聚类初始化，使用  等聚类方法对特征进行聚类，得到  个部件
• 第  步：得到初始的  个部件，为每一个部件引入全连接层，预测  维向量，对应每一个通道有多大概率属于该部件，初始标签为第  步聚类结果
• 第  步：得到  个  维向量，对特征图进行加权求和，得到  个部件的注意力
• 第  步：将虚拟部件对应区域特征进行池化得到特征向量，进行分类

2 数据集读取

2.1 dataset

import os
import random

# 定义一个列表，用于txt文件内存放路径及标签数据
data_list = []
# 初始化类别标签
class_label = -1
# 加载dataset图片数据
dataset_path = './data/CUB_200_2011/images/'
# 遍历文件，依次将文件名存入上述定义列表当中
for root,_,filenames in os.walk(dataset_path):
    for i in filenames:
        data = root+"/"+i+"\\t"+str(class_label)+"\\n"
        print(data)
        data_list.append(data)   # 依次添加，不清空
    class_label += 1
# 打乱txt文件中的数据，保证下面分类进行测试集与训练集每个标签都有涉及
random.shuffle(data_list)

# 定义训练文本数据列表
train_list = []
# 将打乱后的总数据列表中的80%的数据用于训练集
for i in range(int(len(data_list) * 0.8)):
    train_list.append(data_list[i])
# 创建并以“写”方式打开train.txt
with open('train.txt', 'w', encoding='UTF-8') as f:
    for train_img in train_list:
        f.write(str(train_img))  # 将训练数据集数据写入train.txt

# 定义测试文本数据列表
eval_list = []
# 将打乱后的总数据列表中的20%的数据用于训练集
for i in range(int(len(data_list) * 0.8),len(data_list)):
    eval_list.append(data_list[i])
# 创建并以“写”方式打开eval.txt
with open('eval.txt', 'w', encoding='UTF-8') as f:
    for eval_img in eval_list:
        f.write(eval_img)   # 将测试数据集数据写入eval.txt

2.2 dataloader

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

# 定义一个dataloader用于等会调用
class Data_Loader(Dataset):
    def __init__(self, txt_path, train_flag=True):
        self.imgs_info = self.get_images(txt_path)
        self.train_flag = train_flag
        self.targetsize = 224                          # 把图片压缩成224X224
 
        # 训练集的处理方法
        self.train_tf = transforms.Compose([
                transforms.Resize(self.targetsize),    # 压缩图片
                transforms.RandomHorizontalFlip(),     # 随机水平反转
                transforms.RandomVerticalFlip(),       # 随机垂直反转图片
                transforms.ToTensor(),                 # 把图片转变为Tensor()格式，pytorch才能读写
            ])

        # 验证集（测试集）的处理方法
        self.val_tf = transforms.Compose([
                transforms.Resize(self.targetsize),
                transforms.ToTensor(),
            ])

    # 通过读取txt文档内容，返回文档中的每一条信息
    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

    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

    # 我们在遍历数据集中返回的每一条数据
    def __getitem__(self, index):
        img_path, label = self.imgs_info[index]     # 读取每一条数据，得到图片路径和标签值
        img = Image.open(img_path)                  # 利用 Pillow打开图片
        img = img.convert('RGB')                    # 将图片转变为RGB格式
        img = self.padding_black(img)
        if self.train_flag:                         # 对训练集和测试集分别处理
            img = self.train_tf(img)
        else:
            img = self.val_tf(img)
        label = int(label)
        return img, label                           # 返回图片和其标签值

    # 我们在遍历数据集时，遍历多少，返回的是数据集的长度
    def __len__(self):
        return len(self.imgs_info)

if __name__ == "__main__":
    train_dataset = Data_Loader("eval.txt", True)
    print("数据个数：", len(train_dataset))
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
                                               batch_size=10,
                                               shuffle=True)
    test_dataset = Data_Loader("eval.txt", False)
    print("数据个数：", len(test_dataset))
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
                                               batch_size=50,
                                               shuffle=True)
    for image, label in test_loader:
        print(image.shape)
        print(label)

3 模型搭建

3.1 基准模型

import torch.optim as optim
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import Data_Loader
from torchvision.models import resnet50
from tensorboardX import SummaryWriter
import time

# 创建一个基准模型类
class Baisc_Net(nn.Module):
    def __init__(self):
        super(Baisc_Net, self).__init__()
        self.features = nn.Sequential(resnet50().conv1,
                                      resnet50().bn1,
                                      resnet50().relu,
                                      resnet50().maxpool,
                                      resnet50().layer1,
                                      resnet50().layer2,
                                      resnet50().layer3,
                                      resnet50().layer4)
        self.classifiers = nn.Linear(100352, 5)

    def forward(self, x):
        x = self.features(x)
        x = x.view(-1, 100352)
        x = self.classifiers(x)
        return x

# 定义一个函数，在训练集上进行，打印输出loss值与acc值，并写入tensorboard中
def baisic_net_train(train_loader, model, criterion, optimizer, epoch):
    model.train()
    # 初始化正确率
    running_corrects = 0.0
    for i, (input, target) in enumerate(train_loader):
        input = input.cuda()
        target = target.cuda()
        output = model(input)     # 将输入输入到模型中，产生一个输出
        loss = criterion(output, target)    # 计算实际输出与目标输出之间的差距，将值传入loss变量中
        _, preds = torch.max(output.data, 1)     # 按行输出该维度预测概率最大的那一个标签
        running_corrects += torch.sum(preds == target).item()   # 计算该轮次中预测正确的标签数总和
        optimizer.zero_grad()    # 梯度清零
        loss.backward()          # 反向传播
        optimizer.step()         # 迭代更新
        if i % 20 == 0:
            print("Training loss = ", loss.item())    # 每轮中的20次输出一次loss
    epoch_acc = running_corrects / dataset_sizes
    print("Training Accuracy = ", epoch_acc)          # 输出每轮的准确率
    writer.add_scalar('contrast figure basic net', epoch_acc, global_step=epoch)     # 将准确率写入到tensorboard中

if __name__ == "__main__":
    train_dir_list = 'train.txt'
    valid_dir_list = 'eval.txt'
    batch_size = 1      # 看显存
    epochs = 50         # 总共训练多少回合

    # 加载数据集
    # 自创了一个data_loader，调用时需要从dataloader.py中进行读取
    # train_data是我们的训练集
    train_data = Data_Loader(train_dir_list, train_flag=True)
    valid_data = Data_Loader(valid_dir_list, train_flag=False)
    dataset_sizes = len(train_data)    # 查看训练数据集数量
    print(dataset_sizes)

    # 用dataloader加载dataset
    # 数据集的读写方式，num_workers控制多线程读写数据集，pin_memory是内存上锁，batch_size是多少个数据并行读取，shuffle是每次读写重新打乱数据集
    train_loader = DataLoader(dataset=train_data, num_workers=0, pin_memory=True, batch_size=batch_size, shuffle=True)
    valid_loader = DataLoader(dataset=valid_data, num_workers=0, pin_memory=True, batch_size=batch_size)

    # 定义网络
    model = Baisc_Net()
    print(model)
    model = model.cuda()

    # 使用交叉熵损失函数
    criterion = nn.CrossEntropyLoss()
    # 利用SGD优化算法
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # 将tensorboard文件写入runs文件夹中
    writer = SummaryWriter('./runs')
    # 定义一个开始时间，用于查看整个模型训练耗时
    start_time = time.time()

    # 开始训练
    for epoch in range(epochs):
        print("*********************   Epoch ", epoch, " ************************")
        baisic_net_train(train_loader, model, criterion, optimizer, epoch)    # 调用前面定义的训练方法
        epoch = epoch + 1
    # 定义一个结束时间
    end_time = time.time()
    # 用开始时间-结束时间=总耗时
    time = end_time - start_time
    print(time)
    # 关闭tensorboard写入
    writer.close()

3.2 与基准模型相对应的双线性模型

import torch.optim as optim
import torch
import time
import torch.nn as nn
from torch.utils.data import DataLoader
from dataloader import Data_Loader
from torchvision.models import resnet50
from tensorboardX import SummaryWriter

# 定义一个双线性模型类
class Bilinear_form_Net(nn.Module):
    def __init__(self):
        super(Bilinear_form_Net, self).__init__()
        self.features = nn.Sequential(resnet50().conv1,
                                      resnet50().bn1,
                                      resnet50().relu,
                                      resnet50().maxpool,
                                      resnet50().layer1,
                                      resnet50().layer2,
                                      resnet50().layer3,
                                      resnet50().layer4)
        self.classifiers = nn.Linear(2048 ** 2, 5)

    def forward(self, x):
        x = self.features(x)
        batch_size = x.size(0)
        x = x.view(batch_size, 2048, x.size(2) ** 2)
        x = (torch.bmm(x, torch.transpose(x, 1, 2)) / 28 ** 2).view(batch_size, -1)
        x = self.classifiers(x)
        return x

# 定义一个双线性模型测试集训练方法
def bilinear_form_net_train(train_loader, model, criterion, optimizer, epoch):#, writer):
    model.train()
    running_corrects = 0.0
    for i, (input, target) in enumerate(train_loader):
        input = input.cuda()
        target = target.cuda()
        output = model(input)
        loss = criterion(output, target)
        _, preds = torch.max(output.data, 1)
        running_corrects += torch.sum(preds == target).item()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if i % 20 == 0:
            print("Training loss = ", loss.item())
    epoch_acc = running_corrects / dataset_sizes
    print("Training Accuracy = ", epoch_acc)
    writer.add_scalar('contrast figure bilinear form net', epoch_acc, global_step=epoch)

if __name__ == "__main__":
    train_dir_list = 'train.txt'
    valid_dir_list = 'eval.txt'
    batch_size = 1
    epochs = 50

    # 加载数据
    train_data = Data_Loader(train_dir_list, train_flag=True)
    valid_data = Data_Loader(valid_dir_list, train_flag=False)
    dataset_sizes = len(train_data)
    print(dataset_sizes)

    # 用dataloader读取dataset
    train_loader = DataLoader(dataset=train_data, num_workers=0, pin_memory=True, batch_size=batch_size, shuffle=True)
    valid_loader = DataLoader(dataset=valid_data, num_workers=0, pin_memory=True, batch_size=batch_size)

    # 实例化双线性模型
    model = Bilinear_form_Net()
    print(model)
    model = model.cuda()

    # 损失函数采用交叉熵损失
    criterion = nn.CrossEntropyLoss()
    # 优化器采用SGD优化器
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # tensorboard文件写入runs文件夹下的文件中
    writer = SummaryWriter('./runs')

    start_time = time.time()
    # 开始训练，迭代epoch次
    for epoch in range(epochs):
        print("*********************   Epoch ", epoch, " ************************")
        bilinear_form_net_train(train_loader, model, criterion, optimizer, epoch)     # 调用训练方法
        epoch = epoch + 1
    end_time = time.time()
    time = end_time - start_time     # 总耗时
    print(time)
    # 关闭tensorboard写入
    writer.close()

4 性能差异比较

4.1 tensorboard查看测试集准确率差异

4.2 耗时比较（单位：秒）

基准模型

双线性模型

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