深度学习笔记（b站小土堆）_小土堆学习笔记

一、必备的数据集操作技巧

1.数据集操作

下列代码对原始数据集进行了处理，方便了对数据集的访问

import torch
from torch.utils.data import Dataset
from PIL import Image
import os


class MyData(Dataset):

    def __init__(self, root_dir, label_dir):  # self 的作用是指定一个类里的全局变量
        self.root_dir = root_dir
        self.label_dir = label_dir
        self.path = os.path.join(self.root_dir, self.label_dir)  # join函数，连接路径
        self.img_path = os.listdir(self.path)

    def __getitem__(self, idex):
        img_name = self.img_path[idex]
        img_item_path = os.path.join(self.root_dir, self.label_dir, img_name)
        img = Image.open(img_item_path)
        label = self.label_dir
        return img, label

    def __len__(self):
        return len(self.img_path)  # 返回列表的长度

root_dir = "dataset/train"
positive_label_dir = "positive"
negative_label_dir = "negative"
positive_dataset = MyData(root_dir, positive_label_dir)
negative_dataset = MyData(root_dir, negative_label_dir)
train_dataset = positive_dataset + negative_dataset  ##数据集的拼接操作

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2.利用txt文件保存label

上述情况，label是体现在文件夹的名称内，而使用最多的情况通常是这样的

positive_label文件夹内包含了与图片同名的txt文件，打开txt文件就是该图片的label，实现给图片创建对应txt的代码如下：

import os

root_dir = "dataset/train"
target_dir = "positive"
img_path = os.listdir(os.path.join(root_dir,target_dir))
label = target_dir
out_dir = "positive_label"
for i in img_path:
    file_name = i.split(".png")[0]
    with open(os.path.join(root_dir,out_dir,"{}.txt".format(file_name)),"w") as f:
        f.write(label)
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二、Pytorch中tensorboard的使用

tensorboard我理解为一个看板，对结果进行可视化展示

1.add_scalar函数的用法

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter("logs") #创建类的一个实例 将事件文件（图像等）存到logs文件夹下
# writer.add_image()
for i in range(100):
    writer.add_scalar("y=0.5x",i,2*i) #第一个i是y轴，第二个i是x轴，改变第一个参数可以避免几个图杂揉在一起的情况



writer.close()

#打开事件文件
#在Terminal窗口，输入命令：tensorboard --logdir=logs
#修改主机接口：tensorboard --logdir=logs --port=6007
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2.add_image函数的用法

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer = SummaryWriter("logs") #创建类的一个实例 将事件文件（图像等）存到logs文件夹下
image_path = "dataset/train/positive/1 (6).png"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL) #将PIL的文件格式转换为array格式，这样才能被add_image读取
#print(img_array.shape)  array转化的图像通常是（H，W，C）格式的，因此要在add_image函数中说明
writer.add_image("test", img_array, 2, dataformats='HWC') #1是指global_step，这里是指第一步
#step可以展现出每一步的变化，非常炫酷

#for i in range(100):
    #writer.add_scalar("y=0.5x",i,2*i) #第一个i是y轴，第二个i是x轴，改变第一个参数可以避免几个图杂揉在一起的情况

writer.close()

#打开事件文件
#在Terminal窗口，输入命令：tensorboard --logdir=logs
#修改主机接口：tensorboard --logdir=logs --port=6007
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三、transform的用法

transform可以理解为一个工具箱，里面有很多工具（class类）

1.基础知识

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
import cv2
#transforms相当于一个工具箱，里面有很多工具
#pycharm的左侧有个”structure“，可以方便的看到源代码的结构，相当于工具箱的说明书，前面几个类是最常用的

####python中的用法
###通过transform.ToTensor 去解决两个问题
###1.transform如何使用
###2.tensor的数据类型相较于其他数据类型有啥区别

img_path = "dataset/train/positive/1 (1).png"
img = Image.open(img_path) #这是PIL格式转化为tensor格式的方法
# print(img) <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=96x160 at 0x17C1459D240>
tensor_trans = transforms.ToTensor() #创建实例：tensor_trans
tensor_img = tensor_trans(img) #将PIL格式的图片转化为tensor数据类型的图片
###2.tensor数据类型内多了很多卷积神经网络需要的参数，这是其他数据类型不具备的特点

cv_img = cv2.imread(img_path)  #数据类型是numpy array

writer = SummaryWriter("logs")

writer.add_image("Tensor_img", tensor_img, 1)

writer.close()
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下面介绍python中类的概念，关于__call__函数的用法

class Person:
    def __call__(self, name):
        print("__call__"+"hello"+" "+name)
    def hello(self, name):
        print("hello"+" "+name)

person = Person()
person("zyj")  #这种调用方法是直接传入一个参数，默认就会传递给__call__函数
person.hello("zy")  #这种调用方法需要用点后接需指定的函数

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2.常见的transforms

下面介绍了transforms中常见的类的用法

from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("dataset/train/positive/1 (14).jpg")

#ToTensor的使用
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("To_Tensor", img_tensor)


#Normalize的使用（归一化）
trans_norm = transforms.Normalize([6,5,0.5],[5,0.5,5]) ###第一个列表表示平均值
#，第二个列表表示标准差
##归一化的过程：
#计算公式：input(channel) = (input[channel] - mean[channel])/std[channel]
#其中，mean和std是transforms.Normalize中需要传入的参数（用户自定义）
#此处img_tensor的范围在[0,1]，经过上述归一化后，范围变为了[-1,1]
img_norm = trans_norm(img_tensor)
writer.add_image("Normalize", img_norm, 1)



##Resize的使用 给定图片的尺寸。如果给两个数字，就会用长宽去匹配
#但是如果只给一个数字，就会用图片最小的边去匹配这个数字
print(img.size)
trans_resize = transforms.Resize((512, 512))
img_resize = trans_resize(img_tensor)
#print(img_resize)
writer.add_image("Resize", img_resize)

##Randomcrop的用法 随机裁剪
trans_random = transforms.RandomCrop(512)
trans_compose2 = transforms.Compose([trans_random, trans_totensor])
for i in range(10):
    img_crop = trans_compose2(img)
    writer.add_image("Randcrop", img_crop, i)


writer.close()
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四、Dataloader的用法

#dataloader的作用是到dataset中取数据
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor())

test_loader = DataLoader(dataset=test_data, batch_size=4, shuffle=True, num_workers=0, drop_last=False)
#batch_size表示每次抓牌的张数；shuffle表示第一次抓取和第二次抓取牌的顺序是否需要打乱
#如果为true表示两次抓取顺序不一样；num_workers表示进程数量，默认为0表示利用主进程
#drop_last表示牌的总数除以batch_size，如果除不尽的话，剩余的牌是否需要舍弃

#测试数据集的第一张图片及target
img, target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("dataloader")
step = 0
for data in test_loader:
    imgs, targets = data
    #print(imgs.shape)-->torch.Size([4, 3, 32, 32]) 四张图片，三个通道
    #print(targets) -->tensor([0, 8, 6, 5])
    writer.add_images("test_data", imgs, step)
    step = step + 1
writer.close()
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五、卷积层操作（一）

1d，2d，3d表示纬度，图片是二维的

input需要有四个信息参数，（minibatch，in_channels，iH，iW）
因此需要利用torch.reshape更改尺寸

import torch
import torch.nn.functional as F
input = torch.tensor([[1, 2, 0, 3, 1],
                      [0, 1, 2, 3, 1],
                      [1, 2, 1, 0, 1],
                      [5, 2, 3, 1, 1],
                      [2, 1, 0, 1, 1]])

kernel = torch.tensor([[1,2,1],
                       [0,1,0],
                       [2,1,0]])

input = torch.reshape(input, (1,1,5,5))
kernel = torch.reshape(kernel, (1,1,3,3))
output = F.conv2d(input,kernel,stride=1)
print(output)
'''
输出为四维是因为前面的reshape
tensor([[[[10, 12, 12],
          [18, 16, 16],
          [13,  9,  4]]]])
'''
##padding 对图像的边缘进行填充

output2 = F.conv2d(input,kernel,stride=1, padding=1)#默认填充0
print(output2)
'''
tensor([[[[ 1,  3,  4, 10,  8],
          [ 5, 10, 12, 12,  7],
          [ 7, 18, 16, 16,  9],
          [11, 13,  9,  4,  6],
          [14, 13,  9,  7,  4]]]])
'''

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六、卷积层操作（二）

上下两个区别是什么？

kernel_size的参数不需要给出，会自动调整
in_channel很好理解，就是图片的通道数
out_channel需要自己设置，当和in_channel的通道数不一样时，卷积核的个数会不一样

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10("./dataset",
 train=False, transform=torchvision.transforms.ToTensor(),
                                       download=False)
dataloader = DataLoader(dataset, batch_size=64)

class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.conv1 = Conv2d(in_channels=3, 
        out_channels=6, kernel_size=3, stride=1, padding=0)
    def forward(self, x):
        x = self.conv1(x)
        return x
zyj = ZYJ()

print(zyj)
'''
ZYJ(
  (conv1): Conv2d(3, 6, kernel_size=(3, 3), stride=(1, 1))
)'''
writer = SummaryWriter("juanji")
step = 0
for data in dataloader:
    imgs, targets = data
    output = zyj(imgs)
    #print(imgs.shape)  #->torch.Size([64, 3, 32, 32])
    #print(output.shape)  #->torch.Size([64, 6, 30, 30])
    writer.add_images("input", imgs, step)
    '''
    此处经常报错
    AssertionError: size of input tensor and input format are different.
    解决办法是writer.add_image—>writer.add_images
    '''
    #output的通道数是6个，无法可视化，这里使用了一个不严谨的方法，将output进行reshape
    output = torch.reshape(output, (-1, 3, 30, 30)) #这里的-1我们并不清楚

    writer.add_images("output", output, step)

    step = step + 1
writer.close()

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上图为vgg16架构
1->2经过了一次卷积+函数激活操作，前后尺寸不变，说明对padding进行了调试，input_channel = 3, output_channel = 64
具体调试（计算方法）如下图

七、最大池化的使用

1.理解池化

参数：

（stride的默认值是核的尺寸）

dilation 空洞卷积。如图

cell_mode可以选择Floor 和 Ceiling模式，默认为False（Floor）模式

计算公式

import torch
from torch import nn
from torch.nn import MaxPool2d

input = torch.tensor([[1,2,0,3,1],
                      [0,1,2,3,1],
                      [1,2,1,0,0],
                      [5,2,3,1,1],
                      [2,1,0,1,1]], dtype=torch.float32)
#注意修改数据类型，否则报错："max_pool2d" not implemented for 'Long'
input = torch.reshape(input, (-1,1,5,5))

class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.maxpool1 = MaxPool2d(kernel_size=3, ceil_mode=True)
    def forward(self, input):
        output = self.maxpool1(input)
        return output
zyj = ZYJ()
output = zyj(input)
print(output)
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八、非线性激活

ReLU函数

import torch
from torch import nn
from torch.nn import ReLU

input = torch.tensor([[1, -0.5],
                      [-1, 3]])
output = torch.reshape(input, (-1, 1, 2, 2))
print(output.shape)

class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.relu1 = ReLU() #inplace默认为False
        # 意思是不在原数据上改动
    def forward(self, input):
        output = self.relu1(input)
        return output

zyj = ZYJ()
output = zyj(input)
print(output)

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以下为对图像处理

import torch
import torchvision
from torch import nn
from torch.nn import ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
dataset = torchvision.datasets.CIFAR10('dataset', train=False, download=False,
                                       transform=torchvision.transforms.ToTensor())
dataloader = DataLoader(dataset, batch_size=64)
class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.relu1 = ReLU() #inplace默认为False
        # 意思是不在原数据上改动
        self.sigmoid1 = Sigmoid()
    def forward(self, input):
        output = self.sigmoid1(input)
        return output
zyj = ZYJ()
writer = SummaryWriter("logs_sigmoid")
step = 0
for data in dataloader:
    imgs, target = data
    writer.add_images("input", imgs, global_step=step)
    output = zyj(imgs)
    writer.add_images("output", output, global_step=step)
    step = step + 1
writer.close()
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九、线性层及其他层的介绍

1.正则化层——BatchNorm2d

2.Recurrent Layers

用得不多

3.Transformer Layers

用得不多

4.Linear Layers（重点）

下图为线性层，对应的in_feature = d, out_feature = L ,bias = True

import torch
import torchvision
from torch import nn
from torch.nn import Linear
from torch.utils.data import DataLoader

dataset = torchvision.datasets.CIFAR10("dataset", train=False, transform=torchvision.transforms.ToTensor()
                                       , download=False)
dataloader = DataLoader(dataset, batch_size=64, drop_last=True)
class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.linear1 = Linear(196608,10)

    def forward(self,input):
        output = self.linear1(input)
        return output
zyj = ZYJ()

for data in dataloader:
    imgs, targets = data
    print(imgs.shape)
    output = torch.reshape(imgs, (1, 1, 1, -1))
    #上行代码可以用 output = torch.flatten(imgs)替代
    #效果一样，最后得到的数据维数不一样
    print(output.shape)
    output = zyj(output)
    print(output.shape)

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5.Dropout Layers

防止过拟合

6.Sparse Layers

自然语言处理使用得多

7.Distance Function

计算两个值的误差

8.Loss Function

…

十、搭建小实战和sequential的使用

网络结构搭建

import torch
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential
from torch.utils.tensorboard import SummaryWriter


class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.model = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)

        )
    def forward(self, input):
        output = self.model(input)
        return output

zyj = ZYJ()
input = torch.ones(64, 3, 32, 32)
output = zyj(input)
print(output.shape)

writer = SummaryWriter("logs_seq")
writer.add_graph(zyj, input)
writer.close()
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十一、损失函数

import torch
from torch import nn
from torch.nn import L1Loss, MSELoss

####L1Loss
inputs = torch.tensor([1, 2, 3],dtype=torch.float32)
targets = torch.tensor([4, 2, 3],dtype=torch.float32)
inputs = torch.reshape(inputs, (1,1,1,3))
targets = torch.reshape(targets, (1,1,1,3))

loss = L1Loss(reduction='sum')
result = loss(inputs, targets)
print(result)

##MSELoss
loss_mse = MSELoss()
result_mse = loss_mse(inputs, targets)
print(result_mse)


##CrossEntropyLoss 交叉熵（原理没听懂）

x = torch.tensor([0.1, 0.2, 0.3])
y = torch.tensor([1])
x = torch.reshape(x, (1, 3))
loss_cross = nn.CrossEntropyLoss()
result_cross = loss_cross(x, y)
print(result_cross)
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下面搭建了一个神经网络

import torch
import torchvision
from torch import nn
from torch.nn import Conv2d, MaxPool2d, Flatten, Linear, Sequential, ReLU, Sigmoid
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

dataset = torchvision.datasets.CIFAR10(root="dataset", train=False, transform=torchvision.transforms.ToTensor()
                                       ,download=False)
dataloader = DataLoader(dataset, batch_size=1)

class ZYJ(nn.Module):
    def __init__(self):
        super(ZYJ, self).__init__()
        self.model = Sequential(
            Conv2d(3, 32, 5, padding=2),
            MaxPool2d(2),
            ReLU(),
            Conv2d(32, 32, 5, padding=2),
            Sigmoid(),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)

        )
    def forward(self, input):
        output = self.model(input)
        return output

zyj = ZYJ()

loss = nn.CrossEntropyLoss()
for data in dataloader:
    imgs, targets = data
    output = zyj(imgs)
    #print(output)
    #print(targets)
    result_loss = loss(output, targets)
    #print(result_loss)
    result_loss.backward()  #反向传播获得梯度，后续用优化器进行优化参数
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十二、优化器

loss = nn.CrossEntropyLoss()
optim = torch.optim.SGD(zyj.parameters(), lr=0.01)
for epoch in range(50):
    running_loss = 0.0

    for data in dataloader:
        imgs, targets = data
        output = zyj(imgs)
        #print(output)
        #print(targets)
        result_loss = loss(output, targets)
        optim.zero_grad()  #将上一次的梯度数据删除
        #print(result_loss)
        result_loss.backward()  #反向传播获得梯度，后续用优化器进行优化参数
        optim.step()
        #print(result_loss)
        running_loss = running_loss + result_loss
    print(running_loss)
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十三、现有模型的使用和修改

pretrained为true时，就是带参数的

改模型两个思路：1.将最后一个线性层的out_feature改成10

2.添加一个线性层

十四、模型的保存

方式一保存的是模型的结构+模型的参数
方式二保存的是模式的参数

载入方法 ，推荐第二种，第一种更简单

十五、完整的模型训练套路

import torch
from torch.utils.tensorboard import SummaryWriter

from model_self import *
import torchvision
from torch.nn import Conv2d
from torch.optim import SGD
from torch.utils.data import DataLoader
from torch import nn
from torch.nn import Sequential, Conv2d, MaxPool2d, Flatten, Linear

# 准备数据及
train_data = torchvision.datasets.CIFAR10('./cifar10', True, transform=torchvision.transforms.ToTensor(),download=False)
test_data = torchvision.datasets.CIFAR10('./cifar10', False, transform=torchvision.transforms.ToTensor(),download=False)


# 求长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据及长度:{}".format(train_data_size))
print("测试数据集长度:{}".format(test_data_size))

# 加载数据及
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)


# 搭建网络


# 创建网络模型
lyy = Lyy()

# 创建损失函数
loss_fn = nn.CrossEntropyLoss()

# 优化器
learning_rate = 1e-2
optimizer = torch.optim.SGD(lyy.parameters(),lr=learning_rate)


# 设置训练网络参数
total_train_step = 0
total_test_step = 0
epoch = 10


# 添加tensorboard
writer = SummaryWriter("logs")


for i in range(epoch):
    print("-----第{}轮训练开始了-----".format(i+1))

    # 训练步骤开始
    for data in train_dataloader:
        imgs, tragets = data
        output = lyy(imgs)
        loss = loss_fn(output, tragets)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step += 1
        if total_train_step % 100 == 0:
            print("训练次数：{}，Loss:{}".format(total_train_step, loss.item()))
            writer.add_scalar("train_loss", loss.item(), total_train_step)

    # 测试步骤开始
    total_test_loss = 0
    total_accuracy = 0

    with torch.no_grad():  #将网络设置为no_grad模式
        for data in test_dataloader:
            imgs, tragets = data
            output = lyy(imgs)
            loss = loss_fn(output, tragets)
            total_test_loss += loss

            accuracy = (output.argmax(1) -- tragets).sum()
            total_accuracy += accuracy

    print("整体测试机上误差：{}".format(total_test_loss))
    print("整体测试机上的正确率：{}".format(total_accuracy/test_data_size))
    writer.add_scalar("test_loss", total_test_loss, total_test_step)
    writer.add_scalar("test_accuracy", total_accuracy/total_test_step)
    total_test_step += 1

    # torch.save(lyy, "lyy_{}.pth".format(i))
    # print("模型已保存")

writer.close()

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十六、利用GPU加速

第一种方法：

第二种方法：device（“cpu”）or device（“cuda”）

十七、如何使用模型？

import torch
import torchvision
from PIL import Image

from model import *

img_path = "test_imgs/1.jpg"
image = Image.open(img_path)
print(image)
image = image.convert('RGB')

transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
                                            torchvision.transforms.ToTensor()])

image = transform(image)
print(image.shape)

model = torch.load("model/tudui_9.pth", map_location=torch.device('cpu'))
print(model)

image = torch.reshape(image, (1, 3, 32, 32))
print(image.shape)
model.eval()
with torch.no_grad():
    #image = image.cuda()
    output = model(image)
print(output)
print(output.argmax(1))

# 'airplane'=0
# 'automobile'=1
# 'brid'=2
# 'cat'=3
# 'deer'=4
# 'dog'=5
# 'frog'=6
# 'horse'=7
# 'ship'=8
# 'truck'=9

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完结撒花（历时一个月，我可真够拖延的）