DA-CS代码解读(1)

GAMBER_123

已于 2023-10-04 21:45:03 修改

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文章标签：深度学习

于 2023-09-25 18:09:53 首次发布

本文链接：https://blog.youkuaiyun.com/hyh14/article/details/133250687

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The semantic extraction part

of the proposed method in "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data".

Train the Classifier (Pragmatic Function)

1) For the MNIST dataset

mnist手写数字识别（1. mnist介绍）（详细讲解） - 知乎1. 介绍 MNIST数据库（Modified National Institute of Standards and Technology database）是一个大型数据库的手写数字是通常用于训练各种图像处理系统。该数据库还广泛用于机器学习领域的培训和测试。它是通过…https://zhuanlan.zhihu.com/p/264960142准备数据：

import torch
import numpy as np
from torchvision.datasets import mnist  # dataset
from torch.utils.data import DataLoader

# 数据变换
def data_transform(x):
    x = np.array(x, dtype='float32') / 255   #转化为矩阵并用255规范化
    x = (x - 0.5) / 0.5
    x = x.reshape((-1,))
    x = torch.from_numpy(x) #Tensor和Numpy相互转换——torch.from_numpy()
    return x

# load data
trainset = mnist.MNIST('./dataset/mnist', train=True, 
                       transform=data_transform, download=True) #下载数据，但是为class
testset = mnist.MNIST('./dataset/mnist', train=False, 
                      transform=data_transform, download=True)
train_data = DataLoader(trainset, batch_size=64, shuffle=True) #根据batch进行数据切片，shuffle表示是否重新洗牌。
test_data = DataLoader(testset, batch_size=128, shuffle=False)

这里与原文有出入，文章中设定说是5w为训练集，其实训练集为6w，测试集为1w。

可以用下面的包，看一下数据的样子。前面reshape扯为一维的了，为了看图变回28x28.

import matplotlib.pyplot as plt
#看图
a_data, a_label = trainset[0]
plt.imshow(a_data.reshape(28,28))
plt.colorbar()
plt.show()

定义训练网络：

from torch import nn
import torch.nn.functional as F

#定义一个class
class MLP(nn.Module):
    # classifier
    def __init__(self):
        super(MLP, self).__init__()    #用于继承父类的init函数 继承自父类nn.Module的属性进行初始化
        self.fc1 = nn.Linear(28 * 28, 500) #线性y=xA+b 输入；输出；若bias为false则不学习b。
        self.fc2 = nn.Linear(500, 250)
        self.fc3 = nn.Linear(250, 125)
        # self.fc4 = nn.Linear(125, 10)
        self.fc4 = nn.Linear(125, 10)

    def forward(self, x):
        # 用一下全连接层fc1，然后做一个激活。
        x = F.relu(self.fc1(x)) #[64,500]
        # 用一下全连接层fc2，然后做一个激活。
        x = F.relu(self.fc2(x)) #[64,250]

        #dim=0，按列求平均 输出 250 个值
        aver_tmp = torch.mean(x, dim=0, out=None) #250
        aver = torch.mean(aver_tmp, dim=0, out=None) #还是输出1个值

        snr = 1
        aver_noise = aver * (1 / 10 **(snr/10)) #1个值
        noise = torch.randn(size=x.shape) * aver_noise #[64,250] 随机噪声

        x = x + noise #[64,250]

        x = F.relu(self.fc3(x)) #[64,125]
        x = self.fc4(x) #[64,10]
        return x

nn.Module是nn中十分重要的类,包含网络各层的定义及forward方法。

定义自已的网络时，需要继承nn.Module类，并实现forward方法。

一般把网络中具有可学习参数的层放在构造函数__init__()中，不具有可学习参数的层(如ReLU)可放在构造函数中，也可不放在构造函数中(而在forward中使用nn.functional来代替)

只要在nn.Module的子类中定义了forward函数，backward函数就会被自动实现(利用Autograd)。

在forward函数中可以使用任何Variable支持的函数，毕竟在整个pytorch构建的图中，是Variable在流动。还可以使用if,for,print,log等python语法.

解惑（一） ----- super(XXX, self).__init__()到底是代表什么含义_super( ,self)__init_底层研究生的博客-优快云博客好文不应该被埋没，应该被更多的人所熟知！！！转载：https://blog.youkuaiyun.com/zyh19980527/article/details/107206483/相信大家在很多场合特别是写神经网络的代码的时候都看到过下面的这种代码：import torchimport torch.nn as nnimport torch.nn.functional as Fclass Net(nn.Module): def __init__(self): super(Net_super( ,self)__inithttps://blog.youkuaiyun.com/dongjinkun/article/details/114575998 重新命名一些函数，以及梯度下降函数

mlp = MLP()  #创建实例类
criterion = nn.CrossEntropyLoss() #调用交叉熵损失函数
# SGD 就是随机梯度下降
optimizer = torch.optim.SGD(mlp.parameters(), 1e-3)
#mlp.parameters()参数包含weight以及b

训练以及测试：

losses = []
acces = []
eval_losses = []
eval_acces = []

for e in range(20):
    train_loss = 0
    train_acc = 0
    mlp.train()
    for im, label in train_data:
        im = Variable(im) #[64,784]
        label = Variable(label) #[64]

        # forward
        out = mlp(im) #[64,10] #最后输出的10 表示0~9种类别
        loss = criterion(out, label) #tensor(2.3046, grad_fn=<NllLossBackward0>)

        # backward
        optimizer.zero_grad() # 将模型的参数梯度初始化为0
        loss.backward()  # 反向传播计算梯度
        optimizer.step() # 更新所有参数

        train_loss += loss.item()
        _, pred = out.max(1) #每行最大值，以及索引

        num_correct = (pred == label).sum().item() #分类对的个数
        acc = num_correct / im.shape[0] #占比  im.shape[0]=64
        train_acc += acc

    losses.append(train_loss / len(train_data)) #938epoch
    acces.append(train_acc / len(train_data))


    eval_loss = 0
    eval_acc = 0
    mlp.eval()
    for im, label in test_data:
        im = Variable(im)
        label = Variable(label)
        out = mlp(im)
        loss = criterion(out, label)

        eval_loss += loss.item()

        _, pred = out.max(1)
        num_correct = (pred == label).sum().item()
        acc = num_correct / im.shape[0]
        eval_acc += acc

    eval_losses.append(eval_loss / len(test_data))
    eval_acces.append(eval_acc / len(test_data))
    print('epoch: {}, Train Loss: {:.6f}, Train Acc: {:.6f}, Eval Loss: {:.6f}, Eval Acc: {:.6f}'
          .format(e, train_loss / len(train_data), train_acc / len(train_data),
                  eval_loss / len(test_data), eval_acc / len(test_data)))

得到的结果之一：
epoch: 0, Train Loss: 2.282670, Train Acc: 0.206706, Eval Loss: 2.258480, Eval Acc: 0.315467

最后保存结果：

# save the model
torch.save(mlp.state_dict(), 'MLP_MNIST.pkl')

（0_）Pytorch之optimizer.zero_grad()-优快云博客optimizer.zero_grad() 功能梯度初始化为零，把loss关于weight的导数变成0为什么每一轮batch都需要设置optimizer.zero_grad根据pytorch中的backward()函数的计算，当网络参量进行反馈时，梯度是被积累的而不是被替换掉。但是在每一个batch时毫无疑问并不需要将两个batch的梯度混合起来累积，因此这里就需要每个batch设置一遍zero_grad 了每个batch必定执行的操作步骤optimizer.zero_grad() # 梯度初始_optimizer.zero_grad()https://blog.youkuaiyun.com/lwqian102112/article/details/121554580

2) For the CIFAR10 dataset

import sys
sys.path.append("...")

添加上一级目录。

import numpy as np
import torch
from torch.autograd import Variable
from torch import nn
from torchvision.datasets import CIFAR10

def conv_relu(in_channels, out_channels, kernel, stride=1, padding=0):
    layer = nn.Sequential(
        nn.Conv2d(in_channels, out_channels, kernel, stride, padding),
        nn.BatchNorm2d(out_channels, eps=1e-3),
        nn.ReLU(True)
    )
    return layer

pytorch教程之nn.Sequential类详解——使用Sequential类来自定义顺序连接模型-优快云博客

简单理解为一个容器。

Conv2d()的参数及用法-优快云博客

BatchNorm2d()参数及用法-优快云博客

定义inception模块：

class inception(nn.Module):
    def __init__(self, in_channel, out1_1, out2_1, out2_3, out3_1, out3_5, out4_1):
        super(inception, self).__init__()
        # the first line
        self.branch1x1 = conv_relu(in_channel, out1_1, 1)

        # the second line
        self.branch3x3 = nn.Sequential(
            conv_relu(in_channel, out2_1, 1),
            conv_relu(out2_1, out2_3, 3, padding=1)
        )

        # the thrid line
        self.branch5x5 = nn.Sequential(
            conv_relu(in_channel, out3_1, 1),
            conv_relu(out3_1, out3_5, 5, padding=2)
        )

        # the fourth line
        self.branch_pool = nn.Sequential(
            nn.MaxPool2d(3, stride=1, padding=1),
            conv_relu(in_channel, out4_1, 1)
        )

    def forward(self, x):
        # forward
        f1 = self.branch1x1(x)
        f2 = self.branch3x3(x)
        f3 = self.branch5x5(x)
        f4 = self.branch_pool(x)
        output = torch.cat((f1, f2, f3, f4), dim=1) #tensor拼接
        return output

check一下输入输出是否一致：

test_net = inception(3, 64, 48, 64, 64, 96, 32)
test_x = Variable(torch.zeros(1, 3, 96, 96))
print('input shape: {} x {} x {}'.format(test_x.shape[1], test_x.shape[2], test_x.shape[3]))
test_y = test_net(test_x)
print('output shape: {} x {} x {}'.format(test_y.shape[1], test_y.shape[2], test_y.shape[3]))

定义goolenet：

class googlenet(nn.Module):
    def __init__(self, in_channel, num_classes, verbose=False):
        super(googlenet, self).__init__()
        self.verbose = verbose

        self.block1 = nn.Sequential(
            conv_relu(in_channel, out_channels=64, kernel=7, stride=2, padding=3),
            nn.MaxPool2d(3, 2)
        )
        self.block2 = nn.Sequential(
            conv_relu(64, 64, kernel=1),
            conv_relu(64, 192, kernel=3, padding=1),
            nn.MaxPool2d(3, 2)
        )
        self.block3 = nn.Sequential(
            inception(192, 64, 96, 128, 16, 32, 32),
            inception(256, 128, 128, 192, 32, 96, 64),
            nn.MaxPool2d(3, 2)
        )
        self.block4 = nn.Sequential(
            inception(480, 192, 96, 208, 16, 48, 64),
            inception(512, 160, 112, 224, 24, 64, 64),
            inception(512, 128, 128, 256, 24, 64, 64),
            inception(512, 112, 144, 288, 32, 64, 64),
            inception(528, 256, 160, 320, 32, 128, 128),
            nn.MaxPool2d(3, 2)
        )
        self.block5 = nn.Sequential(
            inception(832, 256, 160, 320, 32, 128, 128),
            inception(832, 384, 182, 384, 48, 128, 128),
            nn.AvgPool2d(2)
        )

        self.classifier = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.block1(x)
        if self.verbose:
            print('block 1 output: {}'.format(x.shape))
        x = self.block2(x)
        if self.verbose:
            print('block 2 output: {}'.format(x.shape))
        x = self.block3(x)
        if self.verbose:
            print('block 3 output: {}'.format(x.shape))
        x = self.block4(x)
        if self.verbose:
            print('block 4 output: {}'.format(x.shape))
        x = self.block5(x)
        if self.verbose:
            print('block 5 output: {}'.format(x.shape))

        x = x.view(x.shape[0], -1)
        x = self.classifier(x)
        return x

view=reshape python中view()函数怎么用？_view函数-优快云博客

深度学习|经典网络：GoogLeNet（一） - 知乎 (zhihu.com)https://zhuanlan.zhihu.com/p/73857137

check一下输出：

test_net = googlenet(3, 10, True)
test_x = Variable(torch.zeros(1, 3, 96, 96))
test_y = test_net(test_x)
print('output: {}'.format(test_y.shape))

定义预处理：

def data_tf(x):
    x = x.resize((96, 96), 2)  # shape of x: (96, 96, 3)
    x = np.array(x, dtype='float32') / 255
    x = (x - 0.5) / 0.5
    x = x.transpose((2, 0, 1))
    x = torch.from_numpy(x)
    return x

下载数据：

train_set = CIFAR10('./data', train=True, transform=data_tf, download=True)
train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = CIFAR10('./data', train=False, transform=data_tf, download=True)
test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)

net = googlenet(3, 10) #输入3通道 输出为10
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

定义ACC函数：

def get_acc(output, label):
    total = output.shape[0]
    _, pred_label = output.max(1)
    num_correct = (pred_label == label).sum().item()
    return num_correct / total

from datetime import datetime

def train(net, train_data, valid_data, num_epochs, optimizer, criterion):
    if torch.cuda.is_available():
        net = net.cuda()
    prev_time = datetime.now()
    for epoch in range(num_epochs):
        train_loss = 0
        train_acc = 0
        net = net.train()
        for im, label in train_data:
            if torch.cuda.is_available():
                im = Variable(im.cuda())
                label = Variable(label.cuda())
            else:
                im = Variable(im)
                label = Variable(label)
            # forward
            output = net(im)
            loss = criterion(output, label)
            # backward
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            train_loss += loss.item()
            train_acc += get_acc(output, label)
        cur_time = datetime.now()
        h, remainder = divmod((cur_time - prev_time).seconds, 3600)
        m, s = divmod(remainder, 60)
        time_str = "Time %02d:%02d:%02d" % (h, m, s)
        if valid_data is not None:
            valid_loss = 0
            valid_acc = 0
            net = net.eval()
            for im, label in valid_data:
                if torch.cuda.is_available():
                    im = Variable(im.cuda(), volatile=True)
                    label = Variable(label.cuda(), volatile=True)
                else:
                    im = Variable(im, volatile=True)
                    label = Variable(label, volatile=True)
                output = net(im)
                loss = criterion(output, label)
                valid_loss += loss.item()
                valid_acc += get_acc(output, label)
            epoch_str = (
                    "Epoch %d. Train Loss: %f, Train Acc: %f, Valid Loss: %f, Valid Acc: %f, "
                    % (epoch, train_loss / len(train_data),
                       train_acc / len(train_data), valid_loss / len(valid_data),
                       valid_acc / len(valid_data)))
        else:
            epoch_str = ("Epoch %d. Train Loss: %f, Train Acc: %f, " %
                         (epoch, train_loss / len(train_data),
                          train_acc / len(train_data)))

        prev_time = cur_time
        print(epoch_str + time_str)
        torch.save(net.state_dict(), 'google_net.pkl')


train(net, train_data, test_data, 20, optimizer, criterion)

Train the Semantic Extraction Part

1) For the MNIST dataset

import torch
from torchvision.datasets import mnist
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pandas as pd
import numpy as np
import copy
import torch
from torch import nn
import scipy
from torch.autograd import Variable
from PIL import Image