Pytorch实战：用经典网络实现猫狗大战

一：数据集准备：

数据集来源于研习社：猫狗大战--经典图像分类题 - AI算法竞赛-AI研习社 (yanxishe.com)

二：读取数据：

class Cat_and_Dog_Dataset(Dataset):def __init__(self, filepath):self.images = []self.labels = []self.transform = transformfor filename in tqdm(os.listdir(filepath)):image = Image.open(filepath + filename)image = image.resize((224,224))         #裁剪图片image = self.transform(image)           #转化为Tensor格式self.images.append(image)if filename.split('_')[0] == 'cat':     #添加标签self.labels.append(0)elif filename.split('_')[0] == 'dog':self.labels.append(1)self.labels = torch.LongTensor(self.labels) #标签转化位Tensor格式print(self.labels)def __getitem__(self, index):           #构造迭代器return self.images[index], self.labels[index]def __len__(self):                      #迭代器长度images = np.array(self.images)len = images.shape[0]return lentrain_data = Cat_and_Dog_Dataset('cat_dog/train/')              #加载训练集
train_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True)  val_data = Cat_and_Dog_Dataset('cat_dog/val/')                  #加载验证集
val_loader = DataLoader(dataset=val_data, batch_size=64, shuffle=True)

二：搭建网络：

class InceptionA(torch.nn.Module):                              #构造Inception层def __init__(self,in_channels):super(InceptionA,self).__init__()self.branch1x1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch5x5_1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch5x5_2 = torch.nn.Conv2d(16,24,kernel_size = 5,padding = 2)self.branch3x3_1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch3x3_2 = torch.nn.Conv2d(16,24,kernel_size = 3,padding = 1)self.branch3x3_3 = torch.nn.Conv2d(24,24,kernel_size = 3,padding = 1)self.branch_pool = torch.nn.Conv2d(in_channels,24,kernel_size = 1)def forward(self,x):branch1x1 = self.branch1x1(x)branch5x5 = self.branch5x5_1(x)branch5x5 = self.branch5x5_2(branch5x5)branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch3x3 = self.branch3x3_3(branch3x3)branch_pool = F.avg_pool2d(x,kernel_size = 3,stride = 1,padding = 1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1,branch3x3,branch5x5,branch_pool]return torch.cat(outputs,dim = 1)class Net(torch.nn.Module):                             #构造网络def __init__(self):super(Net,self).__init__()self.conv1 = torch.nn.Conv2d(3,10,kernel_size = 5)self.conv2 = torch.nn.Conv2d(88,20,kernel_size = 5)self.incep1 = InceptionA(in_channels = 10)self.incep2 = InceptionA(in_channels = 20)self.mp = torch.nn.MaxPool2d(2)self.fc = torch.nn.Linear(247192,2)def forward(self,x):in_size = x.size(0)x = self.mp(F.relu((self.conv1(x))))x = self.incep1(x)x = self.mp(F.relu((self.conv2(x))))x = self.incep2(x)x = x.view(in_size,-1)x = self.fc(x)return x

三：构建损失函数和优化器：

criterion = torch.nn.CrossEntropyLoss()         #构造损失函数
optimizer = optim.SGD(model.parameters(),lr = 0.001)        #构造优化器

四：训练模型：

def train(epoch):running_loss = 0.0                              #训练模型for batch_idx,data in enumerate(train_loader,0):inputs,targets = datainputs, targets = inputs.to(device), targets.to(device)optimizer.zero_grad()                       #梯度清零outputs = model(inputs)                     #前向传播loss = criterion(outputs,targets)           #计算损失loss.backward()                             #逆向传播optimizer.step()                            #梯度递进running_loss += loss.item()print('train loss: %.3f' % (running_loss/batch_idx))

五：验证模型：

def val():                                          #验证模型精度correct = 0total = 0with torch.no_grad():                           #不需要梯度，减少计算量for data in val_loader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()print('accuracy on test set: %d %% ' % (100*correct/total))return correct/total

源码：

import torch
import numpy as np
from torch.utils.data import DataLoader,Dataset
from torchvision import transforms
import torchvision
import torch.nn.functional as F
import torch.optim as optim
import os
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as pltbatch_size = 64transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])#设置transformclass Cat_and_Dog_Dataset(Dataset):def __init__(self, filepath):self.images = []self.labels = []self.transform = transformfor filename in tqdm(os.listdir(filepath)):image = Image.open(filepath + filename)image = image.resize((224,224))         #裁剪图片image = self.transform(image)           #转化为Tensor格式self.images.append(image)if filename.split('_')[0] == 'cat':     #添加标签self.labels.append(0)elif filename.split('_')[0] == 'dog':self.labels.append(1)self.labels = torch.LongTensor(self.labels) #标签转化位Tensor格式print(self.labels)def __getitem__(self, index):           #构造迭代器return self.images[index], self.labels[index]def __len__(self):                      #迭代器长度images = np.array(self.images)len = images.shape[0]return lentrain_data = Cat_and_Dog_Dataset('cat_dog/train/')              #加载训练集
train_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True)  val_data = Cat_and_Dog_Dataset('cat_dog/val/')                  #加载验证集
val_loader = DataLoader(dataset=val_data, batch_size=64, shuffle=True)class InceptionA(torch.nn.Module):                              #构造Inception层def __init__(self,in_channels):super(InceptionA,self).__init__()self.branch1x1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch5x5_1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch5x5_2 = torch.nn.Conv2d(16,24,kernel_size = 5,padding = 2)self.branch3x3_1 = torch.nn.Conv2d(in_channels,16,kernel_size = 1)self.branch3x3_2 = torch.nn.Conv2d(16,24,kernel_size = 3,padding = 1)self.branch3x3_3 = torch.nn.Conv2d(24,24,kernel_size = 3,padding = 1)self.branch_pool = torch.nn.Conv2d(in_channels,24,kernel_size = 1)def forward(self,x):branch1x1 = self.branch1x1(x)branch5x5 = self.branch5x5_1(x)branch5x5 = self.branch5x5_2(branch5x5)branch3x3 = self.branch3x3_1(x)branch3x3 = self.branch3x3_2(branch3x3)branch3x3 = self.branch3x3_3(branch3x3)branch_pool = F.avg_pool2d(x,kernel_size = 3,stride = 1,padding = 1)branch_pool = self.branch_pool(branch_pool)outputs = [branch1x1,branch3x3,branch5x5,branch_pool]return torch.cat(outputs,dim = 1)class Net(torch.nn.Module):                             #构造网络def __init__(self):super(Net,self).__init__()self.conv1 = torch.nn.Conv2d(3,10,kernel_size = 5)self.conv2 = torch.nn.Conv2d(88,20,kernel_size = 5)self.incep1 = InceptionA(in_channels = 10)self.incep2 = InceptionA(in_channels = 20)self.mp = torch.nn.MaxPool2d(2)self.fc = torch.nn.Linear(247192,2)def forward(self,x):in_size = x.size(0)x = self.mp(F.relu((self.conv1(x))))x = self.incep1(x)x = self.mp(F.relu((self.conv2(x))))x = self.incep2(x)x = x.view(in_size,-1)x = self.fc(x)return xmodel = Net()device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)            #转换为cuda格式criterion = torch.nn.CrossEntropyLoss()         #构造损失函数
optimizer = optim.SGD(model.parameters(),lr = 0.001)        #构造优化器def train(epoch):running_loss = 0.0                              #训练模型for batch_idx,data in enumerate(train_loader,0):inputs,targets = datainputs, targets = inputs.to(device), targets.to(device)optimizer.zero_grad()                       #梯度清零outputs = model(inputs)                     #前向传播loss = criterion(outputs,targets)           #计算损失loss.backward()                             #逆向传播optimizer.step()                            #梯度递进running_loss += loss.item()print('train loss: %.3f' % (running_loss/batch_idx))def val():                                          #验证模型精度correct = 0total = 0with torch.no_grad():                           #不需要梯度，减少计算量for data in val_loader:images, labels = dataimages, labels = images.to(device), labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, dim=1)total += labels.size(0)correct += (predicted == labels).sum().item()print('accuracy on test set: %d %% ' % (100*correct/total))return correct/totalif __name__ == '__main__':accuracy_list = []epoch_list = []for epoch in range(10):train(epoch)acc = val()accuracy_list.append(acc)epoch_list.append(epoch)plt.plot(epoch_list,accuracy_list)plt.xlabel(epoch)plt.ylabel(accuracy_list)plt.show()

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