基于GoogLeNet实现Cifar-10图像分类项目python源码（高分项目）.zip

#!/usr/bin/env python # coding: utf-8 # In[1]: import torch import torch.nn as nn import torch.nn.functional as F import torchvision import torchvision.transforms as transforms import numpy as np import datetime as dt # In[3]: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print (f'device:{device}') # Hyper-paramesters num_epochs = 12 batch_size = 10 learning_rate = 0.001 # In[ ]: # dataset has PILImage images of rang [0,1] # we transform them to Tensors of normalized range [-1,1] transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))]) # In[5]: train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False) # In[6]: classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # In[7]: class GoogLeNet(nn.Module): def __init__(self, in_channels=3, num_classes=1000): super(GoogLeNet, self).__init__() # 输入通道是3，处理后的图像大小为112×112(原图像为224×224) self.conv1 = Conv_block(in_channels=in_channels, out_channels=64, kernel_size=(7,7), stride=(2,2), padding=(3,3)) # 输出8×8 self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) # self.conv2 = Conv_block(64, 192, kernel_size=3, stride=1, padding=1) self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inception3a = Inception_block(192, 64, 96, 128, 16, 32, 32) self.inception3b = Inception_block(256, 128, 128, 192, 32, 96, 64) self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inception4a = Inception_block(480, 192, 96, 208, 16,48, 64) self.inception4b = Inception_block(512, 160, 112, 224, 24, 64, 64) self.inception4c = Inception_block(512, 128, 128, 256, 24, 64, 64) self.inception4d = Inception_block(512, 112, 144, 288, 32, 64, 64) self.inception4e = Inception_block(528, 256, 160, 320, 32, 128, 128) self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.inception5a = Inception_block(832, 256, 160, 320, 32, 128, 128) self.inception5b = Inception_block(832, 384, 192, 384, 48, 128, 128) self.avgpool = nn.AvgPool2d(kernel_size=1, stride=1) # 224/2/2/2/2/2 = 7 32/2/2/2/2/2 = 1 self.dropout = nn.Dropout(p=0.4) self.fc1 = nn.Linear(1024, num_classes) def forward(self, x): x = self.conv1(x) x = self.maxpool1(x) x = self.conv2(x) x = self.maxpool2(x) x = self.inception3a(x) x = self.inception3b(x) x = self.maxpool3(x) x = self.inception4a(x) x = self.inception4b(x) x = self.inception4c(x) x = self.inception4d(x) x = self.inception4e(x) x = self.maxpool4(x) x = self.inception5a(x) x = self.inception5b(x) x = self.avgpool(x) # 可以不调用。 x = x.reshape(x.shape[0], -1) x = self.dropout(x) x = self.fc1(x) return x # In[2]: class Inception_block(nn.Module): def __init__(self, in_channels, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, out_1x1pool): super(Inception_block,self).__init__() self.branch1 = Conv_block(in_channels, out_1x1, kernel_size=1) self.branch2 =nn.Sequential( Conv_block(in_channels, red_3x3, kernel_size=1), Conv_block(red_3x3, out_3x3, kernel_size=3, padding = 1) ) self.branch3=nn.Sequential( Conv_block(in_channels, red_5x5, kernel_size=1), Conv_block(red_5x5, out_5x5, kernel_size=5, padding = 2) ) self.branch4 = nn.Sequential( nn.MaxPool2d(kernel_size =3, stride=1, padding=1), Conv_block(in_channels, out_1x1pool, kernel_size=1) ) def forward(self, x): return torch.cat([self.branch1(x), self.branch2(x), self.branch3(x), self.branch4(x)],1) # In[9]: class Conv_block(nn.Module): def __init__(self, in_channels, out_channels, **kwargs): super(Conv_block, self).__init__() self.relu = nn.ReLU() self.conv = nn.Conv2d(in_channels, out_channels, **kwargs) self.batchnorm = nn.BatchNorm2d(out_channels) def forward(self, x): return self.relu(self.batchnorm(self.conv(x))) # In[10]: model = GoogLeNet(in_channels=3, num_classes = 10).to(device) criterion = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) n_total_steps = len(train_loader) t1 = dt.datetime.now() print (f'Training started at {t1}') # In[11]: for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): images = images.to(device) labels = labels.to(device) outputs = model(images) loss = criterion(outputs, labels) optimizer.zero_grad() loss.backward() optimizer.step() if (i+1) % 1000 == 0: print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], loss: {loss.item():.4f}') # In[12]: t2 = dt.datetime.now() print(f'Finished Training at {t2}') print(f'Training time : {t2-t1}') with torch.no_grad(): n_correct = 0 n_samples = 0 n_class_correct = [0 for i in range(10)] n_class_samples = [0 for i in range(10)] for images, labels in test_loader: images = images.to(device) labels = labels.to(device) outputs = model(images) _, predicted = torch.max(outputs, 1) n_samples += labels.size(0) n_correct += (predicted == labels).sum().item() for i in range (batch_size): label = labels[i] pred = predicted[i] if label == pred: n_class_correct[label] += 1 n_class_samples[label] += 1 acc = 100.0 * n_correct / n_samples print (f'Accuracy of the network: {acc} %') for i in range(10): acc = 100.0 * n_class_correct[i] / n_class_samples[i] print (f'Accuracy of {classes[i]}: {acc} %')