ResNet-18结构

基本结点

代码实现

import torch
import torch.nn as nn
from torch.nn import functional as Fclass RestNetBasicBlock(nn.Module):def __init__(self, in_channels, out_channels, stride):super(RestNetBasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)self.bn1 = nn.BatchNorm2d(out_channels)self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride, padding=1)self.bn2 = nn.BatchNorm2d(out_channels)def forward(self, x):output = self.conv1(x)output = F.relu(self.bn1(output))output = self.conv2(output)output = self.bn2(output)return F.relu(x + output)class RestNetDownBlock(nn.Module):def __init__(self, in_channels, out_channels, stride):super(RestNetDownBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride[0], padding=1)self.bn1 = nn.BatchNorm2d(out_channels)self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=stride[1], padding=1)self.bn2 = nn.BatchNorm2d(out_channels)self.extra = nn.Sequential(nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride[0], padding=0),nn.BatchNorm2d(out_channels))def forward(self, x):extra_x = self.extra(x)output = self.conv1(x)out = F.relu(self.bn1(output))out = self.conv2(out)out = self.bn2(out)return F.relu(extra_x + out)class RestNet18(nn.Module):def __init__(self):super(RestNet18, self).__init__()self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)self.bn1 = nn.BatchNorm2d(64)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = nn.Sequential(RestNetBasicBlock(64, 64, 1),RestNetBasicBlock(64, 64, 1))self.layer2 = nn.Sequential(RestNetDownBlock(64, 128, [2, 1]),RestNetBasicBlock(128, 128, 1))self.layer3 = nn.Sequential(RestNetDownBlock(128, 256, [2, 1]),RestNetBasicBlock(256, 256, 1))self.layer4 = nn.Sequential(RestNetDownBlock(256, 512, [2, 1]),RestNetBasicBlock(512, 512, 1))self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1, 1))self.fc = nn.Linear(512, 10)def forward(self, x):out = self.conv1(x)out = self.layer1(out)out = self.layer2(out)out = self.layer3(out)out = self.layer4(out)out = self.avgpool(out)out = out.reshape(x.shape[0], -1)out = self.fc(out)return out

用来预测CIFAR-10数据集

数据集

官网链接：CIFAR-10 DATASET

测试代码

import torch
from torch import nn, optim
import torchvision.transforms as transforms
from torchvision import datasets
from torch.utils.data import DataLoader
from restnet18.restnet18 import RestNet18#  用CIFAR-10 数据集进行实验def main():batchsz = 128cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), download=True)cifar_train = DataLoader(cifar_train, batch_size=batchsz, shuffle=True)cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([transforms.Resize((32, 32)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])]), download=True)cifar_test = DataLoader(cifar_test, batch_size=batchsz, shuffle=True)x, label = iter(cifar_train).next()print('x:', x.shape, 'label:', label.shape)device = torch.device('cuda')# model = Lenet5().to(device)model = RestNet18().to(device)criteon = nn.CrossEntropyLoss().to(device)optimizer = optim.Adam(model.parameters(), lr=1e-3)print(model)for epoch in range(1000):model.train()for batchidx, (x, label) in enumerate(cifar_train):# [b, 3, 32, 32]# [b]x, label = x.to(device), label.to(device)logits = model(x)# logits: [b, 10]# label:  [b]# loss: tensor scalarloss = criteon(logits, label)# backpropoptimizer.zero_grad()loss.backward()optimizer.step()print(epoch, 'loss:', loss.item())model.eval()with torch.no_grad():# testtotal_correct = 0total_num = 0for x, label in cifar_test:# [b, 3, 32, 32]# [b]x, label = x.to(device), label.to(device)# [b, 10]logits = model(x)# [b]pred = logits.argmax(dim=1)# [b] vs [b] => scalar tensorcorrect = torch.eq(pred, label).float().sum().item()total_correct += correcttotal_num += x.size(0)# print(correct)acc = total_correct / total_numprint(epoch, 'test acc:', acc)if __name__ == '__main__':main()

运行结果

感觉挺low的，迭代50多次能达到80多的准确率

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PyTorch实现ResNet18

ResNet-18结构

基本结点

代码实现

用来预测CIFAR-10数据集

数据集

测试代码

运行结果

PyTorch实现ResNet18相关推荐

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