Resnet18 PyTorch复现代码

以下是一个简单的Resnet18的PyTorch复现代码，包括了网络结构的定义和训练部分的代码。代码中使用了CIFAR10数据集作为示例数据集，可以根据自己的需求更改数据集。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 定义ResNet基本块类
class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2

相关问题

resnet18代码复现pytorch

### 回答1：
可以使用PyTorch自带的torchvision.models模块中的resnet18模型进行复现。

示例代码：

import torch
from torchvision import models

resnet18 = models.resnet18(pretrained=True)

# 打印模型结构
print(resnet18)

如果需要训练resnet18，可以使用如下代码:

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(resnet18.parameters(), lr=0.001, momentum=0.9)

# 训练过程
for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs; data is a list of [inputs, labels]
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = resnet18(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
    print('Epoch %d loss: %.3f' %(epoch + 1, running_loss / (i+1)))

print('Finished Training')

注意: 上面代码块中的trainloader是训练数据的读取方式。

### 回答2：
ResNet是一种非常流行的深度神经网络模型，它的结构深层，难于训练，容易发生梯度消失的问题。为了解决这个问题，ResNet在模型中引入了跨层连接（Shortcut Connection），让信息可以直接从前一层通过跨层连接到达后续层，从而避免了梯度消失的问题，使得训练更加容易。ResNet18是ResNet系列网络模型中的一种，它比较轻量级，适合在计算资源有限的情况下使用，今天我们来讲一下如何在PyTorch中复现ResNet18的代码。

1. 导入必要的库

在进行代码复现之前，我们需要先导入必要的库，包括PyTorch库、torchvision库和numpy库。

import torch
import torchvision.models as models
import torchvision.transforms as transforms
import numpy as np

2. 定义模型结构

在复现ResNet18的代码之前，我们需要先了解一下ResNet18的结构。ResNet18包括18个卷积层和一个全连接层，其中18个卷积层被分为4个阶段（每个阶段包含若干个卷积层），每个阶段还包含一个Downsampling操作，用于降低特征图的空间大小，其中第一个阶段不需要Downsampling操作。

在PyTorch中可以使用torchvision.models.resnet18()函数来创建ResNet18模型，该函数会自动下载预训练模型参数，或者设置pretrained=False来创建一个没有预训练模型参数的模型。我们在这里使用没有预训练模型参数的模型。为了让输出层的大小符合我们的需求，我们需要修改全连接层的输入特征数量和输出特征数量。

#定义resnet18模型结构
def resnet18(num_classes):
    model = models.resnet18(pretrained=False)
    num_ftrs = model.fc.in_features
    model.fc = torch.nn.Linear(num_ftrs, num_classes)
    return model

3. 数据预处理和模型训练

在进行训练之前，我们需要对数据进行预处理，这里我们使用PyTorch中提供的transforms库对数据进行预处理。另外，我们还需要定义损失函数和优化器。

#进行数据预处理
data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}

#定义损失函数和优化器
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

接下来，我们可以使用PyTorch中的Dataset和Dataloader来加载数据集，进行模型训练。在训练过程中，我们需要从数据集中取出一个batch的数据，经过模型计算和损失函数计算得到损失值，并对模型参数进行优化。每经过若干次迭代，我们可以对模型在验证集上的表现进行评估，从而得到模型的准确率。

#加载数据集
data_dir = 'data/'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),data_transforms[x]) for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,shuffle=True, num_workers=4) for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}

#模型训练
for epoch in range(num_epochs):
    for phase in ['train', 'val']:
        if phase == 'train':
            model.train()
        else:
            model.eval()

        running_loss = 0.0
        running_corrects = 0

        for inputs, labels in dataloaders[phase]:
            inputs = inputs.to(device)
            labels = labels.to(device)

            optimizer.zero_grad()

            with torch.set_grad_enabled(phase == 'train'):
                outputs = model(inputs)
                loss = criterion(outputs, labels)

                _, preds = torch.max(outputs, 1)

                if phase == 'train':
                    loss.backward()
                    optimizer.step()

            running_loss += loss.item() * inputs.size(0)
            running_corrects += torch.sum(preds == labels.data)

        epoch_loss = running_loss / dataset_sizes[phase]
        epoch_acc = running_corrects.double() / dataset_sizes[phase]

        print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

4. 结论

通过以上步骤，我们成功地用PyTorch中的库复现了ResNet18的代码，并使用ImageNet数据集对其进行了训练和验证。ResNet18是一个比较轻量级的深度神经网络模型，适合在计算资源有限的情况下使用，与其他类型的模型相比，在处理图像分类问题时具有更好的性能。通过不断的优化和调整，我们可以获得更好的训练结果，提高模型的准确率。

### 回答3：
ResNet（Residual Network）是2015年ImageNet图像识别挑战中的冠军，也是深度残差网络（Deep Residual Network）的基础。ResNet模型利用残差块（Residual block）实现深度网络，使用跨层的连接（Shortcut connection）优化网络拟合能力，减少梯度消失的问题，可有效地提高网络表现。

在本文中，我们将回答如何使用pytorch编写并复现ResNet18模型的代码。

首先，我们需要安装好pytorch包，以及相关的torchvision包。然后，按照以下步骤进行：

1.导入相关模块

import torch

from torch import nn

from torch.nn import functional as F

2.定义残差块

class ResidualBlock(nn.Module):

def __init__(self, in_channels, out_channels, stride=1):

super(ResidualBlock, self).__init__()

self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)

self.bn1 = nn.BatchNorm2d(out_channels)

self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)

self.bn2 = nn.BatchNorm2d(out_channels)

self.shortcut = nn.Sequential()

if stride != 1 or in_channels != out_channels:

self.shortcut = nn.Sequential(

nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),

nn.BatchNorm2d(out_channels)

)

def forward(self, x):

out = F.relu(self.bn1(self.conv1(x)))

out = self.bn2(self.conv2(out))

out += self.shortcut(x)

out = F.relu(out)

return out

3.定义ResNet18网络结构

class ResNet18(nn.Module):

def __init__(self, num_classes=1000):

super(ResNet18, self).__init__()

self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)

self.bn1 = nn.BatchNorm2d(64)

self.layer1 = nn.Sequential(

ResidualBlock(64, 64),

ResidualBlock(64, 64)

)

self.layer2 = nn.Sequential(

ResidualBlock(64, 128, stride=2),

ResidualBlock(128, 128)

)

self.layer3 = nn.Sequential(

ResidualBlock(128, 256, stride=2),

ResidualBlock(256, 256)

)

self.layer4 = nn.Sequential(

ResidualBlock(256, 512, stride=2),

ResidualBlock(512, 512)

)

self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

self.fc = nn.Linear(512, num_classes)

def forward(self, x):

out = F.relu(self.bn1(self.conv1(x)))

out = F.max_pool2d(out, kernel_size=3, stride=2, padding=1)

out = self.layer1(out)

out = self.layer2(out)

out = self.layer3(out)

out = self.layer4(out)

out = self.avgpool(out)

out = out.view(out.size(0), -1)

out = self.fc(out)

return out

4.添加数据集和损失函数的定义

model = ResNet18()

criterion = nn.CrossEntropyLoss()

optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

5.开始训练模型

for epoch in range(10):

train_loss = 0.0

for i, (inputs, labels) in enumerate(trainloader, 0):

inputs, labels = inputs.to(device), labels.to(device)

optimizer.zero_grad()

outputs = model(inputs)

loss = criterion(outputs, labels)

loss.backward()

optimizer.step()

train_loss += loss.item()

if i % 2000 == 1999:

print('[%d, %5d] loss: %.3f' %

(epoch + 1, i + 1, train_loss / 2000))

train_loss = 0.0

6.评估模型

correct = 0

total = 0

with torch.no_grad():

for data in testloader:

images, labels = data

images, labels = images.to(device), labels.to(device)

outputs = model(images)

_, predicted = torch.max(outputs.data, 1)

total += labels.size(0)

correct += (predicted == labels).sum().item()

print('Accuracy of the network on the test images: %d %%' % (

100 * correct / total))

7.保存模型

torch.save(model.state_dict(), 'resnet18.pth')

到此为止，我们已经完成了ResNet18模型的代码复现，并成功对其进行了训练和评估。如果您想要更好地理解这个过程，可以看看相关的学习资料和视频课程，这将会对您的研究和实践产生巨大帮助。

resnet50代码复现pytorch

引用中提到的Grad-CAM:Visual Explanations from Deep Networks via Gradient-based Localization的复现采用了resnet50预训练网络，下面是一个简单的resnet50代码复现的步骤：

1.导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader

2.定义ResNet50模型：

class ResNet50(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet50, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(64, 3)
        self.layer2 = self._make_layer(128, 4, stride=2)
        self.layer3 = self._make_layer(256, 6, stride=2)
        self.layer4 = self._make_layer(512, 3, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * 1 * 1, num_classes)

    def _make_layer(self, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * 4:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * 4, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * 4),
            )

        layers = []
        layers.append(Bottleneck(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * 4
        for _ in range(1, blocks):
            layers.append(Bottleneck(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

3.定义Bottleneck模块：

class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out

4.定义数据预处理：

transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

5.加载数据集：

train_dataset = datasets.ImageFolder('path/to/train', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

val_dataset = datasets.ImageFolder('path/to/val', transform=transform)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

6.定义损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4)

7.训练模型：

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) % 100 == 0:
            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

8.测试模型：

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in val_loader:
        images = images.to(device)
        labels = labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))