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利用Pytorch实现ResNet网络构建及模型训练

作者:实力

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构建网络

ResNet由一系列堆叠的残差块组成,其主要作用是通过无限制地增加网络深度,从而使其更加强大。在建立ResNet模型之前,让我们先定义4个层,每个层由多个残差块组成。这些层的目的是降低空间尺寸,同时增加通道数量。

以ResNet50为例,我们可以使用以下代码来定义ResNet网络:

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super().__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
(续)
即模型需要在输入层加入一些 normalization 和激活层。
```python
import torch.nn.init as init
class Flatten(nn.Module):
    def __init__(self):
        super().__init__()
    def forward(self, x):
        return x.view(x.size(0), -1)
class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super().__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.layer1 = nn.Sequential(
            ResidualBlock(64, 256, stride=1),
            *[ResidualBlock(256, 256) for _ in range(1, 3)]
        )
        self.layer2 = nn.Sequential(
            ResidualBlock(256, 512, stride=2),
            *[ResidualBlock(512, 512) for _ in range(1, 4)]
        )
        self.layer3 = nn.Sequential(
            ResidualBlock(512, 1024, stride=2),
            *[ResidualBlock(1024, 1024) for _ in range(1, 6)]
        )
        self.layer4 = nn.Sequential(
            ResidualBlock(1024, 2048, stride=2),
            *[ResidualBlock(2048, 2048) for _ in range(1, 3)]
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = Flatten()
        self.fc = nn.Linear(2048, num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.avgpool(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x

改进点如下:

训练模型

我们现在已经实现了ResNet50模型,接下来我们将解释如何训练和测试该模型。

首先我们需要定义损失函数和优化器。在这里,我们使用交叉熵损失函数,以及Adam优化器。

import torch.optim as optim
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet(num_classes=1000).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

在使用PyTorch进行训练时,我们通常会创建一个循环,为每个批次的输入数据计算损失并对模型参数进行更新。以下是该循环的代码:

def train(model, optimizer, criterion, train_loader, device):
    model.train()
    train_loss = 0
    correct = 0
    total = 0
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        inputs, targets = inputs.to(device), targets.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()
        train_loss += loss.item()
        _, predicted = outputs.max(1)
        total += targets.size(0)
        correct += predicted.eq(targets).sum().item()
    acc = 100 * correct / total
    avg_loss = train_loss / len(train_loader)
    return acc, avg_loss

在上面的训练循环中,我们首先通过model.train()代表进入训练模式。然后使用optimizer.zero_grad()清除

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