BasicBlock nn layer
时间: 2023-07-15 17:13:44 浏览: 40
BasicBlock是一个基本的神经网络层,通常用于构建卷积神经网络(CNN)。它由两层卷积层和一个恒等映射(identity mapping)组成。其中,第一层卷积层用于提取特征,第二层卷积层用于将特征进一步压缩,而恒等映射则用于保留输入的信息。
在实际使用中,BasicBlock通常会被组合成更深层次的网络结构,比如ResNet。通过多个BasicBlock的堆叠,可以增加网络的深度和复杂度,从而提高模型的准确率。
相关问题
帮我用pytorch写一个resnet18的模型,模型中不要引用BasicBlock,每一个layer都是用nn.sequential实现
下面是一个用nn.Sequential实现的ResNet18模型,每个layer都是由多个卷积层和池化层组成的:
```python
import torch.nn as nn
class ResNet18(nn.Module):
def __init__(self, num_classes=10):
super(ResNet18, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True)
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True)
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True)
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True)
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
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
```
这个模型的实现与原始的ResNet18稍有不同,因为在原始的ResNet18中,每个layer都是由多个BasicBlock组成的,而在这个实现中,每个layer都是由多个卷积层和池化层组成的。但是,这个实现的效果应该与原始的ResNet18相当。
帮我用pytorch写一个resnet18的模型,模型中不要引用BasicBlock,模型的第一层layer都是用nn.sequential
好的,以下是使用 PyTorch 实现的 ResNet18 模型,其中第一层 layer 使用了 nn.Sequential:
```python
import torch
import torch.nn as nn
class ResNetBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride=1):
super(ResNetBlock, 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.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.stride = stride
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(out_channels)
) if stride != 1 or in_channels != out_channels else None
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)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet18(nn.Module):
def __init__(self, num_classes=1000):
super(ResNet18, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
self.layer1 = nn.Sequential(
ResNetBlock(64, 64),
ResNetBlock(64, 64)
)
self.layer2 = nn.Sequential(
ResNetBlock(64, 128, stride=2),
ResNetBlock(128, 128)
)
self.layer3 = nn.Sequential(
ResNetBlock(128, 256, stride=2),
ResNetBlock(256, 256)
)
self.layer4 = nn.Sequential(
ResNetBlock(256, 512, stride=2),
ResNetBlock(512, 512)
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
```
其中 `ResNetBlock` 是 ResNet 中的一个基本块,实现了两个卷积层和一个残差连接,与 BasicBlock 的功能类似。`ResNet18` 是整个 ResNet18 模型,由多个 ResNetBlock 组成的不同层级和一个全连接层组成。在 `ResNet18` 中,第一层使用了 nn.Sequential 实现,而其他层则是使用多个 ResNetBlock 组成的 nn.Sequential 实现的。