AvgPool = nn.ModuleList()
时间: 2024-06-15 13:08:27 浏览: 19
AvgPool = nn.ModuleList() 是一个空的模块列表,用于存储 nn.Module 的子模块。nn.ModuleList() 是 PyTorch 中的一个类,它可以用来定义一个模块列表,其中每个元素都是一个 nn.Module 的实例。
nn.ModuleList() 可以用于将多个子模块组合在一起,以便在神经网络中使用。在这种情况下,AvgPool = nn.ModuleList() 可能是用来存储多个平均池化层(AvgPool)的实例。
平均池化层是卷积神经网络中常用的一种池化操作,它将输入的特征图划分为不重叠的区域,并对每个区域内的特征值取平均值作为输出。这有助于减少特征图的尺寸,并提取出更加重要的特征。
如果你想了解更多关于 AvgPool = nn.ModuleList() 的具体用法和相关信息,请提供更多上下文或者具体问题,我将尽力帮助你解答。
相关问题
要求不借助resnest.torch复现哦
好的,那么我将使用PyTorch自己实现resnest50模型,并将通道数改为1。
resnest50模型是由4个stage组成,每个stage包含多个基本块(basic block)。我们可以根据论文中的描述和代码来实现这个模型。
```python
import torch.nn as nn
import torch
class ResNeStBottleneck(nn.Module):
def __init__(self, in_channels, out_channels, stride, cardinality, base_width, dilation=1):
super(ResNeStBottleneck, self).__init__()
width_ratio = out_channels / (cardinality * base_width)
D = int(out_channels * (dilation / 64))
self.conv1 = nn.Conv2d(in_channels, base_width * cardinality, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(base_width * cardinality)
self.conv2 = nn.Conv2d(base_width * cardinality, base_width * cardinality, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, groups=cardinality, bias=False)
self.bn2 = nn.BatchNorm2d(base_width * cardinality)
self.conv3 = nn.Conv2d(base_width * cardinality, D, kernel_size=1, stride=1, padding=0, bias=False)
self.bn3 = nn.BatchNorm2d(D)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
self.width_ratio = width_ratio
self.cardinality = cardinality
self.base_width = base_width
self.D = D
def forward(self, x):
residual = 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 residual.shape[1] != out.shape[1]:
residual = torch.nn.functional.avg_pool2d(residual, kernel_size=1, stride=self.stride)
residual = torch.cat((residual, torch.zeros_like(residual)), dim=1)
out = out + residual
out = self.relu(out)
return out
class ResNeStStage(nn.Module):
def __init__(self, in_channels, out_channels, stride, num_blocks, cardinality, base_width, dilation=1):
super(ResNeStStage, self).__init__()
self.blocks = nn.ModuleList()
for i in range(num_blocks):
self.blocks.append(ResNeStBottleneck(in_channels, out_channels, stride if i == 0 else 1, cardinality, base_width, dilation))
def forward(self, x):
for block in self.blocks:
x = block(x)
return x
class ResNeSt50(nn.Module):
def __init__(self, num_classes=1000):
super(ResNeSt50, self).__init__()
self.conv1 = nn.Conv2d(1, 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.stage1 = ResNeStStage(64, 256, stride=1, num_blocks=3, cardinality=1, base_width=64)
self.stage2 = ResNeStStage(256, 512, stride=2, num_blocks=4, cardinality=32, base_width=4)
self.stage3 = ResNeStStage(512, 1024, stride=2, num_blocks=6, cardinality=32, base_width=4)
self.stage4 = ResNeStStage(1024, 2048, stride=2, num_blocks=3, cardinality=32, base_width=4)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(2048, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.stage1(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
在这里,我们定义了ResNeStBottleneck类,它实现了resnest50中的基本块。我们还定义了ResNeStStage类,它实现了resnest50中的stage。最后,我们定义了ResNeSt50类,它实现了整个resnest50模型。
在ResNeStBottleneck类中,我们首先定义了3个卷积层和3个Batch Normalization层。接下来,我们将输入x和残差连接进行加和操作,并将结果通过ReLU激活函数。在forward函数中,我们实现了前向传播。
在ResNeStStage类中,我们使用nn.ModuleList来存储多个ResNeStBottleneck块,并在forward函数中将x传递到每个块中。
在ResNeSt50类中,我们首先定义了输入卷积层和Batch Normalization层,并将结果通过ReLU激活函数。接下来,我们定义了4个stage,并将它们串联在一起。最后,我们定义了全局平均池化层和全连接层。
现在,我们已经成功地实现了resnest50模型,并将通道数改为1。
基于cifar10各类模型代码
好的,下面是基于 CIFAR-10 数据集的各类模型的 PyTorch 实现代码:
1. LeNet-5
```python
import torch.nn as nn
class LeNet5(nn.Module):
def __init__(self, num_classes=10):
super(LeNet5, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 6, kernel_size=5),
nn.Tanh(),
nn.AvgPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5),
nn.Tanh(),
nn.AvgPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(16 * 5 * 5, 120),
nn.Tanh(),
nn.Linear(120, 84),
nn.Tanh(),
nn.Linear(84, num_classes),
)
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
```
2. AlexNet
```python
import torch.nn as nn
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
```
3. VGG-11
```python
import torch.nn as nn
class VGG11(nn.Module):
def __init__(self, num_classes=10):
super(VGG11, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
nn.Linear(512 * 7 * 7, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, num_classes),
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
return x
```
4. ResNet-18
```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, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
)
self.layer2 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
)
self.layer3 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
)
self.layer4 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
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.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
```
5. DenseNet-121
```python
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_channels, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv1 = nn.Conv2d(in_channels, 4 * growth_rate, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(4 * growth_rate)
self.conv2 = nn.Conv2d(4 * growth_rate, growth_rate, kernel_size=3, padding=1, bias=False)
def forward(self, x):
out = self.conv1(F.relu(self.bn1(x)))
out = self.conv2(F.relu(self.bn2(out)))
out = torch.cat([out, x], 1)
return out
class DenseBlock(nn.Module):
def __init__(self, in_channels, num_layers, growth_rate):
super(DenseBlock, self).__init__()
self.layer = nn.ModuleList()
for i in range(num_layers):
in_channels += i * growth_rate
self.layer.append(Bottleneck(in_channels, growth_rate))
def forward(self, x):
for layer in self.layer:
x = layer(x)
return x
class Transition(nn.Module):
def __init__(self, in_channels, out_channels):
super(Transition, self).__init__()
self.bn = nn.BatchNorm2d(in_channels)
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
self.avgpool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
x = self.conv(F.relu(self.bn(x)))
x = self.avgpool(x)
return x
class DenseNet121(nn.Module):
def __init__(self, num_classes=10):
super(DenseNet121, self).__init__()
self.conv = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.block1 = DenseBlock(64, num_layers=6, growth_rate=32)
self.trans1 = Transition(224, 128)
self.block2 = DenseBlock(128, num_layers=12, growth_rate=32)
self.trans2 = Transition(448, 256)
self.block3 = DenseBlock(256, num_layers=24, growth_rate=32)
self.trans3 = Transition(1024, 512)
self.block4 = DenseBlock(512, num_layers=16, growth_rate=32)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(1024, num_classes)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.block1(x)
x = self.trans1(x)
x = self.block2(x)
x = self.trans2(x)
x = self.block3(x)
x = self.trans3(x)
x = self.block4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
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