pytorch densenet121
时间: 2023-11-12 13:55:30 浏览: 143
DenseNet-121是一种在PyTorch中实现的深度学习模型,121表示它有121个层。这包括120个卷积层和1个全连接层。在构建DenseNet-121时,可以使用_densenet()函数,它会调用DenseNet()来构建模型。如果需要使用预训练模型,_densenet()函数还会负责加载预训练模型。在DenseBlock中,每个DenseLayer的输入特征图的通道数会依次增加growth_rate(对于DenseNet-121来说是32)。
相关问题
pytorch中densenet121
DenseNet(密集网络)是一种深度学习模型,其中每个层都连接到前一层的所有层,形成了一个密集的连接结构。DenseNet121是DenseNet系列中的一员,它特别指定了有121个层,适用于图像识别任务。它的特点是减少了冗余计算、提高了特征再利用,并有助于解决梯度消失的问题。
DenseNet121的基本组件包括密集块(Dense Block)、过渡层(Transition Layer)以及输入和输出层。每一层都通过残差连接(Residual Connections)将输入直接添加到输出,使得训练变得更加容易。该模型已经在ImageNet数据集上预训练好了,可以直接在PyTorch中加载并用于迁移学习。
在PyTorch中,你可以使用`torchvision.models`模块来加载预训练的DenseNet121模型,例如:
```python
from torchvision import models
model = models.densenet121(pretrained=True)
```
这将会加载一个已经预训练好的DenseNet121模型,`pretrained=True`表示模型的权重来自于ImageNet数据集。
pytorch搭建densenet121
要使用PyTorch搭建Densenet121模型,可以参考以下步骤:
1. 导入必要的库和模块:
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
```
2. 定义DenseBlock内部的Bottleneck层:
```python
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([x, out], 1)
return out
```
3. 定义DenseBlock:
```python
class DenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers):
super(DenseBlock, self).__init__()
layers = []
for _ in range(num_layers):
layers.append(Bottleneck(in_channels, growth_rate))
in_channels += growth_rate
self.layers = nn.ModuleList(layers)
def forward(self, x):
for layer in self.layers:
x = layer(x)
return x
```
4. 定义过渡层:
```python
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.pool = nn.AvgPool2d(kernel_size=2, stride=2)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
out = self.pool(out)
return out
```
5. 定义Densenet模型:
```python
class DenseNet(nn.Module):
def __init__(self, num_blocks, growth_rate=32, num_classes=1000):
super(DenseNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Densenet层
in_channels = 64
self.dense1 = self._make_dense_block(in_channels, growth_rate, num_blocks[0])
in_channels += growth_rate * num_blocks[0]
self.trans1 = self._make_transition(in_channels, in_channels // 2)
in_channels = in_channels // 2
self.dense2 = self._make_dense_block(in_channels, growth_rate, num_blocks[1])
in_channels += growth_rate * num_blocks[1]
self.trans2 = self._make_transition(in_channels, in_channels // 2)
in_channels = in_channels // 2
self.dense3 = self._make_dense_block(in_channels, growth_rate, num_blocks[2])
in_channels += growth_rate * num_blocks[2]
self.trans3 = self._make_transition(in_channels, in_channels // 2)
in_channels = in_channels // 2
self.dense4 = self._make_dense_block(in_channels, growth_rate, num_blocks[3])
in_channels += growth_rate * num_blocks[3]
self.bn2 = nn.BatchNorm2d(in_channels)
self.fc = nn.Linear(in_channels, num_classes)
def _make_dense_block(self, in_channels, growth_rate, num_layers):
layers = []
for _ in range(num_layers):
layers.append(Bottleneck(in_channels, growth_rate))
in_channels += growth_rate
return nn.Sequential(*layers)
def _make_transition(self, in_channels, out_channels):
return Transition(in_channels, out_channels)
def forward(self, x):
out = self.pool1(F.relu(self.bn1(self.conv1(x))))
out = self.dense1(out)
out = self.trans1(out)
out = self.dense2(out)
out = self.trans2(out)
out = self.dense3(out)
out = self.trans3(out)
out = self.dense4(out)
out = F.avg_pool2d(F.relu(self.bn2(out)), kernel_size=7)
out = out.view(out.size(0), -1)
out = self.fc(out)
return out
```
6. 创建Densenet121模型:
```python
def densenet_121(num_classes=1000):
return DenseNet([6, 12, 24, 16], growth_rate=32, num_classes=num_classes)
```
以上就是使用PyTorch搭建Densenet121模型的过程。如果有任何问题,请随时提问。
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