以下是CondenseNet的简单实现代码：
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_channels, out_channels, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride,
padding=1, bias=False)
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
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)
def forward(self, x):
out = self.conv(F.relu(self.bn(x)))
out = F.avg_pool2d(out, 2)
return out
class CondenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, num_layers, drop_rate=0.0):
super(CondenseBlock, self).__init__()
self.num_layers = num_layers
self.drop_rate = drop_rate
self.layers = nn.ModuleList([Bottleneck(in_channels + i * growth_rate, growth_rate)
for i in range(num_layers)])
def forward(self, x):
for i in range(self.num_layers):
if i == 0:
out = self.layers[i](x)
else:
out = self.layers[i](out)
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = torch.cat([x, out], 1)
return out
class CondenseNet(nn.Module):
def __init__(self, growth_rate=12, block_config=(18, 18, 18),
num_classes=10, drop_rate=0):
super(CondenseNet, self).__init__()
self.growth_rate = growth_rate
# Initial convolution
self.conv1 = nn.Conv2d(3, 2 * growth_rate, kernel_size=3, padding=1,
bias=False)
# First block
self.block1 = CondenseBlock(2 * growth_rate, growth_rate, block_config[0],
drop_rate=drop_rate)
self.trans1 = Transition(2 * growth_rate + block_config[0] * growth_rate,
int(1.5 * growth_rate))
# Second block
self.block2 = CondenseBlock(int(1.5 * growth_rate), growth_rate,
block_config[1], drop_rate=drop_rate)
self.trans2 = Transition(int(1.5 * growth_rate) + block_config[1] * growth_rate,
int(2.25 * growth_rate))
# Third block
self.block3 = CondenseBlock(int(2.25 * growth_rate), growth_rate,
block_config[2], drop_rate=drop_rate)
# Final batch norm
self.bn = nn.BatchNorm2d(int(2.25 * growth_rate))
# Linear layer
self.linear = nn.Linear(int(2.25 * growth_rate), num_classes)
def forward(self, x):
out = self.conv1(x)
out = F.max_pool2d(out, 3, stride=2, padding=1)
out = self.block1(out)
out = self.trans1(out)
out = self.block2(out)
out = self.trans2(out)
out = self.block3(out)
out = F.relu(self.bn(out))
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
```
这里实现了CondenseNet网络的主要结构，包括Bottleneck、Transition和CondenseBlock三个模块。其中，Bottleneck表示CondenseBlock中的基本单元，Transition用于进行通道数调整，CondenseBlock则是CondenseNet的主要模块，由多个Bottleneck组成。在模型的前部分，使用了一个3x3卷积和一个最大池化层，而在最后一层则是一个全局平均池化层和一个全连接层。
CondenseNet是一种优化DenseNet的深度学习模型，通过引入learned group convolutions和训练时剪枝，减少计算量和内存占用。在保持与DenseNet相似准确性的同时，只需1/10的训练时间。文章对比了ShuffleNet和MobileNet等模型，探讨了卷积group操作、训练剪枝和跨block连接的作用。
最低0.47元/天 解锁文章
扫一扫
389
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
