结构化CNN模型构建与测试
- 前言
- GoogLeNet
- 结构
- Inception块
- 模型构建
- resNet18
- 模型结构
- 残差块
- 模型构建
- denseNet
- 模型结构
- DenseBlock
- transition_block
- 模型构建
- 结尾
前言
在本专栏的上一篇博客中我们介绍了常用的线性模型，在本文中我们将介绍GoogleNet、resNet、denseNet这类结构化的模型的构建方式。
GoogLeNet
结构
整体的结构似乎有些吓人，但其实他也是用了块的思维，仔细观察可以发现，他中间一段很多层的结构都是相似的
Inception块
这个块就是其中重复的块，这个块分成了四个分支：1x1卷积、1x1卷积+3x3卷积、1x1卷积+5x5卷积、3x3卷积+1x1卷积，最后将这四个分支通道合并
class Inception(nn.Module):
def __init__(self, in_channels, c1, c2, c3, c4, **kwargs):
super(Inception, self).__init__(**kwargs)
self.p1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
self.p2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
self.p3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
self.p4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)
def forward(self, x):
p1 = F.relu(self.p1_1(x))
p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
p4 = F.relu(self.p4_2(self.p4_1(x)))
return torch.cat((p1, p2, p3, p4), dim=1)
模型构建
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
nn.ReLU(),
nn.Conv2d(64, 192, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
Inception(256, 128, (128, 192), (32, 96), 64),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
b4 = nn.Sequential(Inception(480, 192, (96, 208), (16, 48), 64),
Inception(512, 160, (112, 224), (24, 64), 64),
Inception(512, 128, (128, 256), (24, 64), 64),
Inception(512, 112, (144, 288), (32, 64), 64),
Inception(528, 256, (160, 320), (32, 128), 128),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
Inception(832, 384, (192, 384), (48, 128), 128),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten()
)
net = nn.Sequential(b1, b2, b3, b4, b5, nn.Linear(1024, 10))
resNet18
模型结构
这个模型似乎要更加简洁一些，因为这里只有两个分支，但是他有两种分支方式，一种是卷积+残差，另外一种是卷积+经过1x1卷积处理过的残差
残差块
class Residual(nn.Module):
def __init__(self, input_channels, num_channels, use_1x1conv=False, strides=1):
super().__init__()
self.conv1 = nn.Conv2d(input_channels, num_channels, kernel_size=3, padding=1, stride=strides)
self.conv2 = nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1)
if use_1x1conv:
self.conv3 = nn.Conv2d(input_channels, num_channels, kernel_size=1, stride=strides)
else:
self.conv3 = None
self.bn1 = nn.BatchNorm2d(num_channels)
self.bn2 = nn.BatchNorm2d(num_channels)
def forward(self, X):
Y = F.relu(self.bn1(self.conv1(X)))
Y = self.bn2(self.conv2(Y))
if self.conv3:
X = self.conv3(X)
Y += X
return F.relu(Y)
构建时依次使用两种分支方式
def resnet_block(input_channels, num_channels, num_residuals, first_block=False):
blk = []
for i in range(num_residuals):
if i == 0 and not first_block:
blk.append(Residual(input_channels, num_channels, use_1x1conv=True, strides=2))
else:
blk.append(Residual(num_channels, num_channels))
return blk
模型构建
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
b2 = nn.Sequential(*resnet_block(64, 64, 2, first_block=True))
b3 = nn.Sequential(*resnet_block(64, 128, 2))
b4 = nn.Sequential(*resnet_block(128, 256, 2))
b5 = nn.Sequential(*resnet_block(256, 512, 2))
net = nn.Sequential(b1, b2, b3, b4, b5,
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(512, 10)
)
denseNet
模型结构
这个模型的主要思路就是把每一次的输出和输入合并起来，同时作为下一层的输入，具体的细节还要结合代码解释
DenseBlock
我们看下面的代码，注意init中的循环以及forward时对于X的处理。我们可以发现，每经过一个conv_block都会将conv_block的输出并入到输入，以此作为下一层的输入
def conv_block(input_channels, num_channels):
return nn.Sequential(nn.BatchNorm2d(input_channels), nn.ReLU(),
nn.Conv2d(input_channels, num_channels, kernel_size=3, padding=1)
)
class DenseBlock(nn.Module):
def __init__(self, num_convs, input_channels, num_channels):
super(DenseBlock, self).__init__()
layer = []
for i in range(num_convs):
layer.append(conv_block(
num_channels * i + input_channels, num_channels
))
self.net = nn.Sequential(*layer)
def forward(self, X):
for blk in self.net:
Y = blk(X)
X = torch.cat((X, Y), dim=1)
return X
transition_block
这个块的主要作用是减少通道，因为在前面的块中，通道数会持续的增长，考虑到计算量，需要在中间加入减少通道的块
def transition_block(input_channels, num_channels):
return nn.Sequential(nn.BatchNorm2d(input_channels), nn.ReLU(),
nn.Conv2d(input_channels, num_channels, kernel_size=1),
nn.AvgPool2d(kernel_size=2, stride=2)
)
模型构建
b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
nn.BatchNorm2d(64), nn.ReLU(),
nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
)
num_channels, growth_rate = 64, 32
num_convs_in_dense_blocks = [4, 4, 4, 4]
blks = []
for i, num_convs in enumerate(num_convs_in_dense_blocks):
blks.append(DenseBlock(num_convs, num_channels, growth_rate))
num_channels += num_convs * growth_rate
if i != len(num_convs_in_dense_blocks) - 1:
blks.append(transition_block(num_channels, num_channels // 2))
num_channels = num_channels // 2
net = nn.Sequential(b1, *blks,
nn.BatchNorm2d(num_channels), nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1)),
nn.Flatten(),
nn.Linear(num_channels, 10)
)
结尾
我们到现在模型就已经构建好了，测试的过程可以参照本专栏的上一篇博客
pytorch深度学习基础（十）——常用线性CNN模型的结构与训练
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