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1. GoogLeNet论文详解
Abstract:
提出了GoogLeNet网络结构——22层，此设计允许在保证计算预算不变的前提下，增加网络的深度和宽度，这个网络结构是基于Hebbian原则和多尺度处理，并且在ILSVRC 2014中的分类任务中获得第一名。
对于大型数据集，最近的趋势是增加层数和每一层的尺寸，同时使用dropout来解决过拟合问题
层尺寸的增大意味着需要更大数量的参数，这会使得网络更容易过拟合，尤其是对于数据集小的情况下层深度的增加会大大增加计算资源的使用，尤其是卷积层的权重为0时，会浪费大量计算资源
1x1 卷积 & 全局平均池化
这两种方法都是为了提高卷积网络的表达能力，改善网络结构的。在Network-in-network中被提出的。
1x1卷积：
可以通过设置1x1卷积核的数量来实现降维或升维实现特征图的通道间的聚合
全局平均池化：
传统CNN网络中，前面堆叠卷积层提取特征，最后通过全连接层分类提取出的特征，但是全连接层很容易导致模型过拟合，并且其参数比较多，为了解决这个问题，出现了dropout，但是在Network-in-network中，作者提出了全局平均池化来解决此问题
将卷积层提取出来的特征图（Feature map）进行相加求平均，然后将这些特征图对应的平均值作为某一类的置信度输入到softmax进行分类（要控制卷积层的最后一层的特征图数量与最终分类数量保持一致）
此方法的好处
减少了参数（相对于全连接层）减轻过拟合求和取平均操作综合了空间信息，提高模型的鲁棒性
缺点：
对特征图的简单相加求平均可能会丢失一些有用信息
网络结构细节：
Inception
1x1卷积应用于3x3卷积和5x5卷积之前，主要作用：降维，降低参数数量 以inception(3a)中3x3卷积为例： input: 28x28x192
不使用 1x1 卷积 其参数数量为：192x3x3x128=221184使用1x1卷积 其参数数量为：192x1x1x96+96x3x3x128=184320 相当于先将channel的维数从192维降到96维
GoogLeNet参数
所有的卷积层都包含ReLu激活层#3x3 reduce：表示Inception结构中3x3卷积层前的1x1卷积核的数量#5x5 reduce：表示Inception结构中的5x5卷积前的1x1卷积核的数量pool proj：表示Inception结构中的最大池化层后的1x1卷积核的数量
GoogLeNet网络结构
红色：池化层蓝色：卷积层+ReLu绿色：拼接操作黄色：softmax激活函数
由于网络的深度相对比较大，能够在所有层保证梯度能传播是一个问题。对此我们增加了2个辅助分类器，在训练期间辅助分类器的权重为0.3，在预测时，这些层会被丢弃。
辅助分类器结构：
AveragePool：滤波器大小（5，5）、步长：3。输出：4*4*512（from 4a）、4*4*528（from 4d）Conv：1x1卷积、卷积核数量：128、步长：1FC：（128*4*4，1024）FC：（1024，1000）
2. 基于Pytorch代码复现：
2.1 模型搭建
import torch
import torch.nn as nn
import torchvision.models as models
from torchsummary import summary
import torch.optim as optim
class GoogLeNet(nn.Module):
def __init__(self, num_classes=1000, aux_logits=True, init_weights=False):
super(GoogLeNet, self).__init__()
self.aux_logits = aux_logits
self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
self.maxpool1 = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
self.conv2 = BasicConv2d(64, 64, kernel_size=1)
self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
self.maxpool2 = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
self.maxpool3 = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
self.maxpool4 = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True)
self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
if self.aux_logits:
self.aux1 = InceptionAux(512, num_classes)
self.aux2 = InceptionAux(528, num_classes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(p=0.2)
self.fc = nn.Linear(1024, num_classes)
if init_weights:
self._init_weight()
def forward(self, x):
# N x 3 x 224 x 224
x = self.conv1(x)
# N x 64 x 112 x 112
x = self.maxpool1(x)
# N x 64 x 56 x 56
x = self.conv2(x)
# N x 64 x 56 x 56
x = self.conv3(x)
# N x 192 x 56 x 56
x = self.maxpool2(x)
# N x 192 x 28 x 28
x = self.inception3a(x)
# N x 256 x 28 x 28
x = self.inception3b(x)
# N x 480 x 28 x 28
x = self.maxpool3(x)
# N x 480 x 14 x 14
x = self.inception4a(x)
# N x 512 x 14 x 14
if self.aux_logits and self.training:
aux1 = self.aux1(x)
x = self.inception4b(x)
# N x 512 x 14 x 14
x = self.inception4c(x)
# N x 512 x 14 x 14
x = self.inception4d(x)
# N x 528 x 14 x 14
if self.aux_logits and self.training:
aux2 = self.aux2(x)
x = self.inception4e(x)
# N x 832 x 14 x 14
x = self.maxpool4(x)
# N x 832 x 7 x 7
x = self.inception5a(x)
# N x 832 x 7 x 7
x = self.inception5b(x)
# N x 1024 x 7 x 7
x = self.avgpool(x)
# N x 1024 x 1 x 1
x = torch.flatten(x, start_dim=1)
# N x 1024
x = self.dropout(x)
x = self.fc(x)
if self.aux_logits and self.training:
return x, aux2, aux1
return x
def _init_weight(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
class Inception(nn.Module):
def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
super(Inception, self).__init__()
self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
self.branch2 = nn.Sequential(
BasicConv2d(in_channels, ch3x3red, kernel_size=1),
BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
)
self.branch3 = nn.Sequential(
BasicConv2d(in_channels, ch5x5red, kernel_size=1),
BasicConv2d(ch5x5red, ch5x5, kernel_size=5, padding=2)
)
self.branch4 = nn.Sequential(
nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
BasicConv2d(in_channels, pool_proj, kernel_size=1)
)
def forward(self, x):
branch1 = self.branch1(x)
branch2 = self.branch2(x)
branch3 = self.branch3(x)
branch4 = self.branch4(x)
outputs = [branch1, branch2, branch3, branch4]
return torch.cat(outputs, dim=1)
class InceptionAux(nn.Module):
def __init__(self, in_channels, num_classes):
super(InceptionAux, self).__init__()
self.averagePool = nn.AdaptiveAvgPool2d((4, 4))
self.conv = BasicConv2d(in_channels, 128, kernel_size=1)
self.aux_classifier = nn.Sequential(
nn.Linear(128 * 4 * 4, 1024),
nn.Dropout(p=0.5),
nn.ReLU(inplace=True),
nn.Linear(1024, num_classes)
)
def forward(self, x):
x = self.averagePool(x)
x = self.conv(x)
x = torch.flatten(x, start_dim=1)
x = self.aux_classifier(x)
return x
class BasicConv2d(nn.Module):
def __init__(self, in_channels, out_channels, **kwargs):
super(BasicConv2d, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, **kwargs)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.relu(x)
return x
2.2 训练结果如下
训练数据集与验证集大小以及训练参数
Using 3306 images for training, 364 images for validation
Using cuda GeForce RTX 2060 device for training
lr: 0.0001
batch_size: 16
使用自己定义的网络训练结果
[epoch 1/10] train_loss: 2.350 val_acc: 0.407
[epoch 2/10] train_loss: 1.912 val_acc: 0.505
[epoch 3/10] train_loss: 1.842 val_acc: 0.511
[epoch 4/10] train_loss: 1.769 val_acc: 0.560
[epoch 5/10] train_loss: 1.746 val_acc: 0.566
[epoch 6/10] train_loss: 1.670 val_acc: 0.621
[epoch 7/10] train_loss: 1.595 val_acc: 0.635
[epoch 8/10] train_loss: 1.538 val_acc: 0.621
[epoch 9/10] train_loss: 1.509 val_acc: 0.681
[epoch 10/10] train_loss: 1.456 val_acc: 0.657
Best acc: 0.681
Finished Training
Train 耗时为：277.0s
使用预训练模型参数训练结果
[epoch 1/10] train_loss: 0.668 val_acc: 0.871
[epoch 2/10] train_loss: 0.359 val_acc: 0.901
[epoch 3/10] train_loss: 0.298 val_acc: 0.923
[epoch 4/10] train_loss: 0.268 val_acc: 0.920
[epoch 5/10] train_loss: 0.252 val_acc: 0.904
[epoch 6/10] train_loss: 0.228 val_acc: 0.923
[epoch 7/10] train_loss: 0.196 val_acc: 0.915
[epoch 8/10] train_loss: 0.210 val_acc: 0.92
[epoch 9/10] train_loss: 0.169 val_acc: 0.918
[epoch 10/10] train_loss: 0.179 val_acc: 0.93
Best acc: 0.931
Finished Training
Train 耗时为：239.9s
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