上一篇当中,使用pytorch搭建了一个squeezenet,效果还行。但是偶然间发现了一个稍微改动的版本,拿来测试一下发现效果会更好,大概网络结构还是没有变,还是如下的第二个版本:
具体看网络结构代码:
python
import torch
import torch.nn as nn
class Fire(nn.Module):
def __init__(self, in_channel, out_channel, squzee_channel):
super().__init__()
self.squeeze = nn.Sequential(
nn.Conv2d(in_channel, squzee_channel, 1),
nn.BatchNorm2d(squzee_channel),
nn.ReLU(inplace=True)
)
self.expand_1x1 = nn.Sequential(
nn.Conv2d(squzee_channel, int(out_channel / 2), 1),
nn.BatchNorm2d(int(out_channel / 2)),
nn.ReLU(inplace=True)
)
self.expand_3x3 = nn.Sequential(
nn.Conv2d(squzee_channel, int(out_channel / 2), 3, padding=1),
nn.BatchNorm2d(int(out_channel / 2)),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.squeeze(x)
x = torch.cat([
self.expand_1x1(x),
self.expand_3x3(x)
], 1)
return x
class SqueezeNet(nn.Module):
"""mobile net with simple bypass"""
def __init__(self, class_num=100):
super().__init__()
self.stem = nn.Sequential(
nn.Conv2d(3, 96, 3, padding=1),
nn.BatchNorm2d(96),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2)
)
self.fire2 = Fire(96, 128, 16)
self.fire3 = Fire(128, 128, 16)
self.fire4 = Fire(128, 256, 32)
self.fire5 = Fire(256, 256, 32)
self.fire6 = Fire(256, 384, 48)
self.fire7 = Fire(384, 384, 48)
self.fire8 = Fire(384, 512, 64)
self.fire9 = Fire(512, 512, 64)
self.conv10 = nn.Conv2d(512, class_num, 1)
self.avg = nn.AdaptiveAvgPool2d(1)
self.maxpool = nn.MaxPool2d(2, 2)
def forward(self, x):
x = self.stem(x)
f2 = self.fire2(x)
f3 = self.fire3(f2) + f2
f4 = self.fire4(f3)
f4 = self.maxpool(f4)
f5 = self.fire5(f4) + f4
f6 = self.fire6(f5)
f7 = self.fire7(f6) + f6
f8 = self.fire8(f7)
f8 = self.maxpool(f8)
f9 = self.fire9(f8)
c10 = self.conv10(f9)
x = self.avg(c10)
# x = x.view(x.size(0), -1)
x = torch.flatten(x, start_dim=1)
return x
def squeezenet(class_num=100):
return SqueezeNet(class_num=class_num)
最大的变化就是在卷积层和relu激活层之间加了个bn层,包括所有的fire结构内。其余就是卷积和池化的kernel_size或stride的微调,包括最后去掉了dropout,以及最后部分的网络结构也稍作调整:
将这个网络结构与上一个的网络结构训练同一训练集,得到的模型测试同一测试集,发现这个模型的准确率会比上一个高几个点。而且上一个模型训练容易不稳定,训练到一半直接梯度爆炸了,需要不断调参也比较麻烦,这个模型lr直接0.1或0.01都能训练很好,所以个人更推荐这个网络模型。
下一篇编辑此网络结构的caffe版本。