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"""squeezenet in pytorch
"""
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)
return x
def squeezenet(class_num=1):
return SqueezeNet(class_num=class_num)
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