| """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) | |