public SqueezeNet(string version = "1_0",
int num_classes = 1000)
{
this.num_classes = num_classes;
if (version == "1_0")
{
this.features = new nn.Sequential(
new nn.Conv2d(3, 96, kernel_size: 7, stride: 2),
new nn.ReLU(),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(96, 16, 64, 64),
new Fire(128, 16, 64, 64),
new Fire(128, 32, 128, 128),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(256, 32, 128, 128),
new Fire(256, 48, 192, 192),
new Fire(384, 48, 192, 192),
new Fire(384, 64, 256, 256),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(512, 64, 256, 256)
);
}
else
{
if (version == "1_1")
{
this.features = new nn.Sequential(
new nn.Conv2d(3, 64, kernel_size: 3, stride: 2),
new nn.ReLU(),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(64, 16, 64, 64),
new Fire(128, 16, 64, 64),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(128, 32, 128, 128),
new Fire(256, 32, 128, 128),
new nn.MaxPool2d(kernel_size: 3, stride: 2),
new Fire(256, 48, 192, 192),
new Fire(384, 48, 192, 192),
new Fire(384, 64, 256, 256),
new Fire(512, 64, 256, 256)
);
}
else
{
throw new torch.TorchException(string.Format("TorchException: Unsupported SqueezeNet version {0}: 1_0 or 1_1 expected.", version));
}
}
var final_conv = new nn.Conv2d(512, this.num_classes, kernel_size: 1);
this.classifier = new nn.Sequential(
new nn.Dropout(p: 0.5),
final_conv,
new nn.ReLU(),
new nn.AvgPool2d(13, 13)
);
}
public Fire(int inplanes,
int squeeze_planes,
int expand1x1_planes,
int expand3x3_planes)
{
this.inplanes = inplanes;
this.squeeze = new nn.Conv2d(inplanes,
squeeze_planes,
kernel_size: 1);
this.squeeze_activation = new nn.ReLU();
this.expand1x1 = new nn.Conv2d(squeeze_planes,
expand1x1_planes,
kernel_size: 1);
this.expand1x1_activation = new nn.ReLU();
this.expand3x3 = new nn.Conv2d(squeeze_planes,
expand3x3_planes,
kernel_size: 3,
padding: 1);
this.expand3x3_activation = new nn.ReLU();
}
