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Глеб Брыкин
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‎Implementation/src/torchvision/models/load_model.cs‎

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using System;
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using System.IO;
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using System.Net;
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using System.Collections.Generic;
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namespace System
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{
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namespace AI
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{
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public static partial class torchvision
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{
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public static partial class models
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{
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static models()
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{
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ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12;
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}
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internal static Dictionary<string, torch.Tensor> __load_model(string path, string fname, string url, bool print = true)
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{
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if(File.Exists(path))
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{
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return (Dictionary<string, torch.Tensor>)torch.load(path);
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}
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else
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{
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if(print)
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{
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Console.WriteLine("Downloading " + fname + " from " + url);
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}
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(new WebClient()).DownloadFile(url, path + fname);
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return (Dictionary<string, torch.Tensor>)torch.load(path + fname);
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}
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}
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}
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}
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}
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}

‎Implementation/src/torchvision/models/squeezenet.cs‎

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using nn = System.AI.torch.nn;
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namespace System
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{
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namespace AI
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{
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public static partial class torchvision
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{
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public static partial class models
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{
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public sealed class Fire : nn.Module
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{
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public int inplanes;
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public nn.Conv2d squeeze;
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public nn.ReLU squeeze_activation;
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public nn.Conv2d expand1x1;
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public nn.ReLU expand1x1_activation;
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public nn.Conv2d expand3x3;
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public nn.ReLU expand3x3_activation;
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public Fire(int inplanes,
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int squeeze_planes,
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int expand1x1_planes,
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int expand3x3_planes)
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{
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this.inplanes = inplanes;
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this.squeeze = new nn.Conv2d(inplanes,
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squeeze_planes,
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kernel_size: 1);
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this.squeeze_activation = new nn.ReLU();
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this.expand1x1 = new nn.Conv2d(squeeze_planes,
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expand1x1_planes,
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kernel_size: 1);
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this.expand1x1_activation = new nn.ReLU();
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this.expand3x3 = new nn.Conv2d(squeeze_planes,
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expand3x3_planes,
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kernel_size: 3,
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padding: 1);
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this.expand3x3_activation = new nn.ReLU();
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}
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public override torch.Tensor forward(torch.Tensor x)
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{
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x = this.squeeze_activation.forward(this.squeeze.forward(x));
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return torch.cat(new torch.Tensor[]{
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this.expand1x1_activation.forward(this.expand1x1.forward(x)),
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this.expand3x3_activation.forward(this.expand3x3.forward(x))
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}, 1);
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}
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}
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public sealed class SqueezeNet : nn.Module
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{
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public nn.Sequential features;
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public nn.Sequential classifier;
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public int num_classes;
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public SqueezeNet(string version = "1_0",
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int num_classes = 1000)
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{
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this.num_classes = num_classes;
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if(version == "1_0")
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{
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this.features = new nn.Sequential(
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new nn.Conv2d(3, 96, kernel_size: 7, stride: 2),
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new nn.ReLU(),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(96, 16, 64, 64),
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new Fire(128, 16, 64, 64),
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new Fire(128, 32, 128, 128),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(256, 32, 128, 128),
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new Fire(256, 48, 192, 192),
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new Fire(384, 48, 192, 192),
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new Fire(384, 64, 256, 256),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(512, 64, 256, 256)
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);
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}
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else
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{
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if(version == "1_1")
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{
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this.features = new nn.Sequential(
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new nn.Conv2d(3, 64, kernel_size: 3, stride: 2),
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new nn.ReLU(),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(64, 16, 64, 64),
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new Fire(128, 16, 64, 64),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(128, 32, 128, 128),
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new Fire(256, 32, 128, 128),
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new nn.MaxPool2d(kernel_size: 3, stride: 2),
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new Fire(256, 48, 192, 192),
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new Fire(384, 48, 192, 192),
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new Fire(384, 64, 256, 256),
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new Fire(512, 64, 256, 256)
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);
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}
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else
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{
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throw new torch.TorchException(string.Format("TorchException: Unsupported SqueezeNet version {0}: 1_0 or 1_1 expected.", version));
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}
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}
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var final_conv = new nn.Conv2d(512, this.num_classes, kernel_size: 1);
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this.classifier = new nn.Sequential(
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new nn.Dropout(p: 0.5),
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final_conv,
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new nn.ReLU(),
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new nn.AvgPool2d(13, 13)
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);
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}
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public torch.Tensor forward(torch.Tensor x)
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{
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if((x.shape[1] != 3) || (x.shape[2] != 224) || (x.shape[3] != 224))
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{
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throw new ArgumentException("Unsupported image size: should be bx3x224x224.");
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}
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x = this.features.forward(x) as torch.Tensor;
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x = this.classifier.forward(x) as torch.Tensor;
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return torch.flatten(x, 1);
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}
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}
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public static SqueezeNet squeezenet1_0(bool pretrained = false, int num_classes = 1000)
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{
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var m = new SqueezeNet("1_0");
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if(pretrained)
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{
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m.load_state_dict(
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__load_model("",
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"squeezenet1_0.thn",
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"https://github.com/ColorfulSoft/System.AI/raw/master/Implementation/src/torchvision/models/thn/squeezenet1_0.thn")
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);
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}
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return m;
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}
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public static SqueezeNet squeezenet1_1(bool pretrained = false, int num_classes = 1000)
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{
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var m = new SqueezeNet("1_1");
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if(pretrained)
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{
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m.load_state_dict(
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__load_model("",
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"squeezenet1_1.thn",
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"https://github.com/ColorfulSoft/System.AI/raw/master/Implementation/src/torchvision/models/thn/squeezenet1_1.thn")
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);
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}
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return m;
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}
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}
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}
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}
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}

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