///<summary>Выполняет стилизацию. Метод выполняется в потоке Stylizer.</summary>
private void Stylize()
{
var content = IOConverters.ImageToTensor(this.ContentImage.Image as Bitmap);
var style = IOConverters.ImageToTensor(this.StyleImage.Image as Bitmap);
var content_features = encoder.Encode(content);
var style_features = encoder.Encode(style);
var Total = Layers.AdaIN(content_features, style_features);
var Decoded = decoder.Decode(Total);
this.ResultImage.Image = IOConverters.TensorToImage(Decoded);
this.Stylized = true;
this.Progress.Value = 0;
this.TotalProgress = 0f;
this.GenerateOrSaveResult.Text = "Сохранить";
this.GenerateOrSaveResult.Click -= this.StopProcess;
this.GenerateOrSaveResult.Click += this.SaveResult;
this.OpenContent.Enabled = true;
this.OpenStyle.Enabled = true;
}
public static Tensor Stylize(Encoder Encoder1,
Decoder Decoder1,
Encoder Encoder2,
Decoder Decoder2,
Encoder Encoder3,
Decoder Decoder3,
Encoder Encoder4,
Decoder Decoder4,
Encoder Encoder5,
Decoder Decoder5,
Tensor Content,
Tensor Style,
TransferMethod Method = TransferMethod.WCT,
float alpha = 0.6f)
{
Encoder1.Step += FixStep;
Decoder1.Step += FixStep;
Encoder2.Step += FixStep;
Decoder2.Step += FixStep;
Encoder3.Step += FixStep;
Decoder3.Step += FixStep;
Encoder4.Step += FixStep;
Decoder4.Step += FixStep;
Encoder5.Step += FixStep;
Decoder5.Step += FixStep;
TotalSteps = 0f;
LastPercent = 0;
if (Method == TransferMethod.WCT)
{
// WCT 5
var Conv5 = Decoder5.Decode(Layers.WCT(Encoder5.Encode(Content), Encoder5.Encode(Style), alpha));
// WCT 4
var Conv4 = Decoder4.Decode(Layers.WCT(Encoder4.Encode(Conv5), Encoder4.Encode(Style), alpha));
// WCT 3
var Conv3 = Decoder3.Decode(Layers.WCT(Encoder3.Encode(Conv4), Encoder3.Encode(Style), alpha));
// WCT 2
var Conv2 = Decoder2.Decode(Layers.WCT(Encoder2.Encode(Conv3), Encoder2.Encode(Style), alpha));
// WCT 1
var Conv1 = Decoder1.Decode(Layers.WCT(Encoder1.Encode(Conv2), Encoder1.Encode(Style), alpha));
// Uninstall
Encoder1.Step -= FixStep;
Decoder1.Step -= FixStep;
Encoder2.Step -= FixStep;
Decoder2.Step -= FixStep;
Encoder3.Step -= FixStep;
Decoder3.Step -= FixStep;
Encoder4.Step -= FixStep;
Decoder4.Step -= FixStep;
Encoder5.Step -= FixStep;
Decoder5.Step -= FixStep;
return(Conv1);
}
else
{
// AdaIN 5
var Conv5 = Decoder5.Decode(Layers.AdaIN(Encoder5.Encode(Content), Encoder5.Encode(Style), alpha));
// AdaIN 4
var Conv4 = Decoder4.Decode(Layers.AdaIN(Encoder4.Encode(Conv5), Encoder4.Encode(Style), alpha));
// AdaIN 3
var Conv3 = Decoder3.Decode(Layers.AdaIN(Encoder3.Encode(Conv4), Encoder3.Encode(Style), alpha));
// AdaIN 2
var Conv2 = Decoder2.Decode(Layers.AdaIN(Encoder2.Encode(Conv3), Encoder2.Encode(Style), alpha));
// AdaIN 1
var Conv1 = Decoder1.Decode(Layers.AdaIN(Encoder1.Encode(Conv2), Encoder1.Encode(Style), alpha));
// Uninstall
Encoder1.Step -= FixStep;
Decoder1.Step -= FixStep;
Encoder2.Step -= FixStep;
Decoder2.Step -= FixStep;
Encoder3.Step -= FixStep;
Decoder3.Step -= FixStep;
Encoder4.Step -= FixStep;
Decoder4.Step -= FixStep;
Encoder5.Step -= FixStep;
Decoder5.Step -= FixStep;
return(Conv1);
}
}
///<summary>Выполняет прямой проход через декодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Decode(Tensor Content, Tensor[] Styles)
{
var Temp = Layers.StyleDecorator(Content, Styles[3]);
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv4_1_Weights, this.Data.Conv4_1_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_4_Weights, this.Data.Conv3_4_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_3_Weights, this.Data.Conv3_3_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_2_Weights, this.Data.Conv3_2_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.AdaIN(Temp, Styles[2]);
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv3_1_Weights, this.Data.Conv3_1_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_2_Weights, this.Data.Conv2_2_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.AdaIN(Temp, Styles[1]);
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv2_1_Weights, this.Data.Conv2_1_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_2_Weights, this.Data.Conv1_2_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.AdaIN(Temp, Styles[0]);
Temp = Layers.Conv2D3x3(Temp, this.Data.Conv1_1_Weights, this.Data.Conv1_1_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
Temp = Layers.Conv2D7x7(Temp, this.Data.Conv_Out_Weights, this.Data.Conv_Out_Biases);
if (this.Step != null)
{
this.Step(1f / 22f * 100f);
}
return(Temp);
}