///<summary>Выполняет прямой проход через кодирующую нейросеть.</summary>
///<param name="Content4_1">Контент со слоя ReLU4_1.</param>
///<param name="Style4_1">Стиль со слоя ReLU4_1.</param>
///<param name="Content5_1">Контент со слоя ReLU5_1.</param>
///<param name="Style5_1">Стиль со слоя ReLU5_1.</param>
public Tensor Stylize(Tensor Content4_1, Tensor Style4_1, Tensor Content5_1, Tensor Style5_1)
{
var Fc4_1 = Layers.Norm(Content4_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var Fs4_1 = Layers.Norm(Style4_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var Fc5_1 = Layers.Norm(Content5_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var Fs5_1 = Layers.Norm(Style5_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var f4_1 = Layers.Conv2D1x1(Fc4_1, this.Data.sanet4_1_f_Weights, this.Data.sanet4_1_f_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var g4_1 = Layers.Conv2D1x1(Fs4_1, this.Data.sanet4_1_g_Weights, this.Data.sanet4_1_g_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var h4_1 = Layers.Conv2D1x1(Style4_1, this.Data.sanet4_1_h_Weights, this.Data.sanet4_1_h_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var f5_1 = Layers.Conv2D1x1(Fc5_1, this.Data.sanet5_1_f_Weights, this.Data.sanet5_1_f_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var g5_1 = Layers.Conv2D1x1(Fs5_1, this.Data.sanet5_1_g_Weights, this.Data.sanet5_1_g_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var h5_1 = Layers.Conv2D1x1(Style5_1, this.Data.sanet5_1_h_Weights, this.Data.sanet5_1_h_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var S4_1 = Tensor.MatMul(f4_1.Flat().Transpose(), g4_1.Flat());
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
S4_1 = Layers.Softmax(S4_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var O4_1 = Layers.ElementwiseSum(Layers.Conv2D1x1(Tensor.MatMul(h4_1.Flat(), S4_1.Transpose()).Unflat(Content4_1.Width, Content4_1.Height), this.Data.sanet4_1_out_conv_Weights, this.Data.sanet4_1_out_conv_Biases), Content4_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var S5_1 = Tensor.MatMul(f5_1.Flat().Transpose(), g5_1.Flat());
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
S5_1 = Layers.Softmax(S5_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var O5_1 = Layers.ElementwiseSum(Layers.Conv2D1x1(Tensor.MatMul(h5_1.Flat(), S5_1.Transpose()).Unflat(Content5_1.Width, Content5_1.Height), this.Data.sanet5_1_out_conv_Weights, this.Data.sanet5_1_out_conv_Biases), Content5_1);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
var CS = Layers.Conv2D3x3(Layers.ElementwiseSum(O4_1, Layers.Upsample2D(O5_1)), this.Data.merge_conv_Weights, this.Data.merge_conv_Biases);
if (this.Step != null)
{
this.Step(1f / 17f * 100f);
}
return(CS);
}