///<summary>Реализует слой StyleSwap.</summary>
///<param name="Content">Карты признаков контентного изображения.</param>
///<param name="Style">Карты признаков стилевого изображения.</param>
public static Tensor StyleSwap(Tensor Content, Tensor Style, int patch_size = 3, int stride = 2)
{
var style_kernels = extract_image_patches(Style, patch_size, stride);
var style_kernels_norm = l2_norm(style_kernels);
var ss_enc = Layers.Conv2D(Content, style_kernels_norm, stride);
var ss_argmax = argmax(ss_enc);
var encC = ss_enc.Depth;
var ss_oh = one_hot(ss_argmax, encC);
var ss_dec = Layers.ConvTranspose2D(ss_oh, style_kernels, stride);
var ss_oh_sum = reduce_sum(ss_oh);
var filter_ones = new Tensor[1] {
ones(patch_size, patch_size, stride)
};
var counting = Layers.ConvTranspose2D(ss_oh_sum, filter_ones, stride);
counting = tile(counting, Content.Depth);
return(divide(ss_dec, counting));
}
///<summary>Выполняет прямой проход через сеть-трансформер.</summary>
///<param name="input">Входные данные.</param>
public Tensor Stylize(Tensor input)
{
var Temp = Layers.Conv2D(input, this.Data.Conv1_Weights, 1);
if (Step != null)
{
Step(10);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.Conv1_Shift, this.Data.Conv1_Scale);
Temp = Layers.ReLU(Temp);
Temp = Layers.Conv2D(Temp, this.Data.Conv2_Weights, 2);
if (Step != null)
{
Step(10);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.Conv2_Shift, this.Data.Conv2_Scale);
Temp = Layers.ReLU(Temp);
Temp = Layers.Conv2D(Temp, this.Data.Conv3_Weights, 2);
if (Step != null)
{
Step(10);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.Conv3_Shift, this.Data.Conv3_Scale);
Temp = Layers.ReLU(Temp);
Temp = Layers.ResBlock(Temp, this.Data.ResBlock1);
if (Step != null)
{
Step(10);
}
Temp = Layers.ResBlock(Temp, this.Data.ResBlock2);
if (Step != null)
{
Step(10);
}
Temp = Layers.ResBlock(Temp, this.Data.ResBlock3);
if (Step != null)
{
Step(10);
}
Temp = Layers.ResBlock(Temp, this.Data.ResBlock4);
if (Step != null)
{
Step(10);
}
Temp = Layers.ResBlock(Temp, this.Data.ResBlock5);
if (Step != null)
{
Step(10);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_Weights, 2);
if (Step != null)
{
Step(10);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_Shift, this.Data.TConv1_Scale);
Temp = Layers.ReLU(Temp);
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_Weights, 2);
if (Step != null)
{
Step(10);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_Shift, this.Data.TConv2_Scale);
Temp = Layers.ReLU(Temp);
Temp = Layers.Conv2D(Temp, this.Data.TConv3_Weights, 1);
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv3_Shift, this.Data.TConv3_Scale);
Temp = Layers.Tanh(Temp);
return(Temp);
}
///<summary>Выполняет прямой проход через декодирующую нейросеть.</summary>
///<param name="input">Входные данные.</param>
public Tensor Decode(Tensor Content, Tensor Style)
{
var Temp = Layers.StyleSwap(Content, Style);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv3_1_Weights, this.Data.TConv3_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv3_1_Shift, this.Data.TConv3_1_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_4_Weights, this.Data.TConv2_4_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_4_Shift, this.Data.TConv2_4_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_3_Weights, this.Data.TConv2_3_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_3_Shift, this.Data.TConv2_3_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_2_Weights, this.Data.TConv2_2_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_2_Shift, this.Data.TConv2_2_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv2_1_Weights, this.Data.TConv2_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv2_1_Shift, this.Data.TConv2_1_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_4_Weights, this.Data.TConv1_4_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_4_Shift, this.Data.TConv1_4_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Upsample2D(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_3_Weights, this.Data.TConv1_3_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_3_Shift, this.Data.TConv1_3_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_2_Weights, this.Data.TConv1_2_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.InstanceNorm2D(Temp, this.Data.TConv1_2_Shift, this.Data.TConv1_2_Scale);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.ConvTranspose2D(Temp, this.Data.TConv1_1_Weights, this.Data.TConv1_1_Biases, 1);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
Temp = Layers.Sigmoid(Temp);
if (this.Step != null)
{
this.Step(1f / 29f * 100f);
}
return(Temp);
}
