public static Tensor ResidualBlock(Tensor input, ResidualBlockData Data)
{
var n = Layers.Conv2D3x3(input, Data.Conv1_Weights, new Tensor(1, 1, 64));
n = Layers.BatchNorm(n, Data.Conv1_Mean, Data.Conv1_Variance, Data.Conv1_Shift, Data.Conv1_Scale);
n = Layers.ReLU(n);
n = Layers.Conv2D3x3(n, Data.Conv2_Weights, new Tensor(1, 1, 64));
n = Layers.BatchNorm(n, Data.Conv2_Mean, Data.Conv2_Variance, Data.Conv2_Shift, Data.Conv2_Scale);
Parallel.For(0, 64, (int d) =>
{
for (int y = 0; y < input.Height; y++)
{
for (int x = 0; x < input.Width; x++)
{
n.Set(x, y, d, n.Get(x, y, d) + input.Get(x, y, d));
}
}
});
return(n);
}
public Tensor Enhance(Tensor input)
{
var n = Layers.Conv2D3x3(input, Data.Conv1_Weights, Data.Conv1_Biases);
n = Layers.ReLU(n);
if (this.Step != null)
{
Step(2);
}
var temp = n;
n = Layers.ResidualBlock(n, Data.Residual1);
if (this.Step != null)
{
Step(7);
}
n = Layers.ResidualBlock(n, Data.Residual2);
if (this.Step != null)
{
Step(12);
}
n = Layers.ResidualBlock(n, Data.Residual3);
if (this.Step != null)
{
Step(17);
}
n = Layers.ResidualBlock(n, Data.Residual4);
if (this.Step != null)
{
Step(22);
}
n = Layers.ResidualBlock(n, Data.Residual5);
if (this.Step != null)
{
Step(27);
}
n = Layers.ResidualBlock(n, Data.Residual6);
if (this.Step != null)
{
Step(32);
}
n = Layers.ResidualBlock(n, Data.Residual7);
if (this.Step != null)
{
Step(37);
}
n = Layers.ResidualBlock(n, Data.Residual8);
if (this.Step != null)
{
Step(42);
}
n = Layers.ResidualBlock(n, Data.Residual9);
if (this.Step != null)
{
Step(47);
}
n = Layers.ResidualBlock(n, Data.Residual10);
if (this.Step != null)
{
Step(52);
}
n = Layers.ResidualBlock(n, Data.Residual11);
if (this.Step != null)
{
Step(57);
}
n = Layers.ResidualBlock(n, Data.Residual12);
if (this.Step != null)
{
Step(62);
}
n = Layers.ResidualBlock(n, Data.Residual13);
if (this.Step != null)
{
Step(67);
}
n = Layers.ResidualBlock(n, Data.Residual14);
if (this.Step != null)
{
Step(72);
}
n = Layers.ResidualBlock(n, Data.Residual15);
if (this.Step != null)
{
Step(77);
}
n = Layers.ResidualBlock(n, Data.Residual16);
if (this.Step != null)
{
Step(82);
}
n = Layers.Conv2D3x3(n, Data.Conv2_Weights, new Tensor(1, 1, 64));
n = Layers.BatchNorm(n, Data.Conv2_Mean, Data.Conv2_Variance, Data.Conv2_Shift, Data.Conv2_Scale);
n = Layers.Elementwise(n, temp);
if (this.Step != null)
{
Step(87);
}
n = Layers.Conv2D3x3(n, Data.Conv3_Weights, Data.Conv3_Biases);
n = Layers.SubpixelConv2D(n);
n = Layers.ReLU(n);
if (this.Step != null)
{
Step(92);
}
n = Layers.Conv2D3x3(n, Data.Conv4_Weights, Data.Conv4_Biases);
n = Layers.SubpixelConv2D(n);
n = Layers.ReLU(n);
if (this.Step != null)
{
Step(97);
}
n = Layers.Conv2D1x1(n, Data.Conv5_Weights, Data.Conv5_Biases);
n = Layers.Tanh(n);
if (this.Step != null)
{
Step(100);
}
return(n);
}
public Tensor Enhance(Tensor input)
{
var Temp = input;
Temp = Layers.Conv2D9x9(Temp, this.Data.Conv1_Weights, this.Data.Conv1_Biases);
if (this.Step != null)
{
Step(5);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
Step(10);
}
Temp = Layers.BatchNorm(
Temp,
this.Data.Conv1_Shift,
this.Data.Conv1_Scale,
this.Data.Conv1_Mean,
this.Data.Conv1_Var);
if (this.Step != null)
{
Step(15);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res1_Conv1_Weights,
this.Data.Res1_Conv2_Weights,
this.Data.Res1_Conv1_Biases,
this.Data.Res1_Conv2_Biases,
this.Data.Res1_Conv1_Shift,
this.Data.Res1_Conv2_Shift,
this.Data.Res1_Conv1_Scale,
this.Data.Res1_Conv2_Scale,
this.Data.Res1_Conv1_Mean,
this.Data.Res1_Conv2_Mean,
this.Data.Res1_Conv1_Var,
this.Data.Res1_Conv2_Var);
if (this.Step != null)
{
Step(20);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res2_Conv1_Weights,
this.Data.Res2_Conv2_Weights,
this.Data.Res2_Conv1_Biases,
this.Data.Res2_Conv2_Biases,
this.Data.Res2_Conv1_Shift,
this.Data.Res2_Conv2_Shift,
this.Data.Res2_Conv1_Scale,
this.Data.Res2_Conv2_Scale,
this.Data.Res2_Conv1_Mean,
this.Data.Res2_Conv2_Mean,
this.Data.Res2_Conv1_Var,
this.Data.Res2_Conv2_Var);
if (this.Step != null)
{
Step(25);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res3_Conv1_Weights,
this.Data.Res3_Conv2_Weights,
this.Data.Res3_Conv1_Biases,
this.Data.Res3_Conv2_Biases,
this.Data.Res3_Conv1_Shift,
this.Data.Res3_Conv2_Shift,
this.Data.Res3_Conv1_Scale,
this.Data.Res3_Conv2_Scale,
this.Data.Res3_Conv1_Mean,
this.Data.Res3_Conv2_Mean,
this.Data.Res3_Conv1_Var,
this.Data.Res3_Conv2_Var);
if (this.Step != null)
{
Step(30);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res4_Conv1_Weights,
this.Data.Res4_Conv2_Weights,
this.Data.Res4_Conv1_Biases,
this.Data.Res4_Conv2_Biases,
this.Data.Res4_Conv1_Shift,
this.Data.Res4_Conv2_Shift,
this.Data.Res4_Conv1_Scale,
this.Data.Res4_Conv2_Scale,
this.Data.Res4_Conv1_Mean,
this.Data.Res4_Conv2_Mean,
this.Data.Res4_Conv1_Var,
this.Data.Res4_Conv2_Var);
if (this.Step != null)
{
Step(35);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res5_Conv1_Weights,
this.Data.Res5_Conv2_Weights,
this.Data.Res5_Conv1_Biases,
this.Data.Res5_Conv2_Biases,
this.Data.Res5_Conv1_Shift,
this.Data.Res5_Conv2_Shift,
this.Data.Res5_Conv1_Scale,
this.Data.Res5_Conv2_Scale,
this.Data.Res5_Conv1_Mean,
this.Data.Res5_Conv2_Mean,
this.Data.Res5_Conv1_Var,
this.Data.Res5_Conv2_Var);
if (this.Step != null)
{
Step(40);
}
Temp = Layers.ResidualBlock(
Temp,
this.Data.Res6_Conv1_Weights,
this.Data.Res6_Conv2_Weights,
this.Data.Res6_Conv1_Biases,
this.Data.Res6_Conv2_Biases,
this.Data.Res6_Conv1_Shift,
this.Data.Res6_Conv2_Shift,
this.Data.Res6_Conv1_Scale,
this.Data.Res6_Conv2_Scale,
this.Data.Res6_Conv1_Mean,
this.Data.Res6_Conv2_Mean,
this.Data.Res6_Conv1_Var,
this.Data.Res6_Conv2_Var);
if (this.Step != null)
{
Step(45);
}
Temp = Layers.Conv2DTranspose3x3(Temp, this.Data.Deconv1_Weights, this.Data.Deconv1_Biases);
if (this.Step != null)
{
Step(50);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
Step(55);
}
Temp = Layers.BatchNorm(Temp, this.Data.Deconv1_Shift, this.Data.Deconv1_Scale, this.Data.Deconv1_Mean, this.Data.Deconv1_Var);
if (this.Step != null)
{
Step(60);
}
Temp = Layers.Conv2DTranspose3x3(Temp, this.Data.Deconv2_Weights, this.Data.Deconv2_Biases);
if (this.Step != null)
{
Step(65);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
Step(70);
}
Temp = Layers.BatchNorm(Temp, this.Data.Deconv2_Shift, this.Data.Deconv2_Scale, this.Data.Deconv2_Mean, this.Data.Deconv2_Var);
if (this.Step != null)
{
Step(75);
}
Temp = Layers.Conv2DTranspose9x9(Temp, this.Data.Deconv3_Weights, this.Data.Deconv3_Biases);
if (this.Step != null)
{
Step(80);
}
Temp = Layers.ReLU(Temp);
if (this.Step != null)
{
Step(85);
}
Temp = Layers.BatchNorm(Temp, this.Data.Deconv3_Shift, this.Data.Deconv3_Scale, this.Data.Deconv3_Mean, this.Data.Deconv3_Var);
if (this.Step != null)
{
Step(90);
}
Temp = Layers.Tanh(Temp);
if (this.Step != null)
{
Step(95);
}
return(Temp);
}