public static void reconstruct_picture(Network net, float[] features, Image recon, Image update, float rate, float momentum, float lambda, int smoothSize, int iters)
{
int iter = 0;
for (iter = 0; iter < iters; ++iter)
{
Image delta = new Image(recon.W, recon.H, recon.C);
NetworkState state = new NetworkState();
state.Input = (float[])recon.Data.Clone();
state.Delta = (float[])delta.Data.Clone();
state.Truth = new float[Network.get_network_output_size(net)];
Array.Copy(features, 0, state.Truth, 0, state.Truth.Length);
Network.forward_network_gpu(net, state);
Network.backward_network_gpu(net, state);
Array.Copy(state.Delta, delta.Data, delta.W * delta.H * delta.C);
Blas.Axpy_cpu(recon.W * recon.H * recon.C, 1, delta.Data, update.Data);
Smooth(recon, update, lambda, smoothSize);
Blas.Axpy_cpu(recon.W * recon.H * recon.C, rate, update.Data, recon.Data);
Blas.Scal_cpu(recon.W * recon.H * recon.C, momentum, update.Data, 1);
LoadArgs.constrain_image(recon);
}
}
private static void Average(List <string> args)
{
string cfgfile = args[2];
string outfile = args[3];
Network net = Parser.parse_network_cfg(cfgfile);
Network sum = Parser.parse_network_cfg(cfgfile);
string weightfile = args[4];
Parser.load_weights(sum, weightfile);
int i, j;
int n = args.Count - 5;
for (i = 0; i < n; ++i)
{
weightfile = args[i + 5];
Parser.load_weights(net, weightfile);
for (j = 0; j < net.N; ++j)
{
Layer l = net.Layers[j];
Layer outl = sum.Layers[j];
if (l.LayerType == LayerType.Convolutional)
{
int num = l.N * l.C * l.Size * l.Size;
Blas.Axpy_cpu(l.N, 1, l.BiasesComplete, outl.BiasesComplete, l.BiasesIndex, outl.BiasesIndex);
Blas.Axpy_cpu(num, 1, l.WeightsComplete, outl.WeightsComplete, l.WeightsIndex, outl.WeightsIndex);
if (l.BatchNormalize)
{
Blas.Axpy_cpu(l.N, 1, l.Scales, outl.Scales);
Blas.Axpy_cpu(l.N, 1, l.RollingMean, outl.RollingMean);
Blas.Axpy_cpu(l.N, 1, l.RollingVariance, outl.RollingVariance);
}
}
if (l.LayerType == LayerType.Connected)
{
Blas.Axpy_cpu(l.Outputs, 1, l.BiasesComplete, outl.BiasesComplete, l.BiasesIndex, outl.BiasesIndex);
Blas.Axpy_cpu(l.Outputs * l.Inputs, 1, l.WeightsComplete, outl.WeightsComplete, l.WeightsIndex, outl.WeightsIndex);
}
}
}
n = n + 1;
for (j = 0; j < net.N; ++j)
{
Layer l = sum.Layers[j];
if (l.LayerType == LayerType.Convolutional)
{
int num = l.N * l.C * l.Size * l.Size;
Blas.Scal_cpu(l.N, 1.0f / n, l.BiasesComplete, 1, l.BiasesIndex);
Blas.Scal_cpu(num, 1.0f / n, l.WeightsComplete, 1, l.WeightsIndex);
if (l.BatchNormalize)
{
Blas.Scal_cpu(l.N, 1.0f / n, l.Scales, 1);
Blas.Scal_cpu(l.N, 1.0f / n, l.RollingMean, 1);
Blas.Scal_cpu(l.N, 1.0f / n, l.RollingVariance, 1);
}
}
if (l.LayerType == LayerType.Connected)
{
Blas.Scal_cpu(l.Outputs, 1.0f / n, l.BiasesComplete, 1, l.BiasesIndex);
Blas.Scal_cpu(l.Outputs * l.Inputs, 1.0f / n, l.WeightsComplete, 1, l.WeightsIndex);
}
}
Parser.save_weights(sum, outfile);
}
