public static void run_nightmare(List <string> args)
{
if (args.Count < 4)
{
Console.Error.Write($"usage: %s %s [cfg] [weights] [Image] [Layer] [options! (optional)]\n", args[0], args[1]);
return;
}
string cfg = args[2];
string weights = args[3];
string input = args[4];
int maxLayer = int.Parse(args[5]);
int range = Utils.find_int_arg(args, "-range", 1);
bool norm = Utils.find_int_arg(args, "-norm", 1) != 0;
int rounds = Utils.find_int_arg(args, "-rounds", 1);
int iters = Utils.find_int_arg(args, "-iters", 10);
int octaves = Utils.find_int_arg(args, "-octaves", 4);
float zoom = Utils.find_int_arg(args, "-zoom", 1);
float rate = Utils.find_int_arg(args, "-rate", .04f);
float thresh = Utils.find_int_arg(args, "-thresh", 1);
float rotate = Utils.find_int_arg(args, "-rotate", 0);
float momentum = Utils.find_int_arg(args, "-momentum", .9f);
float lambda = Utils.find_int_arg(args, "-lambda", .01f);
string prefix = Utils.find_int_arg(args, "-prefix", "");
bool reconstruct = Utils.find_arg(args, "-reconstruct");
int smoothSize = Utils.find_int_arg(args, "-smooth", 1);
Network net = Parser.parse_network_cfg(cfg);
Parser.load_weights(net, weights);
string cfgbase = Utils.Basecfg(cfg);
string imbase = Utils.Basecfg(input);
Network.set_batch_network(net, 1);
Image im = LoadArgs.load_image_color(input, 0, 0);
float[] features = new float[0];
Image update = null;
if (reconstruct)
{
Network.resize_network(net, im.W, im.H);
int zz = 0;
Network.network_predict(net, im.Data);
Image outIm = Network.get_network_image(net);
Image crop = LoadArgs.crop_image(outIm, zz, zz, outIm.W - 2 * zz, outIm.H - 2 * zz);
Image fIm = LoadArgs.resize_image(crop, outIm.W, outIm.H);
Console.Write($"%d features\n", outIm.W * outIm.H * outIm.C);
im = LoadArgs.resize_image(im, im.W, im.H);
fIm = LoadArgs.resize_image(fIm, fIm.W, fIm.H);
features = fIm.Data;
int i;
for (i = 0; i < 14 * 14 * 512; ++i)
{
features[i] += Utils.rand_uniform(-.19f, .19f);
}
im = LoadArgs.make_random_image(im.W, im.H, im.C);
update = new Image(im.W, im.H, im.C);
}
int e;
int n;
for (e = 0; e < rounds; ++e)
{
Console.Error.Write($"Iteration: ");
for (n = 0; n < iters; ++n)
{
Console.Error.Write($"%d, ", n);
if (reconstruct)
{
reconstruct_picture(net, features, im, update, rate, momentum, lambda, smoothSize, 1);
//if ((n+1)%30 == 0) rate *= .5;
LoadArgs.show_image(im, "reconstruction");
CvInvoke.WaitKey(10);
}
else
{
int layer = maxLayer + Utils.Rand.Next() % range - range / 2;
int octave = Utils.Rand.Next() % octaves;
optimize_picture(net, im, layer, 1 / (float)Math.Pow(1.33333333, octave), rate, thresh, norm);
}
}
Console.Error.Write($"done\n");
string buff;
if (!string.IsNullOrEmpty(prefix))
{
buff = $"{prefix}_{imbase}_{cfgbase}_{maxLayer}_{e:06}%s/%s_%s_%d_%06d";
}
else
{
buff = $"{imbase}_{cfgbase}_{maxLayer}_{e:06}";
}
Console.Write($"%d %s\n", e, buff);
LoadArgs.save_image(im, buff);
//LoadArgs.show_image(im, buff);
//CvInvoke.WaitKey();
if (rotate != 0)
{
Image rot = LoadArgs.rotate_image(im, rotate);
im = rot;
}
Image crop = LoadArgs.crop_image(im, (int)(im.W * (1f - zoom) / 2f), (int)(im.H * (1f - zoom) / 2f), (int)(im.W * zoom), (int)(im.H * zoom));
Image resized = LoadArgs.resize_image(crop, im.W, im.H);
im = resized;
}
}
