private static void label_classifier(string datacfg, string filename, string weightfile)
{
int i;
Network net = Parser.parse_network_cfg(filename);
Network.set_batch_network(net, 1);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "names", "Data.Data/labels.list");
string testList = OptionList.option_find_str(options, "test", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
string[] labels = Data.Data.get_labels(labelList);
string[] paths = Data.Data.GetPaths(testList);
int m = paths.Length;
for (i = 0; i < m; ++i)
{
Image im = LoadArgs.load_image_color(paths[i], 0, 0);
Image resized = LoadArgs.resize_min(im, net.W);
Image crop = LoadArgs.crop_image(resized, (resized.W - net.W) / 2, (resized.H - net.H) / 2, net.W, net.H);
float[] pred = Network.network_predict(net, crop.Data);
int ind = Utils.max_index(pred, classes);
Console.Write($"%s\n", labels[ind]);
}
}
public static void predict_classifier(string datacfg, string cfgfile, string weightfile, string filename, int top)
{
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
var options = OptionList.read_data_cfg(datacfg);
string nameList = OptionList.option_find_str(options, "names", "");
if (string.IsNullOrEmpty(nameList))
{
nameList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
}
if (top == 0)
{
top = OptionList.option_find_int(options, "top", 1);
}
int i = 0;
string[] names = Data.Data.get_labels(nameList);
var sw = new Stopwatch();
int[] indexes = new int[top];
string input = "";
int size = net.W;
while (true)
{
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input))
{
return;
}
input = input.TrimEnd();
}
Image im = LoadArgs.load_image_color(input, 0, 0);
Image r = LoadArgs.resize_min(im, size);
Network.resize_network(net, r.W, r.H);
Console.Write($"%d %d\n", r.W, r.H);
float[] x = r.Data;
sw.Reset();
sw.Start();
float[] predictions = Network.network_predict(net, x);
if (net.Hierarchy != null)
{
net.Hierarchy.Hierarchy_predictions(predictions, 0, net.Outputs, false);
}
Utils.top_k(predictions, net.Outputs, top, indexes);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
for (i = 0; i < top; ++i)
{
int index = indexes[i];
if (net.Hierarchy != null)
{
Console.Write($"%d, %s: %f, parent: %s \n", index, names[index], predictions[index], (net.Hierarchy.Parent[index] >= 0) ? names[net.Hierarchy.Parent[index]] : "Root");
}
else
{
Console.Write($"%s: %f\n", names[index], predictions[index]);
}
}
if (!string.IsNullOrEmpty(filename))
{
break;
}
}
}
private static void try_classifier(string datacfg, string cfgfile, string weightfile, string filename, int layerNum)
{
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
var options = OptionList.read_data_cfg(datacfg);
string nameList = OptionList.option_find_str(options, "names", "");
if (string.IsNullOrEmpty(nameList))
{
nameList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
}
int top = OptionList.option_find_int(options, "top", 1);
int i = 0;
string[] names = Data.Data.get_labels(nameList);
var sw = new Stopwatch();
int[] indexes = new int[top];
string input = "";
while (true)
{
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input))
{
return;
}
input = input.TrimEnd();
}
Image orig = LoadArgs.load_image_color(input, 0, 0);
Image r = LoadArgs.resize_min(orig, 256);
Image im = LoadArgs.crop_image(r, (r.W - 224 - 1) / 2 + 1, (r.H - 224 - 1) / 2 + 1, 224, 224);
float[] mean = { 0.48263312050943f, 0.45230225481413f, 0.40099074308742f };
float[] std = { 0.22590347483426f, 0.22120921437787f, 0.22103996251583f };
float[] var = new float[3];
var[0] = std[0] * std[0];
var[1] = std[1] * std[1];
var[2] = std[2] * std[2];
Blas.Normalize_cpu(im.Data, mean, var, 1, 3, im.W * im.H);
float[] x = im.Data;
sw.Reset();
sw.Start();
float[] predictions = Network.network_predict(net, x);
Layer l = net.Layers[layerNum];
for (i = 0; i < l.C; ++i)
{
if (l.RollingMean.Length > i)
{
Console.Write($"%f %f %f\n", l.RollingMean[i], l.RollingVariance[i], l.Scales[i]);
}
}
Array.Copy(l.OutputGpu, l.Output, l.Outputs);
for (i = 0; i < l.Outputs; ++i)
{
Console.Write($"%f\n", l.Output[i]);
}
Network.top_predictions(net, top, indexes);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
for (i = 0; i < top; ++i)
{
int index = indexes[i];
Console.Write($"%s: %f\n", names[index], predictions[index]);
}
if (!string.IsNullOrEmpty(filename))
{
break;
}
}
}
private static void validate_classifier_multi(string datacfg, string filename, string weightfile)
{
int i, j;
Network net = Parser.parse_network_cfg(filename);
Network.set_batch_network(net, 1);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string validList = OptionList.option_find_str(options, "valid", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
int topk = OptionList.option_find_int(options, "top", 1);
string[] labels = Data.Data.get_labels(labelList);
int[] scales = { 224, 288, 320, 352, 384 };
int nscales = scales.Length;
string[] paths = Data.Data.GetPaths(validList);
int m = paths.Length;
float avgAcc = 0;
float avgTopk = 0;
int[] indexes = new int[topk];
for (i = 0; i < m; ++i)
{
int class2 = -1;
string path = paths[i];
for (j = 0; j < classes; ++j)
{
if (path.Contains(labels[j]))
{
class2 = j;
break;
}
}
float[] pred = new float[classes];
Image im = LoadArgs.load_image_color(paths[i], 0, 0);
for (j = 0; j < nscales; ++j)
{
Image r = LoadArgs.resize_min(im, scales[j]);
Network.resize_network(net, r.W, r.H);
float[] p = Network.network_predict(net, r.Data);
if (net.Hierarchy != null)
{
net.Hierarchy.Hierarchy_predictions(p, 0, net.Outputs, true);
}
Blas.Axpy_cpu(classes, 1, p, pred);
LoadArgs.flip_image(r);
p = Network.network_predict(net, r.Data);
Blas.Axpy_cpu(classes, 1, p, pred);
}
Utils.top_k(pred, classes, topk, indexes);
if (indexes[0] == class2)
{
avgAcc += 1;
}
for (j = 0; j < topk; ++j)
{
if (indexes[j] == class2)
{
avgTopk += 1;
}
}
Console.Write($"%d: top 1: %f, top %d: %f\n", i, avgAcc / (i + 1), topk, avgTopk / (i + 1));
}
}
private static void validate_classifier_single(string datacfg, string filename, string weightfile)
{
int i, j;
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string leafList = OptionList.option_find_str(options, "leaves", "");
if (!string.IsNullOrEmpty(leafList))
{
net.Hierarchy.Change_leaves(leafList);
}
string validList = OptionList.option_find_str(options, "valid", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
int topk = OptionList.option_find_int(options, "top", 1);
string[] labels = Data.Data.get_labels(labelList);
string[] paths = Data.Data.GetPaths(validList);
int m = paths.Length;
float avgAcc = 0;
float avgTopk = 0;
int[] indexes = new int[topk];
for (i = 0; i < m; ++i)
{
int class2 = -1;
string path = paths[i];
for (j = 0; j < classes; ++j)
{
if (path.Contains(labels[j]))
{
class2 = j;
break;
}
}
Image im = LoadArgs.load_image_color(paths[i], 0, 0);
Image resized = LoadArgs.resize_min(im, net.W);
Image crop = LoadArgs.crop_image(resized, (resized.W - net.W) / 2, (resized.H - net.H) / 2, net.W, net.H);
float[] pred = Network.network_predict(net, crop.Data);
if (net.Hierarchy != null)
{
net.Hierarchy.Hierarchy_predictions(pred, 0, net.Outputs, false);
}
Utils.top_k(pred, classes, topk, indexes);
if (indexes[0] == class2)
{
avgAcc += 1;
}
for (j = 0; j < topk; ++j)
{
if (indexes[j] == class2)
{
avgTopk += 1;
}
}
Console.Write($"%d: top 1: %f, top %d: %f\n", i, avgAcc / (i + 1), topk, avgTopk / (i + 1));
}
}
private static void test_tag(string cfgfile, string weightfile, string filename)
{
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
Utils.Rand = new Random(2222222);
int i = 0;
string[] names = Data.Data.get_labels("Data.Data/tags.txt");
var sw = new Stopwatch();
int[] indexes = new int[10];
string input = "";
int size = net.W;
while (true)
{
if (!string.IsNullOrEmpty(filename))
{
input = filename;
}
else
{
Console.Write($"Enter Image Path: ");
input = Console.ReadLine();
if (string.IsNullOrEmpty(input))
{
return;
}
input = input.TrimEnd();
}
Image im = LoadArgs.load_image_color(input, 0, 0);
Image r = LoadArgs.resize_min(im, size);
Network.resize_network(net, r.W, r.H);
Console.Write($"%d %d\n", r.W, r.H);
float[] x = r.Data;
sw.Reset();
sw.Start();
float[] predictions = Network.network_predict(net, x);
Network.top_predictions(net, 10, indexes);
sw.Stop();
Console.Write($"%s: Predicted ini %f seconds.\n", input, sw.Elapsed.Seconds);
for (i = 0; i < 10; ++i)
{
int index = indexes[i];
Console.Write($"%.1f%%: %s\n", predictions[index] * 100, names[index]);
}
if (!string.IsNullOrEmpty(filename))
{
break;
}
}
}
