private static void test_cifar_csvtrain(string filename, string weightfile)
{
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Data.Data test = Data.Data.load_all_cifar10();
Matrix pred = Network.network_predict_data(net, test);
int i;
for (i = 0; i < test.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, test.X.Vals[i]);
LoadArgs.flip_image(im);
}
Matrix pred2 = Network.network_predict_data(net, test);
pred.scale_matrix(.5f);
pred2.scale_matrix(.5f);
Matrix.matrix_add_matrix(pred2, pred);
pred.to_csv();
Console.Error.Write($"Accuracy: %f\n", Matrix.matrix_topk_accuracy(test.Y, pred, 1));
}
private static void eval_cifar_csv()
{
Data.Data test = Data.Data.load_cifar10_data("Data.Data/cifar/cifar-10-batches-bin/test_batch.bin");
Matrix pred = new Matrix("results/combined.csv");
Console.Error.Write($"%d %d\n", pred.Rows, pred.Cols);
Console.Error.Write($"Accuracy: %f\n", Matrix.matrix_topk_accuracy(test.Y, pred, 1));
}
private static void train_cifar(string cfgfile, string weightfile)
{
float avgLoss = -1;
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
string backupDirectory = "/home/pjreddie/backup/";
int n = 50000;
Data.Data.get_labels("Data.Data/cifar/labels.txt");
int epoch = (net.Seen) / n;
Data.Data train = Data.Data.load_all_cifar10();
var sw = new Stopwatch();
while (Network.get_current_batch(net) < net.MaxBatches || net.MaxBatches == 0)
{
sw.Reset();
sw.Start();
float loss = Network.train_network_sgd(net, train, 1);
if (avgLoss == -1)
{
avgLoss = loss;
}
avgLoss = avgLoss * .95f + loss * .05f;
sw.Stop();
Console.Write(
$"{Network.get_current_batch(net)}, {net.Seen / n:.3}: {loss}, {avgLoss} avg, {Network.get_current_rate(net)} rate, {sw.Elapsed.Seconds} seconds, {net.Seen} images\n");
if (net.Seen / n > epoch)
{
epoch = net.Seen / n;
string buff = $"{backupDirectory}/{basec}_{epoch}.Weights";
Parser.save_weights(net, buff);
}
if (Network.get_current_batch(net) % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}.backup";
Parser.save_weights(net, buff);
}
}
string buff2 = $"{backupDirectory}/{basec}.Weights";
Parser.save_weights(net, buff2);
}
private static void extract_cifar()
{
string[] labels = { "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck" };
int i;
Data.Data train = Data.Data.load_all_cifar10();
Data.Data test = Data.Data.load_cifar10_data("Data.Data/cifar/cifar-10-batches-bin/test_batch.bin");
for (i = 0; i < train.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, train.X.Vals[i]);
int sclass = Utils.max_index(train.Y.Vals[i], 10);
string buff = $"Data.Data/cifar/train/{i}_{labels[sclass]}";
LoadArgs.save_image_png(im, buff);
}
for (i = 0; i < test.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, test.X.Vals[i]);
int sclass = Utils.max_index(test.Y.Vals[i], 10);
string buff = $"Data.Data/cifar/test/{i}_{labels[sclass]}";
LoadArgs.save_image_png(im, buff);
}
}
private static void test_cifar(string filename, string weightfile)
{
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var sw = new Stopwatch();
float avgAcc = 0;
Data.Data test = Data.Data.load_cifar10_data("Data.Data/cifar/cifar-10-batches-bin/test_batch.bin");
sw.Start();
float[] acc = Network.network_accuracies(net, test, 2);
avgAcc += acc[0];
sw.Stop();
Console.Write($"top1: %f, %lf seconds, %d images\n", avgAcc, sw.Elapsed.Seconds, test.X.Rows);
}
private static void test_cifar_multi(string filename, string weightfile)
{
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Network.set_batch_network(net, 1);
float avgAcc = 0;
Data.Data test = Data.Data.load_cifar10_data("Data.Data/cifar/cifar-10-batches-bin/test_batch.bin");
int i;
for (i = 0; i < test.X.Rows; ++i)
{
Image im = new Image(32, 32, 3, test.X.Vals[i]);
float[] pred = new float[10];
float[] p = Network.network_predict(net, im.Data);
Blas.Axpy_cpu(10, 1, p, pred);
LoadArgs.flip_image(im);
p = Network.network_predict(net, im.Data);
Blas.Axpy_cpu(10, 1, p, pred);
int index = Utils.max_index(pred, 10);
int sclass = Utils.max_index(test.Y.Vals[i], 10);
if (index == sclass)
{
avgAcc += 1;
}
Console.Write($"%4d: %.2f%%\n", i, 100f * avgAcc / (i + 1));
}
}
private static void train_writing(string cfgfile, string weightfile)
{
string backupDirectory = "/home/pjreddie/backup/";
float avgLoss = -1;
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
int imgs = net.Batch * net.Subdivisions;
string[] paths = Data.Data.GetPaths("figures.list");
var sw = new Stopwatch();
int n = paths.Length;
Console.Write($"N: %d\n", n);
Image outf = Network.get_network_image(net);
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.OutW = outf.W;
args.OutH = outf.H;
args.Paths = paths;
args.N = imgs;
args.M = n;
args.D = buffer;
args.Type = DataType.WritingData;
Thread loadThread = Data.Data.load_data_in_thread(args);
int epoch = (net.Seen) / n;
while (Network.get_current_batch(net) < net.MaxBatches || net.MaxBatches == 0)
{
sw.Reset();
sw.Start();
loadThread.Join();
var train = buffer;
loadThread = Data.Data.load_data_in_thread(args);
sw.Stop();
Console.Write($"Loaded %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = Network.train_network(net, train);
if (avgLoss == -1)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
Console.Write($"%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", Network.get_current_batch(net), (float)(net.Seen) / n, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, net.Seen);
if (Network.get_current_batch(net) % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}_batch_{Network.get_current_batch(net)}.Weights";
Parser.save_weights(net, buff);
}
if (net.Seen / n > epoch)
{
epoch = net.Seen / n;
string buff = $"{backupDirectory}/{basec}_{epoch}.Weights";
Parser.save_weights(net, buff);
}
}
}
private static void validate_compare(string filename, string weightfile)
{
int i = 0;
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
string[] paths = Data.Data.GetPaths("Data.Data/compare.val.list");
int n = paths.Length / 2;
var sw = new Stopwatch();
int correct = 0;
int total = 0;
int splits = 10;
int num = (i + 1) * n / splits - i * n / splits;
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.Classes = 20;
args.N = num;
args.M = 0;
args.D = buffer;
args.Type = DataType.CompareData;
Thread loadThread = Data.Data.load_data_in_thread(args);
for (i = 1; i <= splits; ++i)
{
sw.Reset();
sw.Start();
loadThread.Join();
var val = buffer;
num = (i + 1) * n / splits - i * n / splits;
string[] part = new string[paths.Length - (i * n / splits)];
Array.Copy(paths, i * n / splits, part, 0, part.Length);
if (i != splits)
{
args.Paths = part;
loadThread = Data.Data.load_data_in_thread(args);
}
sw.Stop();
Console.Write($"Loaded: %d images ini %lf seconds\n", val.X.Rows, sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
Matrix pred = Network.network_predict_data(net, val);
int j, k;
for (j = 0; j < val.Y.Rows; ++j)
{
for (k = 0; k < 20; ++k)
{
if (val.Y.Vals[j][k * 2] != val.Y.Vals[j][k * 2 + 1])
{
++total;
if ((val.Y.Vals[j][k * 2] < val.Y.Vals[j][k * 2 + 1]) == (pred.Vals[j][k * 2] < pred.Vals[j][k * 2 + 1]))
{
++correct;
}
}
}
}
sw.Stop();
Console.Write($"%d: Acc: %f, %lf seconds, %d images\n", i, (float)correct / total, sw.Elapsed.Seconds, val.X.Rows);
}
}
private static void train_compare(string cfgfile, string weightfile)
{
float avgLoss = -1;
string basec = Utils.Basecfg(cfgfile);
string backupDirectory = "/home/pjreddie/backup/";
Console.Write($"%s\n", basec);
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
int imgs = 1024;
string[] paths = Data.Data.GetPaths("Data.Data/compare.train.list");
int n = paths.Length;
Console.Write($"%d\n", n);
var sw = new Stopwatch();
Thread loadThread;
Data.Data train;
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.Classes = 20;
args.N = imgs;
args.M = n;
args.D = buffer;
args.Type = DataType.CompareData;
loadThread = Data.Data.load_data_in_thread(args);
int epoch = net.Seen / n;
int i = 0;
while (true)
{
++i;
sw.Reset();
sw.Start();
loadThread.Join();
train = buffer;
loadThread = Data.Data.load_data_in_thread(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = Network.train_network(net, train);
if (avgLoss == -1)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
sw.Stop();
Console.Write($"%.3f: %f, %f avg, %lf seconds, %d images\n", (float)net.Seen / n, loss, avgLoss, sw.Elapsed.Seconds, net.Seen);
if (i % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}_{epoch}_minor_{i}.Weights";
Parser.save_weights(net, buff);
}
if (net.Seen / n > epoch)
{
epoch = net.Seen / n;
i = 0;
string buff = $"{backupDirectory}/{basec}_{epoch}.Weights";
Parser.save_weights(net, buff);
if (epoch % 22 == 0)
{
net.LearningRate *= .1f;
}
}
}
loadThread.Join();
}
private static void validate_classifier_crop(string datacfg, string filename, string weightfile)
{
int i = 0;
Network net = Parser.parse_network_cfg(filename);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string validList = OptionList.option_find_str(options, "valid", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
int topk = OptionList.option_find_int(options, "top", 1);
string[] labels = Data.Data.get_labels(labelList);
string[] paths = Data.Data.GetPaths(validList);
int m = paths.Length;
var sw = new Stopwatch();
float avgAcc = 0;
float avgTopk = 0;
int splits = m / 1000;
int num = (i + 1) * m / splits - i * m / splits;
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.Classes = classes;
args.N = num;
args.M = 0;
args.Labels = labels;
args.D = buffer;
args.Type = DataType.OldClassificationData;
Thread loadThread = Data.Data.load_data_in_thread(args);
for (i = 1; i <= splits; ++i)
{
sw.Reset();
sw.Start();
loadThread.Join();
var val = buffer;
num = (i + 1) * m / splits - i * m / splits;
string[] part = new string[paths.Length - (i * m / splits)];
Array.Copy(paths, i * m / splits, part, 0, part.Length);
if (i != splits)
{
args.Paths = part;
loadThread = Data.Data.load_data_in_thread(args);
}
sw.Stop();
Console.Write($"Loaded: %d images ini %lf seconds\n", val.X.Rows, sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float[] acc = Network.network_accuracies(net, val, topk);
avgAcc += acc[0];
avgTopk += acc[1];
sw.Stop();
Console.Write($"%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avgAcc / i, topk, avgTopk / i, sw.Elapsed.Seconds, val.X.Rows);
}
}
private static void test_classifier(string datacfg, string cfgfile, string weightfile, int targetLayer)
{
int curr = 0;
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
var options = OptionList.read_data_cfg(datacfg);
string testList = OptionList.option_find_str(options, "test", "Data.Data/test.list");
int classes = OptionList.option_find_int(options, "classes", 2);
string[] paths = Data.Data.GetPaths(testList);
int m = paths.Length;
var sw = new Stopwatch();
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.Classes = classes;
args.N = net.Batch;
args.M = 0;
args.Labels = new string[0];
args.D = buffer;
args.Type = DataType.OldClassificationData;
Thread loadThread = Data.Data.load_data_in_thread(args);
for (curr = net.Batch; curr < m; curr += net.Batch)
{
sw.Reset();
sw.Start();
loadThread.Join();
var val = buffer;
if (curr < m)
{
args.Paths = new string[paths.Length - curr];
Array.Copy(paths, curr, args.Paths, 0, args.Paths.Length);
if (curr + net.Batch > m)
{
args.N = m - curr;
}
loadThread = Data.Data.load_data_in_thread(args);
}
sw.Stop();
Console.Error.Write($"Loaded: %d images ini %lf seconds\n", val.X.Rows, sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
Matrix pred = Network.network_predict_data(net, val);
int i, j;
for (i = 0; i < pred.Rows; ++i)
{
Console.Write($"%s", paths[curr - net.Batch + i]);
for (j = 0; j < pred.Cols; ++j)
{
Console.Write($"\t%g", pred.Vals[i][j]);
}
Console.Write($"\n");
}
sw.Stop();
Console.Error.Write($"%lf seconds, %d images, %d total\n", sw.Elapsed.Seconds, val.X.Rows, curr);
}
}
private static void train_classifier(string datacfg, string cfgfile, string weightfile, int[] gpus, int ngpus, bool clear)
{
int i;
float avgLoss = -1;
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
Console.Write($"%d\n", ngpus);
Network[] nets = new Network[ngpus];
int seed = Utils.Rand.Next();
for (i = 0; i < ngpus; ++i)
{
nets[i] = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(nets[i], weightfile);
}
if (clear)
{
nets[i].Seen = 0;
}
nets[i].LearningRate *= ngpus;
}
Network net = nets[0];
int imgs = net.Batch * net.Subdivisions * ngpus;
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
var options = OptionList.read_data_cfg(datacfg);
string backupDirectory = OptionList.option_find_str(options, "backup", "/backup/");
string labelList = OptionList.option_find_str(options, "labels", "Data.Data/labels.list");
string trainList = OptionList.option_find_str(options, "train", "Data.Data/train.list");
int classes = OptionList.option_find_int(options, "classes", 2);
string[] labels = Data.Data.get_labels(labelList);
string[] paths = Data.Data.GetPaths(trainList);
Console.Write($"%d\n", paths.Length);
int n = paths.Length;
var sw = new Stopwatch();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Threads = 32;
args.Hierarchy = net.Hierarchy;
args.Min = net.MinCrop;
args.Max = net.MaxCrop;
args.Angle = net.Angle;
args.Aspect = net.Aspect;
args.Exposure = net.Exposure;
args.Saturation = net.Saturation;
args.Hue = net.Hue;
args.Size = net.W;
args.Paths = paths;
args.Classes = classes;
args.N = imgs;
args.M = n;
args.Labels = labels;
args.Type = DataType.ClassificationData;
Data.Data train;
Data.Data buffer = new Data.Data();
Thread loadThread;
args.D = buffer;
loadThread = Data.Data.load_data(args);
int epoch = (net.Seen) / n;
while (Network.get_current_batch(net) < net.MaxBatches || net.MaxBatches == 0)
{
sw.Reset();
sw.Start();
loadThread.Join();
train = buffer;
loadThread = Data.Data.load_data(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = 0;
if (ngpus == 1)
{
loss = Network.train_network(net, train);
}
else
{
loss = Network.train_networks(nets, ngpus, train, 4);
}
if (avgLoss == -1)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
Console.Write($"%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", Network.get_current_batch(net), (float)(net.Seen) / n, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, net.Seen);
if (net.Seen / n > epoch)
{
epoch = net.Seen / n;
string buff = $"{backupDirectory}/{basec}_{epoch}.Weights";
Parser.save_weights(net, buff);
}
if (Network.get_current_batch(net) % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}.backup";
Parser.save_weights(net, buff);
}
}
string buff2 = $"{backupDirectory}/{basec}.Weights";
Parser.save_weights(net, buff2);
}
private static void train_yolo(string cfgfile, string weightfile)
{
string trainImages = "/Data.Data/voc/train.txt";
string backupDirectory = "/home/pjreddie/backup/";
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
float avgLoss = -1;
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
int imgs = net.Batch * net.Subdivisions;
int i = net.Seen / imgs;
Data.Data buffer = new Data.Data();
Layer l = net.Layers[net.N - 1];
int side = l.Side;
int classes = l.Classes;
float jitter = l.Jitter;
string[] paths = Data.Data.GetPaths(trainImages);
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.N = imgs;
args.M = paths.Length;
args.Classes = classes;
args.Jitter = jitter;
args.NumBoxes = side;
args.D = buffer;
args.Type = DataType.RegionData;
args.Angle = net.Angle;
args.Exposure = net.Exposure;
args.Saturation = net.Saturation;
args.Hue = net.Hue;
Thread loadThread = Data.Data.load_data_in_thread(args);
var sw = new Stopwatch();
while (Network.get_current_batch(net) < net.MaxBatches)
{
i += 1;
sw.Start();
loadThread.Join();
var train = buffer;
loadThread = Data.Data.load_data_in_thread(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = Network.train_network(net, train);
if (avgLoss < 0)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
sw.Stop();
Console.Write($"%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, i * imgs);
if (i % 1000 == 0 || (i < 1000 && i % 100 == 0))
{
string buff = $"{backupDirectory}/{basec}_{i}.Weights";
Parser.save_weights(net, buff);
}
}
string buff2 = $"{backupDirectory}/{basec}_final.Weights";
Parser.save_weights(net, buff2);
}
private static void train_super(string cfgfile, string weightfile)
{
string trainImages = "/Data.Data/imagenet/imagenet1k.train.list";
string backupDirectory = "/home/pjreddie/backup/";
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
float avgLoss = -1;
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
int imgs = net.Batch * net.Subdivisions;
int i = net.Seen / imgs;
Data.Data buffer = new Data.Data();
string[] paths = Data.Data.GetPaths(trainImages);
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Scale = 4;
args.Paths = paths;
args.N = imgs;
args.M = paths.Length;
args.D = buffer;
args.Type = DataType.SuperData;
Thread loadThread = Data.Data.load_data_in_thread(args);
var sw = new Stopwatch();
//while(i*imgs < N*120){
while (Network.get_current_batch(net) < net.MaxBatches)
{
i += 1;
sw.Start();
loadThread.Join();
var train = buffer;
loadThread = Data.Data.load_data_in_thread(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = Network.train_network(net, train);
if (avgLoss < 0)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
sw.Stop();
Console.Write($"%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, i * imgs);
if (i % 1000 == 0)
{
string buffa = $"{backupDirectory}/{basec}_{i}.Weights";
Parser.save_weights(net, buffa);
}
if (i % 100 == 0)
{
string buffb = $"{backupDirectory}/{basec}.backup";
Parser.save_weights(net, buffb);
}
}
string buff = $"{backupDirectory}/{basec}_final.Weights";
Parser.save_weights(net, buff);
}
private static void train_tag(string cfgfile, string weightfile, bool clear)
{
float avgLoss = -1;
string basec = Utils.Basecfg(cfgfile);
string backupDirectory = "/home/pjreddie/backup/";
Console.Write($"%s\n", basec);
Network net = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(net, weightfile);
}
if (clear)
{
net.Seen = 0;
}
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
int imgs = 1024;
string[] paths = Data.Data.GetPaths("/home/pjreddie/tag/train.list");
Console.Write($"%d\n", paths.Length);
int n = paths.Length;
var sw = new Stopwatch();
Thread loadThread;
Data.Data train;
Data.Data buffer = new Data.Data();
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Min = net.W;
args.Max = net.MaxCrop;
args.Size = net.W;
args.Paths = paths;
args.Classes = net.Outputs;
args.N = imgs;
args.M = n;
args.D = buffer;
args.Type = DataType.TagData;
args.Angle = net.Angle;
args.Exposure = net.Exposure;
args.Saturation = net.Saturation;
args.Hue = net.Hue;
Console.Error.Write($"%d classes\n", net.Outputs);
loadThread = Data.Data.load_data_in_thread(args);
int epoch = (net.Seen) / n;
while (Network.get_current_batch(net) < net.MaxBatches || net.MaxBatches == 0)
{
sw.Reset();
sw.Start();
loadThread.Join();
train = buffer;
loadThread = Data.Data.load_data_in_thread(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss = Network.train_network(net, train);
if (avgLoss == -1)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
Console.Write(
$"%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", Network.get_current_batch(net), (float)(net.Seen) / n, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, net.Seen);
if (net.Seen / n > epoch)
{
epoch = net.Seen / n;
string buff = $"{backupDirectory}/{basec}_{epoch}.Weights";
Parser.save_weights(net, buff);
}
if (Network.get_current_batch(net) % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}.backup";
Parser.save_weights(net, buff);
}
}
string buff2 = $"{backupDirectory}/{basec}.Weights";
Parser.save_weights(net, buff2);
loadThread.Join();
}
private static void train_detector(string datacfg, string cfgfile, string weightfile, bool clear)
{
var options = OptionList.read_data_cfg(datacfg);
string trainImages = OptionList.option_find_str(options, "train", "Data.Data/train.list");
string backupDirectory = OptionList.option_find_str(options, "backup", "/backup/");
string basec = Utils.Basecfg(cfgfile);
Console.Write($"%s\n", basec);
float avgLoss = -1;
Network[] nets = new Network[1];
int seed = Utils.Rand.Next();
int i;
for (i = 0; i < 1; ++i)
{
nets[i] = Parser.parse_network_cfg(cfgfile);
if (string.IsNullOrEmpty(weightfile))
{
Parser.load_weights(nets[i], weightfile);
}
if (clear)
{
nets[i].Seen = 0;
}
nets[i].LearningRate *= 1;
}
Network net = nets[0];
int imgs = net.Batch * net.Subdivisions * 1;
Console.Write($"Learning Rate: %g, Momentum: %g, Decay: %g\n", net.LearningRate, net.Momentum, net.Decay);
Data.Data buffer = new Data.Data();
Layer l = net.Layers[net.N - 1];
int classes = l.Classes;
float jitter = l.Jitter;
string[] paths = Data.Data.GetPaths(trainImages);
LoadArgs args = new LoadArgs();
args.W = net.W;
args.H = net.H;
args.Paths = paths;
args.N = imgs;
args.M = paths.Length;
args.Classes = classes;
args.Jitter = jitter;
args.NumBoxes = l.MaxBoxes;
args.D = buffer;
args.Type = DataType.DetectionData;
args.Threads = 8;
args.Angle = net.Angle;
args.Exposure = net.Exposure;
args.Saturation = net.Saturation;
args.Hue = net.Hue;
Thread loadThread = Data.Data.load_data(args);
var sw = new Stopwatch();
int count = 0;
while (Network.get_current_batch(net) < net.MaxBatches)
{
Data.Data train;
if (l.Random && count++ % 10 == 0)
{
Console.Write($"Resizing\n");
int dim = (Utils.Rand.Next() % 10 + 10) * 32;
if (Network.get_current_batch(net) + 100 > net.MaxBatches)
{
dim = 544;
}
Console.Write($"%d\n", dim);
args.W = dim;
args.H = dim;
loadThread.Join();
loadThread = Data.Data.load_data(args);
for (i = 0; i < 1; ++i)
{
Network.resize_network(nets[i], dim, dim);
}
net = nets[0];
}
sw.Start();
loadThread.Join();
train = buffer;
loadThread = Data.Data.load_data(args);
sw.Stop();
Console.Write($"Loaded: %lf seconds\n", sw.Elapsed.Seconds);
sw.Reset();
sw.Start();
float loss;
loss = Network.train_network(net, train);
if (avgLoss < 0)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
i = Network.get_current_batch(net);
sw.Stop();
Console.Write($"%d: %f, %f avg, %f rate, %lf seconds, %d images\n", Network.get_current_batch(net), loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds, i * imgs);
if (i % 1000 == 0 || (i < 1000 && i % 100 == 0))
{
string buffa = $"{backupDirectory}/{basec}_{i}.Weights";
Parser.save_weights(net, buffa);
}
}
string buff = $"{backupDirectory}/{basec}_final.Weights";
Parser.save_weights(net, buff);
}