private static FloatPair get_rnn_data(byte[] text, int[] offsets, int characters, int len, int batch, int steps)
{
float[] x = new float[batch * steps * characters];
float[] y = new float[batch * steps * characters];
int i, j;
for (i = 0; i < batch; ++i)
{
for (j = 0; j < steps; ++j)
{
byte curr = text[(offsets[i]) % len];
byte next = text[(offsets[i] + 1) % len];
x[(j * batch + i) * characters + curr] = 1;
y[(j * batch + i) * characters + next] = 1;
offsets[i] = (offsets[i] + 1) % len;
if (curr > 255 || curr <= 0 || next > 255 || next <= 0)
{
Utils.Error("Bad char");
}
}
}
FloatPair p = new FloatPair();
p.X = x;
p.Y = y;
return(p);
}
private static FloatPair get_rnn_vid_data(Network net, string[] files, int n, int batch, int steps)
{
int b;
Image outIm = Network.get_network_image(net);
int outputSize = outIm.W * outIm.H * outIm.C;
Console.Write($"%d %d %d\n", outIm.W, outIm.H, outIm.C);
float[] feats = new float[net.Batch * batch * outputSize];
for (b = 0; b < batch; ++b)
{
int inputSize = net.W * net.H * net.C;
float[] input = new float[inputSize * net.Batch];
string filename = files[Utils.Rand.Next() % n];
using (VideoCapture cap = new VideoCapture(filename))
{
int frames = (int)cap.GetCaptureProperty(CapProp.FrameCount);
int index = Utils.Rand.Next() % (frames - steps - 2);
if (frames < (steps + 4))
{
--b;
continue;
}
Console.Write($"frames: %d, index: %d\n", frames, index);
cap.SetCaptureProperty(CapProp.PosFrames, index);
int i;
for (i = 0; i < net.Batch; ++i)
{
using (Mat src = cap.QueryFrame())
{
Image im = new Image(src);
LoadArgs.rgbgr_image(im);
Image re = LoadArgs.resize_image(im, net.W, net.H);
Array.Copy(re.Data, 0, input, i * inputSize, inputSize);
}
}
float[] output = Network.network_predict(net, input);
for (i = 0; i < net.Batch; ++i)
{
Array.Copy(output, i * outputSize, feats, (b + i * batch) * outputSize, outputSize);
}
}
}
FloatPair p = new FloatPair();
p.X = feats;
p.Y = new float[feats.Length - outputSize * batch];
Array.Copy(feats, outputSize * batch, p.Y, 0, p.Y.Length);
return(p);
}
private static void train_vid_rnn(string cfgfile, string weightfile)
{
string trainVideos = "Data.Data/vid/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;
string[] paths = Data.Data.GetPaths(trainVideos);
int n = paths.Length;
var sw = new Stopwatch();
int steps = net.TimeSteps;
int batch = net.Batch / net.TimeSteps;
Network extractor = Parser.parse_network_cfg("cfg/extractor.cfg");
Parser.load_weights(extractor, "/home/pjreddie/trained/yolo-coco.conv");
while (Network.get_current_batch(net) < net.MaxBatches)
{
i += 1;
sw.Reset();
sw.Start();
FloatPair p = get_rnn_vid_data(extractor, paths, n, batch, steps);
float loss = Network.train_network_datum(net, p.X, p.Y) / (net.Batch);
if (avgLoss < 0)
{
avgLoss = loss;
}
avgLoss = avgLoss * .9f + loss * .1f;
sw.Stop();
Console.Error.Write($"%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avgLoss, Network.get_current_rate(net), sw.Elapsed.Seconds);
if (i % 100 == 0)
{
string buff = $"{backupDirectory}/{basec}_{i}.Weights";
Parser.save_weights(net, buff);
}
if (i % 10 == 0)
{
string buff = $"{backupDirectory}/{basec}.backup";
Parser.save_weights(net, buff);
}
}
string buff2 = $"{backupDirectory}/{basec}_final.Weights";
Parser.save_weights(net, buff2);
}
