protected void rescale(Floatarray outv, Floatarray sub)
{
if (sub.Rank() != 2)
throw new Exception("CHECK_ARG: sub.Rank()==2");
int csize = PGeti("csize");
int indent = PGeti("indent");
float s = Math.Max(sub.Dim(0), sub.Dim(1)) / (float)(csize - indent - indent);
if (PGeti("noupscale") > 0 && s < 1.0f)
s = 1.0f;
float sig = s * PGetf("aa");
float dx = (csize * s - sub.Dim(0)) / 2;
float dy = (csize * s - sub.Dim(1)) / 2;
if (sig > 1e-3f)
Gauss.Gauss2d(sub, sig, sig);
outv.Resize(csize, csize);
outv.Fill(0f);
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
float x = i * s - dx;
float y = j * s - dy;
if (x < 0 || x >= sub.Dim(0)) continue;
if (y < 0 || y >= sub.Dim(1)) continue;
float value = ImgOps.bilin(sub, x, y);
outv[i, j] = value;
}
}
/*Global.Debugf("fe", "{0} {1} ({2}) -> {3} {4} ({5})\n",
sub.Dim(0), sub.Dim(1), NarrayUtil.Max(sub),
outv.Dim(0), outv.Dim(1), NarrayUtil.Max(outv));*/
}
public TrainInfo TestDense(IDataset ts)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
if (first)
{
first = false;
BeginTestEpoch(HrLenet, linput.Height, linput.Width, ts.nSamples()); // init test
}
try
{
if (C2i.ContainsKey(ts.Cls(i)))
{
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
}
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// do test one epoch
try
{
EndAndRunTestEpoch(HrLenet, ref trInfo);
}
catch (Exception e)
{
GetStdout(sbout); // get test messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTestEpoch");
}
throw new Exception("Exception in EndAndRunTestEpoch\r\n" + e.Message);
}
return(trInfo);
}
public static void putd1 <T, S>(Floatarray image, Floatarray slice, int index)
{
if (!(slice.Rank() == 1 && slice.Dim(0) == image.Dim(0)))
{
throw new Exception("ASSERT: slice.Rank()==1 && slice.Dim(0)==image.Dim(1)");
}
for (int i = 0; i < image.Dim(0); i++)
{
image.UnsafePut(i, index, slice.UnsafeAt(i));
}
}
protected override float Outputs(OutputVector result, Floatarray v)
{
result.Clear();
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
// byte array input
StdInput vinput = new StdInput(v);
byte[] buffer = vinput.GetDataBuffer();
// char classifier compute outputs
if (CharClass.AsciiTarget)
{
// net output 0..~ (lower - winner)
CharClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, result);
}
else
{
// net output 0..1; (higher - winner)
CharClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, result);
}
// junk classifier
if (PGetb("junk") && !DisableJunk && !JunkClass.IsEmpty)
{
OutputVector jv = new OutputVector();
if (JunkClass.AsciiTarget)
{
JunkClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
result[jc()] = jv.Value(1);
}
else
{
//result.Normalize();
result.Normalize2();
JunkClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
jv.Normalize2();
for (int i = 0; i < result.nKeys(); i++)
{
result.Values[i] *= jv.Value(0);
}
result[jc()] = jv.Value(1);
}
}
return(0.0f);
}
protected void rescale(Floatarray outv, Floatarray sub)
{
if (sub.Rank() != 2)
{
throw new Exception("CHECK_ARG: sub.Rank()==2");
}
int csize = PGeti("csize");
int indent = PGeti("indent");
float s = Math.Max(sub.Dim(0), sub.Dim(1)) / (float)(csize - indent - indent);
if (PGeti("noupscale") > 0 && s < 1.0f)
{
s = 1.0f;
}
float sig = s * PGetf("aa");
float dx = (csize * s - sub.Dim(0)) / 2;
float dy = (csize * s - sub.Dim(1)) / 2;
if (sig > 1e-3f)
{
Gauss.Gauss2d(sub, sig, sig);
}
outv.Resize(csize, csize);
outv.Fill(0f);
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
float x = i * s - dx;
float y = j * s - dy;
if (x < 0 || x >= sub.Dim(0))
{
continue;
}
if (y < 0 || y >= sub.Dim(1))
{
continue;
}
float value = ImgOps.bilin(sub, x, y);
outv[i, j] = value;
}
}
/*Global.Debugf("fe", "{0} {1} ({2}) -> {3} {4} ({5})\n",
* sub.Dim(0), sub.Dim(1), NarrayUtil.Max(sub),
* outv.Dim(0), outv.Dim(1), NarrayUtil.Max(outv));*/
}
protected void rescale(Floatarray v, Floatarray input)
{
if (input.Rank() != 2)
{
throw new Exception("CHECK_ARG: sub.Rank()==2");
}
Floatarray sub = new Floatarray();
// find the largest connected component
// and crop to its bounding box
// (use a binary version of the character
// to compute the bounding box)
Intarray components = new Intarray();
float threshold = PGetf("threshold") * NarrayUtil.Max(input);
Global.Debugf("biggestcc", "threshold {0}", threshold);
components.MakeLike(input);
components.Fill(0);
for (int i = 0; i < components.Length(); i++)
{
components[i] = (input[i] > threshold ? 1 : 0);
}
int n = ImgLabels.label_components(ref components);
Intarray totals = new Intarray(n + 1);
totals.Fill(0);
for (int i = 0; i < components.Length(); i++)
{
totals[components[i]]++;
}
totals[0] = 0;
Narray <Rect> boxes = new Narray <Rect>();
ImgLabels.bounding_boxes(ref boxes, components);
int biggest = NarrayUtil.ArgMax(totals);
Rect r = boxes[biggest];
int pad = (int)(PGetf("pad") + 0.5f);
r.PadBy(pad, pad);
Global.Debugf("biggestcc", "({0}) {1}[{2}] :: {3} {4} {5} {6}",
n, biggest, totals[biggest],
r.x0, r.y0, r.x1, r.y1);
// now perform normal feature extraction
// (use the original grayscale input)
sub = input;
ImgMisc.Crop(sub, r);
int csize = PGeti("csize");
float s = Math.Max(sub.Dim(0), sub.Dim(1)) / (float)csize;
if (PGetf("noupscale") > 0 && s < 1.0f)
{
s = 1.0f;
}
float sig = s * PGetf("aa");
float dx = (csize * s - sub.Dim(0)) / 2f;
float dy = (csize * s - sub.Dim(1)) / 2f;
if (sig > 1e-3f)
{
Gauss.Gauss2d(sub, sig, sig);
}
v.Resize(csize, csize);
v.Fill(0f);
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
float x = i * s - dx;
float y = j * s - dy;
if (x < 0 || x >= sub.Dim(0))
{
continue;
}
if (y < 0 || y >= sub.Dim(1))
{
continue;
}
float value = ImgOps.bilin(sub, x, y);
v[i, j] = value;
}
}
/*Global.Debugf("biggestcc", "{0} {1} ({2}) -> {3} {4} ({5})",
* sub.Dim(0), sub.Dim(1), NarrayUtil.Max(sub),
* v.Dim(0), v.Dim(1), NarrayUtil.Max(v));*/
}
public virtual TrainInfo TrainBatch(IDataset ds, IDataset ts, int epochs)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Train Dataset to Lenet
for (int i = 0; i < ds.nSamples(); i++)
{
ds.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
try
{
if (first)
{
first = false;
//StartRedirectStdout(); // start redirect cout to string buffer
BeginTrainEpoch(HrLenet, linput.Height, linput.Width, ds.nSamples(), ts.nSamples()); // init train
}
if (C2i.ContainsKey(ds.Cls(i)))
{
AddSampleToTrainOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ds.Cls(i)]);
}
else
{
Global.Debugf("error", "class '{0}' is not contained in the char list", (char)ds.Cls(i));
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTrainOfEpoch");
}
throw new Exception("Exception in AddSampleToTrainOfEpoch\r\n" + e.Message);
}
}
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
{
v.Reshape(csize, csize, 0, 0);
}
StdInput linput = new StdInput(v);
try
{
if (C2i.ContainsKey(ts.Cls(i)))
{
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
}
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// debug save mnist
//SaveTrainMnist(HrLenet, "debug-images-idx3-ubyte", "debug-labels-idx1-ubyte");
// do train epochs
for (int epoch = 0; epoch < epochs; epoch++)
{
trInfo = new TrainInfo();
try
{
DateTime startDate = DateTime.Now;
EndAndRunTrainEpoch(HrLenet, ref trInfo); // do train one epoch
// show train info
Global.Debugf("info",
String.Format("|{0,7}| Energy:{1:0.#####} Correct:{2:0.00#%} Errors:{3:0.00#%} Count:{4} ",
trInfo.age, trInfo.energy, (trInfo.correct / (float)trInfo.size),
(trInfo.error / (float)trInfo.size), trInfo.size));
Global.Debugf("info",
String.Format(" TEST Energy={0:0.#####} Correct={1:0.00#%} Errors={2:0.00#%} Count={3} ",
trInfo.tenergy, (trInfo.tcorrect / (float)trInfo.tsize),
(trInfo.terror / (float)trInfo.tsize), trInfo.tsize));
TimeSpan spanTrain = DateTime.Now - startDate;
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)spanTrain.TotalMinutes, spanTrain.Seconds));
// get dll stdout messages
GetStdout(sbout);
if (sbout.Length > 0)
{
Console.Write(sbout.ToString());
}
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
{
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTrainEpoch");
}
throw new Exception("Exception in EndAndRunTrainEpoch\r\n" + e.Message);
}
}
return(trInfo);
}
protected override float Outputs(OutputVector result, Floatarray v)
{
result.Clear();
if (v.Rank() == 1)
v.Reshape(csize, csize, 0, 0);
// byte array input
StdInput vinput = new StdInput(v);
byte[] buffer = vinput.GetDataBuffer();
// char classifier compute outputs
if (CharClass.AsciiTarget)
// net output 0..~ (lower - winner)
CharClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, result);
else
// net output 0..1; (higher - winner)
CharClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, result);
// junk classifier
if (PGetb("junk") && !DisableJunk && !JunkClass.IsEmpty)
{
OutputVector jv = new OutputVector();
if (JunkClass.AsciiTarget)
{
JunkClass.ComputeOutputs(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
result[jc()] = jv.Value(1);
}
else
{
//result.Normalize();
result.Normalize2();
JunkClass.ComputeOutputsRaw(buffer, vinput.Length, vinput.Height, vinput.Width, jv);
jv.Normalize2();
for (int i = 0; i < result.nKeys(); i++)
result.Values[i] *= jv.Value(0);
result[jc()] = jv.Value(1);
}
}
return 0.0f;
}
public virtual TrainInfo TrainBatch(IDataset ds, IDataset ts, int epochs)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Train Dataset to Lenet
for (int i = 0; i < ds.nSamples(); i++)
{
ds.Input(v, i);
if (v.Rank() == 1)
v.Reshape(csize, csize, 0, 0);
StdInput linput = new StdInput(v);
try
{
if (first)
{
first = false;
//StartRedirectStdout(); // start redirect cout to string buffer
BeginTrainEpoch(HrLenet, linput.Height, linput.Width, ds.nSamples(), ts.nSamples()); // init train
}
if (C2i.ContainsKey(ds.Cls(i)))
AddSampleToTrainOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ds.Cls(i)]);
else
Global.Debugf("error", "class '{0}' is not contained in the char list", (char)ds.Cls(i));
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTrainOfEpoch");
throw new Exception("Exception in AddSampleToTrainOfEpoch\r\n" + e.Message);
}
}
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
v.Reshape(csize, csize, 0, 0);
StdInput linput = new StdInput(v);
try
{
if (C2i.ContainsKey(ts.Cls(i)))
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// debug save mnist
//SaveTrainMnist(HrLenet, "debug-images-idx3-ubyte", "debug-labels-idx1-ubyte");
// do train epochs
for (int epoch = 0; epoch < epochs; epoch++)
{
trInfo = new TrainInfo();
try
{
DateTime startDate = DateTime.Now;
EndAndRunTrainEpoch(HrLenet, ref trInfo); // do train one epoch
// show train info
Global.Debugf("info",
String.Format("|{0,7}| Energy:{1:0.#####} Correct:{2:0.00#%} Errors:{3:0.00#%} Count:{4} ",
trInfo.age, trInfo.energy, (trInfo.correct / (float)trInfo.size),
(trInfo.error / (float)trInfo.size), trInfo.size) );
Global.Debugf("info",
String.Format(" TEST Energy={0:0.#####} Correct={1:0.00#%} Errors={2:0.00#%} Count={3} ",
trInfo.tenergy, (trInfo.tcorrect / (float)trInfo.tsize),
(trInfo.terror / (float)trInfo.tsize), trInfo.tsize));
TimeSpan spanTrain = DateTime.Now - startDate;
Global.Debugf("info", String.Format(" training time: {0} minutes, {1} seconds",
(int)spanTrain.TotalMinutes, spanTrain.Seconds));
// get dll stdout messages
GetStdout(sbout);
if (sbout.Length > 0)
Console.Write(sbout.ToString());
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTrainEpoch");
throw new Exception("Exception in EndAndRunTrainEpoch\r\n" + e.Message);
}
}
return trInfo;
}
public TrainInfo TestDense(IDataset ts)
{
TrainInfo trInfo = new TrainInfo();
bool first = true;
Floatarray v = new Floatarray();
// Send Test Dataset to Lenet
for (int i = 0; i < ts.nSamples(); i++)
{
ts.Input(v, i);
if (v.Rank() == 1)
v.Reshape(csize, csize, 0, 0);
StdInput linput = new StdInput(v);
if (first)
{
first = false;
BeginTestEpoch(HrLenet, linput.Height, linput.Width, ts.nSamples()); // init test
}
try
{
if (C2i.ContainsKey(ts.Cls(i)))
AddSampleToTestOfEpoch(HrLenet, linput.GetDataBuffer(), linput.Length, C2i[ts.Cls(i)]);
}
catch (Exception e)
{
GetStdout(sbout); // get train messages
if (sbout.Length > 0)
Global.Debugf("error", sbout.ToString() + "\r\nException in AddSampleToTestOfEpoch");
throw new Exception("Exception in AddSampleToTestOfEpoch\r\n" + e.Message);
}
}
// do test one epoch
try
{
EndAndRunTestEpoch(HrLenet, ref trInfo);
}
catch (Exception e)
{
GetStdout(sbout); // get test messages
if (sbout.Length > 0)
Global.Debugf("error", sbout.ToString() + "\r\nException in EndAndRunTestEpoch");
throw new Exception("Exception in EndAndRunTestEpoch\r\n" + e.Message);
}
return trInfo;
}
protected void rescale(Floatarray v, Floatarray input)
{
if (input.Rank() != 2)
throw new Exception("CHECK_ARG: sub.Rank()==2");
Floatarray sub = new Floatarray();
// find the largest connected component
// and crop to its bounding box
// (use a binary version of the character
// to compute the bounding box)
Intarray components = new Intarray();
float threshold = PGetf("threshold") * NarrayUtil.Max(input);
Global.Debugf("biggestcc", "threshold {0}", threshold);
components.MakeLike(input);
components.Fill(0);
for (int i = 0; i < components.Length(); i++)
components[i] = (input[i] > threshold ? 1 : 0);
int n = ImgLabels.label_components(ref components);
Intarray totals = new Intarray(n + 1);
totals.Fill(0);
for (int i = 0; i < components.Length(); i++)
totals[components[i]]++;
totals[0] = 0;
Narray<Rect> boxes = new Narray<Rect>();
ImgLabels.bounding_boxes(ref boxes, components);
int biggest = NarrayUtil.ArgMax(totals);
Rect r = boxes[biggest];
int pad = (int)(PGetf("pad") + 0.5f);
r.PadBy(pad, pad);
Global.Debugf("biggestcc", "({0}) {1}[{2}] :: {3} {4} {5} {6}",
n, biggest, totals[biggest],
r.x0, r.y0, r.x1, r.y1);
// now perform normal feature extraction
// (use the original grayscale input)
sub = input;
ImgMisc.Crop(sub, r);
int csize = PGeti("csize");
float s = Math.Max(sub.Dim(0), sub.Dim(1))/(float)csize;
if(PGetf("noupscale") > 0 && s < 1.0f)
s = 1.0f;
float sig = s * PGetf("aa");
float dx = (csize*s-sub.Dim(0))/2f;
float dy = (csize*s-sub.Dim(1))/2f;
if(sig > 1e-3f)
Gauss.Gauss2d(sub, sig, sig);
v.Resize(csize, csize);
v.Fill(0f);
for (int i = 0; i < csize; i++)
{
for (int j = 0; j < csize; j++)
{
float x = i * s - dx;
float y = j * s - dy;
if (x < 0 || x >= sub.Dim(0)) continue;
if (y < 0 || y >= sub.Dim(1)) continue;
float value = ImgOps.bilin(sub, x, y);
v[i, j] = value;
}
}
/*Global.Debugf("biggestcc", "{0} {1} ({2}) -> {3} {4} ({5})",
sub.Dim(0), sub.Dim(1), NarrayUtil.Max(sub),
v.Dim(0), v.Dim(1), NarrayUtil.Max(v));*/
}