private void backward_cuda(BlobCollection <T> colTop, List <bool> rgbPropagateDown, BlobCollection <T> colBottom)
{
m_log.CHECK(!rgbPropagateDown[1], "Cannot backpropagate to sequence indicators.");
// TODO: skip backpropagation to inputs and parameters inside the unrolled
// net according to propagate_down[0] and propagate_down[2]. For now just
// backprop to inputs and parameters unconditionally, as either the inputs or
// the parameters do need backward (or Net would have set
// layer_needs_backward[i] = false for this layer).
m_unrolledNet.Backward(m_nLastLayerIndex);
}
private void make_step(string strLayer, double dfSigma, double dfStepSize = 1.5, int nFocusLabel = -1, float[] rgDirectInputs = null)
{
Blob <T> blobSrc = m_net.blob_by_name("data"); // input image is stored in Net's 'data' blob
Blob <T> blobDst = m_net.blob_by_name(strLayer);
int nDstIdx = m_net.layer_index_by_name(strLayer);
double dfLoss;
m_net.Forward(out dfLoss);
if (nFocusLabel < 0 && rgDirectInputs == null)
{
m_cuda.copy(blobDst.count(), blobDst.gpu_data, blobDst.mutable_gpu_diff);
}
else if (rgDirectInputs != null)
{
blobDst.SetDiff(0);
for (int i = 0; i < rgDirectInputs.Length && i < blobDst.count(); i++)
{
if (rgDirectInputs[i] != 0)
{
blobDst.SetDiff(rgDirectInputs[i], i);
}
}
}
else
{
if (nFocusLabel >= blobDst.count())
{
throw new Exception("The focus label '" + nFocusLabel + "' is greater than the number of outputs for blob '" + blobDst.Name + "' -- it only has '" + blobDst.count().ToString() + "' outputs!");
}
blobDst.SetDiff(0);
blobDst.SetDiff(1.0, nFocusLabel);
}
m_net.Backward(nDstIdx);
// Apply normalized ascent step to the input image.
double dfAsum = Utility.ConvertVal <T>(blobSrc.asum_diff());
double dfMean = dfAsum / blobSrc.count();
double dfStep = dfStepSize / dfMean;
m_cuda.scal(blobSrc.count(), dfStep, blobSrc.mutable_gpu_diff);
m_cuda.add(blobSrc.count(), blobSrc.gpu_diff, blobSrc.gpu_data, blobSrc.mutable_gpu_data);
if (dfSigma != 0)
{
m_cuda.gaussian_blur(blobSrc.count(), blobSrc.channels, blobSrc.height, blobSrc.width, dfSigma, blobSrc.gpu_data, m_blobBlur.mutable_gpu_data);
blobSrc.CopyFrom(m_blobBlur);
}
}
//-----------------------------------------------------------------------------------------
// Simple Classification (using direct net surgery)
//-----------------------------------------------------------------------------------------
/// <summary>
/// The SimpleClassification sample is designed to show how to manually train the MNIST dataset using raw image data stored
/// in the \ProgramData\MyCaffe\test_data\images\mnist\training directory (previously loaded with the 'Export Images'
/// sample above).
/// </summary>
/// <remarks>
/// IMPORTANT: This sample is for demonstration, using the Simplest Classification method is the fastest recommended method that uses the Image Database.
///
/// This sample requires that you have already loaded the MNIST dataset into SQL (or SQLEXPRESS) using the MyCaffe
/// Test Application by selecting its 'Database | Load MNIST...' menu item.
/// </remarks>
/// <param name="sender">Specifies the event sender.</param>
/// <param name="e">Specifies the event argument.</param>
private void btnSimpleClassification_Click(object sender, EventArgs e)
{
Stopwatch sw = new Stopwatch();
int nBatchSize = 32;
SettingsCaffe settings = new SettingsCaffe();
settings.GpuIds = "0";
if (!Directory.Exists(m_strImageDirTesting) || !Directory.Exists(m_strImageDirTraining))
{
MessageBox.Show("You must first export the MNIST images by pressing the Export button!", "Export Needed", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
m_rgstrTrainingFiles = Directory.GetFiles(m_strImageDirTraining);
m_rgstrTrainingFiles = m_rgstrTrainingFiles.Where(p => p.Contains(".png")).ToArray();
m_rgstrTestingFiles = Directory.GetFiles(m_strImageDirTesting);
m_rgstrTestingFiles = m_rgstrTestingFiles.Where(p => p.Contains(".png")).ToArray();
string strSolver;
string strModel;
load_descriptors("mnist", out strSolver, out strModel); // Load the descriptors from their respective files (installed by MyCaffe Test Application install)
strModel = fixup_model(strModel, nBatchSize);
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, m_log, m_evtCancel);
mycaffe.Load(Phase.TRAIN, // using the training phase.
strSolver, // solver descriptor, that specifies to use the SGD solver.
strModel, // simple LENET model descriptor.
null, null, null, false, null, false, false); // no weights are loaded and no image database is used.
// Perform your own training
Solver <float> solver = mycaffe.GetInternalSolver();
Net <float> net = mycaffe.GetInternalNet(Phase.TRAIN);
Blob <float> dataBlob = net.blob_by_name("data");
Blob <float> labelBlob = net.blob_by_name("label");
sw.Start();
int nIterations = 5000;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTrainingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward and backward passes.
double dfLoss;
net.Forward(out dfLoss);
net.ClearParamDiffs();
net.Backward();
// Apply the gradients calculated during Backward.
solver.ApplyUpdate(i);
// Output the loss.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("Loss = " + dfLoss.ToString());
sw.Restart();
}
}
// Run testing using the MyCaffe control (who's internal Run net is already updated
// for it shares its weight memory with the training net.
net = mycaffe.GetInternalNet(Phase.TEST);
dataBlob = net.blob_by_name("data");
labelBlob = net.blob_by_name("label");
float fTotalAccuracy = 0;
nIterations = 100;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTestingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward pass.
double dfLoss;
BlobCollection <float> res = net.Forward(out dfLoss);
fTotalAccuracy += res[0].GetData(0);
// Output the training progress.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("training...");
sw.Restart();
}
}
double dfAccuracy = (double)fTotalAccuracy / (double)nIterations;
m_log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
MessageBox.Show("Average Accuracy = " + dfAccuracy.ToString("P"), "Traing/Test on MNIST Completed", MessageBoxButtons.OK, MessageBoxIcon.Information);
}
//-----------------------------------------------------------------------------------------
// Simple Classification (using direct net surgery)
//-----------------------------------------------------------------------------------------
/// <summary>
/// The SimpleClassification sample is designed to show how to manually train the MNIST dataset using raw image data stored
/// in the \ProgramData\MyCaffe\test_data\images\mnist\training directory (previously loaded with the 'Export Images'
/// sample above).
/// </summary>
/// <remarks>
/// This sample requires that you have already loaded the MNIST dataset into SQL (or SQLEXPRESS) using the MyCaffe
/// Test Application by selecting its 'Database | Load MNIST...' menu item.
/// </remarks>
/// <param name="sender">Specifies the event sender.</param>
/// <param name="e">Specifies the event argument.</param>
private void btnSimpleClassification_Click(object sender, EventArgs e)
{
Stopwatch sw = new Stopwatch();
int nBatchSize = 32;
SettingsCaffe settings = new SettingsCaffe();
settings.GpuIds = "0";
if (!Directory.Exists(m_strImageDirTraining) || !Directory.Exists(m_strImageDirTesting))
{
string strMsg = "You must first expand the MNIST dataset into the following directories:" + Environment.NewLine;
strMsg += "Training Images: '" + m_strImageDirTraining + "'" + Environment.NewLine;
strMsg += "Testing Images: '" + m_strImageDirTesting + "'" + Environment.NewLine + Environment.NewLine;
strMsg += "If you have Microsoft SQL or SQL Express installed, selecting the 'Export' button from the 'ImageClassification' project will export these images for you." + Environment.NewLine + Environment.NewLine;
strMsg += "If you DO NOT have Microsoft SQL or SQL Express, running the MyCaffe Test Application and selecting the 'Database | Load MNIST...' menu item with the 'Export to file only' check box checked, will export the images for you without SQL." + Environment.NewLine + Environment.NewLine;
strMsg += "To get the MNIST *.gz data files, please see http://yann.lecun.com/exdb/mnist/";
MessageBox.Show(strMsg, "Images Not Found", MessageBoxButtons.OK, MessageBoxIcon.Error);
return;
}
m_rgstrTrainingFiles = Directory.GetFiles(m_strImageDirTraining);
m_rgstrTrainingFiles = m_rgstrTrainingFiles.Where(p => p.Contains(".png")).ToArray();
m_rgstrTestingFiles = Directory.GetFiles(m_strImageDirTesting);
m_rgstrTestingFiles = m_rgstrTestingFiles.Where(p => p.Contains(".png")).ToArray();
string strSolver;
string strModel;
load_descriptors("mnist", out strSolver, out strModel); // Load the descriptors from their respective files (installed by MyCaffe Test Application install)
strModel = fixup_model(strModel, nBatchSize);
MyCaffeControl <float> mycaffe = new MyCaffeControl <float>(settings, m_log, m_evtCancel);
mycaffe.LoadLite(Phase.TRAIN, // using the training phase.
strSolver, // solver descriptor, that specifies to use the SGD solver.
strModel, // simple LENET model descriptor.
null); // no weights are loaded.
// Perform your own training
Solver <float> solver = mycaffe.GetInternalSolver();
Net <float> net = mycaffe.GetInternalNet(Phase.TRAIN);
Blob <float> dataBlob = net.blob_by_name("data");
Blob <float> labelBlob = net.blob_by_name("label");
sw.Start();
int nIterations = 5000;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTrainingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward and backward passes.
double dfLoss;
net.Forward(out dfLoss);
net.ClearParamDiffs();
net.Backward();
// Apply the gradients calculated during Backward.
solver.ApplyUpdate(i);
// Output the loss.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("Loss = " + dfLoss.ToString());
sw.Restart();
}
}
// Run testing using the MyCaffe control (who's internal Run net is already updated
// for it shares its weight memory with the training net.
net = mycaffe.GetInternalNet(Phase.TEST);
dataBlob = net.blob_by_name("data");
labelBlob = net.blob_by_name("label");
float fTotalAccuracy = 0;
nIterations = 100;
for (int i = 0; i < nIterations; i++)
{
// Load the data into the data and label blobs.
loadData(m_rgstrTestingFiles, nBatchSize, dataBlob, labelBlob);
// Run the forward pass.
double dfLoss;
BlobCollection <float> res = net.Forward(out dfLoss);
fTotalAccuracy += res[0].GetData(0);
// Output the training progress.
if (sw.Elapsed.TotalMilliseconds > 1000)
{
m_log.Progress = (double)i / (double)nIterations;
m_log.WriteLine("training...");
sw.Restart();
}
}
double dfAccuracy = (double)fTotalAccuracy / (double)nIterations;
m_log.WriteLine("Accuracy = " + dfAccuracy.ToString("P"));
MessageBox.Show("Average Accuracy = " + dfAccuracy.ToString("P"), "Traing/Test on MNIST Completed", MessageBoxButtons.OK, MessageBoxIcon.Information);
// Save the trained weights for use later.
saveWeights(mycaffe, "my_weights");
Bitmap bmp = new Bitmap(m_rgstrTestingFiles[0]);
ResultCollection results = mycaffe.Run(bmp);
MyCaffeControl <float> mycaffe2 = mycaffe.Clone(0);
ResultCollection results2 = mycaffe2.Run(bmp);
// Release resources used.
mycaffe.Dispose();
mycaffe2.Dispose();
}
