Merge pull request #30 from cvjena/master

Pull changes for v1.3.0

CONTRIBUTORS

@@ -2,6 +2,9 @@ Contributors from the Computer Vision Group of the Friedrich Schiller University
  Dipl.-Inf. Sven Sickert
  Marcel Simon M.Sc.
  Dr. rer. nat. Erik Rodner
+
+Contributors from GitHub:
+ lolz0r (RGB regression)
 
 FindSQLite3.CMake (New BSD License):
  Andreas Schneider

example/labelmefacade.net

@@ -2,13 +2,13 @@
 
 # Network configuration
 ?convolutional kernels=16 size=7x7
-?maxpooling size=2x2
+?amaxpooling size=2x2 stride=2x2
 ?tanh
 
-?convolutional size=5x5 kernels=12
+?convolutional kernels=12 size=5x5
 ?tanh
 
-?convolutional size=5x5 kernels=64
+?convolutional kernels=96 size=5x5
 ?tanh
 
 ?fullyconnected neurons=192
@@ -18,12 +18,15 @@
 ?output
 
 # Learning settings
-l1=0.001
-l2=0.0005
-lr=0.0001
-gamma=0.003
+method=patch
+l1=0.000
+l2=0.0008
+lr=0.02
+gamma=0.00003
 momentum=0.9
 exponent=0.75
-iterations=100
-sbatchsize=10
-pbatchsize=2
+iterations=10000
+sbatchsize=1
+pbatchsize=96
+mu=1.75
+eta=0.1

src/math/TensorMath.cpp

@@ -194,7 +194,7 @@ void TensorMath::IM2COL(const Tensor& source, const int source_width, const int
       FATAL("Error setting kernel args: " << (signed int) error);
     }
 
-    size_t global_work_size[] = {target_width * target_height, target_maps, samples};
+    size_t global_work_size[] = {(size_t)(target_width * target_height), (size_t)target_maps, (size_t)samples};
 
     error = clEnqueueNDRangeKernel (CLHelper::queue, CLHelper::k_im2col, 3, NULL,
         global_work_size, NULL, 0, NULL, NULL);
@@ -294,7 +294,7 @@ void TensorMath::COL2IM(Tensor& source, const int source_width, const int source
       FATAL("Error setting kernel args: " << (signed int) error);
     }
 
-    size_t global_work_size[] = {source_width * source_height, maps, samples};
+    size_t global_work_size[] = {(size_t)(source_width * source_height), (size_t)maps, (size_t)samples};
 
     error = clEnqueueNDRangeKernel (CLHelper::queue, CLHelper::k_col2im, 3, NULL,
         global_work_size, NULL, 0, NULL, NULL);
@@ -365,7 +365,7 @@ void TensorMath::SETSAMPLE(Tensor& A, const int smA, const datum value)
       FATAL("Error setting kernel args: " << (signed int) error);
     }
 
-    size_t global_work_size[] = {smA == -1 ? A.elements() : A.width() * A.height() * A.samples()};
+    size_t global_work_size[] = {(size_t)(smA == -1 ? A.elements() : A.width() * A.height() * A.samples())};
 
     error = clEnqueueNDRangeKernel (CLHelper::queue, CLHelper::k_setValue, 1, NULL,
         global_work_size, NULL, 0, NULL, NULL);
@@ -416,7 +416,7 @@ void TensorMath::SMS(const Tensor& source, Tensor& target)
       FATAL("Error setting kernel args: " << (signed int) error);
     }
 
-    size_t global_work_size[] = {target.elements()};
+    size_t global_work_size[] = {(size_t)target.elements()};
 
     error = clEnqueueNDRangeKernel (CLHelper::queue, CLHelper::k_sms, 1, NULL,
         global_work_size, NULL, 0, NULL, NULL);
@@ -451,4 +451,4 @@ void TensorMath::SMS(const Tensor& source, Tensor& target)
   target.hint_ignore_content_ = false;
 }
 
-}
+}

src/net/Trainer.cpp

@@ -153,8 +153,8 @@ void Trainer::Epoch() {
 	for (NetGraphNode* training_node : graph_.GetTrainingNodes())
 		(dynamic_cast<TrainingLayer*>(training_node->layer))->SetTestingMode(false);
 
-  LOGDEBUG << "Epoch: " << epoch_ << ", it: " << iterations <<
-           ", bsize: " << first_training_layer_->GetBatchSize() * settings_.sbatchsize << ", lr0: " <<
+  LOGINFO << "Epoch: " << epoch_ << ", it: " << iterations <<
+           ", bsize: " << first_training_layer_->GetBatchSize() * settings_.sbatchsize << ", current lr: " <<
            CalculateLR (epoch_ * iterations) << std::endl;
 
   auto t_begin = std::chrono::system_clock::now();

tools/trainNetwork.cpp

@@ -98,6 +98,7 @@ int main (int argc, char* argv[]) {
   settings.testing_ratio = 1 * it_factor;
 
   // Load dataset
+  LOGINFO << "Loading dataset, this can take a long time depending on the size!" << std::flush;
   Conv::Dataset* dataset = nullptr;
 
   if (patchwise_training) {