[BUGFIX] Fix gated activations in WaveNet

NAM/wavenet.cpp

@@ -37,15 +37,14 @@ void nam::wavenet::_Layer::process_(const Eigen::MatrixXf& input, const Eigen::M
   }
   else
   {
-    this->_activation->apply(this->_z.topRows(channels));
-    activations::Activation::get_activation("Sigmoid")->apply(this->_z.bottomRows(channels));
-    // activations::Activation::get_activation("Sigmoid")->apply(this->_z.block(channels, 0, channels,
-    // this->_z.cols()));
-
+    // CAREFUL: .topRows() and .bottomRows() won't be memory-contiguous for a column-major matrix (Issue 125). Need to
+    // do this column-wise:
+    for (long i = 0; i < _z.cols(); i++)
+    {
+      this->_activation->apply(this->_z.block(0, i, channels, 1));
+      activations::Activation::get_activation("Sigmoid")->apply(this->_z.block(channels, i, channels, 1));
+    }
     this->_z.topRows(channels).array() *= this->_z.bottomRows(channels).array();
-    // this->_z.topRows(channels) = this->_z.topRows(channels).cwiseProduct(
-    //   this->_z.bottomRows(channels)
-    // );
   }
 
   head_input += this->_z.topRows(channels);

tools/run_tests.cpp

@@ -5,6 +5,7 @@
 #include "test/test_activations.cpp"
 #include "test/test_dsp.cpp"
 #include "test/test_get_dsp.cpp"
+#include "test/test_wavenet.cpp"
 
 int main()
 {
@@ -32,6 +33,8 @@ int main()
   test_get_dsp::test_null_input_level();
   test_get_dsp::test_null_output_level();
 
+  test_wavenet::test_gated();
+
   std::cout << "Success!" << std::endl;
   return 0;
 }

tools/test/test_wavenet.cpp

@@ -0,0 +1,76 @@
+// Tests for the WaveNet
+
+#include <Eigen/Dense>
+#include <cassert>
+#include <iostream>
+
+#include "NAM/wavenet.h"
+
+namespace test_wavenet
+{
+void test_gated()
+{
+  // Assert correct nuemrics of the gating activation.
+  // Issue 101
+  const int conditionSize = 1;
+  const int channels = 1;
+  const int kernelSize = 1;
+  const int dilation = 1;
+  const std::string activation = "ReLU";
+  const bool gated = true;
+  auto layer = nam::wavenet::_Layer(conditionSize, channels, kernelSize, dilation, activation, gated);
+
+  // Conv, input mixin, 1x1
+  std::vector<float> weights{
+    // Conv (weight, bias)  NOTE: 2 channels out bc gated, so shapes are (2,1,1), (2,)
+    1.0f, 1.0f, 0.0f, 0.0f,
+    // Input mixin (weight only: (2,1,1))
+    1.0f, -1.0f,
+    // 1x1 (weight (1,1,1), bias (1,))
+    // NOTE: Weights are (1,1) on conv, (1,-1), so the inputs sum on the upper channel and cancel on the lower.
+    // This should give us a nice zero if the input & condition are the same, so that'll sigmoid to 0.5 for the
+    // gate.
+    1.0f, 0.0f};
+  auto it = weights.begin();
+  layer.set_weights_(it);
+  assert(it == weights.end());
+
+  const long numFrames = 4;
+  layer.set_num_frames_(numFrames);
+
+  Eigen::MatrixXf input, condition, headInput, output;
+  input.resize(channels, numFrames);
+  condition.resize(channels, numFrames);
+  headInput.resize(channels, numFrames);
+  output.resize(channels, numFrames);
+
+  const float signalValue = 0.25f;
+  input.fill(signalValue);
+  condition.fill(signalValue);
+  // So input & condition will sum to 0.5 on the top channel (-> ReLU), cancel to 0 on bottom (-> sigmoid)
+
+  headInput.setZero();
+  output.setZero();
+
+  layer.process_(input, condition, headInput, output, 0, 0);
+
+  // 0.25 + 0.25 -> 0.5 for conv & input mixin top channel
+  // (0 on bottom channel)
+  // Top ReLU -> preseves 0.5
+  // Bottom sigmoid 0->0.5
+  // Product is 0.25
+  // 1x1 is unity
+  // Skip-connect -> 0.25 (input) + 0.25 (output) -> 0.5 output
+  // head output gets 0+0.25 = 0.25
+  const float expectedOutput = 0.5;
+  const float expectedHeadInput = 0.25;
+  for (int i = 0; i < numFrames; i++)
+  {
+    const float actualOutput = output(0, i);
+    const float actualHeadInput = headInput(0, i);
+    // std::cout << actualOutput << std::endl;
+    assert(actualOutput == expectedOutput);
+    assert(actualHeadInput == expectedHeadInput);
+  }
+}
+}; // namespace test_wavenet