Release 18.08

[platform/upstream/armnn.git] / src / armnn / backends / RefWorkloads / ConvImpl.hpp

diff --git a/src/armnn/backends/RefWorkloads/ConvImpl.hpp b/src/armnn/backends/RefWorkloads/ConvImpl.hpp
index 8b66b0b..b7d5d17 100644 (file)
--- a/src/armnn/backends/RefWorkloads/ConvImpl.hpp
+++ b/src/armnn/backends/RefWorkloads/ConvImpl.hpp
@@ -18,7 +18,7 @@
 namespace armnn
 {
 
-/// Performs multiplication of a integer with a multiplier which is less than one,
+/// Performs multiplication of an integer with a multiplier which is less than one,
 /// using quantized integer arithmetic which is consistent with AndroidNN's CPU executor.
 struct QuantizedMultiplierSmallerThanOne
 {
@@ -28,21 +28,21 @@ public:
     /// The implementation of this function is adapted from Android NN's QuantizeMultiplierSmallerThanOne().
     QuantizedMultiplierSmallerThanOne(float multiplier);
 
-    /// The implementation of this function is adapted from Android NN's MultiplyByQuantizedMultiplierSmallerThanOne()
+    /// The implementation of this function is adapted from Android NN's MultiplyByQuantizedMultiplierSmallerThanOne().
     int32_t operator*(int32_t rhs) const;
 
 private:
-    /// The implementation of this function is adapted from gemmlowp's SaturatingRoundingDoublingHighMul()
+    /// The implementation of this function is adapted from gemmlowp's SaturatingRoundingDoublingHighMul().
     static int32_t SaturatingRoundingDoublingHighMul(int32_t a, int32_t b);
 
-    /// The implementation of this function is adapted from gemmlowp's RoundingDivideByPOT()
+    /// The implementation of this function is adapted from gemmlowp's RoundingDivideByPOT().
     static int32_t RoundingDivideByPOT(int32_t x, int exponent);
 
     int32_t m_Multiplier;
     int32_t m_RightShift;
 };
 
-/// an implementation shared by normal and depthwise convolution
+/// An implementation shared by normal and depthwise convolution.
 template<typename ConvData, typename InputType, typename BiasType, typename AccumulatorType>
 static void ConvImpl(ConvData data,
                      const InputType* inputData,
@@ -55,6 +55,7 @@ static void ConvImpl(ConvData data,
                      InputType* outputData,
                      float outputScale,
                      int32_t outputOffset,
+                     const TensorInfo& filterInfo,
                      bool depthwise = false)
 {
     if (data.m_Parameters.m_BiasEnabled && !biasData)
@@ -64,7 +65,6 @@ static void ConvImpl(ConvData data,
 
     const TensorInfo& inputInfo0 = GetTensorInfo(data.m_Inputs[0]);
     const TensorInfo& outputInfo0 = GetTensorInfo(data.m_Outputs[0]);
-    const TensorInfo& filterInfo = data.m_Weight->GetTensorInfo();
 
     unsigned int depthMult      = depthwise ? filterInfo.GetShape()[0] : 1;
     unsigned int channelsInput  = filterInfo.GetShape()[1];
@@ -84,7 +84,7 @@ static void ConvImpl(ConvData data,
     unsigned int hStride  = data.m_Parameters.m_StrideY;
     unsigned int xStride  = data.m_Parameters.m_StrideX;
 
-    // the world's least efficient convolution
+    // The world's least efficient convolution.
     for (unsigned int batchIdx = 0; batchIdx < batchSize; batchIdx++)
     {
         for (unsigned int cOutput = 0; cOutput < channelsOutput; cOutput++)
@@ -93,11 +93,11 @@ static void ConvImpl(ConvData data,
             {
                 for (unsigned int xOutput = 0; xOutput < widthOutput; xOutput++)
                 {
-                    // this loop goes over each output element
+                    // This loop goes over each output element.
                     AccumulatorType sum = AccumulatorType();
 
-                    // for depthwise, each output channel corresponds to exactly one input channel
-                    // for normal, must loop over each input channel
+                    // For depthwise, each output channel corresponds to exactly one input channel.
+                    // For normal, must loop over each input channel.
                     for (unsigned int cInput = 0; cInput < (depthwise ? 1 : channelsInput); cInput++)
                     {
                         unsigned int depthwiseMultiplierIdx = 0;
@@ -111,11 +111,11 @@ static void ConvImpl(ConvData data,
                         {
                             for (unsigned int xFilter = 0; xFilter < widthFilter; xFilter++)
                             {
-                                // this loop goes over each input element for each output element
+                                // This loop goes over each input element for each output element.
 
                                 unsigned int filterIndex;
 
-                                // since dimensionality of kernel depends on depthwiseness, so does index
+                                // Since dimensionality of kernel depends on depthwiseness, so does index.
                                 if (depthwise)
                                 {
                                     filterIndex = depthwiseMultiplierIdx * widthFilter * heightFilter * channelsInput +
@@ -138,7 +138,7 @@ static void ConvImpl(ConvData data,
 
                                 AccumulatorType inputValue;
 
-                                // check if we're in the padding
+                                // Check if we're in the padding.
                                 if (yInput < paddingTop || yInput >= heightInput + paddingTop ||
                                     xInput < paddingLeft || xInput >= widthInput + paddingLeft )
                                 {