IVGCVSW-3452 Support dynamic output shape in hal_1_2::HalPolicy::ConvertConv2d
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: I8694e1f1c62da6f74eb356558b17a63758ccfdad
diff --git a/OutputShapeUtils.cpp b/OutputShapeUtils.cpp
index b6cdb31..6a9bf90 100644
--- a/OutputShapeUtils.cpp
+++ b/OutputShapeUtils.cpp
@@ -5,6 +5,8 @@
#include "OutputShapeUtils.hpp"
+#include <DataLayoutIndexed.hpp>
+
#include <algorithm>
#include <vector>
@@ -54,6 +56,48 @@
return outputInfo.GetNumElements() == 0u;
}
+TensorShape InferConvolution2dOutputShape(const TensorShape& inputShape,
+ const TensorShape& kernelShape,
+ const Convolution2dDescriptor& descriptor)
+{
+ if (inputShape.GetNumDimensions() != 4)
+ {
+ throw InvalidArgumentException("Input shape for Convolution2d must be 4D");
+ }
+
+ armnnUtils::DataLayoutIndexed dataLayoutIndex(descriptor.m_DataLayout);
+
+ const unsigned int cIndex = dataLayoutIndex.GetChannelsIndex();
+ const unsigned int wIndex = dataLayoutIndex.GetWidthIndex();
+ const unsigned int hIndex = dataLayoutIndex.GetHeightIndex();
+
+ const unsigned int wInput = inputShape[wIndex];
+ const unsigned int hInput = inputShape[hIndex];
+
+ const unsigned int wKernel = kernelShape[wIndex];
+ const unsigned int wDilated = wKernel + (descriptor.m_DilationX - 1) * (wKernel - 1);
+
+ const unsigned int wRead = (wInput + descriptor.m_PadLeft + descriptor.m_PadRight) - wDilated;
+ const unsigned int wOutput = 1 + (wRead / descriptor.m_StrideX);
+
+ const unsigned int hKernel = kernelShape[hIndex];
+ const unsigned int hDilated = hKernel + (descriptor.m_DilationY - 1) * (hKernel - 1);
+
+ const unsigned int hRead = (hInput + descriptor.m_PadTop + descriptor.m_PadBottom) - hDilated;
+ const unsigned int hOutput = 1 + (hRead / descriptor.m_StrideY);
+
+ const unsigned int batches = inputShape[0];
+ const unsigned int channels = kernelShape[0];
+
+ TensorShape outputShape(4);
+ outputShape[0] = batches;
+ outputShape[cIndex] = channels;
+ outputShape[wIndex] = wOutput;
+ outputShape[hIndex] = hOutput;
+
+ return outputShape;
+}
+
TensorShape InferMaximumOutputShape(const armnn::TensorShape& input0Shape,
const armnn::TensorShape& input1Shape)
{