1 files changed, 89 insertions, 0 deletions
diff --git a/src/armnn/layers/BatchToSpaceNdLayer.cpp b/src/armnn/layers/BatchToSpaceNdLayer.cpp
new file mode 100644
index 0000000000..595ce4a7fe
--- /dev/null
+++ b/src/armnn/layers/BatchToSpaceNdLayer.cpp
@@ -0,0 +1,89 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "BatchToSpaceNdLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+#include "LayerWithParameters.hpp"
+#include "BatchToSpaceNdLayer.hpp"
+
+#include <armnn/TypesUtils.hpp>
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/WorkloadData.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+namespace armnn
+{
+
+BatchToSpaceNdLayer::BatchToSpaceNdLayer(const armnn::BatchToSpaceNdDescriptor& param, const char* name)
+    : LayerWithParameters(1, 1, LayerType::BatchToSpaceNd, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> BatchToSpaceNdLayer::CreateWorkload(const Graph& graph,
+                                                               const IWorkloadFactory& factory) const
+{
+    BatchToSpaceNdQueueDescriptor descriptor;
+
+    return factory.CreateBatchToSpaceNd(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+BatchToSpaceNdLayer* BatchToSpaceNdLayer::Clone(Graph& graph) const
+{
+    auto layer = CloneBase<BatchToSpaceNdLayer>(graph, m_Param, GetName());
+    return std::move(layer);
+}
+
+void BatchToSpaceNdLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(1, CHECK_LOCATION());
+
+    auto inferredShapes = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape()});
+
+    BOOST_ASSERT(inferredShapes.size() == 1);
+
+    ConditionalThrowIfNotEqual<LayerValidationException>(
+        "BatchToSpaceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+        GetOutputSlot(0).GetTensorInfo().GetShape(),inferredShapes[0]);
+}
+
+std::vector<TensorShape> BatchToSpaceNdLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+    const DataLayoutIndexed & dataLayout = m_Param.m_DataLayout;
+    const TensorShape& inputShape = inputShapes[0];
+    unsigned int inBatchSize = inputShape[0];
+    unsigned int channelSize = inputShape[dataLayout.GetChannelsIndex()];
+
+    std::vector<unsigned int> theBlockShape = m_Param.m_BlockShape;
+
+    unsigned int overallSize = inBatchSize;
+
+    for (unsigned int i = 0; i < theBlockShape.size(); ++i)
+    {
+        overallSize = overallSize * theBlockShape.at(i);
+    }
+
+    std::vector<std::vector<unsigned int>> crops = m_Param.m_Crops;
+
+    std::vector<unsigned int> yCrops = crops[0];
+    std::vector<unsigned int> xCrops = crops[1];
+
+    unsigned int inputHeight = inputShape[dataLayout.GetHeightIndex()];
+    unsigned int outputHeight = theBlockShape.at(0) * (inputHeight - (yCrops[0] + yCrops[1]));
+
+    unsigned int inputWidth = inputShape[dataLayout.GetWidthIndex()];
+    unsigned int outputWidth = theBlockShape.at(1) * (inputWidth - (xCrops[0] + xCrops[1]));
+
+    unsigned int outputBatchSize = overallSize / (outputHeight * outputWidth);
+
+    if (dataLayout == DataLayout::NHWC)
+    {
+        return std::vector<TensorShape>({ TensorShape({ outputBatchSize, outputHeight, outputWidth, channelSize }) });
+    }
+    else
+    {
+        return std::vector<TensorShape>({ TensorShape({ outputBatchSize, channelSize, outputHeight, outputWidth }) });
+    }
+}
+} // namespace armnn