1 files changed, 119 insertions, 6 deletions

diff --git a/delegate/src/Slice.hpp b/delegate/src/Slice.hpp
index 0311abf41c..a237034bb6 100644
--- a/delegate/src/Slice.hpp
+++ b/delegate/src/Slice.hpp
@@ -21,13 +21,126 @@ TfLiteStatus VisitSliceOperator(DelegateData& delegateData,
                                 int nodeIndex,
                                 int32_t sliceOperatorCode)
 {
-    armnn::IgnoreUnused(delegateData,
-                        tfLiteContext,
-                        tfLiteNode,
-                        nodeIndex,
-                        sliceOperatorCode);
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 4, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
 
-    return kTfLiteError;
+    // Read inputs [input, begin, end, strides]
+    int numInputs = tfLiteNode->inputs->size;
+    std::vector<const TfLiteTensor*> tfLiteInputs;
+    tfLiteInputs.reserve(numInputs);
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    for (int i = 0; i < numInputs; i++)
+    {
+        const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]];
+        tfLiteInputs.push_back(inputTensor);
+        if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex))
+        {
+            return kTfLiteError;
+        }
+    }
+
+    // We save the begin, end and strides tensors in our descriptor. Therefore we have to read those values from inputs
+    int inputRank = tfLiteInputs[0]->dims->size;
+    auto ReadInt32Input = [&](int inputIndex, std::vector<int32_t>& outputData) ->  TfLiteStatus
+    {
+        if (tfLiteInputs[inputIndex]->type != kTfLiteInt32)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to "
+                    "be of type int32. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int rank = tfLiteInputs[inputIndex]->dims->size;
+        if (rank != 1)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to "
+                    "be a 1D-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int numValues = tfLiteInputs[inputIndex]->dims->data[0];
+        if (numValues != inputRank)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The number of values in the Begin-, End- and Stride-Tensors of the "
+                    "StridedSlice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        // return tensor data
+        auto* tensorDataPtr = tflite::GetTensorData<int32_t>(tfLiteInputs[inputIndex]);
+        outputData.assign(tensorDataPtr, tensorDataPtr+numValues);
+        return kTfLiteOk;
+    };
+
+    std::vector<int32_t> beginData;
+    if (ReadInt32Input(1, beginData) != kTfLiteOk)
+        return kTfLiteError;
+    std::vector<int32_t> endData;
+    if (ReadInt32Input(2, endData) != kTfLiteOk)
+        return kTfLiteError;
+    std::vector<int32_t> strideData;
+    if (ReadInt32Input(3, strideData) != kTfLiteOk)
+        return kTfLiteError;
+
+    // parse built in options
+    auto* stridedSliceParams = reinterpret_cast<TfLiteStridedSliceParams*>(tfLiteNode->builtin_data);
+
+    // Write all data to the descriptor
+    armnn::StridedSliceDescriptor descriptor;
+    descriptor.m_Begin          = std::move(beginData);
+    descriptor.m_End            = std::move(endData);
+    descriptor.m_Stride         = std::move(strideData);
+    descriptor.m_BeginMask      = stridedSliceParams->begin_mask;
+    descriptor.m_EllipsisMask   = stridedSliceParams->ellipsis_mask;
+    descriptor.m_EndMask        = stridedSliceParams->end_mask;
+    descriptor.m_NewAxisMask    = stridedSliceParams->new_axis_mask;
+    descriptor.m_ShrinkAxisMask = stridedSliceParams->shrink_axis_mask;
+    descriptor.m_DataLayout     = armnn::DataLayout::NHWC;
+
+    // Validate output
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+    bool isSupported = false;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                                   tfLiteContext,
+                                   IsStridedSliceSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   inputTensorInfo,
+                                   outInfo,
+                                   descriptor);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add a StridedSlice layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddStridedSliceLayer(descriptor);
+    ARMNN_ASSERT(layer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // Connect
+    return Connect(layer, tfLiteNode, delegateData);
 }
 
 } // namespace armnnDelegate