1 files changed, 108 insertions, 2 deletions
diff --git a/delegate/src/Pooling.hpp b/delegate/src/Pooling.hpp
index ff3d668545..28e26f6504 100644
--- a/delegate/src/Pooling.hpp
+++ b/delegate/src/Pooling.hpp
@@ -13,13 +13,119 @@
 namespace armnnDelegate
 {
 
+void CalcPadding(uint32_t inputSize,
+                 uint32_t filterSize,
+                 uint32_t stride,
+                 uint32_t dilation,
+                 uint32_t& paddingFront,
+                 uint32_t& paddingBack,
+                 TfLitePadding padding)
+{
+    paddingFront = 0;
+    paddingBack = 0;
+    if (padding == kTfLitePaddingSame)
+    {
+        uint32_t outputSize = (inputSize + stride - 1) / stride;
+        uint32_t dilatedSize = filterSize + (dilation - 1) * (filterSize - 1);
+        uint32_t temp = (outputSize - 1) * stride + dilatedSize;
+        if (temp > inputSize)
+        {
+            paddingFront = (temp - inputSize) / 2;
+            paddingBack = (temp - inputSize) - paddingFront;
+        }
+    }
+}
+
 TfLiteStatus VisitPoolingOperator(DelegateData& delegateData,
                                   TfLiteContext* tfLiteContext,
                                   TfLiteNode* tfLiteNode,
                                   int nodeIndex,
-                                  int32_t poolingOperatorCode)
+                                  int32_t tfLitePoolingOperatorCode)
 {
-    return kTfLiteError;
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+    if (IsDynamicTensor(tfLiteInputTensor))
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+            tfLitePoolingOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (IsDynamicTensor(tfLiteOutputTensor))
+    {
+        TF_LITE_MAYBE_KERNEL_LOG(
+            tfLiteContext,
+            "TfLiteArmnnDelegate: Dynamic output tensors are not supported in operator #%d node #%d: ",
+            tfLitePoolingOperatorCode, nodeIndex);
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+    armnn::PoolingAlgorithm poolingAlgorithm;
+    switch(tfLitePoolingOperatorCode)
+    {
+        case kTfLiteBuiltinMaxPool2d:
+            poolingAlgorithm = armnn::PoolingAlgorithm::Max;
+            break;
+        default:
+            return kTfLiteError;
+    }
+
+    armnn::Pooling2dDescriptor descriptor;
+    descriptor.m_PoolType = poolingAlgorithm;
+
+    auto* params = reinterpret_cast<TfLitePoolParams*>(tfLiteNode->builtin_data);
+    descriptor.m_PoolWidth = params->filter_width;
+    descriptor.m_PoolHeight = params->filter_height;
+    descriptor.m_StrideX = params->stride_width;
+    descriptor.m_StrideY = params->stride_height;
+    descriptor.m_DataLayout = armnn::DataLayout::NHWC;
+
+    unsigned int inputHeight = inputTensorInfo.GetShape()[1];
+    unsigned int inputWidth  = inputTensorInfo.GetShape()[2];
+
+    CalcPadding(inputHeight, descriptor.m_PoolHeight, descriptor.m_StrideY, 1u,
+                descriptor.m_PadTop, descriptor.m_PadBottom, params->padding);
+    CalcPadding(inputWidth, descriptor.m_PoolWidth, descriptor.m_StrideX, 1u,
+                descriptor.m_PadLeft, descriptor.m_PadRight, params->padding);
+
+    bool isSupported = false;
+    auto validateFunc = [&](const armnn::TensorInfo& outputTensorInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                                   tfLiteContext,
+                                   IsPooling2dSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   inputTensorInfo,
+                                   outputTensorInfo,
+                                   descriptor);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    armnn::IConnectableLayer* poolingLayer = delegateData.m_Network->AddPooling2dLayer(descriptor);
+    ARMNN_ASSERT(poolingLayer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = poolingLayer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+    Connect(poolingLayer, tfLiteNode, delegateData);
+
+    // Check activation
+    TfLiteFusedActivation activationType = params->activation;
+    return FusedActivation(tfLiteContext, tfLiteNode, activationType, poolingLayer, 0, delegateData);
 }
 
 } // namespace armnnDelegate