#pragma once
-#include <armnn/utility/IgnoreUnused.hpp>
-
#include <tensorflow/lite/builtin_ops.h>
#include <tensorflow/lite/c/builtin_op_data.h>
#include <tensorflow/lite/c/common.h>
TfLiteContext* tfLiteContext,
TfLiteNode* tfLiteNode,
int nodeIndex,
- int32_t padOperatorCode)
+ int32_t tfLitePadOperatorCode)
{
- armnn::IgnoreUnused(delegateData,
- tfLiteContext,
- tfLiteNode,
- nodeIndex,
- padOperatorCode);
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ switch(tfLitePadOperatorCode)
+ {
+ case kTfLiteBuiltinPad:
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+ break;
+ case kTfLiteBuiltinPadv2:
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex));
+ break;
+ default:
+ return kTfLiteError;
+ }
+
+ const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+ const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+ const TfLiteTensor& tfLitepaddingTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+
+ if (IsDynamicTensor(tfLiteInputTensor))
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Dynamic input tensors are not supported in operator #%d node #%d: ",
+ tfLitePadOperatorCode, 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: ",
+ tfLitePadOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+
+ const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+ const armnn::TensorInfo& paddingTensorInfo = GetTensorInfoForTfLiteTensor(tfLitepaddingTensor);
+ const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+ // Get the padding data from the input tensor
+ auto* paddingData = tflite::GetTensorData<int32_t>(&tfLitepaddingTensor);
+
+ size_t step = 2;
+ armnn::PadDescriptor descriptor;
+ for (unsigned int i = 0; i < paddingTensorInfo.GetNumElements() / step; ++i)
+ {
+ descriptor.m_PadList.emplace_back(paddingData[i * step], paddingData[i * step + 1]);
+ }
+
+ if (tfLitePadOperatorCode == kTfLiteBuiltinPad && inputTensorInfo.IsQuantized())
+ {
+ descriptor.m_PadValue = inputTensorInfo.GetQuantizationOffset();
+ }
+ else if (tfLitePadOperatorCode == kTfLiteBuiltinPadv2)
+ {
+ const TfLiteTensor& tfLitepaddingValue = tfLiteTensors[tfLiteNode->inputs->data[2]];
+ armnn::TensorInfo paddingValueTensorInfo = GetTensorInfoForTfLiteTensor(tfLitepaddingValue);
+ if (paddingValueTensorInfo.GetNumElements() != 1)
+ {
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Multiple padding value are not supported in operator #%d node #%d: ",
+ tfLitePadOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+ // Get the padding value from the input tensor
+ switch (tfLitepaddingValue.type)
+ {
+ case kTfLiteFloat32:
+ descriptor.m_PadValue = tflite::GetTensorData<float>(&tfLitepaddingValue)[0];
+ break;
+ case kTfLiteUInt8:
+ descriptor.m_PadValue = tflite::GetTensorData<uint8>(&tfLitepaddingValue)[0];
+ break;
+ case kTfLiteInt8:
+ descriptor.m_PadValue = tflite::GetTensorData<int8>(&tfLitepaddingValue)[0];
+ break;
+ case kTfLiteInt16:
+ descriptor.m_PadValue = tflite::GetTensorData<int16>(&tfLitepaddingValue)[0];
+ break;
+ default:
+ TF_LITE_MAYBE_KERNEL_LOG(
+ tfLiteContext,
+ "TfLiteArmnnDelegate: Padding value datatype is not supported in operator #%d node #%d: ",
+ tfLitePadOperatorCode, nodeIndex);
+ return kTfLiteError;
+ }
+ }
+
+ if (!delegateData.m_Network)
+ {
+ bool isSupported = false;
+ FORWARD_LAYER_SUPPORT_FUNC(__func__,
+ tfLiteContext,
+ IsPadSupported,
+ delegateData.m_Backends,
+ isSupported,
+ inputTensorInfo,
+ outputTensorInfo,
+ descriptor);
+
+ return isSupported ? kTfLiteOk : kTfLiteError;
+ }
+
+ armnn::IConnectableLayer* padLayer = delegateData.m_Network->AddPadLayer(descriptor);
+ ARMNN_ASSERT(padLayer != nullptr);
+
+ armnn::IOutputSlot& outputSlot = padLayer->GetOutputSlot(0);
+ outputSlot.SetTensorInfo(outputTensorInfo);
- return kTfLiteError;
+ return Connect(padLayer, tfLiteNode, delegateData);
}
} // namespace armnnDelegate
--- /dev/null
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "PadTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void Pad2dTest(std::vector<armnn::BackendId>& backends,
+ tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD,
+ float pad = 0.0f)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 2, 2, 2 };
+ std::vector<int32_t> outputShape { 3, 5, 6 };
+ std::vector<int32_t> paddingShape { 3, 2 };
+
+ std::vector<float> inputValues = { 0.0f, 4.0f,
+ 2.0f, -5.0f,
+ 6.0f, 1.0f,
+ 5.0f, -2.0f };
+
+ std::vector<float> expectedOutputValues = { pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, 0.0f, 4.0f, pad, pad,
+ pad, pad, 2.0f, -5.0f, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, 6.0f, 1.0f, pad, pad,
+ pad, pad, 5.0f, -2.0f, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad };
+
+ std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 };
+
+ PadTest<float>(padOperatorCode,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ paddingShape,
+ outputShape,
+ inputValues,
+ paddingDim,
+ expectedOutputValues,
+ pad);
+}
+
+void Pad3dTest(std::vector<armnn::BackendId>& backends,
+ tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD,
+ float pad = 0.0f)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 2, 2, 2 };
+ std::vector<int32_t> outputShape { 3, 5, 6 };
+ std::vector<int32_t> paddingShape { 3, 2 };
+
+ std::vector<float> inputValues = { 0.0f, 4.0f,
+ 2.0f, 5.0f,
+ 6.0f, 1.0f,
+ 5.0f, 2.0f };
+
+ std::vector<float> expectedOutputValues = { pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, 0.0f, 4.0f, pad, pad,
+ pad, pad, 2.0f, 5.0f, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, 6.0f, 1.0f, pad, pad,
+ pad, pad, 5.0f, 2.0f, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad,
+ pad, pad, pad, pad, pad, pad };
+
+ std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 };
+
+ PadTest<float>(padOperatorCode,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ paddingShape,
+ outputShape,
+ inputValues,
+ paddingDim,
+ expectedOutputValues,
+ pad);
+}
+
+void Pad4dTest(std::vector<armnn::BackendId>& backends,
+ tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD,
+ float pad = 0.0f)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 2, 2, 3, 2 };
+ std::vector<int32_t> outputShape { 4, 5, 7, 4 };
+ std::vector<int32_t> paddingShape { 4, 2 };
+
+ std::vector<float> inputValues = { 0.0f, 1.0f,
+ 2.0f, 3.0f,
+ 4.0f, 5.0f,
+
+ 6.0f, 7.0f,
+ 8.0f, 9.0f,
+ 10.0f, 11.0f,
+
+ 12.0f, 13.0f,
+ 14.0f, 15.0f,
+ 16.0f, 17.0f,
+
+ 18.0f, 19.0f,
+ 20.0f, 21.0f,
+ 22.0f, 23.0f };
+
+ std::vector<float> expectedOutputValues = { pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, 0.0f, 1.0f, pad,
+ pad, 2.0f, 3.0f, pad,
+ pad, 4.0f, 5.0f, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, 6.0f, 7.0f, pad,
+ pad, 8.0f, 9.0f, pad,
+ pad, 10.0f, 11.0f, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, 12.0f, 13.0f, pad,
+ pad, 14.0f, 15.0f, pad,
+ pad, 16.0f, 17.0f, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, 18.0f, 19.0f, pad,
+ pad, 20.0f, 21.0f, pad,
+ pad, 22.0f, 23.0f, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad,
+ pad, pad, pad, pad };
+
+ std::vector<int32_t> paddingDim = { 1, 1, 2, 1, 3, 1, 1, 1 };
+
+ PadTest<float>(padOperatorCode,
+ ::tflite::TensorType_FLOAT32,
+ backends,
+ inputShape,
+ paddingShape,
+ outputShape,
+ inputValues,
+ paddingDim,
+ expectedOutputValues,
+ pad);
+}
+
+void PadInt8Test(std::vector<armnn::BackendId>& backends,
+ tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD,
+ int8_t paddingValue = 0,
+ int8_t p = 3,
+ float quantizationScale = -2.0f,
+ int32_t quantizationOffset = 3)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 2, 2, 2 };
+ std::vector<int32_t> outputShape { 3, 5, 6 };
+ std::vector<int32_t> paddingShape { 3, 2 };
+
+ std::vector<int8_t> inputValues = { 0, 4,
+ 2, -5,
+ 6, 1,
+ 5, -2 };
+
+ std::vector<int8_t> expectedOutputValues = { p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, 0, 4, p, p,
+ p, p, 2, -5, p, p,
+ p, p, p, p, p, p,
+
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, 6, 1, p, p,
+ p, p, 5, -2, p, p,
+ p, p, p, p, p, p,
+
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p };
+
+ std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 };
+
+ PadTest<int8_t>(padOperatorCode,
+ ::tflite::TensorType_INT8,
+ backends,
+ inputShape,
+ paddingShape,
+ outputShape,
+ inputValues,
+ paddingDim,
+ expectedOutputValues,
+ paddingValue,
+ quantizationScale,
+ quantizationOffset);
+}
+
+void PadUint8Test(std::vector<armnn::BackendId>& backends,
+ tflite::BuiltinOperator padOperatorCode = tflite::BuiltinOperator_PAD,
+ uint8_t paddingValue = 0,
+ uint8_t p = 3,
+ float quantizationScale = -2.0f,
+ int32_t quantizationOffset = 3)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 2, 2, 2 };
+ std::vector<int32_t> outputShape { 3, 5, 6 };
+ std::vector<int32_t> paddingShape { 3, 2 };
+
+ std::vector<uint8_t> inputValues = { 0, 4,
+ 2, 5,
+ 6, 1,
+ 5, 2 };
+
+ std::vector<uint8_t> expectedOutputValues = { p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, 0, 4, p, p,
+ p, p, 2, 5, p, p,
+ p, p, p, p, p, p,
+
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, 6, 1, p, p,
+ p, p, 5, 2, p, p,
+ p, p, p, p, p, p,
+
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p,
+ p, p, p, p, p, p };
+
+ std::vector<int32_t> paddingDim = { 0, 1, 2, 1, 2, 2 };
+
+ PadTest<uint8_t>(padOperatorCode,
+ ::tflite::TensorType_UINT8,
+ backends,
+ inputShape,
+ paddingShape,
+ outputShape,
+ inputValues,
+ paddingDim,
+ expectedOutputValues,
+ paddingValue,
+ quantizationScale,
+ quantizationOffset);
+}
+
+TEST_SUITE("Pad_CpuRefTests")
+{
+
+TEST_CASE ("Pad2d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad2dTest(backends);
+}
+
+TEST_CASE ("Pad3d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad3dTest(backends);
+}
+
+TEST_CASE ("Pad4d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad4dTest(backends);
+}
+
+TEST_CASE ("Pad_Int8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ PadInt8Test(backends);
+}
+
+TEST_CASE ("Pad_Uint8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ PadUint8Test(backends);
+}
+
+TEST_CASE ("PadV22d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5);
+}
+
+TEST_CASE ("PadV23d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0);
+}
+
+TEST_CASE ("PadV24d_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33);
+}
+
+TEST_CASE ("PadV2_Int8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+TEST_CASE ("PadV2_Uint8_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+} // TEST_SUITE("Pad_CpuRefTests")
+
+TEST_SUITE("Pad_CpuAccTests")
+{
+
+TEST_CASE ("Pad2d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad2dTest(backends);
+}
+
+TEST_CASE ("Pad3d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad3dTest(backends);
+}
+
+TEST_CASE ("Pad4d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad4dTest(backends);
+}
+
+TEST_CASE ("Pad_Int8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ PadInt8Test(backends);
+}
+
+TEST_CASE ("Pad_Uint8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ PadUint8Test(backends);
+}
+
+TEST_CASE ("PadV22d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5);
+}
+
+TEST_CASE ("PadV23d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0);
+}
+
+TEST_CASE ("PadV24d_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33);
+}
+
+TEST_CASE ("PadV2_Int8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+TEST_CASE ("PadV2_Uint8_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+} // TEST_SUITE("Pad_CpuAccTests")
+
+TEST_SUITE("Pad_GpuAccTests")
+{
+
+TEST_CASE ("Pad2d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad2dTest(backends);
+}
+
+TEST_CASE ("Pad3d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad3dTest(backends);
+}
+
+TEST_CASE ("Pad4d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad4dTest(backends);
+}
+
+TEST_CASE ("Pad_Int8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ PadInt8Test(backends);
+}
+
+TEST_CASE ("Pad_Uint8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ PadUint8Test(backends);
+}
+
+TEST_CASE ("PadV22d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad2dTest(backends, tflite::BuiltinOperator_PADV2, -2.5);
+}
+
+TEST_CASE ("PadV23d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad3dTest(backends, tflite::BuiltinOperator_PADV2, 2.0);
+}
+
+TEST_CASE ("PadV24d_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ Pad4dTest(backends, tflite::BuiltinOperator_PADV2, -1.33);
+}
+
+TEST_CASE ("PadV2_Int8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ PadInt8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+TEST_CASE ("PadV2_Uint8_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ PadUint8Test(backends, tflite::BuiltinOperator_PADV2, -1, -1);
+}
+
+} // TEST_SUITE("Pad_GpuAccTests")
+
+} // namespace armnnDelegate
\ No newline at end of file
--- /dev/null
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/interpreter.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/model.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+#include <tensorflow/lite/version.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+
+template <typename T>
+std::vector<char> CreatePadTfLiteModel(
+ tflite::BuiltinOperator padOperatorCode,
+ tflite::TensorType tensorType,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& paddingTensorShape,
+ const std::vector<int32_t>& outputTensorShape,
+ const std::vector<int32_t>& paddingDim,
+ const std::vector<T> paddingValue,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ auto quantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>({ quantScale }),
+ flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+ auto inputTensor = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("input"),
+ quantizationParameters);
+
+ auto paddingTensor = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(paddingTensorShape.data(),
+ paddingTensorShape.size()),
+ tflite::TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("padding"));
+
+ auto outputTensor = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 2,
+ flatBufferBuilder.CreateString("output"),
+ quantizationParameters);
+
+ std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, paddingTensor, outputTensor};
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingDim.data()),
+ sizeof(int32_t) * paddingDim.size())));
+ buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+ std::vector<int32_t> operatorInputs;
+ std::vector<int> subgraphInputs;
+
+ tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_PadOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions;
+
+ if (padOperatorCode == tflite::BuiltinOperator_PAD)
+ {
+ operatorInputs = {{ 0, 1 }};
+ subgraphInputs = {{ 0, 1 }};
+ operatorBuiltinOptions = CreatePadOptions(flatBufferBuilder).Union();
+
+ }
+ else if (padOperatorCode == tflite::BuiltinOperator_PADV2)
+ {
+ buffers.push_back(
+ CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(paddingValue.data()),
+ sizeof(T))));
+
+ const std::vector<int32_t> shape = { 1 };
+ auto padValueTensor = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(shape.data(),
+ shape.size()),
+ tensorType,
+ 3,
+ flatBufferBuilder.CreateString("paddingValue"),
+ quantizationParameters);
+
+ tensors.push_back(padValueTensor);
+
+ operatorInputs = {{ 0, 1, 3 }};
+ subgraphInputs = {{ 0, 1, 3 }};
+
+ operatorBuiltinOptionsType = BuiltinOptions_PadV2Options;
+ operatorBuiltinOptions = CreatePadV2Options(flatBufferBuilder).Union();
+ }
+
+ // create operator
+ const std::vector<int32_t> operatorOutputs{{ 2 }};
+ flatbuffers::Offset <Operator> redefineOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOptions);
+
+ const std::vector<int> subgraphOutputs{{ 2 }};
+ flatbuffers::Offset <SubGraph> subgraph =
+ CreateSubGraph(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(tensors.data(), tensors.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()),
+ flatBufferBuilder.CreateVector(&redefineOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Pad Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+ padOperatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+ flatBufferBuilder.Finish(flatbufferModel);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+template <typename T>
+void PadTest(tflite::BuiltinOperator padOperatorCode,
+ tflite::TensorType tensorType,
+ const std::vector<armnn::BackendId>& backends,
+ const std::vector<int32_t>& inputShape,
+ const std::vector<int32_t>& paddingShape,
+ std::vector<int32_t>& outputShape,
+ std::vector<T>& inputValues,
+ std::vector<int32_t>& paddingDim,
+ std::vector<T>& expectedOutputValues,
+ T paddingValue,
+ float quantScale = 1.0f,
+ int quantOffset = 0)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreatePadTfLiteModel<T>(padOperatorCode,
+ tensorType,
+ inputShape,
+ paddingShape,
+ outputShape,
+ paddingDim,
+ {paddingValue},
+ quantScale,
+ quantOffset);
+
+ const Model* tfLiteModel = GetModel(modelBuffer.data());
+ CHECK(tfLiteModel != nullptr);
+
+ std::unique_ptr<Interpreter> armnnDelegateInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegateInterpreter) == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter != nullptr);
+ CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteInterpreter;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteInterpreter) == kTfLiteOk);
+ CHECK(tfLiteInterpreter != nullptr);
+ CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk);
+
+ // Create the ArmNN Delegate
+ armnnDelegate::DelegateOptions delegateOptions(backends);
+ std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
+ CHECK(theArmnnDelegate != nullptr);
+ // Modify armnnDelegateInterpreter to use armnnDelegate
+ CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data
+ armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues);
+ armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
+
+ // Run EnqueueWorkload
+ CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+ armnnDelegate::CompareOutputData<T>(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
+}
+
+} // anonymous namespace
\ No newline at end of file
projects / platform / upstream / armnn.git / commitdiff