IVGCVSW-7886 Add TILE to delegate and opaque delegate
* Adding support for Tile in classic and opaque delegates
* CMake files updated
* Tests added
Signed-off-by: Tianle Cheng <tianle.cheng@arm.com>
Change-Id: I9b52cea3480eb71961cbccb1a346805f73b5661a
diff --git a/delegate/test/TileTest.cpp b/delegate/test/TileTest.cpp
new file mode 100644
index 0000000..2e20859
--- /dev/null
+++ b/delegate/test/TileTest.cpp
@@ -0,0 +1,91 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "TileTestHelper.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 <schema_generated.h>
+#include <tensorflow/lite/version.h>
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+void TileFloat32Test(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data
+ std::vector<float> inputValues =
+ {
+ 0.f, 1.f, 2.f,
+ 3.f, 4.f, 5.f
+ };
+
+ // Set output data
+ std::vector<float> expectedOutputValues =
+ {
+ 0.f, 1.f, 2.f, 0.f, 1.f, 2.f,
+ 3.f, 4.f, 5.f, 3.f, 4.f, 5.f,
+
+ 0.f, 1.f, 2.f, 0.f, 1.f, 2.f,
+ 3.f, 4.f, 5.f, 3.f, 4.f, 5.f
+ };
+
+ // The multiples
+ const std::vector<int32_t> multiplesValues = { 2, 2 };
+
+ // Set shapes
+ const std::vector<int32_t> inputShape = { 2, 3 };
+ const std::vector<int32_t> multiplesShape = { 2 };
+ const std::vector<int32_t> expectedOutputShape = { 4, 6 };
+
+ TileFP32TestImpl(tflite::BuiltinOperator_TILE,
+ backends,
+ inputValues,
+ inputShape,
+ multiplesValues,
+ multiplesShape,
+ expectedOutputValues,
+ expectedOutputShape);
+}
+
+#if defined(TILE_GPUACC)
+TEST_SUITE("TileTests_GpuAccTests")
+{
+
+ TEST_CASE ("Tile_Float32_GpuAcc_Test")
+ {
+ std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+ TileFloat32Test(backends);
+ }
+
+} // TEST_SUITE("Tile_Float32_GpuAcc_Test")
+#endif
+
+TEST_SUITE("TileTests_CpuAccTests")
+{
+
+ TEST_CASE ("Tile_Float32_CpuAcc_Test")
+ {
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+ TileFloat32Test(backends);
+ }
+
+} // TEST_SUITE("Tile_Float32_CpuAcc_Test")
+
+TEST_SUITE("TileTests_CpuRefTests")
+{
+
+ TEST_CASE ("Tile_Float32_CpuRef_Test")
+ {
+ std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+ TileFloat32Test(backends);
+ }
+
+} // TEST_SUITE("Tile_Float32_CpuRef_Test")
+
+} // namespace armnnDelegate
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diff --git a/delegate/test/TileTestHelper.hpp b/delegate/test/TileTestHelper.hpp
new file mode 100644
index 0000000..f376612
--- /dev/null
+++ b/delegate/test/TileTestHelper.hpp
@@ -0,0 +1,149 @@
+//
+// Copyright © 2023 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <DelegateTestInterpreter.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/kernels/register.h>
+#include <tensorflow/lite/version.h>
+
+#include <schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace
+{
+std::vector<char> CreateTileTfLiteModel(tflite::BuiltinOperator operatorCode,
+ tflite::TensorType inputTensorType,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& multiplesTensorData,
+ const std::vector<int32_t>& multiplesTensorShape,
+ const std::vector<int32_t>& outputTensorShape)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+ buffers.push_back(CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(
+ reinterpret_cast<const uint8_t*>(multiplesTensorData.data()),
+ sizeof(int32_t) * multiplesTensorData.size())));
+ buffers.push_back(CreateBuffer(flatBufferBuilder));
+
+ std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ inputTensorType,
+ 1,
+ flatBufferBuilder.CreateString("input_tensor"));
+
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(multiplesTensorShape.data(),
+ multiplesTensorShape.size()),
+ TensorType_INT32,
+ 2,
+ flatBufferBuilder.CreateString("axis_input_tensor"));
+
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ inputTensorType,
+ 3,
+ flatBufferBuilder.CreateString("output_tensor"));
+
+ // Create Operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+ flatbuffers::Offset<void> operatorBuiltinOption = 0;
+
+ const std::vector<int> operatorInputs {0, 1};
+ const std::vector<int> operatorOutputs {2};
+
+ flatbuffers::Offset<Operator> tileOperator =
+ CreateOperator(flatBufferBuilder,
+ 0,
+ flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
+ flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
+ operatorBuiltinOptionsType,
+ operatorBuiltinOption);
+
+ const std::vector<int> subgraphInputs{0, 1};
+ 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(&tileOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: Tile Operator Model");
+ flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode);
+
+ flatbuffers::Offset <Model> flatbufferModel =
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&opCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+
+ flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER);
+
+ return std::vector<char>(flatBufferBuilder.GetBufferPointer(),
+ flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize());
+}
+
+void TileFP32TestImpl(tflite::BuiltinOperator operatorCode,
+ std::vector<armnn::BackendId>& backends,
+ std::vector<float>& inputValues,
+ std::vector<int32_t> inputShape,
+ std::vector<int32_t> multiplesValues,
+ std::vector<int32_t> multiplesShapes,
+ std::vector<float>& expectedOutputValues,
+ std::vector<int32_t> expectedOutputShape)
+{
+ using namespace delegateTestInterpreter;
+
+ std::vector<char> modelBuffer = CreateTileTfLiteModel(operatorCode,
+ ::tflite::TensorType::TensorType_FLOAT32,
+ inputShape,
+ multiplesValues,
+ multiplesShapes,
+ expectedOutputShape);
+
+ // Setup interpreter with just TFLite Runtime.
+ auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer);
+ CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk);
+ CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+ CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk);
+ CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk);
+ std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0);
+ std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0);
+
+ // Setup interpreter with Arm NN Delegate applied.
+ auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends);
+ CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk);
+ CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk);
+ CHECK(armnnInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk);
+ CHECK(armnnInterpreter.Invoke() == kTfLiteOk);
+ std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0);
+ std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0);
+
+ armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues);
+ armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape);
+
+ tfLiteInterpreter.Cleanup();
+ armnnInterpreter.Cleanup();
+}
+
+} // anonymous namespace
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