IVGCVSW-6980 Delegate support for slice operator
Signed-off-by: Cathal Corbett <cathal.corbett@arm.com>
Change-Id: I90d800160b070e25d999b5102a7ce6d3e0ed6a81
diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index 847a8a0..fe5c962 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -202,6 +202,8 @@
src/test/ShapeTestHelper.hpp
src/test/SliceTest.cpp
src/test/SliceTestHelper.hpp
+ src/test/StridedSliceTest.cpp
+ src/test/StridedSliceTestHelper.hpp
src/test/SplitTest.cpp
src/test/SplitTestHelper.hpp
src/test/TestUtils.hpp
diff --git a/delegate/src/Slice.hpp b/delegate/src/Slice.hpp
index 6e355ae..cbcb45e 100644
--- a/delegate/src/Slice.hpp
+++ b/delegate/src/Slice.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -21,10 +21,10 @@
int nodeIndex,
int32_t sliceOperatorCode)
{
- TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 4, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 3, nodeIndex));
TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
- // Read inputs [input, begin, end, strides]
+ // Read inputs [input, begin, size]
int numInputs = tfLiteNode->inputs->size;
std::vector<const TfLiteTensor*> tfLiteInputs;
tfLiteInputs.reserve(numInputs);
@@ -39,15 +39,15 @@
}
}
- // We save the begin, end and strides tensors in our descriptor. Therefore we have to read those values from inputs
+ // We save the begin and size 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
+ auto ReadInt32Input = [&](int inputIndex, std::vector<uint32_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 "
+ "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to "
"be of type int32. Operator: #%d node #%d: ",
sliceOperatorCode, nodeIndex);
return kTfLiteError;
@@ -57,7 +57,7 @@
{
TF_LITE_MAYBE_KERNEL_LOG(
tfLiteContext,
- "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to "
+ "TfLiteArmnnDelegate: The Begin- and Size-Tensors of the Slice operation need to "
"be a 1D-Tensor. Operator: #%d node #%d: ",
sliceOperatorCode, nodeIndex);
return kTfLiteError;
@@ -67,41 +67,26 @@
{
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: ",
+ "TfLiteArmnnDelegate: The number of values in the Begin- and Size-Tensors of the "
+ "Slice 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]);
+ auto* tensorDataPtr = tflite::GetTensorData<uint32_t>(tfLiteInputs[inputIndex]);
outputData.assign(tensorDataPtr, tensorDataPtr+numValues);
return kTfLiteOk;
};
- std::vector<int32_t> beginData;
- if (ReadInt32Input(1, beginData) != kTfLiteOk)
+ std::vector<uint32_t> begin;
+ if (ReadInt32Input(1, begin) != kTfLiteOk)
return kTfLiteError;
- std::vector<int32_t> endData;
- if (ReadInt32Input(2, endData) != kTfLiteOk)
+ std::vector<uint32_t> size;
+ if (ReadInt32Input(2, size) != 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;
+ armnn::SliceDescriptor descriptor(begin, size);
// Validate output
const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
@@ -118,7 +103,7 @@
{
FORWARD_LAYER_SUPPORT_FUNC("SLICE",
tfLiteContext,
- IsStridedSliceSupported,
+ IsSliceSupported,
delegateData.m_Backends,
isSupported,
inputTensorInfo,
@@ -133,7 +118,7 @@
}
// Add a StridedSlice layer
- armnn::IConnectableLayer* layer = delegateData.m_Network->AddStridedSliceLayer(descriptor);
+ armnn::IConnectableLayer* layer = delegateData.m_Network->AddSliceLayer(descriptor);
ARMNN_ASSERT(layer != nullptr);
armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
diff --git a/delegate/src/StridedSlice.hpp b/delegate/src/StridedSlice.hpp
new file mode 100644
index 0000000..515c819
--- /dev/null
+++ b/delegate/src/StridedSlice.hpp
@@ -0,0 +1,146 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#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>
+#include <tensorflow/lite/minimal_logging.h>
+
+namespace armnnDelegate
+{
+
+TfLiteStatus VisitStridedSliceOperator(DelegateData& delegateData,
+ TfLiteContext* tfLiteContext,
+ TfLiteNode* tfLiteNode,
+ int nodeIndex,
+ int32_t sliceOperatorCode)
+{
+ TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 4, nodeIndex));
+ TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+ // 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("STRIDED_SLICE",
+ 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
diff --git a/delegate/src/armnn_delegate.cpp b/delegate/src/armnn_delegate.cpp
index b2ad160..4d95522 100644
--- a/delegate/src/armnn_delegate.cpp
+++ b/delegate/src/armnn_delegate.cpp
@@ -34,6 +34,7 @@
#include "Round.hpp"
#include "Shape.hpp"
#include "Slice.hpp"
+#include "StridedSlice.hpp"
#include "Softmax.hpp"
#include "SpaceDepth.hpp"
#include "Split.hpp"
@@ -963,12 +964,18 @@
tfLiteNode,
nodeIndex,
kTfLiteBuiltinSqueeze);
- case kTfLiteBuiltinStridedSlice:
+ case kTfLiteBuiltinSlice:
return VisitSliceOperator(delegateData,
tfLiteContext,
tfLiteNode,
nodeIndex,
- kTfLiteBuiltinStridedSlice);
+ kTfLiteBuiltinSlice);
+ case kTfLiteBuiltinStridedSlice:
+ return VisitStridedSliceOperator(delegateData,
+ tfLiteContext,
+ tfLiteNode,
+ nodeIndex,
+ kTfLiteBuiltinStridedSlice);
case kTfLiteBuiltinSum:
return VisitReduceOperator(delegateData,
tfLiteContext,
diff --git a/delegate/src/test/SliceTest.cpp b/delegate/src/test/SliceTest.cpp
index bd05849..1d7133f 100644
--- a/delegate/src/test/SliceTest.cpp
+++ b/delegate/src/test/SliceTest.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -8,236 +8,74 @@
#include <armnn_delegate.hpp>
#include <flatbuffers/flatbuffers.h>
-#include <tensorflow/lite/schema/schema_generated.h>
#include <doctest/doctest.h>
namespace armnnDelegate
{
-void StridedSlice4DTest(std::vector<armnn::BackendId>& backends)
+void SliceFixtureSimpleTest(std::vector<armnn::BackendId>& backends)
{
- std::vector<int32_t> inputShape { 3, 2, 3, 1 };
- std::vector<int32_t> outputShape { 1, 2, 3, 1 };
- std::vector<int32_t> beginShape { 4 };
- std::vector<int32_t> endShape { 4 };
- std::vector<int32_t> strideShape { 4 };
+ std::vector<int32_t> inputShape { 3, 2, 3 };
+ std::vector<int32_t> outputShape { 2, 1, 3 };
+ std::vector<int32_t> beginShape { 3 };
+ std::vector<int32_t> sizeShape { 3 };
- std::vector<int32_t> beginData { 1, 0, 0, 0 };
- std::vector<int32_t> endData { 2, 2, 3, 1 };
- std::vector<int32_t> strideData { 1, 1, 1, 1 };
+ std::vector<int32_t> beginData { 1, 0, 0 };
+ std::vector<int32_t> sizeData { 2, 1, 3 };
std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
- std::vector<float> outputData { 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f };
+ std::vector<float> outputData { 3.0f, 3.0f, 3.0f,
+ 5.0f, 5.0f, 5.0f };
- StridedSliceTestImpl<float>(
- backends,
- inputData,
- outputData,
- beginData,
- endData,
- strideData,
- inputShape,
- beginShape,
- endShape,
- strideShape,
- outputShape
- );
+ SliceTestImpl<float>(
+ backends,
+ inputData,
+ outputData,
+ beginData,
+ sizeData,
+ inputShape,
+ beginShape,
+ sizeShape,
+ outputShape);
}
-void StridedSlice4DReverseTest(std::vector<armnn::BackendId>& backends)
-{
- std::vector<int32_t> inputShape { 3, 2, 3, 1 };
- std::vector<int32_t> outputShape { 1, 2, 3, 1 };
- std::vector<int32_t> beginShape { 4 };
- std::vector<int32_t> endShape { 4 };
- std::vector<int32_t> strideShape { 4 };
-
- std::vector<int32_t> beginData { 1, -1, 0, 0 };
- std::vector<int32_t> endData { 2, -3, 3, 1 };
- std::vector<int32_t> strideData { 1, -1, 1, 1 };
- std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
- 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
- 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
- std::vector<float> outputData { 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f };
-
- StridedSliceTestImpl<float>(
- backends,
- inputData,
- outputData,
- beginData,
- endData,
- strideData,
- inputShape,
- beginShape,
- endShape,
- strideShape,
- outputShape
- );
-}
-
-void StridedSliceSimpleStrideTest(std::vector<armnn::BackendId>& backends)
-{
- std::vector<int32_t> inputShape { 3, 2, 3, 1 };
- std::vector<int32_t> outputShape { 2, 1, 2, 1 };
- std::vector<int32_t> beginShape { 4 };
- std::vector<int32_t> endShape { 4 };
- std::vector<int32_t> strideShape { 4 };
-
- std::vector<int32_t> beginData { 0, 0, 0, 0 };
- std::vector<int32_t> endData { 3, 2, 3, 1 };
- std::vector<int32_t> strideData { 2, 2, 2, 1 };
- std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
- 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
- 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
- std::vector<float> outputData { 1.0f, 1.0f,
- 5.0f, 5.0f };
-
- StridedSliceTestImpl<float>(
- backends,
- inputData,
- outputData,
- beginData,
- endData,
- strideData,
- inputShape,
- beginShape,
- endShape,
- strideShape,
- outputShape
- );
-}
-
-void StridedSliceSimpleRangeMaskTest(std::vector<armnn::BackendId>& backends)
-{
- std::vector<int32_t> inputShape { 3, 2, 3, 1 };
- std::vector<int32_t> outputShape { 3, 2, 3, 1 };
- std::vector<int32_t> beginShape { 4 };
- std::vector<int32_t> endShape { 4 };
- std::vector<int32_t> strideShape { 4 };
-
- std::vector<int32_t> beginData { 1, 1, 1, 1 };
- std::vector<int32_t> endData { 1, 1, 1, 1 };
- std::vector<int32_t> strideData { 1, 1, 1, 1 };
-
- int beginMask = -1;
- int endMask = -1;
-
- std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
- 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
- 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
- std::vector<float> outputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
- 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
- 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
-
- StridedSliceTestImpl<float>(
- backends,
- inputData,
- outputData,
- beginData,
- endData,
- strideData,
- inputShape,
- beginShape,
- endShape,
- strideShape,
- outputShape,
- beginMask,
- endMask
- );
-}
-
-
-TEST_SUITE("StridedSlice_CpuRefTests")
+TEST_SUITE("Slice_CpuRefTests")
{
-TEST_CASE ("StridedSlice_4D_CpuRef_Test")
+TEST_CASE ("Slice_Simple_CpuRef_Test")
{
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- StridedSlice4DTest(backends);
+ SliceFixtureSimpleTest(backends);
}
-TEST_CASE ("StridedSlice_4D_Reverse_CpuRef_Test")
+} // Slice_CpuRefTests TestSuite
+
+
+
+TEST_SUITE("Slice_CpuAccTests")
+{
+
+TEST_CASE ("Slice_Simple_CpuAcc_Test")
{
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- StridedSlice4DReverseTest(backends);
+ SliceFixtureSimpleTest(backends);
}
-TEST_CASE ("StridedSlice_SimpleStride_CpuRef_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- StridedSliceSimpleStrideTest(backends);
-}
-
-TEST_CASE ("StridedSlice_SimpleRange_CpuRef_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
- StridedSliceSimpleRangeMaskTest(backends);
-}
-
-} // StridedSlice_CpuRefTests TestSuite
-
-
-
-TEST_SUITE("StridedSlice_CpuAccTests")
-{
-
-TEST_CASE ("StridedSlice_4D_CpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- StridedSlice4DTest(backends);
-}
-
-TEST_CASE ("StridedSlice_4D_Reverse_CpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- StridedSlice4DReverseTest(backends);
-}
-
-TEST_CASE ("StridedSlice_SimpleStride_CpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- StridedSliceSimpleStrideTest(backends);
-}
-
-TEST_CASE ("StridedSlice_SimpleRange_CpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
- StridedSliceSimpleRangeMaskTest(backends);
-}
-
-} // StridedSlice_CpuAccTests TestSuite
+} // Slice_CpuAccTests TestSuite
TEST_SUITE("StridedSlice_GpuAccTests")
{
-TEST_CASE ("StridedSlice_4D_GpuAcc_Test")
+TEST_CASE ("Slice_Simple_GpuAcc_Test")
{
- std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- StridedSlice4DTest(backends);
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ SliceFixtureSimpleTest(backends);
}
-TEST_CASE ("StridedSlice_4D_Reverse_GpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- StridedSlice4DReverseTest(backends);
-}
-
-TEST_CASE ("StridedSlice_SimpleStride_GpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- StridedSliceSimpleStrideTest(backends);
-}
-
-TEST_CASE ("StridedSlice_SimpleRange_GpuAcc_Test")
-{
- std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
- StridedSliceSimpleRangeMaskTest(backends);
-}
-
-} // StridedSlice_GpuAccTests TestSuite
+} // Slice_GpuAccTests TestSuite
} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/SliceTestHelper.hpp b/delegate/src/test/SliceTestHelper.hpp
index abaa807..4a2537f 100644
--- a/delegate/src/test/SliceTestHelper.hpp
+++ b/delegate/src/test/SliceTestHelper.hpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -24,61 +24,27 @@
namespace
{
-struct StridedSliceParams
-{
- StridedSliceParams(std::vector<int32_t>& inputTensorShape,
- std::vector<int32_t>& beginTensorData,
- std::vector<int32_t>& endTensorData,
- std::vector<int32_t>& strideTensorData,
- std::vector<int32_t>& outputTensorShape,
- armnn::StridedSliceDescriptor& descriptor)
- : m_InputTensorShape(inputTensorShape),
- m_BeginTensorData(beginTensorData),
- m_EndTensorData(endTensorData),
- m_StrideTensorData(strideTensorData),
- m_OutputTensorShape(outputTensorShape),
- m_Descriptor (descriptor) {}
-
- std::vector<int32_t> m_InputTensorShape;
- std::vector<int32_t> m_BeginTensorData;
- std::vector<int32_t> m_EndTensorData;
- std::vector<int32_t> m_StrideTensorData;
- std::vector<int32_t> m_OutputTensorShape;
- armnn::StridedSliceDescriptor m_Descriptor;
-};
-
std::vector<char> CreateSliceTfLiteModel(tflite::TensorType tensorType,
const std::vector<int32_t>& inputTensorShape,
const std::vector<int32_t>& beginTensorData,
- const std::vector<int32_t>& endTensorData,
- const std::vector<int32_t>& strideTensorData,
+ const std::vector<int32_t>& sizeTensorData,
const std::vector<int32_t>& beginTensorShape,
- const std::vector<int32_t>& endTensorShape,
- const std::vector<int32_t>& strideTensorShape,
- const std::vector<int32_t>& outputTensorShape,
- const int32_t beginMask,
- const int32_t endMask,
- const int32_t ellipsisMask,
- const int32_t newAxisMask,
- const int32_t ShrinkAxisMask,
- const armnn::DataLayout& dataLayout)
+ const std::vector<int32_t>& sizeTensorShape,
+ const std::vector<int32_t>& outputTensorShape)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
- std::array<flatbuffers::Offset<tflite::Buffer>, 4> buffers;
+ std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers;
buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
buffers[1] = CreateBuffer(flatBufferBuilder,
flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()),
sizeof(int32_t) * beginTensorData.size()));
buffers[2] = CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()),
- sizeof(int32_t) * endTensorData.size()));
- buffers[3] = CreateBuffer(flatBufferBuilder,
- flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()),
- sizeof(int32_t) * strideTensorData.size()));
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(sizeTensorData.data()),
+ sizeof(int32_t) * sizeTensorData.size()));
- std::array<flatbuffers::Offset<Tensor>, 5> tensors;
+ std::array<flatbuffers::Offset<Tensor>, 4> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
inputTensorShape.size()),
@@ -92,18 +58,12 @@
1,
flatBufferBuilder.CreateString("begin_tensor"));
tensors[2] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(),
- endTensorShape.size()),
+ flatBufferBuilder.CreateVector<int32_t>(sizeTensorShape.data(),
+ sizeTensorShape.size()),
::tflite::TensorType_INT32,
2,
- flatBufferBuilder.CreateString("end_tensor"));
+ flatBufferBuilder.CreateString("size_tensor"));
tensors[3] = CreateTensor(flatBufferBuilder,
- flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(),
- strideTensorShape.size()),
- ::tflite::TensorType_INT32,
- 3,
- flatBufferBuilder.CreateString("stride_tensor"));
- tensors[4] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
tensorType,
@@ -112,45 +72,40 @@
// create operator
- tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions;
- flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder,
- beginMask,
- endMask,
- ellipsisMask,
- newAxisMask,
- ShrinkAxisMask).Union();
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SliceOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateSliceOptions(flatBufferBuilder).Union();
- const std::vector<int> operatorInputs{ 0, 1, 2, 3 };
- const std::vector<int> operatorOutputs{ 4 };
+ const std::vector<int> operatorInputs{ 0, 1, 2 };
+ const std::vector<int> operatorOutputs{ 3 };
flatbuffers::Offset <Operator> sliceOperator =
- CreateOperator(flatBufferBuilder,
- 0,
- flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
- flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
- operatorBuiltinOptionsType,
- operatorBuiltinOptions);
+ 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> subgraphInputs{ 0, 1, 2, 3 };
- const std::vector<int> subgraphOutputs{ 4 };
+ const std::vector<int> subgraphInputs{ 0, 1, 2 };
+ const std::vector<int> subgraphOutputs{ 3 };
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(&sliceOperator, 1));
+ 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(&sliceOperator, 1));
flatbuffers::Offset <flatbuffers::String> modelDescription =
- flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model");
+ flatBufferBuilder.CreateString("ArmnnDelegate: Slice Operator Model");
flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
- BuiltinOperator_STRIDED_SLICE);
+ BuiltinOperator_SLICE);
flatbuffers::Offset <Model> flatbufferModel =
- CreateModel(flatBufferBuilder,
- TFLITE_SCHEMA_VERSION,
- flatBufferBuilder.CreateVector(&operatorCode, 1),
- flatBufferBuilder.CreateVector(&subgraph, 1),
- modelDescription,
- flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
+ CreateModel(flatBufferBuilder,
+ TFLITE_SCHEMA_VERSION,
+ flatBufferBuilder.CreateVector(&operatorCode, 1),
+ flatBufferBuilder.CreateVector(&subgraph, 1),
+ modelDescription,
+ flatBufferBuilder.CreateVector(buffers.data(), buffers.size()));
flatBufferBuilder.Finish(flatbufferModel);
@@ -159,62 +114,46 @@
}
template <typename T>
-void StridedSliceTestImpl(std::vector<armnn::BackendId>& backends,
- std::vector<T>& inputValues,
- std::vector<T>& expectedOutputValues,
- std::vector<int32_t>& beginTensorData,
- std::vector<int32_t>& endTensorData,
- std::vector<int32_t>& strideTensorData,
- std::vector<int32_t>& inputTensorShape,
- std::vector<int32_t>& beginTensorShape,
- std::vector<int32_t>& endTensorShape,
- std::vector<int32_t>& strideTensorShape,
- std::vector<int32_t>& outputTensorShape,
- const int32_t beginMask = 0,
- const int32_t endMask = 0,
- const int32_t ellipsisMask = 0,
- const int32_t newAxisMask = 0,
- const int32_t ShrinkAxisMask = 0,
- const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC)
+void SliceTestImpl(std::vector<armnn::BackendId>& backends,
+ std::vector<T>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ std::vector<int32_t>& beginTensorData,
+ std::vector<int32_t>& sizeTensorData,
+ std::vector<int32_t>& inputTensorShape,
+ std::vector<int32_t>& beginTensorShape,
+ std::vector<int32_t>& sizeTensorShape,
+ std::vector<int32_t>& outputTensorShape)
{
using namespace tflite;
std::vector<char> modelBuffer = CreateSliceTfLiteModel(
- ::tflite::TensorType_FLOAT32,
- inputTensorShape,
- beginTensorData,
- endTensorData,
- strideTensorData,
- beginTensorShape,
- endTensorShape,
- strideTensorShape,
- outputTensorShape,
- beginMask,
- endMask,
- ellipsisMask,
- newAxisMask,
- ShrinkAxisMask,
- dataLayout);
+ ::tflite::TensorType_FLOAT32,
+ inputTensorShape,
+ beginTensorData,
+ sizeTensorData,
+ beginTensorShape,
+ sizeTensorShape,
+ outputTensorShape);
auto tfLiteModel = GetModel(modelBuffer.data());
// Create TfLite Interpreters
std::unique_ptr<Interpreter> armnnDelegate;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
- (&armnnDelegate) == kTfLiteOk);
+ (&armnnDelegate) == kTfLiteOk);
CHECK(armnnDelegate != nullptr);
CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk);
std::unique_ptr<Interpreter> tfLiteDelegate;
CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
- (&tfLiteDelegate) == kTfLiteOk);
+ (&tfLiteDelegate) == kTfLiteOk);
CHECK(tfLiteDelegate != nullptr);
CHECK(tfLiteDelegate->AllocateTensors() == kTfLiteOk);
// Create the ArmNN Delegate
armnnDelegate::DelegateOptions delegateOptions(backends);
std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)>
- theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
- armnnDelegate::TfLiteArmnnDelegateDelete);
+ theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions),
+ armnnDelegate::TfLiteArmnnDelegateDelete);
CHECK(theArmnnDelegate != nullptr);
// Modify armnnDelegateInterpreter to use armnnDelegate
@@ -236,6 +175,6 @@
tfLiteDelegate.reset(nullptr);
armnnDelegate.reset(nullptr);
-} // End of StridedSlice Test
+} // End of Slice Test
} // anonymous namespace
\ No newline at end of file
diff --git a/delegate/src/test/StridedSliceTest.cpp b/delegate/src/test/StridedSliceTest.cpp
new file mode 100644
index 0000000..43aea8a
--- /dev/null
+++ b/delegate/src/test/StridedSliceTest.cpp
@@ -0,0 +1,241 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "StridedSliceTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void StridedSlice4DTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 3, 2, 3, 1 };
+ std::vector<int32_t> outputShape { 1, 2, 3, 1 };
+ std::vector<int32_t> beginShape { 4 };
+ std::vector<int32_t> endShape { 4 };
+ std::vector<int32_t> strideShape { 4 };
+
+ std::vector<int32_t> beginData { 1, 0, 0, 0 };
+ std::vector<int32_t> endData { 2, 2, 3, 1 };
+ std::vector<int32_t> strideData { 1, 1, 1, 1 };
+ std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+ 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+ std::vector<float> outputData { 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f };
+
+ StridedSliceTestImpl<float>(
+ backends,
+ inputData,
+ outputData,
+ beginData,
+ endData,
+ strideData,
+ inputShape,
+ beginShape,
+ endShape,
+ strideShape,
+ outputShape
+ );
+}
+
+void StridedSlice4DReverseTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 3, 2, 3, 1 };
+ std::vector<int32_t> outputShape { 1, 2, 3, 1 };
+ std::vector<int32_t> beginShape { 4 };
+ std::vector<int32_t> endShape { 4 };
+ std::vector<int32_t> strideShape { 4 };
+
+ std::vector<int32_t> beginData { 1, -1, 0, 0 };
+ std::vector<int32_t> endData { 2, -3, 3, 1 };
+ std::vector<int32_t> strideData { 1, -1, 1, 1 };
+ std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+ 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+ std::vector<float> outputData { 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f };
+
+ StridedSliceTestImpl<float>(
+ backends,
+ inputData,
+ outputData,
+ beginData,
+ endData,
+ strideData,
+ inputShape,
+ beginShape,
+ endShape,
+ strideShape,
+ outputShape
+ );
+}
+
+void StridedSliceSimpleStrideTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 3, 2, 3, 1 };
+ std::vector<int32_t> outputShape { 2, 1, 2, 1 };
+ std::vector<int32_t> beginShape { 4 };
+ std::vector<int32_t> endShape { 4 };
+ std::vector<int32_t> strideShape { 4 };
+
+ std::vector<int32_t> beginData { 0, 0, 0, 0 };
+ std::vector<int32_t> endData { 3, 2, 3, 1 };
+ std::vector<int32_t> strideData { 2, 2, 2, 1 };
+ std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+ 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+ std::vector<float> outputData { 1.0f, 1.0f,
+ 5.0f, 5.0f };
+
+ StridedSliceTestImpl<float>(
+ backends,
+ inputData,
+ outputData,
+ beginData,
+ endData,
+ strideData,
+ inputShape,
+ beginShape,
+ endShape,
+ strideShape,
+ outputShape
+ );
+}
+
+void StridedSliceSimpleRangeMaskTest(std::vector<armnn::BackendId>& backends)
+{
+ std::vector<int32_t> inputShape { 3, 2, 3, 1 };
+ std::vector<int32_t> outputShape { 3, 2, 3, 1 };
+ std::vector<int32_t> beginShape { 4 };
+ std::vector<int32_t> endShape { 4 };
+ std::vector<int32_t> strideShape { 4 };
+
+ std::vector<int32_t> beginData { 1, 1, 1, 1 };
+ std::vector<int32_t> endData { 1, 1, 1, 1 };
+ std::vector<int32_t> strideData { 1, 1, 1, 1 };
+
+ int beginMask = -1;
+ int endMask = -1;
+
+ std::vector<float> inputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+ 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+ std::vector<float> outputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+ 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+ 5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+
+ StridedSliceTestImpl<float>(
+ backends,
+ inputData,
+ outputData,
+ beginData,
+ endData,
+ strideData,
+ inputShape,
+ beginShape,
+ endShape,
+ strideShape,
+ outputShape,
+ beginMask,
+ endMask
+ );
+}
+
+TEST_SUITE("StridedSlice_CpuRefTests")
+{
+
+TEST_CASE ("StridedSlice_4D_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_CpuRefTests TestSuite
+
+
+
+TEST_SUITE("StridedSlice_CpuAccTests")
+{
+
+TEST_CASE ("StridedSlice_4D_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_CpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+ StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_CpuAccTests TestSuite
+
+
+
+TEST_SUITE("StridedSlice_GpuAccTests")
+{
+
+TEST_CASE ("StridedSlice_4D_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_GpuAcc_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+ StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_GpuAccTests TestSuite
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/StridedSliceTestHelper.hpp b/delegate/src/test/StridedSliceTestHelper.hpp
new file mode 100644
index 0000000..2bca4fd
--- /dev/null
+++ b/delegate/src/test/StridedSliceTestHelper.hpp
@@ -0,0 +1,218 @@
+//
+// Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <armnn/DescriptorsFwd.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>
+
+#include <string>
+
+namespace
+{
+
+std::vector<char> CreateStridedSliceTfLiteModel(tflite::TensorType tensorType,
+ const std::vector<int32_t>& inputTensorShape,
+ const std::vector<int32_t>& beginTensorData,
+ const std::vector<int32_t>& endTensorData,
+ const std::vector<int32_t>& strideTensorData,
+ const std::vector<int32_t>& beginTensorShape,
+ const std::vector<int32_t>& endTensorShape,
+ const std::vector<int32_t>& strideTensorShape,
+ const std::vector<int32_t>& outputTensorShape,
+ const int32_t beginMask,
+ const int32_t endMask,
+ const int32_t ellipsisMask,
+ const int32_t newAxisMask,
+ const int32_t ShrinkAxisMask,
+ const armnn::DataLayout& dataLayout)
+{
+ using namespace tflite;
+ flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+ std::array<flatbuffers::Offset<tflite::Buffer>, 4> buffers;
+ buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+ buffers[1] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()),
+ sizeof(int32_t) * beginTensorData.size()));
+ buffers[2] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()),
+ sizeof(int32_t) * endTensorData.size()));
+ buffers[3] = CreateBuffer(flatBufferBuilder,
+ flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()),
+ sizeof(int32_t) * strideTensorData.size()));
+
+ std::array<flatbuffers::Offset<Tensor>, 5> tensors;
+ tensors[0] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+ inputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("input"));
+ tensors[1] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(),
+ beginTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 1,
+ flatBufferBuilder.CreateString("begin_tensor"));
+ tensors[2] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(),
+ endTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 2,
+ flatBufferBuilder.CreateString("end_tensor"));
+ tensors[3] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(),
+ strideTensorShape.size()),
+ ::tflite::TensorType_INT32,
+ 3,
+ flatBufferBuilder.CreateString("stride_tensor"));
+ tensors[4] = CreateTensor(flatBufferBuilder,
+ flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+ outputTensorShape.size()),
+ tensorType,
+ 0,
+ flatBufferBuilder.CreateString("output"));
+
+
+ // create operator
+ tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions;
+ flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder,
+ beginMask,
+ endMask,
+ ellipsisMask,
+ newAxisMask,
+ ShrinkAxisMask).Union();
+
+ const std::vector<int> operatorInputs{ 0, 1, 2, 3 };
+ const std::vector<int> operatorOutputs{ 4 };
+ flatbuffers::Offset <Operator> sliceOperator =
+ 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> subgraphInputs{ 0, 1, 2, 3 };
+ const std::vector<int> subgraphOutputs{ 4 };
+ 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(&sliceOperator, 1));
+
+ flatbuffers::Offset <flatbuffers::String> modelDescription =
+ flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model");
+ flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+ BuiltinOperator_STRIDED_SLICE);
+
+ 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 StridedSliceTestImpl(std::vector<armnn::BackendId>& backends,
+ std::vector<T>& inputValues,
+ std::vector<T>& expectedOutputValues,
+ std::vector<int32_t>& beginTensorData,
+ std::vector<int32_t>& endTensorData,
+ std::vector<int32_t>& strideTensorData,
+ std::vector<int32_t>& inputTensorShape,
+ std::vector<int32_t>& beginTensorShape,
+ std::vector<int32_t>& endTensorShape,
+ std::vector<int32_t>& strideTensorShape,
+ std::vector<int32_t>& outputTensorShape,
+ const int32_t beginMask = 0,
+ const int32_t endMask = 0,
+ const int32_t ellipsisMask = 0,
+ const int32_t newAxisMask = 0,
+ const int32_t ShrinkAxisMask = 0,
+ const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC)
+{
+ using namespace tflite;
+ std::vector<char> modelBuffer = CreateStridedSliceTfLiteModel(
+ ::tflite::TensorType_FLOAT32,
+ inputTensorShape,
+ beginTensorData,
+ endTensorData,
+ strideTensorData,
+ beginTensorShape,
+ endTensorShape,
+ strideTensorShape,
+ outputTensorShape,
+ beginMask,
+ endMask,
+ ellipsisMask,
+ newAxisMask,
+ ShrinkAxisMask,
+ dataLayout);
+
+ auto tfLiteModel = GetModel(modelBuffer.data());
+
+ // Create TfLite Interpreters
+ std::unique_ptr<Interpreter> armnnDelegate;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&armnnDelegate) == kTfLiteOk);
+ CHECK(armnnDelegate != nullptr);
+ CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk);
+
+ std::unique_ptr<Interpreter> tfLiteDelegate;
+ CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+ (&tfLiteDelegate) == kTfLiteOk);
+ CHECK(tfLiteDelegate != nullptr);
+ CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+ // Set input data
+ armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues);
+ armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues);
+
+ // Run EnqueWorkload
+ CHECK(tfLiteDelegate->Invoke() == kTfLiteOk);
+ CHECK(armnnDelegate->Invoke() == kTfLiteOk);
+
+ // Compare output data
+ armnnDelegate::CompareOutputData<T>(tfLiteDelegate,
+ armnnDelegate,
+ outputTensorShape,
+ expectedOutputValues);
+
+ tfLiteDelegate.reset(nullptr);
+ armnnDelegate.reset(nullptr);
+} // End of StridedSlice Test
+
+} // anonymous namespace
\ No newline at end of file
diff --git a/src/armnnTfLiteParser/test/Slice.cpp b/src/armnnTfLiteParser/test/Slice.cpp
index a2a791f..83c0b73 100644
--- a/src/armnnTfLiteParser/test/Slice.cpp
+++ b/src/armnnTfLiteParser/test/Slice.cpp
@@ -174,9 +174,9 @@
struct DynamicSliceFixtureD213 : SliceFixture
{
DynamicSliceFixtureD213() : SliceFixture("[ 3, 2, 3 ]",
- "[ ]",
- "[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]",
- "[ 255, 255, 255, 255, 1, 0, 0, 0, 255, 255, 255, 255 ]") {}
+ "[ ]",
+ "[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]",
+ "[ 255, 255, 255, 255, 1, 0, 0, 0, 255, 255, 255, 255 ]") {}
};
TEST_CASE_FIXTURE(DynamicSliceFixtureD213, "DynamicSliceD213")