Revert "Revert "MLCE-1093 Reshape and concat invalid results""
This reverts commit 008270f8c1359a7d62c2f881326b4d3f0d8b7b56.
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: If8f5151aa349ff3834f03391e813669e5c51ed66
diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp
index f466ab1..5d424af 100644
--- a/src/backends/aclCommon/ArmComputeUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeUtils.hpp
@@ -9,6 +9,7 @@
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <armnn/backends/WorkloadData.hpp>
+#include <armnnUtils/TensorUtils.hpp>
#include <arm_compute/runtime/FunctionDescriptors.h>
#include <arm_compute/function_info/FullyConnectedLayerInfo.h>
@@ -248,13 +249,20 @@
unsigned int numDimensions = desc.GetNumDimensions();
std::set<unsigned int> splitAxis;
- for (unsigned int i = 0; i < numSplit; ++i)
+ if (desc.HasAxis())
{
- for (unsigned int dimIdx = 0; dimIdx < numDimensions; ++dimIdx)
+ splitAxis.insert(armnnUtils::GetUnsignedAxis(desc.GetNumDimensions(), desc.GetAxis()));
+ }
+ else
+ {
+ for (unsigned int i = 0; i < numSplit; ++i)
{
- if (desc.GetViewSizes(i)[dimIdx] != input[dimIdx])
+ for (unsigned int dimIdx = 0; dimIdx < numDimensions; ++dimIdx)
{
- splitAxis.insert(dimIdx);
+ if (desc.GetViewSizes(i)[dimIdx] != input[dimIdx])
+ {
+ splitAxis.insert(dimIdx);
+ }
}
}
}
diff --git a/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp
index 9e3d83c..13483e5 100644
--- a/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp
+++ b/src/backends/backendsCommon/test/layerTests/SplitterTestImpl.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017, 2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2019-2020,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -199,11 +199,29 @@
// Do the first split
armnn::SplitterQueueDescriptor data;
+ data.m_Parameters = armnn::SplitterDescriptor(2, 3);
+
armnn::WorkloadInfo info;
AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get());
AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get());
+ data.m_Parameters.SetViewSize(0, 0, outputChannels1);
+ data.m_Parameters.SetViewSize(0, 1, outputHeight1);
+ data.m_Parameters.SetViewSize(0, 2, outputWidth1);
+
+ data.m_Parameters.SetViewSize(1, 0, outputChannels2);
+ data.m_Parameters.SetViewSize(1, 1, outputHeight2);
+ data.m_Parameters.SetViewSize(1, 2, outputWidth2);
+
+ data.m_Parameters.SetViewOriginCoord(0, 0, 0);
+ data.m_Parameters.SetViewOriginCoord(0, 1, 0);
+ data.m_Parameters.SetViewOriginCoord(0, 2, 0);
+
+ data.m_Parameters.SetViewOriginCoord(1, 0, 1);
+ data.m_Parameters.SetViewOriginCoord(1, 1, 0);
+ data.m_Parameters.SetViewOriginCoord(1, 2, 0);
+
data.m_ViewOrigins.push_back(window1);
data.m_ViewOrigins.push_back(window2);
@@ -224,11 +242,29 @@
// Do the second split.
armnn::SplitterQueueDescriptor data2;
+ data2.m_Parameters = armnn::SplitterDescriptor(2, 3);
+
armnn::WorkloadInfo info2;
AddInputToWorkload(data2, info2, outputTensorInfo2, outputHandle2.get());
AddOutputToWorkload(data2, info2, outputTensorInfo3, outputHandle3.get());
AddOutputToWorkload(data2, info2, outputTensorInfo4, outputHandle4.get());
+ data2.m_Parameters.SetViewSize(0, 0, outputChannels1);
+ data2.m_Parameters.SetViewSize(0, 1, outputHeight1);
+ data2.m_Parameters.SetViewSize(0, 2, outputWidth1);
+
+ data2.m_Parameters.SetViewSize(1, 0, outputChannels2);
+ data2.m_Parameters.SetViewSize(1, 1, outputHeight2);
+ data2.m_Parameters.SetViewSize(1, 2, outputWidth1);
+
+ data2.m_Parameters.SetViewOriginCoord(0, 0, 0);
+ data2.m_Parameters.SetViewOriginCoord(0, 1, 0);
+ data2.m_Parameters.SetViewOriginCoord(0, 2, 0);
+
+ data2.m_Parameters.SetViewOriginCoord(1, 0, 1);
+ data2.m_Parameters.SetViewOriginCoord(1, 1, 0);
+ data2.m_Parameters.SetViewOriginCoord(1, 2, 0);
+
data2.m_ViewOrigins.push_back(window3);
data2.m_ViewOrigins.push_back(window4);
@@ -307,6 +343,17 @@
AddInputToWorkload(data, info, tensorInfo, inputHandle.get());
AddOutputToWorkload(data, info, tensorInfo, outputHandle.get());
+ data.m_Parameters = armnn::SplitterDescriptor(1, 3);
+ data.m_Parameters.SetAxis(0);
+
+ data.m_Parameters.SetViewSize(0, 0, 3);
+ data.m_Parameters.SetViewSize(0, 1, 6);
+ data.m_Parameters.SetViewSize(0, 2, 5);
+
+ data.m_Parameters.SetViewOriginCoord(0, 0, 0);
+ data.m_Parameters.SetViewOriginCoord(0, 1, 0);
+ data.m_Parameters.SetViewOriginCoord(0, 2, 0);
+
data.m_ViewOrigins.push_back(window);
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::Splitter,
diff --git a/src/backends/cl/ClTensorHandleFactory.cpp b/src/backends/cl/ClTensorHandleFactory.cpp
index 82e41d3..be3ca5e 100644
--- a/src/backends/cl/ClTensorHandleFactory.cpp
+++ b/src/backends/cl/ClTensorHandleFactory.cpp
@@ -103,7 +103,7 @@
bool ClTensorHandleFactory::SupportsSubTensors() const
{
- return true;
+ return false;
}
MemorySourceFlags ClTensorHandleFactory::GetExportFlags() const
diff --git a/src/backends/neon/NeonTensorHandleFactory.cpp b/src/backends/neon/NeonTensorHandleFactory.cpp
index ce3ce5c..2597b5f 100644
--- a/src/backends/neon/NeonTensorHandleFactory.cpp
+++ b/src/backends/neon/NeonTensorHandleFactory.cpp
@@ -104,7 +104,7 @@
bool NeonTensorHandleFactory::SupportsSubTensors() const
{
- return true;
+ return false;
}
MemorySourceFlags NeonTensorHandleFactory::GetExportFlags() const
diff --git a/src/backends/neon/test/NeonTensorHandleTests.cpp b/src/backends/neon/test/NeonTensorHandleTests.cpp
index c8e781b..a94e4dd 100644
--- a/src/backends/neon/test/NeonTensorHandleTests.cpp
+++ b/src/backends/neon/test/NeonTensorHandleTests.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2021,2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <Graph.hpp>
@@ -89,81 +89,6 @@
CHECK(capabilities[0].m_Value);
}
-TEST_CASE("ConcatOnXorYSubTensorsNoPaddingRequiredTest")
-{
- armnn::INetworkPtr net(armnn::INetwork::Create());
-
- // Set up tensor infos
- const armnn::TensorInfo inputInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32);
- const armnn::TensorInfo intermediateInfo = armnn::TensorInfo({2, 3, 2, 2}, armnn::DataType::Float32);
- const armnn::TensorInfo outputInfo = armnn::TensorInfo({2, 3, 4, 2}, armnn::DataType::Float32);
-
- armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs);
-
- // Create the network
- armnn::IConnectableLayer* const input0Layer = net->AddInputLayer(0, "input_0");
- input0Layer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- armnn::IConnectableLayer* elementwiseUnaryLayer0 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_0");
- elementwiseUnaryLayer0->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
- input0Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer0->GetInputSlot(0));
-
- armnn::IConnectableLayer* const input1Layer = net->AddInputLayer(1, "input_1");
- input1Layer->GetOutputSlot(0).SetTensorInfo(inputInfo);
- armnn::IConnectableLayer* elementwiseUnaryLayer1 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseUnary_1");
- elementwiseUnaryLayer1->GetOutputSlot(0).SetTensorInfo(intermediateInfo);
- input1Layer->GetOutputSlot(0).Connect(elementwiseUnaryLayer1->GetInputSlot(0));
-
- std::array<armnn::TensorShape, 2> concatInputShapes = { intermediateInfo.GetShape(), intermediateInfo.GetShape() };
- armnn::IConnectableLayer* const concatLayer = net->AddConcatLayer(armnn::CreateDescriptorForConcatenation(
- concatInputShapes.begin(), concatInputShapes.end(), 2), "concatenation");
- concatLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
- elementwiseUnaryLayer0->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(0));
- elementwiseUnaryLayer1->GetOutputSlot(0).Connect(concatLayer->GetInputSlot(1));
-
- armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output");
- concatLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
- armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-
- const armnn::Graph& theGraph = GetGraphForTesting(optimizedNet.get());
-
- // Load graph into runtime
- armnn::NetworkId networkIdentifier;
- runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));
-
- // now check the concat how many sub-tensors it is using..
- auto TraceSubTensorHandleAncestry = [](armnn::ITensorHandle* const subTensorHandle)
- {
- if (subTensorHandle && subTensorHandle->GetParent())
- {
- return true;
- }
- return false;
- };
-
- for (auto&& layer : theGraph)
- {
- if(layer->GetType() == armnn::LayerType::Concat)
- {
- unsigned int numberOfSubTensors = 0;
- for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
- {
- const armnn::OutputSlot* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();
- if (TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData()))
- {
- ++numberOfSubTensors;
- }
- }
- // sub-tensors should be supported in this configuration
- ARMNN_ASSERT(numberOfSubTensors > 0);
- }
- }
-}
-
TEST_CASE("ConcatonXorYPaddingRequiredTest")
{
armnn::INetworkPtr net(armnn::INetwork::Create());
@@ -247,212 +172,6 @@
ARMNN_ASSERT(numberOfSubTensors == 0);
}
-TEST_CASE("SplitteronXorYNoPaddingRequiredTest")
-{
- using namespace armnn;
-
- unsigned int splitAxis = 2;
- unsigned int numSplit = 2;
-
- const TensorShape& inputShape = { 2, 3, 4, 2 };
- const armnn::TensorInfo intermediateInfo = armnn::TensorInfo({ 2, 3, 2, 2 }, armnn::DataType::Float32);
- const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 },
- { 2, 3, 2, 2 }};
- const float qScale = 1.0f;
- const int32_t qOffset = 0;
-
- // Creates structures for input & output.
- std::vector<float> inputData{
- 1, 2,
- 3, 4,
- 5, 6,
- 7, 8,
- 9, 10,
- 11, 12,
- 13, 14,
- 15, 16,
- 17, 18,
- 19, 20,
- 21, 22,
- 23, 24,
- 25, 26,
- 27, 28,
- 29, 30,
- 31, 32,
- 33, 34,
- 35, 36,
- 37, 38,
- 39, 40,
- 41, 42,
- 43, 44,
- 45, 46,
- 47, 48
- };
-
- std::vector<float> expectedOutput0{
- 1, 2,
- 3, 4,
- 9, 10,
- 11, 12,
- 17, 18,
- 19, 20,
- 25, 26,
- 27, 28,
- 33, 34,
- 35, 36,
- 41, 42,
- 43, 44
- };
-
- std::vector<float> expectedOutput1{
- 5, 6,
- 7, 8,
- 13, 14,
- 15, 16,
- 21, 22,
- 23, 24,
- 29, 30,
- 31, 32,
- 37, 38,
- 39, 40,
- 45, 46,
- 47, 48
- };
-
- // Builds up the structure of the network.
- INetworkPtr net(INetwork::Create());
-
- TensorInfo inputTensorInfo(inputShape, armnn::DataType::Float32, qScale, qOffset);
-
- armnn::ElementwiseUnaryDescriptor descriptor(armnn::UnaryOperation::Abs);
-
- // Splitter
- std::vector<unsigned int> splitterDimSizes(inputShape.GetNumDimensions());
-
- // Add current input shape to splitterDimSizes
- for (unsigned int i = 0; i < inputShape.GetNumDimensions(); ++i)
- {
- splitterDimSizes[i] = inputTensorInfo.GetShape()[i];
- }
-
- if (splitterDimSizes[splitAxis] % numSplit != 0)
- {
- throw ParseException("Number of splits must evenly divide the dimension");
- }
-
- splitterDimSizes[splitAxis] /= numSplit;
-
- SplitterDescriptor splitDesc(numSplit, inputShape.GetNumDimensions());
-
- for (unsigned int g = 0; g < numSplit; ++g)
- {
- // Set the size of the views.
- for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx)
- {
- splitDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]);
- }
- splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g);
- }
- IConnectableLayer* input = net->AddInputLayer(0, "input");
- IConnectableLayer* elementWiseUnary0 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseunary_0");
- IConnectableLayer* elementWiseUnary1 = net->AddElementwiseUnaryLayer(descriptor, "elementwiseunary_0");
- IConnectableLayer* splitter = net->AddSplitterLayer(splitDesc, "splitter");
-
- // Connections
- Connect(input, splitter, inputTensorInfo, 0, 0);
- Connect(splitter, elementWiseUnary0, intermediateInfo, 0, 0);
- Connect(splitter, elementWiseUnary1, intermediateInfo, 1, 0);
-
- std::vector<IConnectableLayer*> pooling2dLayers{elementWiseUnary0, elementWiseUnary1};
-
- for (unsigned int i = 0; i < outputShapes.size(); ++i)
- {
- TensorInfo outputTensorInfo(outputShapes[i], armnn::DataType::Float32, qScale, qOffset);
- IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i));
- Connect(pooling2dLayers[i], output, outputTensorInfo, 0, 0);
- }
-
- std::map<int, std::vector<float>> inputTensorData = {{ 0,inputData }};
- std::map<int, std::vector<float>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }};
-
- armnn::IRuntime::CreationOptions options;
- armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
- std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
- armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec());
-
- const armnn::Graph& theGraph = GetGraphForTesting(optimizedNet.get());
-
- // Load graph into runtime
- armnn::NetworkId networkIdentifier;
- runtime->LoadNetwork(networkIdentifier, std::move(optimizedNet));
-
- // now check the concat how many sub-tensors it is using..
- auto TraceSubTensorHandleAncestry = [](armnn::ITensorHandle* const subTensorHandle)
- {
- if (subTensorHandle && subTensorHandle->GetParent())
- {
- return true;
- }
- return false;
- };
-
- for (auto&& layer : theGraph)
- {
- if(layer->GetType() == armnn::LayerType::ElementwiseUnary)
- {
- unsigned int numberOfSubTensors = 0;
- for (unsigned int i = 0; i < layer->GetNumInputSlots(); ++i)
- {
- const armnn::OutputSlot* slot = layer->GetInputSlot(i).GetConnectedOutputSlot();
- if (TraceSubTensorHandleAncestry(slot->GetOutputHandler().GetData()))
- {
- ++numberOfSubTensors;
- }
- }
- // sub-tensors should be supported in this configuration
- ARMNN_ASSERT(numberOfSubTensors > 0);
- }
- }
-
- InputTensors inputTensors;
- inputTensors.reserve(inputTensorData.size());
- for (auto&& it : inputTensorData)
- {
- TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, it.first);
- inputTensorInfo.SetConstant(true);
- inputTensors.push_back({it.first,
- ConstTensor(inputTensorInfo, it.second.data())});
- }
- OutputTensors outputTensors;
- outputTensors.reserve(expectedOutputData.size());
- std::map<int, std::vector<float>> outputStorage;
- for (auto&& it : expectedOutputData)
- {
- std::vector<float> out(it.second.size());
- outputStorage.emplace(it.first, out);
- outputTensors.push_back({it.first,
- Tensor(runtime->GetOutputTensorInfo(networkIdentifier, it.first),
- outputStorage.at(it.first).data())});
- }
-
- // Does the inference.
- runtime->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);
-
- // Checks the results.
- float tolerance = 0.000001f;
- for (auto&& it : expectedOutputData)
- {
- std::vector<float> out = outputStorage.at(it.first);
- for (unsigned int i = 0; i < out.size(); ++i)
- {
- CHECK_MESSAGE(Compare<armnn::DataType::Float32>(it.second[i], out[i], tolerance) == true,
- "Actual output: " << out[i] << ". Expected output:" << it.second[i]);
-
- }
- }
-}
-
TEST_CASE("SplitteronXorYPaddingRequiredTest")
{
using namespace armnn;