IVGCVSW-5818 Enable import on GPU
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I4e4eb107aa2bfa09625840d738001f33152e6792
diff --git a/src/backends/cl/test/ClFallbackTests.cpp b/src/backends/cl/test/ClFallbackTests.cpp
index eec3afe..183b8ca 100644
--- a/src/backends/cl/test/ClFallbackTests.cpp
+++ b/src/backends/cl/test/ClFallbackTests.cpp
@@ -11,7 +11,7 @@
BOOST_AUTO_TEST_SUITE(ClFallback)
-BOOST_AUTO_TEST_CASE(ClImportEnabledFallbackToNeon, * boost::unit_test::disabled())
+BOOST_AUTO_TEST_CASE(ClImportEnabledFallbackToNeon)
{
using namespace armnn;
@@ -34,7 +34,7 @@
add->GetOutputSlot(0).Connect(sub->GetInputSlot(1));
sub->GetOutputSlot(0).Connect(output->GetInputSlot(0));
- TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32);
+ TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32);
input0->GetOutputSlot(0).SetTensorInfo(info);
input1->GetOutputSlot(0).SetTensorInfo(info);
@@ -82,30 +82,49 @@
runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);
// Creates structures for input & output
- std::vector<float> inputData0
+ std::vector<float> inputValue0
{
- 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f
+ 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f
};
- std::vector<float> inputData1
+ std::vector<float> inputValue1
{
- 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f
+ 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f
};
std::vector<float> inputData2
{
- 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f
+ 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f
};
- std::vector<float> outputData(12);
+ std::vector<float> outputData(16);
std::vector<float> expectedOutput
{
- 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f
+ 11.0f, 9.0f, 7.0f, 5.0f, 3.0f, 1.0f, -1.0f, -3.0f, -5.0f, -7.0f, -9.0f, -11.0f, 11.0f, 9.0f, 7.0f, 5.0f
};
+ // Prepare aligned data
+ unsigned int numElements = info.GetNumElements();
+ size_t totalBytes = numElements * sizeof(float);
+ const size_t alignment = 64;
+ size_t space = totalBytes + alignment + alignment;
+ auto inputData0 = std::make_unique<uint8_t[]>(space);
+ void* alignedInputPtr0 = inputData0.get();
+ BOOST_CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space));
+
+ auto* intputPtr0 = reinterpret_cast<float*>(alignedInputPtr0);
+ std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0);
+
+ auto inputData1 = std::make_unique<uint8_t[]>(space);
+ void* alignedInputPtr1 = inputData1.get();
+ BOOST_CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space));
+
+ auto* intputPtr1 = reinterpret_cast<float*>(alignedInputPtr1);
+ std::copy(inputValue1.begin(), inputValue1.end(), intputPtr1);
+
InputTensors inputTensors
{
- { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },
- { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },
+ { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), alignedInputPtr0) },
+ { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), alignedInputPtr1) },
{ 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }
};
OutputTensors outputTensors
@@ -134,6 +153,8 @@
// Check output is as expected
BOOST_TEST(outputData == expectedOutput);
+
+ runtime->UnloadNetwork(netId);
}
BOOST_AUTO_TEST_CASE(ClImportDisabledFallbackToNeon)
@@ -258,7 +279,7 @@
BOOST_TEST(outputData == expectedOutput);
}
-BOOST_AUTO_TEST_CASE(ClImportEnabledFallbackSubgraphToNeon, * boost::unit_test::disabled())
+BOOST_AUTO_TEST_CASE(ClImportEnabledFallbackSubgraphToNeon)
{
using namespace armnn;
@@ -269,6 +290,10 @@
INetworkPtr net(INetwork::Create());
Pooling2dDescriptor desc;
+ desc.m_PoolWidth = 2;
+ desc.m_PoolHeight = 2;
+ desc.m_StrideX = 2;
+ desc.m_StrideY = 2;
IConnectableLayer* input0 = net->AddInputLayer(0, "input0");
IConnectableLayer* input1 = net->AddInputLayer(1, "input1");
@@ -285,8 +310,8 @@
sub->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0));
- TensorInfo info = TensorInfo({ 1, 2, 3, 2 }, DataType::Float32);
- TensorInfo poolingInfo = TensorInfo({ 1, 2, 1, 1 }, DataType::Float32);
+ TensorInfo info = TensorInfo({ 1, 2, 4, 2 }, DataType::Float32);
+ TensorInfo poolingInfo = TensorInfo({ 1, 2, 2, 1 }, DataType::Float32);
input0->GetOutputSlot(0).SetTensorInfo(info);
input1->GetOutputSlot(0).SetTensorInfo(info);
@@ -340,27 +365,45 @@
runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);
// Creates structures for input & output
- std::vector<float> inputData0
+ std::vector<float> inputValue0
{
- 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f
+ 1.0f, 1.0f, 2.0f, 2.0f, 2.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 6.0f, 1.0f, 1.0f, 2.0f, 2.0f
};
- std::vector<float> inputData1
+ std::vector<float> inputValue1
{
- 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f
+ 0.0f, 1.0f, 1.0f, 2.0f, 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 5.0f, 5.0f, 6.0f, 0.0f, 1.0f, 1.0f, 2.0f
};
std::vector<float> inputData2
{
- 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f
+ 12.0f, 11.0f, 10.0f, 9.0f, 8.0f, 7.0f, 6.0f, 5.0f, 4.0f, 3.0f, 2.0f, 1.0f, 12.0f, 11.0f, 10.0f, 9.0f
};
- std::vector<float> outputData(2);
+ std::vector<float> outputData(4);
- std::vector<float> expectedOutput{ 11.0f, -1.0f };
+ std::vector<float> expectedOutput{ 11.0f, 3.0f, -5.0f, 11.0f };
+
+ unsigned int numElements = info.GetNumElements();
+ size_t totalBytes = numElements * sizeof(float);
+ const size_t alignment = 64;
+ size_t space = totalBytes + alignment + alignment;
+ auto inputData0 = std::make_unique<uint8_t[]>(space);
+ void* alignedInputPtr0 = inputData0.get();
+ BOOST_CHECK(std::align(alignment, totalBytes, alignedInputPtr0, space));
+
+ auto* intputPtr0 = reinterpret_cast<float*>(alignedInputPtr0);
+ std::copy(inputValue0.begin(), inputValue0.end(), intputPtr0);
+
+ auto inputData1 = std::make_unique<uint8_t[]>(space);
+ void* alignedInputPtr1 = inputData1.get();
+ BOOST_CHECK(std::align(alignment, totalBytes, alignedInputPtr1, space));
+
+ auto* intputPtr1 = reinterpret_cast<float*>(alignedInputPtr1);
+ std::copy(inputValue1.begin(), inputValue1.end(), intputPtr1);
InputTensors inputTensors
{
- { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData0.data()) },
- { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), inputData1.data()) },
+ { 0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), alignedInputPtr0) },
+ { 1, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 1), alignedInputPtr1) },
{ 2, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 2), inputData2.data()) }
};
OutputTensors outputTensors
@@ -393,6 +436,8 @@
// Check output is as expected
BOOST_TEST(outputData == expectedOutput);
+
+ runtime->UnloadNetwork(netId);
}
BOOST_AUTO_TEST_CASE(ClImportDisableFallbackSubgraphToNeon)