IVGCVSW-2503 Refactor RefElementwiseWorkload around Equal and Greater
* Remove Equal and Greater from RefElementwiseWorkload
* Create RefComparisonWorkload and add Equal and Greater
* Update ElementwiseFunction for different input/output types
* Update TfParser to create Equal/Greater with Boolean output
* Update relevant tests to check for Boolean comparison
Change-Id: I299b7f2121769c960ac0c6139764a5f3c89c9c32
diff --git a/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp b/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp
index 1d6cf1d..6f685eb 100644
--- a/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp
+++ b/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp
@@ -17,7 +17,7 @@
namespace
{
-template<typename armnn::DataType DataType>
+template<armnn::DataType ArmnnTypeInput, armnn::DataType ArmnnTypeOutput>
INetworkPtr CreateArithmeticNetwork(const std::vector<TensorShape>& inputShapes,
const TensorShape& outputShape,
const LayerType type,
@@ -39,22 +39,25 @@
for (unsigned int i = 0; i < inputShapes.size(); ++i)
{
- TensorInfo inputTensorInfo(inputShapes[i], DataType, qScale, qOffset);
+ TensorInfo inputTensorInfo(inputShapes[i], ArmnnTypeInput, qScale, qOffset);
IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i));
Connect(input, arithmeticLayer, inputTensorInfo, 0, i);
}
- TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
+ TensorInfo outputTensorInfo(outputShape, ArmnnTypeOutput, qScale, qOffset);
IConnectableLayer* output = net->AddOutputLayer(0, "output");
Connect(arithmeticLayer, output, outputTensorInfo, 0, 0);
return net;
}
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+template<armnn::DataType ArmnnInputType,
+ armnn::DataType ArmnnOutputType,
+ typename TInput = armnn::ResolveType<ArmnnInputType>,
+ typename TOutput = armnn::ResolveType<ArmnnOutputType>>
void ArithmeticSimpleEndToEnd(const std::vector<BackendId>& backends,
const LayerType type,
- const std::vector<T> expectedOutput)
+ const std::vector<TOutput> expectedOutput)
{
using namespace armnn;
@@ -62,26 +65,29 @@
const TensorShape& outputShape = { 2, 2, 2, 2 };
// Builds up the structure of the network
- INetworkPtr net = CreateArithmeticNetwork<ArmnnType>(inputShapes, outputShape, type);
+ INetworkPtr net = CreateArithmeticNetwork<ArmnnInputType, ArmnnOutputType>(inputShapes, outputShape, type);
BOOST_TEST_CHECKPOINT("create a network");
- const std::vector<T> input0({ 1, 1, 1, 1, 5, 5, 5, 5,
- 3, 3, 3, 3, 4, 4, 4, 4 });
+ const std::vector<TInput> input0({ 1, 1, 1, 1, 5, 5, 5, 5,
+ 3, 3, 3, 3, 4, 4, 4, 4 });
- const std::vector<T> input1({ 1, 1, 1, 1, 3, 3, 3, 3,
- 5, 5, 5, 5, 4, 4, 4, 4 });
+ const std::vector<TInput> input1({ 1, 1, 1, 1, 3, 3, 3, 3,
+ 5, 5, 5, 5, 4, 4, 4, 4 });
- std::map<int, std::vector<T>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
- std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};
+ std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
+ std::map<int, std::vector<TOutput>> expectedOutputData = {{ 0, expectedOutput }};
- EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
+ EndToEndLayerTestImpl<TInput, TOutput>(move(net), inputTensorData, expectedOutputData, backends);
}
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+template<armnn::DataType ArmnnInputType,
+ armnn::DataType ArmnnOutputType,
+ typename TInput = armnn::ResolveType<ArmnnInputType>,
+ typename TOutput = armnn::ResolveType<ArmnnOutputType>>
void ArithmeticBroadcastEndToEnd(const std::vector<BackendId>& backends,
const LayerType type,
- const std::vector<T> expectedOutput)
+ const std::vector<TOutput> expectedOutput)
{
using namespace armnn;
@@ -89,19 +95,19 @@
const TensorShape& outputShape = { 1, 2, 2, 3 };
// Builds up the structure of the network
- INetworkPtr net = CreateArithmeticNetwork<ArmnnType>(inputShapes, outputShape, type);
+ INetworkPtr net = CreateArithmeticNetwork<ArmnnInputType, ArmnnOutputType>(inputShapes, outputShape, type);
BOOST_TEST_CHECKPOINT("create a network");
- const std::vector<T> input0({ 1, 2, 3, 1, 0, 6,
- 7, 8, 9, 10, 11, 12 });
+ const std::vector<TInput> input0({ 1, 2, 3, 1, 0, 6,
+ 7, 8, 9, 10, 11, 12 });
- const std::vector<T> input1({ 1, 1, 3 });
+ const std::vector<TInput> input1({ 1, 1, 3 });
- std::map<int, std::vector<T>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
- std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }};
+ std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
+ std::map<int, std::vector<TOutput>> expectedOutputData = {{ 0, expectedOutput }};
- EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
+ EndToEndLayerTestImpl<TInput, TOutput>(move(net), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace