IVGCVSW-2354 Caffe SqueezeNet through armnn Cl and Neon
* Compute Softmax Acl axis for Cl and Neon
* Add unittests for Softmax in 3D and 4D
* Correct input and output layer names in CaffeSqueezeNet inference test
Change-Id: I2d369d9a2db19c40f2af3341039dd33f0c5637b1
Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp
index 3d5b9ca..b4673f7 100644
--- a/src/backends/aclCommon/ArmComputeUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeUtils.hpp
@@ -120,4 +120,14 @@
return fc_info;
}
+inline unsigned int ComputeSoftmaxAclAxis(const armnn::TensorInfo& tensor)
+{
+ unsigned int dim = tensor.GetNumDimensions();
+
+ BOOST_ASSERT(dim != 0);
+
+ // Currently ArmNN support axis 1.
+ return dim - 1;
+}
+
} // namespace armnn
diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp
index ce02fed..a088aaa 100644
--- a/src/backends/backendsCommon/test/LayerTests.cpp
+++ b/src/backends/backendsCommon/test/LayerTests.cpp
@@ -907,6 +907,22 @@
return SimpleSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta);
}
+LayerTestResult<float,3> Simple3dSoftmaxTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ return Simple3dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta);
+}
+
+LayerTestResult<float,4> Simple4dSoftmaxTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ return Simple4dSoftmaxTestImpl<armnn::DataType::Float32>(workloadFactory, memoryManager, beta);
+}
+
LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
@@ -915,6 +931,22 @@
return SimpleSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta);
}
+LayerTestResult<uint8_t,3> Simple3dSoftmaxUint8Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ return Simple3dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta);
+}
+
+LayerTestResult<uint8_t,4> Simple4dSoftmaxUint8Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ return Simple4dSoftmaxTestImpl<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, beta);
+}
+
LayerTestResult<float,4> CompareNormalizationTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp
index 587ffe9..a8cb553 100644
--- a/src/backends/backendsCommon/test/LayerTests.hpp
+++ b/src/backends/backendsCommon/test/LayerTests.hpp
@@ -335,11 +335,31 @@
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta);
+LayerTestResult<float, 3> Simple3dSoftmaxTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta);
+
+LayerTestResult<float, 4> Simple4dSoftmaxTest(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta);
+
LayerTestResult<uint8_t, 2> SimpleSoftmaxUint8Test(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
float beta);
+LayerTestResult<uint8_t,3> Simple3dSoftmaxUint8Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta);
+
+LayerTestResult<uint8_t,4> Simple4dSoftmaxUint8Test(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta);
+
LayerTestResult<float, 4> SimpleSigmoidTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager);
diff --git a/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp b/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp
index 25ceda1..8081950 100644
--- a/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp
+++ b/src/backends/backendsCommon/test/SoftmaxTestImpl.hpp
@@ -19,34 +19,35 @@
#include <algorithm>
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
+template<armnn::DataType ArmnnType, std::size_t n, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, n> SimpleSoftmaxBaseTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- float beta)
+ float beta,
+ const armnn::TensorShape& inputShape,
+ const std::vector<float>& outputData)
{
using std::exp;
+ const float qScale = 1.f / 256.f;
+ const int qOffset = 0;
+
armnn::TensorInfo inputTensorInfo;
armnn::TensorInfo outputTensorInfo;
- unsigned int inputShape[] = { 2, 4 };
-
- inputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
- float qScale = 1.f / 256.f;
- int qOffset = 0;
+ inputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
- outputTensorInfo = armnn::TensorInfo(2, inputShape, ArmnnType);
+ outputTensorInfo = armnn::TensorInfo(inputShape, ArmnnType);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
- LayerTestResult<T, 2> ret(outputTensorInfo);
+ LayerTestResult<T, n> ret(outputTensorInfo);
// Each row is independently softmax'd.
- auto input = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(
- QuantizedVector<T>(qScale, 0, {
+ auto input = MakeTensor<T, n>(inputTensorInfo, std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, {
0.f, 1.f, 0.f, 0.f,
.5f, 0.f, 0.f, 0.f,
})));
@@ -65,35 +66,76 @@
inputHandle->Allocate();
outputHandle->Allocate();
- CopyDataToITensorHandle(inputHandle.get(), &input[0][0]);
+ CopyDataToITensorHandle(inputHandle.get(), input.origin());
+
+ BOOST_ASSERT(workload);
ExecuteWorkload(*workload, memoryManager);
- CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get());
+ CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get());
- float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),
- exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };
- float sum0 = x0[0] + x0[1] + x0[2] + x0[3];
- float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),
- exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };
- float sum1 = x1[0] + x1[1] + x1[2] + x1[3];
-
- ret.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>(
- QuantizedVector<T>(qScale, qOffset, {
- x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
- x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1
- })));
+ std::vector<T> expectedOutput = std::vector<T>(
+ QuantizedVector<T>(qScale, qOffset, outputData));
+ ret.outputExpected = MakeTensor<T, n>(outputTensorInfo, expectedOutput);
return ret;
}
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-LayerTestResult<T, 2> CompareSoftmaxTestImpl(
+LayerTestResult<T, 2> SimpleSoftmaxTestImpl(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
- armnn::IWorkloadFactory& refWorkloadFactory,
float beta)
{
+ using std::exp;
+ const armnn::TensorShape inputShape{ 2, 4 };
+
+ float x0[4] = { exp((0.f - 1.0f) * beta), exp((1.0f - 1.0f) * beta),
+ exp((0.0f - 1.0f) * beta), exp((0.0f - 1.0f) * beta) };
+ float sum0 = x0[0] + x0[1] + x0[2] + x0[3];
+ float x1[4] = { exp((0.5f - 0.5f) * beta), exp((0.0f - 0.5f) * beta),
+ exp((0.0f - 0.5f) * beta), exp((0.0f - 0.5f) * beta) };
+ float sum1 = x1[0] + x1[1] + x1[2] + x1[3];
+
+ const std::vector<float> outputData = { x0[0] / sum0, x0[1] / sum0, x0[2] / sum0, x0[3] / sum0,
+ x1[0] / sum1, x1[1] / sum1, x1[2] / sum1, x1[3] / sum1 };
+
+ return SimpleSoftmaxBaseTestImpl<ArmnnType, 2>(workloadFactory, memoryManager, beta, inputShape, outputData);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 3> Simple3dSoftmaxTestImpl(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ const armnn::TensorShape inputShape{ 1, 8, 1 };
+ const std::vector<float> outputData = { 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,
+ 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f };
+
+ return SimpleSoftmaxBaseTestImpl<ArmnnType, 3>(workloadFactory, memoryManager, beta, inputShape, outputData);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> Simple4dSoftmaxTestImpl(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ float beta)
+{
+ const armnn::TensorShape inputShape{ 1, 8, 1, 1 };
+ const std::vector<float> outputData = { 0.0964599f, 0.26220518f, 0.0964599f, 0.0964599f,
+ 0.15903549f, 0.0964599f, 0.0964599f, 0.0964599f };
+
+ return SimpleSoftmaxBaseTestImpl<ArmnnType, 4>(workloadFactory, memoryManager, beta, inputShape, outputData);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 2> CompareSoftmaxTestImpl(
+ armnn::IWorkloadFactory& workloadFactory,
+ const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+ armnn::IWorkloadFactory& refWorkloadFactory,
+ float beta)
+{
const int batchSize = 20;
const int channels = 30;
diff --git a/src/backends/cl/test/ClLayerTests.cpp b/src/backends/cl/test/ClLayerTests.cpp
index 23b4dc4..58b9ba7 100644
--- a/src/backends/cl/test/ClLayerTests.cpp
+++ b/src/backends/cl/test/ClLayerTests.cpp
@@ -34,11 +34,6 @@
// Activation
ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest)
-ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f)
-ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f)
-ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f)
-ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f)
-
ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest)
ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest)
@@ -377,6 +372,22 @@
ARMNN_AUTO_TEST_CASE(GreaterBroadcast1ElementUint8, GreaterBroadcast1ElementUint8Test)
ARMNN_AUTO_TEST_CASE(GreaterBroadcast1DVectorUint8, GreaterBroadcast1DVectorUint8Test)
+// Softmax
+ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f)
+ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f)
+ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f)
+
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmax, Simple3dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmaxUint8, Simple3dSoftmaxUint8Test, 1.0f)
+
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmax, Simple4dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmaxUint8, Simple4dSoftmaxUint8Test, 1.0f)
+
+ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta1WithReference, CompareSoftmaxTest, 1.0f)
+ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta2WithReference, CompareSoftmaxTest, 2.0f)
+ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxUint8, CompareSoftmaxUint8Test, 1.0f)
+
// Space To Batch Nd
ARMNN_AUTO_TEST_CASE(SpaceToBatchNdSimpleFloat32, SpaceToBatchNdSimpleFloat32Test)
ARMNN_AUTO_TEST_CASE(SpaceToBatchNdMultiChannelsFloat32, SpaceToBatchNdMultiChannelsFloat32Test)
@@ -445,10 +456,6 @@
armnn::NormalizationAlgorithmChannel::Across,
armnn::NormalizationAlgorithmMethod::LocalBrightness)
-ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta1WithReference, CompareSoftmaxTest, 1.0f)
-ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxBeta2WithReference, CompareSoftmaxTest, 2.0f)
-ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareSoftmaxUint8, CompareSoftmaxUint8Test, 1.0f)
-
ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareMaxPooling2dWithRef, ComparePooling2dTest, armnn::PoolingAlgorithm::Max)
ARMNN_COMPARE_REF_AUTO_TEST_CASE(CompareAveragePooling2dWithRef, ComparePooling2dTest, armnn::PoolingAlgorithm::Average)
diff --git a/src/backends/cl/workloads/ClSoftmaxFloatWorkload.cpp b/src/backends/cl/workloads/ClSoftmaxFloatWorkload.cpp
index bb56802..c78ab03 100644
--- a/src/backends/cl/workloads/ClSoftmaxFloatWorkload.cpp
+++ b/src/backends/cl/workloads/ClSoftmaxFloatWorkload.cpp
@@ -4,11 +4,12 @@
//
#include "ClSoftmaxFloatWorkload.hpp"
+#include "ClWorkloadUtils.hpp"
+
+#include <aclCommon/ArmComputeUtils.hpp>
#include <cl/ClTensorHandle.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
-#include "ClWorkloadUtils.hpp"
-
namespace armnn
{
@@ -21,7 +22,9 @@
arm_compute::ICLTensor& input = static_cast<ClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ICLTensor& output = static_cast<ClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
- m_SoftmaxLayer.configure(&input, &output, m_Data.m_Parameters.m_Beta);
+
+ unsigned int aclAxis = ComputeSoftmaxAclAxis(info.m_InputTensorInfos[0]);
+ m_SoftmaxLayer.configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
}
void ClSoftmaxFloatWorkload::Execute() const
diff --git a/src/backends/cl/workloads/ClSoftmaxUint8Workload.cpp b/src/backends/cl/workloads/ClSoftmaxUint8Workload.cpp
index c386e38..086f375 100644
--- a/src/backends/cl/workloads/ClSoftmaxUint8Workload.cpp
+++ b/src/backends/cl/workloads/ClSoftmaxUint8Workload.cpp
@@ -4,11 +4,12 @@
//
#include "ClSoftmaxUint8Workload.hpp"
+#include "ClWorkloadUtils.hpp"
+
+#include <aclCommon/ArmComputeUtils.hpp>
#include <cl/ClTensorHandle.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
-#include "ClWorkloadUtils.hpp"
-
namespace armnn
{
@@ -30,7 +31,8 @@
"Invalid quantization for output. Only scale = 1.0f / 256.0f and offset = 0 supported");
}
- m_SoftmaxLayer.configure(&input, &output, descriptor.m_Parameters.m_Beta);
+ unsigned int aclAxis = ComputeSoftmaxAclAxis(info.m_InputTensorInfos[0]);
+ m_SoftmaxLayer.configure(&input, &output, descriptor.m_Parameters.m_Beta, aclAxis);
}
void ClSoftmaxUint8Workload::Execute() const
diff --git a/src/backends/neon/test/NeonLayerTests.cpp b/src/backends/neon/test/NeonLayerTests.cpp
index 01773db..b34e2dd 100644
--- a/src/backends/neon/test/NeonLayerTests.cpp
+++ b/src/backends/neon/test/NeonLayerTests.cpp
@@ -275,14 +275,21 @@
// Activation
ARMNN_AUTO_TEST_CASE(ConstantLinearActivation, ConstantLinearActivationTest)
+// ReLu
+ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest)
+ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest)
+
+// Softmax
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f)
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f)
-
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f)
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f)
-ARMNN_AUTO_TEST_CASE(ReLu1Uint8, BoundedReLuUint8UpperAndLowerBoundTest)
-ARMNN_AUTO_TEST_CASE(ReLu6Uint8, BoundedReLuUint8UpperBoundOnlyTest)
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmaxBeta1, Simple3dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmaxBeta1Uint8, Simple3dSoftmaxUint8Test, 1.0f)
+
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmaxBeta1, Simple4dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmaxBeta1Uint8, Simple4dSoftmaxUint8Test, 1.0f)
// Splitter
ARMNN_AUTO_TEST_CASE(SimpleSplitter, SplitterTest)
diff --git a/src/backends/neon/workloads/NeonSoftmaxFloatWorkload.cpp b/src/backends/neon/workloads/NeonSoftmaxFloatWorkload.cpp
index d9c78bb..afc6135 100644
--- a/src/backends/neon/workloads/NeonSoftmaxFloatWorkload.cpp
+++ b/src/backends/neon/workloads/NeonSoftmaxFloatWorkload.cpp
@@ -7,6 +7,7 @@
#include "NeonWorkloadUtils.hpp"
+#include <aclCommon/ArmComputeUtils.hpp>
#include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h>
namespace armnn
@@ -22,8 +23,9 @@
arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
+ unsigned int aclAxis = ComputeSoftmaxAclAxis(info.m_InputTensorInfos[0]);
auto layer = std::make_unique<arm_compute::NESoftmaxLayer>(memoryManager);
- layer->configure(&input, &output, m_Data.m_Parameters.m_Beta);
+ layer->configure(&input, &output, m_Data.m_Parameters.m_Beta, aclAxis);
m_SoftmaxLayer.reset(layer.release());
}
diff --git a/src/backends/neon/workloads/NeonSoftmaxUint8Workload.cpp b/src/backends/neon/workloads/NeonSoftmaxUint8Workload.cpp
index f780589..7f295d6 100644
--- a/src/backends/neon/workloads/NeonSoftmaxUint8Workload.cpp
+++ b/src/backends/neon/workloads/NeonSoftmaxUint8Workload.cpp
@@ -4,9 +4,10 @@
//
#include "NeonSoftmaxUint8Workload.hpp"
-
#include "NeonWorkloadUtils.hpp"
+#include <aclCommon/ArmComputeUtils.hpp>
+
#include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h>
namespace armnn
@@ -29,9 +30,10 @@
throw InvalidArgumentException(
"Invalid quantization for output. Only scale = 1.0f / 256.0f and offset = 0 supported");
}
+ unsigned int aclAxis = ComputeSoftmaxAclAxis(info.m_InputTensorInfos[0]);
auto layer = std::make_unique<arm_compute::NESoftmaxLayer>(memoryManager);
- layer->configure(&input, &output, descriptor.m_Parameters.m_Beta);
+ layer->configure(&input, &output, descriptor.m_Parameters.m_Beta, aclAxis);
m_SoftmaxLayer.reset(layer.release());
}
diff --git a/src/backends/reference/test/RefLayerTests.cpp b/src/backends/reference/test/RefLayerTests.cpp
index 4be917f..13ea82d 100644
--- a/src/backends/reference/test/RefLayerTests.cpp
+++ b/src/backends/reference/test/RefLayerTests.cpp
@@ -176,11 +176,19 @@
ARMNN_AUTO_TEST_CASE(SimpleNormalizationWithin, SimpleNormalizationWithinTest)
ARMNN_AUTO_TEST_CASE(SimpleNormalizationAcrossNhwc, SimpleNormalizationAcrossNhwcTest)
+// Softmax
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1, SimpleSoftmaxTest, 1.0f)
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2, SimpleSoftmaxTest, 2.0f)
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta1Uint8, SimpleSoftmaxUint8Test, 1.0f)
ARMNN_AUTO_TEST_CASE(SimpleSoftmaxBeta2Uint8, SimpleSoftmaxUint8Test, 2.0f)
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmax, Simple3dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple3dSoftmaxUint8, Simple3dSoftmaxUint8Test, 1.0f)
+
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmax, Simple4dSoftmaxTest, 1.0f)
+ARMNN_AUTO_TEST_CASE(Simple4dSoftmaxUint8, Simple4dSoftmaxUint8Test, 1.0f)
+
+// Sigmoid
ARMNN_AUTO_TEST_CASE(SimpleSigmoid, SimpleSigmoidTest)
ARMNN_AUTO_TEST_CASE(SimpleSigmoidUint8, SimpleSigmoidUint8Test)
diff --git a/tests/CaffeSqueezeNet1_0-Armnn/CaffeSqueezeNet1_0-Armnn.cpp b/tests/CaffeSqueezeNet1_0-Armnn/CaffeSqueezeNet1_0-Armnn.cpp
index 5eaf2ef..f4c16fe 100644
--- a/tests/CaffeSqueezeNet1_0-Armnn/CaffeSqueezeNet1_0-Armnn.cpp
+++ b/tests/CaffeSqueezeNet1_0-Armnn/CaffeSqueezeNet1_0-Armnn.cpp
@@ -8,8 +8,13 @@
int main(int argc, char* argv[])
{
- return armnn::test::ClassifierInferenceTestMain<CaffePreprocessor, armnnCaffeParser::ICaffeParser>(
+ using DataType = float;
+ using DatabaseType = CaffePreprocessor;
+ using ParserType = armnnCaffeParser::ICaffeParser;
+ using ModelType = InferenceModel<ParserType, DataType>;
+
+ return armnn::test::ClassifierInferenceTestMain<DatabaseType, ParserType>(
argc, argv, "squeezenet.caffemodel", true,
- "data", "output", { 0 },
- [](const char* dataDir) { return CaffePreprocessor(dataDir); });
+ "input", "prob", { 0 },
+ [](const char* dataDir, const ModelType &) { return CaffePreprocessor(dataDir); });
}