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/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;