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-rw-r--r--src/backends/backendsCommon/test/ActivationEndToEndTestImpl.hpp214
1 files changed, 150 insertions, 64 deletions
diff --git a/src/backends/backendsCommon/test/ActivationEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/ActivationEndToEndTestImpl.hpp
index 996e7605d2..09d71923b6 100644
--- a/src/backends/backendsCommon/test/ActivationEndToEndTestImpl.hpp
+++ b/src/backends/backendsCommon/test/ActivationEndToEndTestImpl.hpp
@@ -57,12 +57,12 @@ armnn::INetworkPtr CreateActivationNetwork(const armnn::TensorInfo& inputInfo,
INetworkPtr net(INetwork::Create());
- IConnectableLayer* input = net->AddInputLayer(0, "input");
- IConnectableLayer* prelu = net->AddActivationLayer(descriptor, ActivationName);
- IConnectableLayer* output = net->AddOutputLayer(0, "output");
+ IConnectableLayer* inputLayer = net->AddInputLayer(0, "input");
+ IConnectableLayer* activationLayer = net->AddActivationLayer(descriptor, ActivationName);
+ IConnectableLayer* outputLayer = net->AddOutputLayer(0, "output");
- Connect(input, prelu, inputInfo, 0, 0);
- Connect(prelu, output, outputInfo, 0, 0);
+ Connect(inputLayer, activationLayer, inputInfo, 0, 0);
+ Connect(activationLayer, outputLayer, outputInfo, 0, 0);
return net;
}
@@ -113,83 +113,169 @@ void ActivationEndToEndImpl(const std::vector<armnn::BackendId>& backends,
tolerance);
}
-/** Executes an end to end test for Elu activation with specific input and expected-output data
- *
- * @tparam ArmnnType The armnn data type for the input and expected-output data
- * @param backends The backends on which to run the test
- */
-template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-void EluEndToEndTest(const std::vector<BackendId>& backends)
+std::vector<float> Activation(const std::vector<float>& input,
+ const ActivationDescriptor& descriptor)
{
- std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f,
- 1.0f, 2.0f, 3.0f, 4.0f };
+ float a = descriptor.m_A;
+ float b = descriptor.m_B;
- std::vector<float> floatExpectedOutputData{ -0.86466471676f, -0.63212055882f, -0.0f, 0.0f,
- 1.0f , 2.0f , 3.0f, 4.0f };
+ std::vector<float> output;
+ output.reserve(input.size());
- float qScale = 1.0f;
- int32_t qOffset = 0;
- armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset, true);
- armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
-
- armnn::ActivationDescriptor descriptor(ActivationFunction::Elu, 1.0);
-
- ActivationEndToEndImpl<ArmnnType>(backends,
- floatInputData,
- floatExpectedOutputData,
- inputInfo,
- outputInfo,
- descriptor);
+ // Compute the result of the activation function.
+ switch (descriptor.m_Function)
+ {
+ case ActivationFunction::Linear:
+ {
+ for (auto in :input)
+ {
+ auto out = a * in + b;
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Sigmoid:
+ {
+ for (auto in :input)
+ {
+ auto out = 1.f / (1.f + expf(-in));
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::ReLu:
+ {
+ for (auto in :input)
+ {
+ auto out = std::max(0.f, in);
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::BoundedReLu:
+ {
+ for (auto in :input)
+ {
+ auto out = std::min(a, std::max(b, in));
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::SoftReLu:
+ {
+ for (auto in :input)
+ {
+ auto out = logf(1.0f + expf(in));
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::LeakyReLu:
+ {
+ for (auto in :input)
+ {
+ auto out = in > 0.0f ? in : (in * a);
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Abs:
+ {
+ for (auto in :input)
+ {
+ auto out = in < 0 ? -in : in;
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Sqrt:
+ {
+ for (auto in :input)
+ {
+ auto out = sqrtf(in);
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Square:
+ {
+ for (auto in :input)
+ {
+ auto out = in * in;
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::TanH:
+ {
+ for (auto in :input)
+ {
+ auto out = a * tanhf(b * in);
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Elu:
+ {
+ for (auto in: input) {
+ auto out = (in >= 0) ? in : a * (expf(in) - 1);
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::HardSwish:
+ {
+ for (auto in :input)
+ {
+ // hard_swish(x) = x * relu6(x+3) / 6
+ // relu6(x) = min(max(x,0),6)
+ auto out = in * (std::min(std::max((in + 3), 0.0f), 6.0f)) / 6;
+ output.push_back(out);
+ }
+ break;
+ }
+ case ActivationFunction::Gelu:
+ {
+ for (auto in :input)
+ {
+ // gelu(x) = x * 1/2 * (1 + erf(x / sqrt(2))),
+ // where erf is Gaussian error function
+ auto out = in * (0.5f * (1.0f + erff(static_cast<float>(in / std::sqrt(2)))));
+ output.push_back(out);
+ }
+ break;
+ }
+ default:
+ {
+ throw InvalidArgumentException("Unsupported activation function");
+ }
+ }
+ return output;
}
-/** Executes an end to end test for HardSwish activation with specific input and expected-output data
+/** Executes an end to end test for activation layers with specific input and expected-output data
*
* @tparam ArmnnType The armnn data type for the input and expected-output data
* @param backends The backends on which to run the test
*/
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
-void HardSwishEndToEndTest(const std::vector<BackendId>& backends)
+void ActivationEndToEndTest(const std::vector<BackendId>& backends,
+ const ActivationFunction activationFunction,
+ const float qScale=1.0f,
+ const int32_t qOffset=0,
+ const float a = 1,
+ const float b = 0)
{
- std::vector<float> floatInputData{ -2.0f, -1.0f, -0.5f, 0.0f,
+ std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f,
1.0f, 2.0f, 3.0f, 4.0f };
- std::vector<float> floatExpectedOutputData{ -0.33333333333f, -0.33333333333f, -0.208333f, 0.0f,
- 0.66666666667f, 1.66666666667f, 3.0f , 4.0f };
-
- float qScale = 1.0f;
- int32_t qOffset = 0;
- armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset, true);
- armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
+ ActivationDescriptor descriptor(activationFunction, a, b);
- armnn::ActivationDescriptor descriptor(ActivationFunction::HardSwish, 1.0);
-
- ActivationEndToEndImpl<ArmnnType>(backends,
- floatInputData,
- floatExpectedOutputData,
- inputInfo,
- outputInfo,
- descriptor);
-}
-
-/** Executes an end to end test for Leaky Relu activation with specific input and expected-output data
- *
- * @tparam ArmnnType The armnn data type for the input and expected-output data
- * @param backends The backends on which to run the test
- */
-template<armnn::DataType ArmnnType>
-void LeakyReluEndToEndTest(const std::vector<BackendId>& backends, const float qScale=1.0f, const int32_t qOffset=0)
-{
- std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f,
- 1.0f, 2.0f, 3.0f, 4.0f };
-
- std::vector<float> floatExpectedOutputData{ -0.02f, -0.01f, -0.0f, 0.0f,
- 1.0f, 2.0f, 3.0f, 4.0f };
+ std::vector<float> floatExpectedOutputData = Activation(floatInputData, descriptor);
armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset, true);
armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
- armnn::ActivationDescriptor descriptor(ActivationFunction::LeakyReLu, static_cast<float>(0.01));
-
ActivationEndToEndImpl<ArmnnType>(backends,
floatInputData,
floatExpectedOutputData,
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-05 18:24:58 +0000