IVGCVSW-3291 Add L2Normalization epsilon value to serialization
Signed-off-by: Ferran Balaguer <ferran.balaguer@arm.com>
Change-Id: Icfff3fb596a03c126a42b1d0c254a68e498df734
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f07295f..976f8de 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -623,6 +623,7 @@
src/armnnDeserializer/test/DeserializeFullyConnected.cpp
src/armnnDeserializer/test/DeserializeGather.cpp
src/armnnDeserializer/test/DeserializeGreater.cpp
+ src/armnnDeserializer/test/DeserializeL2Normalization.cpp
src/armnnDeserializer/test/DeserializeMean.cpp
src/armnnDeserializer/test/DeserializeMultiplication.cpp
src/armnnDeserializer/test/DeserializeNormalization.cpp
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index b23ed97..90ca6d3 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -1187,6 +1187,7 @@
auto layerName = GetLayerName(graph, layerIndex);
armnn::L2NormalizationDescriptor descriptor;
descriptor.m_DataLayout = ToDataLayout(flatBufferDescriptor->dataLayout());
+ descriptor.m_Eps = flatBufferDescriptor->eps();
IConnectableLayer* layer = m_Network->AddL2NormalizationLayer(descriptor, layerName.c_str());
layer->GetOutputSlot(0).SetTensorInfo(outputInfo);
diff --git a/src/armnnDeserializer/test/DeserializeL2Normalization.cpp b/src/armnnDeserializer/test/DeserializeL2Normalization.cpp
new file mode 100644
index 0000000..d8604a5
--- /dev/null
+++ b/src/armnnDeserializer/test/DeserializeL2Normalization.cpp
@@ -0,0 +1,142 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "ParserFlatbuffersSerializeFixture.hpp"
+#include "../Deserializer.hpp"
+
+#include <string>
+#include <iostream>
+
+BOOST_AUTO_TEST_SUITE(Deserializer)
+
+struct L2NormalizationFixture : public ParserFlatbuffersSerializeFixture
+{
+ explicit L2NormalizationFixture(const std::string &inputShape,
+ const std::string &outputShape,
+ const std::string &dataType,
+ const std::string &dataLayout,
+ const std::string epsilon)
+ {
+ m_JsonString = R"(
+ {
+ inputIds: [0],
+ outputIds: [2],
+ layers: [
+ {
+ layer_type: "InputLayer",
+ layer: {
+ base: {
+ layerBindingId: 0,
+ base: {
+ index: 0,
+ layerName: "InputLayer",
+ layerType: "Input",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:0, outputSlotIndex:0 },
+ }],
+ outputSlots: [{
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + inputShape + R"(,
+ dataType: ")" + dataType + R"(",
+ quantizationScale: 0.5,
+ quantizationOffset: 0
+ },
+ }]
+ },
+ }
+ },
+ },
+ {
+ layer_type: "L2NormalizationLayer",
+ layer : {
+ base: {
+ index:1,
+ layerName: "L2NormalizationLayer",
+ layerType: "L2Normalization",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:0, outputSlotIndex:0 },
+ }],
+ outputSlots: [{
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + outputShape + R"(,
+ dataType: ")" + dataType + R"("
+ },
+ }],
+ },
+ descriptor: {
+ dataLayout: ")" + dataLayout + R"(",
+ eps: )" + epsilon + R"(
+ },
+ },
+ },
+ {
+ layer_type: "OutputLayer",
+ layer: {
+ base:{
+ layerBindingId: 0,
+ base: {
+ index: 2,
+ layerName: "OutputLayer",
+ layerType: "Output",
+ inputSlots: [{
+ index: 0,
+ connection: {sourceLayerIndex:1, outputSlotIndex:0 },
+ }],
+ outputSlots: [ {
+ index: 0,
+ tensorInfo: {
+ dimensions: )" + outputShape + R"(,
+ dataType: ")" + dataType + R"("
+ },
+ }],
+ }
+ }},
+ }]
+ }
+)";
+ Setup();
+ }
+};
+
+struct L2NormFixture : L2NormalizationFixture
+{
+ // Using a non standard epsilon value of 1e-8
+ L2NormFixture():L2NormalizationFixture("[ 1, 3, 1, 1 ]",
+ "[ 1, 3, 1, 1 ]",
+ "Float32",
+ "NCHW",
+ "0.00000001"){}
+};
+
+BOOST_FIXTURE_TEST_CASE(L2NormalizationFloat32, L2NormFixture)
+{
+ // 1 / sqrt(1^2 + 2^2 + 3^2)
+ const float approxInvL2Norm = 0.267261f;
+
+ RunTest<4, armnn::DataType::Float32>(0,
+ {{"InputLayer", { 1.0f, 2.0f, 3.0f }}},
+ {{"OutputLayer",{ 1.0f * approxInvL2Norm,
+ 2.0f * approxInvL2Norm,
+ 3.0f * approxInvL2Norm }}});
+}
+
+BOOST_FIXTURE_TEST_CASE(L2NormalizationEpsilonLimitFloat32, L2NormFixture)
+{
+ // 1 / sqrt(1e-8)
+ const float approxInvL2Norm = 10000;
+
+ RunTest<4, armnn::DataType::Float32>(0,
+ {{"InputLayer", { 0.00000001f, 0.00000002f, 0.00000003f }}},
+ {{"OutputLayer",{ 0.00000001f * approxInvL2Norm,
+ 0.00000002f * approxInvL2Norm,
+ 0.00000003f * approxInvL2Norm }}});
+}
+
+BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnSerializer/ArmnnSchema.fbs b/src/armnnSerializer/ArmnnSchema.fbs
index db5672f..7947893 100644
--- a/src/armnnSerializer/ArmnnSchema.fbs
+++ b/src/armnnSerializer/ArmnnSchema.fbs
@@ -224,6 +224,7 @@
table L2NormalizationDescriptor {
dataLayout:DataLayout = NCHW;
+ eps:float = 1e-12;
}
table MinimumLayer {
diff --git a/src/armnnSerializer/Serializer.cpp b/src/armnnSerializer/Serializer.cpp
index dabe977..efadbb3 100644
--- a/src/armnnSerializer/Serializer.cpp
+++ b/src/armnnSerializer/Serializer.cpp
@@ -380,9 +380,11 @@
// Create the FlatBuffer L2Normalization Descriptor
auto fbDescriptor = serializer::CreateL2NormalizationDescriptor(
- m_flatBufferBuilder, GetFlatBufferDataLayout(l2NormalizationDescriptor.m_DataLayout));
+ m_flatBufferBuilder,
+ GetFlatBufferDataLayout(l2NormalizationDescriptor.m_DataLayout),
+ l2NormalizationDescriptor.m_Eps);
- // Create Flatuffer layer
+ // Create FlatBuffer layer
auto fbLayer = serializer::CreateL2NormalizationLayer(m_flatBufferBuilder, fbBaseLayer, fbDescriptor);
CreateAnyLayer(fbLayer.o, serializer::Layer::Layer_L2NormalizationLayer);
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp
index 812a478..ddebd14 100644
--- a/src/armnnSerializer/test/SerializerTests.cpp
+++ b/src/armnnSerializer/test/SerializerTests.cpp
@@ -1046,39 +1046,41 @@
deserializedNetwork->Accept(verifier);
}
+class L2NormalizationLayerVerifier : public LayerVerifierBase
+{
+public:
+ L2NormalizationLayerVerifier(const std::string& layerName,
+ const std::vector<armnn::TensorInfo>& inputInfos,
+ const std::vector<armnn::TensorInfo>& outputInfos,
+ const armnn::L2NormalizationDescriptor& descriptor)
+ : LayerVerifierBase(layerName, inputInfos, outputInfos)
+ , m_Descriptor(descriptor) {}
+
+ void VisitL2NormalizationLayer(const armnn::IConnectableLayer* layer,
+ const armnn::L2NormalizationDescriptor& descriptor,
+ const char* name) override
+ {
+ VerifyNameAndConnections(layer, name);
+ VerifyDescriptor(descriptor);
+ }
+private:
+ void VerifyDescriptor(const armnn::L2NormalizationDescriptor& descriptor)
+ {
+ BOOST_TEST(descriptor.m_Eps == m_Descriptor.m_Eps);
+ BOOST_TEST(GetDataLayoutName(descriptor.m_DataLayout) == GetDataLayoutName(m_Descriptor.m_DataLayout));
+ }
+
+ armnn::L2NormalizationDescriptor m_Descriptor;
+};
+
BOOST_AUTO_TEST_CASE(SerializeL2Normalization)
{
- class L2NormalizationLayerVerifier : public LayerVerifierBase
- {
- public:
- L2NormalizationLayerVerifier(const std::string& layerName,
- const std::vector<armnn::TensorInfo>& inputInfos,
- const std::vector<armnn::TensorInfo>& outputInfos,
- const armnn::L2NormalizationDescriptor& descriptor)
- : LayerVerifierBase(layerName, inputInfos, outputInfos)
- , m_Descriptor(descriptor) {}
-
- void VisitL2NormalizationLayer(const armnn::IConnectableLayer* layer,
- const armnn::L2NormalizationDescriptor& descriptor,
- const char* name) override
- {
- VerifyNameAndConnections(layer, name);
- VerifyDescriptor(descriptor);
- }
- private:
- void VerifyDescriptor(const armnn::L2NormalizationDescriptor& descriptor)
- {
- BOOST_TEST(GetDataLayoutName(descriptor.m_DataLayout) == GetDataLayoutName(m_Descriptor.m_DataLayout));
- }
-
- armnn::L2NormalizationDescriptor m_Descriptor;
- };
-
const std::string l2NormLayerName("l2Normalization");
const armnn::TensorInfo info({1, 2, 1, 5}, armnn::DataType::Float32);
armnn::L2NormalizationDescriptor desc;
desc.m_DataLayout = armnn::DataLayout::NCHW;
+ desc.m_Eps = 0.0001f;
armnn::INetworkPtr network = armnn::INetwork::Create();
armnn::IConnectableLayer* const inputLayer0 = network->AddInputLayer(0);
@@ -1098,6 +1100,62 @@
deserializedNetwork->Accept(verifier);
}
+BOOST_AUTO_TEST_CASE(EnsureL2NormalizationBackwardCompatibility)
+{
+ // The hex array below is a flat buffer containing a simple network with one input
+ // a L2Normalization layer and an output layer with dimensions as per the tensor infos below.
+ //
+ // This test verifies that we can still read back these old style
+ // models without the normalization epsilon value.
+ unsigned int size = 508;
+ const unsigned char l2NormalizationModel[] = {
+ 0x10,0x00,0x00,0x00,0x00,0x00,0x0A,0x00,0x10,0x00,0x04,0x00,0x08,0x00,0x0C,0x00,0x0A,0x00,0x00,0x00,
+ 0x0C,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x1C,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x3C,0x01,0x00,0x00,
+ 0x74,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+ 0x02,0x00,0x00,0x00,0xE8,0xFE,0xFF,0xFF,0x00,0x00,0x00,0x0B,0x04,0x00,0x00,0x00,0xD6,0xFE,0xFF,0xFF,
+ 0x0C,0x00,0x00,0x00,0x00,0x00,0x06,0x00,0x08,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,
+ 0x9E,0xFF,0xFF,0xFF,0x02,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+ 0x14,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00,
+ 0x00,0x00,0x00,0x00,0x4C,0xFF,0xFF,0xFF,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x44,0xFF,0xFF,0xFF,
+ 0x00,0x00,0x00,0x20,0x0C,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x04,0x00,0x08,0x00,0x08,0x00,0x00,0x00,
+ 0x20,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x04,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x0E,0x00,
+ 0x18,0x00,0x04,0x00,0x08,0x00,0x0C,0x00,0x10,0x00,0x14,0x00,0x0E,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+ 0x10,0x00,0x00,0x00,0x1F,0x00,0x00,0x00,0x1C,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x0F,0x00,0x00,0x00,
+ 0x6C,0x32,0x4E,0x6F,0x72,0x6D,0x61,0x6C,0x69,0x7A,0x61,0x74,0x69,0x6F,0x6E,0x00,0x01,0x00,0x00,0x00,
+ 0x48,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0C,0x00,0x00,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x04,0x00,
+ 0x08,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x52,0xFF,0xFF,0xFF,0x00,0x00,0x00,0x01,0x08,0x00,0x00,0x00,
+ 0x00,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+ 0x05,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x00,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,
+ 0x00,0x00,0x00,0x00,0x08,0x00,0x0C,0x00,0x07,0x00,0x08,0x00,0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x09,
+ 0x04,0x00,0x00,0x00,0xF6,0xFF,0xFF,0xFF,0x0C,0x00,0x00,0x00,0x00,0x00,0x06,0x00,0x0A,0x00,0x04,0x00,
+ 0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x00,0x00,0x0E,0x00,0x14,0x00,0x00,0x00,0x04,0x00,0x08,0x00,
+ 0x0C,0x00,0x10,0x00,0x0E,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+ 0x10,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,
+ 0x0C,0x00,0x00,0x00,0x08,0x00,0x0A,0x00,0x00,0x00,0x04,0x00,0x08,0x00,0x00,0x00,0x10,0x00,0x00,0x00,
+ 0x00,0x00,0x0A,0x00,0x10,0x00,0x08,0x00,0x07,0x00,0x0C,0x00,0x0A,0x00,0x00,0x00,0x00,0x00,0x00,0x01,
+ 0x08,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,
+ 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0 };
+
+ std::stringstream ss;
+ for (unsigned int i = 0; i < size; ++i)
+ {
+ ss << l2NormalizationModel[i];
+ }
+ std::string l2NormalizationLayerNetwork = ss.str();
+ armnn::INetworkPtr deserializedNetwork = DeserializeNetwork(l2NormalizationLayerNetwork);
+ BOOST_CHECK(deserializedNetwork);
+ const std::string layerName("l2Normalization");
+ const armnn::TensorInfo inputInfo = armnn::TensorInfo({1, 2, 1, 5}, armnn::DataType::Float32);
+
+ armnn::L2NormalizationDescriptor desc;
+ desc.m_DataLayout = armnn::DataLayout::NCHW;
+ // Since this variable does not exist in the l2NormalizationModel[] dump, the default value will be loaded.
+ desc.m_Eps = 1e-12f;
+
+ L2NormalizationLayerVerifier verifier(layerName, {inputInfo}, {inputInfo}, desc);
+ deserializedNetwork->Accept(verifier);
+}
+
BOOST_AUTO_TEST_CASE(SerializeMaximum)
{
class MaximumLayerVerifier : public LayerVerifierBase