IVGCVSW-5826 Change weights layout for depthwise to [1,H,W,I*M]
* This change is necessary because tflite uses a [1,H,W,I*M] format
and uses the I*M dimension for per axis quantization. Our previous
layout [M,I,H,W] can't handle the correlating quantization scales.
* Updates Onnx-, TfLiteParser and TfliteDelegate
* Updates the CpuRef, CpuAcc and GpuAcc backends
* Adjusts unit tests
* Adds test to ensure models with old layout can still be read and
executed
* Adds conversion function to previous layout [1,H,W,I*M] --> [M,I,H,W]
which can be used by backend developers
!android-nn-driver:5553
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ifef23368b8c3702cf315a5838d214f7dc13c0152
diff --git a/src/armnnDeserializer/Deserializer.cpp b/src/armnnDeserializer/Deserializer.cpp
index 976986e..7951589 100644
--- a/src/armnnDeserializer/Deserializer.cpp
+++ b/src/armnnDeserializer/Deserializer.cpp
@@ -927,6 +927,7 @@
if (graph->featureVersions())
{
versions.m_BindingIdScheme = graph->featureVersions()->bindingIdsScheme();
+ versions.m_WeightsLayoutScheme = graph->featureVersions()->weightsLayoutScheme();
}
return versions;
@@ -1420,19 +1421,51 @@
descriptor.m_BiasEnabled = serializerDescriptor->biasEnabled();;
descriptor.m_DataLayout = ToDataLayout(serializerDescriptor->dataLayout());
- armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
- armnn::ConstTensor biases;
+ IConnectableLayer* layer;
armnn::Optional<armnn::ConstTensor> optionalBiases = armnn::EmptyOptional();
if (descriptor.m_BiasEnabled)
{
- biases = ToConstTensor(serializerLayer->biases());
+ armnn::ConstTensor biases = ToConstTensor(serializerLayer->biases());
optionalBiases = armnn::Optional<armnn::ConstTensor>(biases);
}
- IConnectableLayer* layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
- weights,
- optionalBiases,
- layerName.c_str());
+
+ armnn::ConstTensor weights = ToConstTensor(serializerLayer->weights());
+ // The data layout for weights in ArmNN used to be [M,I,H,W] but now it's changed to [1,H,W,I*M]
+ // When reading older flatbuffer files we need to add a permutation to get to the new layout.
+ if (this->GetFeatureVersions(graph).m_WeightsLayoutScheme <= 0)
+ {
+ // Permute weights [ H, W, M, I ] --> [ 1, H, W, I*M ]
+ // Step1: [ M, I, H, W ] --> [ H, W, I, M]
+ PermutationVector permutationVector = { 3, 2, 0, 1 };
+ armnn::TensorInfo weightsInfo = weights.GetInfo();
+ std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[weightsInfo.GetNumBytes()]);
+ weightsInfo = armnnUtils::Permuted(weightsInfo, permutationVector);
+ armnnUtils::Permute(weightsInfo.GetShape(), permutationVector,
+ weights.GetMemoryArea(), permuteBuffer.get(),
+ GetDataTypeSize(weightsInfo.GetDataType()));
+
+ // Step2: Reshape [ H, W, I, M] --> [ 1, H, W, I*M ]
+ auto weightsShape = weightsInfo.GetShape();
+ weightsInfo.SetShape({1,
+ weightsShape[0],
+ weightsShape[1],
+ weightsShape[2]*weightsShape[3]});
+
+ armnn::ConstTensor weightsPermuted(weightsInfo, permuteBuffer.get());
+
+ layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
+ weightsPermuted,
+ optionalBiases,
+ layerName.c_str());
+ }
+ else
+ {
+ layer = m_Network->AddDepthwiseConvolution2dLayer(descriptor,
+ weights,
+ optionalBiases,
+ layerName.c_str());
+ }
armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
diff --git a/src/armnnDeserializer/Deserializer.hpp b/src/armnnDeserializer/Deserializer.hpp
index 3465011..8f38058 100644
--- a/src/armnnDeserializer/Deserializer.hpp
+++ b/src/armnnDeserializer/Deserializer.hpp
@@ -163,6 +163,9 @@
{
// Default values to zero for backward compatibility
unsigned int m_BindingIdScheme = 0;
+
+ // Default values to zero for backward compatibility
+ unsigned int m_WeightsLayoutScheme = 0;
};
FeatureVersions GetFeatureVersions(GraphPtr graph);
diff --git a/src/armnnDeserializer/test/DeserializeDepthwiseConv2d.cpp b/src/armnnDeserializer/test/DeserializeDepthwiseConv2d.cpp
new file mode 100644
index 0000000..83dede1
--- /dev/null
+++ b/src/armnnDeserializer/test/DeserializeDepthwiseConv2d.cpp
@@ -0,0 +1,233 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ParserFlatbuffersSerializeFixture.hpp"
+
+#include <armnnDeserializer/IDeserializer.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+#include <string>
+
+BOOST_AUTO_TEST_SUITE(Deserializer)
+
+struct DepthwiseConv2dFlatbufferVersion1Fixture : public ParserFlatbuffersSerializeFixture
+{
+ explicit DepthwiseConv2dFlatbufferVersion1Fixture()
+ {
+ m_JsonString = R"(
+ {
+ "layers": [
+ {
+ "layer_type": "InputLayer",
+ "layer": {
+ "base": {
+ "base": {
+ "index": 0,
+ "layerName": "Input",
+ "layerType": "Input",
+ "inputSlots": [
+
+ ],
+ "outputSlots": [
+ {
+ "index": 0,
+ "tensorInfo": {
+ "dimensions": [
+ 1,
+ 3,
+ 3,
+ 3
+ ],
+ "dataType": "QAsymmS8",
+ "quantizationScale": 1.0,
+ "quantizationOffset": 0,
+ "quantizationDim": 0,
+ "dimensionality": 1,
+ "dimensionSpecificity": [
+ true,
+ true,
+ true,
+ true
+ ]
+ }
+ }
+ ]
+ },
+ "layerBindingId": 0
+ }
+ }
+ },
+ {
+ "layer_type": "DepthwiseConvolution2dLayer",
+ "layer": {
+ "base": {
+ "index": 1,
+ "layerName": "depwiseConvolution2dWithPerAxis",
+ "layerType": "DepthwiseConvolution2d",
+ "inputSlots": [
+ {
+ "index": 0,
+ "connection": {
+ "sourceLayerIndex": 0,
+ "outputSlotIndex": 0
+ }
+ }
+ ],
+ "outputSlots": [
+ {
+ "index": 0,
+ "tensorInfo": {
+ "dimensions": [
+ 1,
+ 3,
+ 3,
+ 3
+ ],
+ "dataType": "QAsymmS8",
+ "quantizationScale": 1.0,
+ "quantizationOffset": 0,
+ "quantizationDim": 0,
+ "dimensionality": 1,
+ "dimensionSpecificity": [
+ true,
+ true,
+ true,
+ true
+ ]
+ }
+ }
+ ]
+ },
+ "descriptor": {
+ "padLeft": 1,
+ "padRight": 1,
+ "padTop": 1,
+ "padBottom": 1,
+ "strideX": 1,
+ "strideY": 1,
+ "dilationX": 1,
+ "dilationY": 1,
+ "biasEnabled": false,
+ "dataLayout": "NHWC"
+ },
+ "weights": {
+ "info": {
+ "dimensions": [
+ 1,
+ 3,
+ 3,
+ 3
+ ],
+ "dataType": "QSymmS8",
+ "quantizationScale": 0.25,
+ "quantizationOffset": 0,
+ "quantizationScales": [
+ 0.25,
+ 0.2,
+ 0.1
+ ],
+ "quantizationDim": 0,
+ "dimensionality": 1,
+ "dimensionSpecificity": [
+ true,
+ true,
+ true,
+ true
+ ]
+ },
+ "data_type": "ByteData",
+ "data": {
+ "data": [
+ 4,
+ 20,
+ 0,
+ 8,
+ 20,
+ 30,
+ 4,
+ 0,
+ 10,
+ 12,
+ 0,
+ 40,
+ 0,
+ 5,
+ 30,
+ 16,
+ 10,
+ 40,
+ 12,
+ 0,
+ 30,
+ 16,
+ 20,
+ 0,
+ 12,
+ 20,
+ 20
+ ]
+ }
+ }
+ }
+ },
+ {
+ "layer_type": "OutputLayer",
+ "layer": {
+ "base": {
+ "base": {
+ "index": 2,
+ "layerName": "Output",
+ "layerType": "Output",
+ "inputSlots": [
+ {
+ "index": 0,
+ "connection": {
+ "sourceLayerIndex": 1,
+ "outputSlotIndex": 0
+ }
+ }
+ ],
+ "outputSlots": [
+
+ ]
+ },
+ "layerBindingId": 0
+ }
+ }
+ }
+ ],
+ "inputIds": [
+ 0
+ ],
+ "outputIds": [
+ 0
+ ],
+ "featureVersions": {
+ "bindingIdsScheme": 1
+ }
+ }
+ )";
+ SetupSingleInputSingleOutput("Input", "Output");
+ }
+};
+
+// This test uses a model that was created before weights layout scheme version was added to our flatbuffers
+// file. It ensures older models can still be read and executed
+// featureVersion weights layout scheme 1 indicates a change in the depthwise weights layout within
+// armm from [M,I,H,W] --> [1,H,W,I*M]
+BOOST_FIXTURE_TEST_CASE(DepthwiseConv2d_FlatbufferVersion1, DepthwiseConv2dFlatbufferVersion1Fixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 3,2,0,0,4,3,0,1,2,
+ 0,1,3,0,4,2,2,2,3,
+ 2,4,3,2,0,4,3,4,0},
+ { 15,60,10,11,37,20, 0,18,17,
+ 20,65,28,28,74,26,12,20,18,
+ 25,36,12,37,42,25,29,14, 9});
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file