GitHub #714: OnnxParser FullyConnectedLayer inferred shape doesn't match
* Added reshape before and after FullyConnected to support dimensions
> 2. This is now consistent with the Delegate and TfLiteParser.
* Refactored AddFullyConnected method to remove duplicate code.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Signed-off-by: Francis Murtagh <francis.murtagh@arm.com>
Change-Id: I04dfeb38dbcac096c5fcd9dcb5e3821d38ce6550
diff --git a/src/armnnOnnxParser/OnnxParser.cpp b/src/armnnOnnxParser/OnnxParser.cpp
index 552d4e4..936216f 100644
--- a/src/armnnOnnxParser/OnnxParser.cpp
+++ b/src/armnnOnnxParser/OnnxParser.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2017,2022 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017,2022-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "OnnxParser.hpp"
@@ -505,9 +505,10 @@
outNames.end(),
[this](std::string name)
{
- return (m_TensorsInfo.count(name) == 0 || m_TensorsInfo[name].m_info == nullptr
- || m_TensorsInfo[name].m_info->GetShape().GetDimensionality() ==
- Dimensionality::NotSpecified);
+ return (m_TensorsInfo.count(name) == 0 ||
+ m_TensorsInfo[name].m_info == nullptr ||
+ m_TensorsInfo[name].m_info->GetShape().GetDimensionality() ==
+ Dimensionality::NotSpecified);
});
std::vector<TensorInfo> outInfo;
//if the output info(s) are not here, we need to compute them
@@ -1148,16 +1149,23 @@
void OnnxParserImpl::AddFullyConnected(const onnx::NodeProto& matmulNode, const onnx::NodeProto* addNode)
{
-
// find matmul inputs
- std::string weightName;
std::string inputName;
+ std::string weightName;
+ std::string biasName;
+ std::string outputName;
CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.input_size()), 2);
CHECK_VALID_SIZE(static_cast<size_t>(matmulNode.output_size()), 1);
VALID_INPUTS(matmulNode, STR_LIST(onnx::TensorProto::FLOAT));
GetInputAndParam(matmulNode, &inputName, &weightName, CHECK_LOCATION());
+ TensorInfo inputInfo = *m_TensorsInfo[inputName].m_info;
+ TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info;
+ TensorInfo biasInfo;
+
+ std::vector<std::string> inputNames;
+
FullyConnectedDescriptor desc;
desc.m_BiasEnabled = addNode != nullptr;
@@ -1165,7 +1173,6 @@
if(desc.m_BiasEnabled)
{
// find bias const
- std::string biasName;
CHECK_VALID_SIZE(static_cast<size_t>(addNode->input_size()), 2);
CHECK_VALID_SIZE(static_cast<size_t>(addNode->output_size()), 1);
VALID_INPUTS(*addNode, STR_LIST(onnx::TensorProto::FLOAT));
@@ -1174,8 +1181,7 @@
//Output shape is [1, weights[1]] and 1d vec in ONNX can be [1,X] so we convert biases to "armnn" 1D
To1DTensor(biasName, CHECK_LOCATION());
- TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info;
- TensorInfo biasInfo = *m_TensorsInfo[biasName].m_info;
+ biasInfo = *m_TensorsInfo[biasName].m_info;
if (weightInfo.GetShape()[1] != biasInfo.GetShape()[0])
{
@@ -1191,61 +1197,114 @@
CHECK_LOCATION().AsString()));
}
- // Just add a FullyConnected layer, weights and biases are handled as inputs now.
- layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str());
- ARMNN_ASSERT(layer != nullptr);
-
- auto outputInfo = ComputeOutputInfo({addNode->output(0)}, layer,
- {m_TensorsInfo[inputName].m_info->GetShape(),
- m_TensorsInfo[weightName].m_info->GetShape()});
- layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
-
- // Add constant layer to store weights/biases and connect to FullyConnected layer..
- if(m_TensorsInfo[weightName].isConstant())
- {
- IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first);
-
- weightInfo.SetConstant();
- weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo);
- weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
- }
-
- if(m_TensorsInfo[biasName].isConstant())
- {
- IConnectableLayer* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(biasName).first);
-
- biasInfo.SetConstant();
- biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
- biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
- }
-
- RegisterInputSlots(layer, {inputName, weightName, biasName});
- RegisterOutputSlots(layer, {addNode->output(0)});
+ inputNames = { inputName, weightName, biasName };
+ outputName = addNode->output(0);
}
else
{
- layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str());
- ARMNN_ASSERT(layer != nullptr);
+ inputNames = { inputName, weightName };
+ outputName = matmulNode.output(0);
+ }
- auto outputInfo = ComputeOutputInfo({matmulNode.output(0)}, layer,
- {m_TensorsInfo[inputName].m_info->GetShape(),
- m_TensorsInfo[weightName].m_info->GetShape()});
- layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+ // Just add a FullyConnected layer, weights and biases are handled as inputs now.
+ layer = m_Network->AddFullyConnectedLayer(desc, matmulNode.name().c_str());
+ ARMNN_ASSERT(layer != nullptr);
- // Add constant layer to store weights and connect to FullyConnected layer.
- if(m_TensorsInfo[weightName].isConstant())
+ if (inputInfo.GetNumDimensions() > 2)
+ {
+ // Add reshape to flatten to 2D [batch_size, input_size],
+ // where "input_size" corresponds to the number of inputs to the layer,
+ // matching the second dimension of weights,
+ // and "batch_size" is calculated by dividing the number of elements by "input_size".
+ std::vector<unsigned int> reshapedDimensions(2);
+ reshapedDimensions[1] = weightInfo.GetShape()[0];
+ reshapedDimensions[0] = inputInfo.GetNumElements() / reshapedDimensions[1];
+
+ if (inputInfo.GetNumElements() % reshapedDimensions[1] != 0)
{
- TensorInfo weightInfo = *m_TensorsInfo[weightName].m_info;
- IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first);
-
- weightInfo.SetConstant();
- weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
- weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo);
+ throw ParseException(
+ fmt::format("Failed to deduce input tensor shape from filter size {} {}",
+ reshapedDimensions[1],
+ CHECK_LOCATION().AsString()));
}
- RegisterInputSlots(layer, {inputName, weightName});
- RegisterOutputSlots(layer, {matmulNode.output(0)});
+ TensorInfo reshapedTensorInfo = inputInfo;
+ reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
+ inputInfo = reshapedTensorInfo;
+
+ ReshapeDescriptor reshapeDescriptor;
+ reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape();
+
+ std::string reshapeLayerName = fmt::format("Reshape_for:{}", layer->GetName());
+ IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor, reshapeLayerName.c_str());
+
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo);
+ reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
+
+ RegisterInputSlots(reshapeLayer, {inputName});
+ inputNames[0] = reshapeLayerName;
}
+
+ auto outputInfo = ComputeOutputInfo({ outputName },
+ layer,
+ { inputInfo.GetShape(),
+ weightInfo.GetShape() });
+ layer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+ RegisterInputSlots(layer, inputNames);
+
+ // Add constant layer to store weights/biases and connect to FullyConnected layer..
+ if(m_TensorsInfo[weightName].isConstant())
+ {
+ IConnectableLayer* weightsLayer = m_Network->AddConstantLayer(CreateConstTensor(weightName).first);
+
+ weightInfo.SetConstant();
+ weightsLayer->GetOutputSlot(0).SetTensorInfo(weightInfo);
+ weightsLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(1u));
+ }
+
+ if(desc.m_BiasEnabled && m_TensorsInfo[biasName].isConstant())
+ {
+ IConnectableLayer* biasLayer = m_Network->AddConstantLayer(CreateConstTensor(biasName).first);
+
+ biasInfo.SetConstant();
+ biasLayer->GetOutputSlot(0).SetTensorInfo(biasInfo);
+ biasLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(2u));
+ }
+
+ if (outputInfo[0].GetNumDimensions() > 2)
+ {
+ // Calculate reshape to flatten to 2D [batch_size, input_size]
+ std::vector<unsigned int> reshapedDimensions(2);
+ reshapedDimensions[1] = weightInfo.GetShape()[1];
+ reshapedDimensions[0] = outputInfo[0].GetNumElements() / reshapedDimensions[1];
+
+ if (outputInfo[0].GetNumElements() % reshapedDimensions[1] != 0)
+ {
+ throw ParseException(
+ fmt::format("Failed to deduce output tensor shape from filter size {} {}",
+ reshapedDimensions[1],
+ CHECK_LOCATION().AsString()));
+ }
+
+ armnn::TensorInfo reshapedOutputTensorInfo = outputInfo[0];
+ reshapedOutputTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
+ layer->GetOutputSlot(0).SetTensorInfo(reshapedOutputTensorInfo);
+
+ ReshapeDescriptor desc;
+ desc.m_TargetShape = outputInfo[0].GetShape();
+
+ std::string reshapeLayerName = fmt::format("ExpandDims_for:{}", layer->GetName());
+ IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(desc, reshapeLayerName.c_str());
+
+ layer->GetOutputSlot(0).Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputInfo[0]);
+
+ RegisterInputSlots(reshapeLayer, {layer->GetName()});
+ layer = reshapeLayer;
+ }
+
+ RegisterOutputSlots(layer, { outputName });
}
void OnnxParserImpl::AddPoolingLayer(const onnx::NodeProto& node, Pooling2dDescriptor& desc)