IVGCVSW-7277 Fixed issues with FullyConnected on certain TFLite models
* TFLite Parser:
* Fixed issue in ParseReshape where the targetShape wasn't always calculated correctly
* Fixed issue in ParseFullyConnected where the wrong name was used for the ReshapeLayer
* Added an ExpandDims to the FullyConnected to ensure that we reshape the output correctly
* TFLite Delegate:
* Added an ExpandDims to the FullyConnected to ensure that we reshape the output correctly
Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I129dfcb8543f8a3a297c0589c841be20ef3b6407
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index a3bec92..66dc13e 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -1,5 +1,5 @@
//
-// Copyright © 2018-2023 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
@@ -497,9 +497,19 @@
}
std::unique_ptr<bool[]> dimMask = std::make_unique<bool[]>(tensorPtr->shape_signature.size());
+ bool batchOnly = true;
for (unsigned int i = 0; i < tensorPtr->shape_signature.size(); ++i)
{
- dimMask[i] = tensorPtr->shape_signature[i] == -1 ? false : true;
+ dimMask[i] = tensorPtr->shape_signature[i] != -1;
+
+ if (i > 0 && !dimMask[i])
+ {
+ batchOnly = false;
+ }
+ }
+ if (batchOnly)
+ {
+ dimMask[0] = true;
}
tensorShape = TensorShape(static_cast<unsigned int>(safeShape.size()), safeShape.data(), dimMask.get());
}
@@ -1163,7 +1173,7 @@
for (unsigned int i = 0u; i < numOutputs; ++i)
{
- layer->GetOutputSlot(i).SetTensorInfo(OutputTensorInfoFromInputs(subgraphIndex, operatorIndex, layer, i, {}));
+ layer->GetOutputSlot(i).SetTensorInfo(ToTensorInfo(outputs[0], true));
}
auto inputTensorIds = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -2969,10 +2979,17 @@
try
{
// We attempt to infer during Runtime.
- TensorShape reshapeShapes =ToTensorInfo(inputs[1]).GetShape();
- reshapeShapes = InputTensorInfo(subgraphIndex, operatorIndex, 1).GetShape();
+ TensorShape reshapeShapes = ToTensorInfo(inputs[1]).GetShape();
+
+ if (reshapeShapes[0] == actualOutputTensorInfo.GetNumDimensions())
+ {
+ for (unsigned int i = 0; i < actualOutputTensorInfo.GetShape().GetNumDimensions(); ++i)
+ {
+ targetShape.push_back(actualOutputTensorInfo.GetShape()[i]);
+ }
+ }
// The parser only supports shape (batch, -1) or (-1) for non-constant shape input.
- if (reshapeShapes[0] > 2)
+ else if (reshapeShapes[0] > 2)
{
throw ParseException(fmt::format("Invalid input shape '{}' in Reshape layer '{}' {}. "
"When inferring during runtime, the parser only supports "
@@ -2981,16 +2998,18 @@
layerName,
CHECK_LOCATION().AsString()));
}
-
- const int32_t numInputElements = inputTensorInfo.GetNumElements();
- const int32_t inputTensorShape = inputTensorInfo.GetShape()[0];
- if (reshapeShapes[0] == 1)
+ else
{
- targetShape = {numInputElements};
- }
- else if (reshapeShapes[0] == 2)
- {
- targetShape = {inputTensorShape, numInputElements / inputTensorShape};
+ const int32_t numInputElements = inputTensorInfo.GetNumElements();
+ const int32_t inputTensorShape = inputTensorInfo.GetShape()[0];
+ if (reshapeShapes[0] == 1)
+ {
+ targetShape = {numInputElements};
+ }
+ else if (reshapeShapes[0] == 2)
+ {
+ targetShape = {inputTensorShape, numInputElements / inputTensorShape};
+ }
}
}
catch (const std::exception& exc)
@@ -3220,7 +3239,6 @@
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
// Add the first input tensor to the registration list
std::vector<unsigned int> tensorIndexesToRegister = {inputTensorIndexes[0]};
- std::vector<unsigned int> ignoreInputWhenRegister = {};
armnn::TensorInfo inputTensorInfo = InputTensorInfo(subgraphIndex, operatorIndex, 0);
desc.m_ConstantWeights = IsConstTensor(inputs[1]);
@@ -3278,14 +3296,12 @@
std::string reshapeLayerName = fmt::format("Reshape_for:{}", layer->GetName());
armnn::ReshapeDescriptor reshapeDescriptor;
reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape();
- armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor, layerName.c_str());
+ armnn::IConnectableLayer* reshapeLayer = m_Network->AddReshapeLayer(reshapeDescriptor,
+ reshapeLayerName.c_str());
reshapeLayer->GetOutputSlot(0).SetTensorInfo(reshapedTensorInfo);
reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
- auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex));
- m_TensorInfos[outputTensorIndexes[0]] = reshapedTensorInfo;
-
RegisterInputSlots(subgraphIndex, operatorIndex, reshapeLayer, {inputTensorIndexes[0]});
// Fc layer connects to the reshape layer, so we skip the first input slot when registering fc's input slots
tensorIndexesToRegister.erase(tensorIndexesToRegister.begin());
@@ -3297,8 +3313,30 @@
armnn::TensorInfo outputTensorInfo = OutputTensorInfoFromShapes(subgraphIndex, operatorIndex, layer, 0,
{ inputTensorInfo.GetShape(),
filterTensorInfo.GetShape() });
+
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+ if (outputTensorInfo.GetNumDimensions() > 2)
+ {
+ // Calculate reshape to flatten to 2D [batch_size, input_size]
+ std::vector<unsigned int> reshapedDimensions(2);
+ reshapedDimensions[1] = filterTensorInfo.GetShape()[0];
+ reshapedDimensions[0] = outputTensorInfo.GetNumElements() / reshapedDimensions[1];
+ armnn::TensorInfo reshapedOutputTensorInfo = outputTensorInfo;
+ if (outputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0)
+ {
+ throw ParseException(
+ fmt::format("Failed to deduce output tensor shape from filter size {} {}",
+ reshapedDimensions[1],
+ CHECK_LOCATION().AsString()));
+ }
+ reshapedOutputTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
+ layer->GetOutputSlot(0).SetTensorInfo(reshapedOutputTensorInfo);
+
+ std::string reshapeLayerName = fmt::format("ExpandDims:{}:{}", subgraphIndex, operatorIndex);
+ layer = AddReshapeLayer(layer, 0, reshapeLayerName, outputTensorInfo);
+ }
+
// we need to add the activation layer and fortunately we don't need to care about the data layout
armnn::IConnectableLayer* fusedActivationLayer = AddFusedActivationLayer(layer, 0,
options->fused_activation_function);
@@ -3306,6 +3344,8 @@
// register the output connection slots for the layer, connections are made after all layers have been created
auto outputTensorIndexes = AsUnsignedVector(GetOutputTensorIds(m_Model, subgraphIndex, operatorIndex));
RegisterOutputSlots(subgraphIndex, operatorIndex, fusedActivationLayer, {outputTensorIndexes[0]});
+
+ m_TensorInfos[outputTensorIndexes[0]] = layer->GetOutputSlot(0).GetTensorInfo();
}
void TfLiteParserImpl::ParseDetectionPostProcess(size_t subgraphIndex, size_t operatorIndex)
@@ -4533,6 +4573,23 @@
RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
}
+armnn::IConnectableLayer* TfLiteParserImpl::AddReshapeLayer(armnn::IConnectableLayer* layer,
+ unsigned int outputSlot,
+ std::string reshapeLayerName,
+ armnn::TensorInfo outputShape)
+{
+ ReshapeDescriptor desc;
+ desc.m_TargetShape = outputShape.GetShape();
+
+ IConnectableLayer* reshapeLayer =
+ m_Network->AddReshapeLayer(desc, reshapeLayerName.c_str());
+
+ auto & prevOutputSlot = layer->GetOutputSlot(outputSlot);
+ prevOutputSlot.Connect(reshapeLayer->GetInputSlot(0));
+ reshapeLayer->GetOutputSlot(0).SetTensorInfo(outputShape);
+ return reshapeLayer;
+}
+
armnn::IConnectableLayer* TfLiteParserImpl::AddFusedActivationLayer(armnn::IConnectableLayer* prevLayer,
unsigned int outputSlot,
tflite::ActivationFunctionType activationType)