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Diffstat (limited to 'src/armnnTfLiteParser/TfLiteParser.cpp')
-rw-r--r--src/armnnTfLiteParser/TfLiteParser.cpp161
1 files changed, 112 insertions, 49 deletions
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 1a4449302e..7b496173cc 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -5,6 +5,7 @@
#include "TfLiteParser.hpp"
+#include <armnn/BackendOptions.hpp>
#include <armnn/Descriptors.hpp>
#include <armnn/Exceptions.hpp>
#include <armnn/Logging.hpp>
@@ -311,8 +312,10 @@ void CalcPadding(uint32_t inputSize,
}
}
-armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, const std::vector<unsigned int>& shapes,
- const armnn::PermutationVector& dimensionMappings = {0, 1, 2, 3})
+armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr,
+ const std::vector<unsigned int>& shapes,
+ const armnn::PermutationVector& dimensionMappings = {0, 1, 2, 3},
+ const bool outputTensor = false)
{
armnn::DataType type;
CHECK_TENSOR_PTR(tensorPtr);
@@ -357,9 +360,14 @@ armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, const std::
}
}
std::vector<unsigned int> safeShape = shapes;
+ bool isDynamic = false;
if (safeShape.size() == 0)
{
safeShape.push_back(1);
+ if (outputTensor)
+ {
+ isDynamic = true;
+ }
}
float quantizationScale = 0.0f;
@@ -383,12 +391,16 @@ armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, const std::
quantizationOffset = boost::numeric_cast<int32_t>(tensorPtr->quantization->zero_point[0]);
}
- armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()),
- safeShape.data(),
- type,
- quantizationScale,
- quantizationOffset);
-
+ TensorShape tensorShape(boost::numeric_cast<unsigned int>(safeShape.size()),
+ safeShape.data());
+ if (isDynamic)
+ {
+ tensorShape = TensorShape(1, false);
+ }
+ armnn::TensorInfo result(tensorShape,
+ type,
+ quantizationScale,
+ quantizationOffset);
return result;
}
else
@@ -402,19 +414,29 @@ armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr, const std::
std::back_inserter(quantizationScales));
// QSymmS8 Per-axis
- armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()),
- safeShape.data(),
- type,
- quantizationScales,
- dimensionMappings[boost::numeric_cast<unsigned int>(
- tensorPtr->quantization->quantized_dimension)]);
+ TensorShape tensorShape(boost::numeric_cast<unsigned int>(safeShape.size()),
+ safeShape.data());
+ if (isDynamic)
+ {
+ tensorShape = TensorShape(1, false);
+ }
+ armnn::TensorInfo result(tensorShape,
+ type,
+ quantizationScales,
+ dimensionMappings[boost::numeric_cast<unsigned int>(
+ tensorPtr->quantization->quantized_dimension)]);
return result;
}
}
else
{
- armnn::TensorInfo result(boost::numeric_cast<unsigned int>(safeShape.size()),
- safeShape.data(),
+ TensorShape tensorShape(boost::numeric_cast<unsigned int>(safeShape.size()),
+ safeShape.data());
+ if (isDynamic)
+ {
+ tensorShape = TensorShape(1, false);
+ }
+ armnn::TensorInfo result(tensorShape,
type,
quantizationScale,
quantizationOffset);
@@ -429,6 +451,14 @@ armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr,
return ToTensorInfo(tensorPtr, dimensions, dimensionMappings);
}
+armnn::TensorInfo ToTensorInfo(TfLiteParser::TensorRawPtr tensorPtr,
+ const bool outputTensor)
+{
+ auto const & dimensions = AsUnsignedVector(tensorPtr->shape);
+ const armnn::PermutationVector& dimensionMappings = {0, 1, 2, 3};
+ return ToTensorInfo(tensorPtr, dimensions, dimensionMappings, outputTensor);
+}
+
template<typename T>
std::pair<armnn::ConstTensor, std::unique_ptr<T[]>>
CreateConstTensorImpl(TfLiteParser::BufferRawPtr bufferPtr,
@@ -659,7 +689,20 @@ INetworkPtr TfLiteParser::CreateNetworkFromBinary(const std::vector<uint8_t> & b
INetworkPtr TfLiteParser::CreateNetworkFromModel()
{
- m_Network = INetwork::Create();
+
+ using NetworkOptions = std::vector<BackendOptions>;
+ NetworkOptions networkOptions = {};
+ if (m_Options && m_Options.value().m_InferAndValidate)
+ {
+ BackendOptions shapeInferenceMethodOption("ShapeInferenceMethod",
+ {
+ { "InferAndValidate", true }
+ });
+
+ networkOptions.push_back(shapeInferenceMethodOption);
+ }
+
+ m_Network = INetwork::Create(networkOptions);
ARMNN_ASSERT(m_Model.get() != nullptr);
if (m_Model->subgraphs.size() != 1)
@@ -734,6 +777,26 @@ INetworkPtr TfLiteParser::CreateNetworkFromModel()
}
}
+ // if InferAndValidate set make sure all the TensorInfo set and all the dynamic output tensors are inferred
+ if (m_Options && m_Options.value().m_InferAndValidate)
+ {
+ for (subgraphIndex = 0;
+ subgraphIndex < m_SubgraphConnections.size();
+ ++subgraphIndex)
+ {
+ if (m_SubgraphConnections[subgraphIndex].size() > 0)
+ {
+ // get the last output slot on the layer
+ auto outputSlot =
+ m_SubgraphConnections[subgraphIndex][m_SubgraphConnections[subgraphIndex].size() - 1].outputSlot;
+ if (outputSlot != nullptr)
+ {
+ outputSlot->IsTensorInfoSet();
+ }
+ }
+ }
+ }
+
return std::move(m_Network);
}
@@ -835,7 +898,7 @@ void TfLiteParser::ParseUnsupportedOperator(size_t subgraphIndex, size_t operato
for (unsigned int i = 0u; i < numOutputs; ++i)
{
- layer->GetOutputSlot(i).SetTensorInfo(ToTensorInfo(outputs[i]));
+ layer->GetOutputSlot(i).SetTensorInfo(ToTensorInfo(outputs[i], true));
}
auto inputTensorIds = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -914,7 +977,7 @@ void TfLiteParser::ParseConv2D(size_t subgraphIndex, size_t operatorIndex)
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// register the input connection slots for the layer, connections are made after all layers have been created
@@ -1001,7 +1064,7 @@ void TfLiteParser::ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorInd
}
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// register the input connection slots for the layer, connections are made after all layers have been created
@@ -1030,7 +1093,7 @@ void TfLiteParser::ParseDequantize(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddDequantizeLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1057,7 +1120,7 @@ void TfLiteParser::ParseExp(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(desc, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1093,7 +1156,7 @@ void TfLiteParser::ParseTranspose(size_t subgraphIndex, size_t operatorIndex)
}
TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddTransposeLayer(desc, layerName.c_str());
@@ -1188,7 +1251,7 @@ void TfLiteParser::ParseTransposeConv(size_t subgraphIndex, size_t operatorIndex
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// only the tensors for the inputs are relevant, exclude the const (filter) tensor
@@ -1241,7 +1304,7 @@ void TfLiteParser::ParseBatchToSpaceND(size_t subgraphIndex, size_t operatorInde
auto layerName = boost::str(boost::format("BatchToSpaceND:%1%:%2%") % subgraphIndex % operatorIndex);
TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddBatchToSpaceNdLayer(desc, layerName.c_str());
@@ -1272,7 +1335,7 @@ void TfLiteParser::ParseL2Normalization(size_t subgraphIndex, size_t operatorInd
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1303,7 +1366,7 @@ void TfLiteParser::ParseMaximum(size_t subgraphIndex, size_t operatorIndex)
TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]);
CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, "Input 0", "Input 1");
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddMaximumLayer(layerName.c_str());
@@ -1340,7 +1403,7 @@ void TfLiteParser::ParseMinimum(size_t subgraphIndex, size_t operatorIndex)
TensorInfo input1TensorInfo = ToTensorInfo(inputs[1]);
CheckMatchingQuantization(inputTensorInfo, input1TensorInfo, layerName, "Input 0", "Input 1");
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddMinimumLayer(layerName.c_str());
@@ -1415,7 +1478,7 @@ void TfLiteParser::ParsePool(size_t subgraphIndex,
auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
CHECK_VALID_SIZE(outputs.size(), 1);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddPooling2dLayer(desc, layerName.c_str());
@@ -1462,7 +1525,7 @@ void TfLiteParser::ParseSlice(size_t subgraphIndex, size_t operatorIndex)
auto layerName = boost::str(boost::format("Slice:%1%:%2%") % subgraphIndex % operatorIndex);
TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* const layer = m_Network->AddSliceLayer(desc, layerName.c_str());
@@ -1495,7 +1558,7 @@ void TfLiteParser::ParseSoftmax(size_t subgraphIndex, size_t operatorIndex)
auto layerName = boost::str(boost::format("Softmax:%1%:%2%") % subgraphIndex % operatorIndex);
IConnectableLayer* const layer = m_Network->AddSoftmaxLayer(desc, layerName.c_str());
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// register the input connection slots for the layer, connections are made after all layers have been created
@@ -1545,7 +1608,7 @@ void TfLiteParser::ParseSpaceToBatchND(size_t subgraphIndex, size_t operatorInde
auto layerName = boost::str(boost::format("SpaceToBatchND:%1%:%2%") % subgraphIndex % operatorIndex);
TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddSpaceToBatchNdLayer(desc, layerName.c_str());
@@ -1692,7 +1755,7 @@ void TfLiteParser::ParseStridedSlice(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddStridedSliceLayer(desc, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1722,7 +1785,7 @@ void TfLiteParser::ParseSub(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddSubtractionLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1761,7 +1824,7 @@ void TfLiteParser::ParseDiv(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddDivisionLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1800,7 +1863,7 @@ void TfLiteParser::ParseAdd(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddAdditionLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1839,7 +1902,7 @@ void TfLiteParser::ParseMul(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddMultiplicationLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1876,7 +1939,7 @@ void TfLiteParser::ParseMean(size_t subgraphIndex, size_t operatorIndex)
desc.m_Axis = axis;
armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
desc.m_KeepDims =
inputTensorInfo.GetNumDimensions() == outputTensorInfo.GetNumDimensions() ?
@@ -1910,7 +1973,7 @@ void TfLiteParser::ParseNeg(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddElementwiseUnaryLayer(descriptor, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -1943,7 +2006,7 @@ void TfLiteParser::ParsePad(size_t subgraphIndex, size_t operatorIndex)
}
auto layerName = boost::str(boost::format("Pad:%1%:%2%") % subgraphIndex % operatorIndex);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
IConnectableLayer* layer = m_Network->AddPadLayer(desc, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
@@ -1971,7 +2034,7 @@ void TfLiteParser::ParseQuantize(size_t subgraphIndex, size_t operatorIndex)
IConnectableLayer* layer = m_Network->AddQuantizeLayer(layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -2073,7 +2136,7 @@ void TfLiteParser::ParseActivation(size_t subgraphIndex, size_t operatorIndex, A
IConnectableLayer* const layer = m_Network->AddActivationLayer(activationDesc, layerName.c_str());
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// register the input connection slots for the layer, connections are made after all layers have been created
@@ -2279,7 +2342,7 @@ void TfLiteParser::ParseResize(size_t subgraphIndex, size_t operatorIndex, Resiz
}
TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
CheckMatchingQuantization(inputTensorInfo, outputTensorInfo, layerName, "Input 0", "Output 0");
IConnectableLayer* layer = m_Network->AddResizeLayer(desc, layerName.c_str());
@@ -2327,7 +2390,7 @@ void TfLiteParser::ParseConcatenation(size_t subgraphIndex, size_t operatorIndex
}
auto layerName = boost::str(boost::format("Concatenation:%1%:%2%") % subgraphIndex % operatorIndex);
- TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
IConnectableLayer* layer = m_Network->AddConcatLayer(concatDescriptor, layerName.c_str());
ARMNN_ASSERT(layer != nullptr);
@@ -2446,7 +2509,7 @@ void TfLiteParser::ParseFullyConnected(size_t subgraphIndex, size_t operatorInde
RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});
}
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// we need to add the activation layer and fortunately we don't need to care about the data layout
@@ -2566,7 +2629,7 @@ void TfLiteParser::ParsePack(size_t subgraphIndex, size_t operatorIndex)
ARMNN_ASSERT(layer != nullptr);
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0], true);
layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
@@ -2660,7 +2723,7 @@ void TfLiteParser::ParseUnpack(size_t subgraphIndex, size_t operatorIndex)
// Create reshape to remove the unpacked dimension for unpack operator of each output from Splitter.
for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k)
{
- armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[k]);
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[k], true);
std::string reshapeLayerName = boost::str(boost::format("Reshape_for:%1%") % layer->GetName());
armnn::ReshapeDescriptor desc;
desc.m_TargetShape = outputTensorInfo.GetShape();
@@ -2757,7 +2820,7 @@ void TfLiteParser::ParseSplit(size_t subgraphIndex, size_t operatorIndex)
for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k)
{
- armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k]);
+ armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k], true);
layer->GetOutputSlot(k).SetTensorInfo(tensorInfo);
}
@@ -2905,7 +2968,7 @@ void TfLiteParser::ParseSplitV(size_t subgraphIndex, size_t operatorIndex)
for (unsigned int k = 0; k < layer->GetNumOutputSlots(); ++k)
{
- armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k]);
+ armnn::TensorInfo tensorInfo = ToTensorInfo(outputs[k], true);
layer->GetOutputSlot(k).SetTensorInfo(tensorInfo);
}
generated by cgit v1.2.1 (git 2.23.0) at 2024-06-04 06:35:09 +0000