IVGCVSW-4932 Introduce ShapeInferenceMethod to TfLite Parser
* Introduced ShapeInferenceMethod::InferAndValidate to TfLiteParser
* Added unit tests
Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Iad4aadce92912e7645b1652019ec4af478b7fc32
diff --git a/include/armnnTfLiteParser/ITfLiteParser.hpp b/include/armnnTfLiteParser/ITfLiteParser.hpp
index de1eae7..a68b719 100644
--- a/include/armnnTfLiteParser/ITfLiteParser.hpp
+++ b/include/armnnTfLiteParser/ITfLiteParser.hpp
@@ -28,9 +28,11 @@
struct TfLiteParserOptions
{
TfLiteParserOptions()
- : m_StandInLayerForUnsupported(false) {}
+ : m_StandInLayerForUnsupported(false),
+ m_InferAndValidate(false) {}
bool m_StandInLayerForUnsupported;
+ bool m_InferAndValidate;
};
static ITfLiteParser* CreateRaw(const armnn::Optional<TfLiteParserOptions>& options = armnn::EmptyOptional());
diff --git a/src/armnn/test/TensorHelpers.hpp b/src/armnn/test/TensorHelpers.hpp
index 286d9f3..4536035 100644
--- a/src/armnn/test/TensorHelpers.hpp
+++ b/src/armnn/test/TensorHelpers.hpp
@@ -74,20 +74,26 @@
template <typename T, std::size_t n>
boost::test_tools::predicate_result CompareTensors(const boost::multi_array<T, n>& a,
const boost::multi_array<T, n>& b,
- bool compareBoolean = false)
+ bool compareBoolean = false,
+ bool isDynamic = false)
{
- // Checks they are same shape.
- for (unsigned int i=0; i<n; i++)
+ if (!isDynamic)
{
- if (a.shape()[i] != b.shape()[i])
+ // Checks they are same shape.
+ for (unsigned int i = 0;
+ i < n;
+ i++)
{
- boost::test_tools::predicate_result res(false);
- res.message() << "Different shapes ["
- << a.shape()[i]
- << "!="
- << b.shape()[i]
- << "]";
- return res;
+ if (a.shape()[i] != b.shape()[i])
+ {
+ boost::test_tools::predicate_result res(false);
+ res.message() << "Different shapes ["
+ << a.shape()[i]
+ << "!="
+ << b.shape()[i]
+ << "]";
+ return res;
+ }
}
}
@@ -190,9 +196,13 @@
// Creates a boost::multi_array with the shape defined by the given TensorInfo and contents defined by the given vector.
template <typename T, std::size_t n>
-boost::multi_array<T, n> MakeTensor(const armnn::TensorInfo& tensorInfo, const std::vector<T>& flat)
+boost::multi_array<T, n> MakeTensor(
+ const armnn::TensorInfo& tensorInfo, const std::vector<T>& flat, bool isDynamic = false)
{
- ARMNN_ASSERT_MSG(flat.size() == tensorInfo.GetNumElements(), "Wrong number of components supplied to tensor");
+ if (!isDynamic)
+ {
+ ARMNN_ASSERT_MSG(flat.size() == tensorInfo.GetNumElements(), "Wrong number of components supplied to tensor");
+ }
std::array<unsigned int, n> shape;
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 1a44493..7b49617 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 @@
}
}
-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 @@
}
}
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 @@
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 @@
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 @@
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::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 @@
}
}
+ // 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 @@
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 @@
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 @@
}
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 @@
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 @@
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 @@
}
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
}
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 @@
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 @@
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 @@
}
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 @@
}
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 @@
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 @@
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 @@
// 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 @@
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 @@
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);
}
diff --git a/src/armnnTfLiteParser/test/Conv2D.cpp b/src/armnnTfLiteParser/test/Conv2D.cpp
index 2eae5f5..8ef827c 100644
--- a/src/armnnTfLiteParser/test/Conv2D.cpp
+++ b/src/armnnTfLiteParser/test/Conv2D.cpp
@@ -234,6 +234,32 @@
});
}
+struct DynamicConv2DWithBiasesFixture : Conv2DWithBiasesFixture
+{
+ DynamicConv2DWithBiasesFixture()
+ : Conv2DWithBiasesFixture("[ 1, 2, 2, 1 ]", // inputShape
+ "[ ]", // outputShape
+ "[ 1, 2, 2, 1 ]", // filterShape
+ "[ 2,1, 0,6 ]", // filterData
+ "[ 1 ]", // biasShape
+ "[ 10, 0, 0, 0 ]", // biasData
+ "1") // stride w and h
+ {}
+};
+
+BOOST_FIXTURE_TEST_CASE( ParseDynamicConv2DWithBias, DynamicConv2DWithBiasesFixture )
+{
+ RunTest<4,
+ armnn::DataType::QAsymmU8,
+ armnn::DataType::QAsymmU8>(0,
+ { { "inputTensor", { 1, 2, 3, 4, } } },
+ { { "outputTensor", { (1*2 + 2*1 + 3*0 + 4*6 + 10)/2,
+ (2*2 + 0*1 + 4*0 + 0*6 + 10)/2,
+ (3*2 + 4*1 + 0*0 + 0*6 + 10)/2,
+ (4*2 + 0*1 + 0*0 + 0*6 + 10)/2} } },
+ true);
+}
+
struct Conv2DShapeTestFixture : Conv2DWithBiasesFixture
{
static std::string GenerateInts(unsigned int n)
diff --git a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
index 2bf08fa..5d220eb 100644
--- a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
+++ b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
@@ -196,4 +196,30 @@
(110+10)/2, (197+10)/2, (158+10)/2 });
}
+struct DynamicDepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture
+{
+ DynamicDepthwiseConvolution2dSameBiasFixture()
+ : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape
+ "[ ]", // outputShape
+ "[ 1, 3, 3, 1 ]", // filterShape
+ "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData
+ "1", // stride w and h
+ "SAME", // padding type
+ "[ 1 ]", // biasShape
+ "[ 10, 0, 0, 0 ]") // biasData
+ {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ParseDynamicDepthwiseConv2DSameBias, DynamicDepthwiseConvolution2dSameBiasFixture)
+{
+ RunTest<4, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0,
+ { { "inputTensor", { 0, 1, 2,
+ 3, 4, 5,
+ 6, 7, 8 } } },
+ { { "outputTensor", { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2,
+ ( 57+10)/2, (120+10)/2, (111+10)/2,
+ (110+10)/2, (197+10)/2, (158+10)/2 } } },
+ true);
+}
+
BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnTfLiteParser/test/Div.cpp b/src/armnnTfLiteParser/test/Div.cpp
index f83e455..10be29d 100644
--- a/src/armnnTfLiteParser/test/Div.cpp
+++ b/src/armnnTfLiteParser/test/Div.cpp
@@ -112,4 +112,30 @@
1.0f, 1.0f, -1.0f } } });
}
+
+struct DynamicDivFixture : public DivFixture
+{
+ DynamicDivFixture() : DivFixture("[ 1, 2, 2, 3 ]", "[ 1, 2, 2, 3 ]", "[ ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(ParseDynamicDiv, DynamicDivFixture)
+{
+ using armnn::DataType;
+ float Inf = std::numeric_limits<float>::infinity();
+ float NaN = std::numeric_limits<float>::quiet_NaN();
+
+ RunTest<4, DataType::Float32, DataType::Float32>(0, {{ "inputTensor1", { 0.0f, 1.0f, 2.0f,
+ 3.0f, 4.0f, 5.0f,
+ 6.0f, 7.0f, 8.0f,
+ 9.0f, 10.0f, -11.0f } },
+ { "inputTensor2", { 0.0f, 0.0f, 4.0f,
+ 3.0f, 40.0f, 5.0f,
+ 6.0f, 7.0f, 8.0f,
+ 9.0f, 10.0f, 11.0f} } },
+ {{ "outputTensor", { NaN, Inf, 0.5f,
+ 1.0f, 0.1f, 1.0f,
+ 1.0f, 1.0f, 1.0f,
+ 1.0f, 1.0f, -1.0f } } }, true);
+}
+
BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnTfLiteParser/test/FullyConnected.cpp b/src/armnnTfLiteParser/test/FullyConnected.cpp
index d1223d5..e7aa908 100644
--- a/src/armnnTfLiteParser/test/FullyConnected.cpp
+++ b/src/armnnTfLiteParser/test/FullyConnected.cpp
@@ -171,4 +171,28 @@
{ (40+10)/2, (400+10)/2 });
}
+struct DynamicFullyConnectedWithBiasMultipleOutputsFixture : FullyConnectedFixture
+{
+ DynamicFullyConnectedWithBiasMultipleOutputsFixture()
+ : FullyConnectedFixture("[ 1, 4, 2, 1 ]", // inputShape
+ "[ ]", // outputShape
+ "[ 1, 4 ]", // filterShape
+ "[ 2, 3, 4, 5 ]", // filterData
+ "[ 1 ]", // biasShape
+ "[ 10, 0, 0, 0 ]" ) // biasData
+ { }
+};
+
+BOOST_FIXTURE_TEST_CASE(
+ DynamicFullyConnectedWithBiasMultipleOutputs,
+ DynamicFullyConnectedWithBiasMultipleOutputsFixture)
+{
+ RunTest<2,
+ armnn::DataType::QAsymmU8,
+ armnn::DataType::QAsymmU8>(0,
+ { { "inputTensor", { 1, 2, 3, 4, 10, 20, 30, 40} } },
+ { { "outputTensor", { (40+10)/2, (400+10)/2 } } },
+ true);
+}
+
BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
index 891e0be..f2f723b 100644
--- a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
+++ b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
@@ -42,6 +42,7 @@
{
ITfLiteParser::TfLiteParserOptions options;
options.m_StandInLayerForUnsupported = true;
+ options.m_InferAndValidate = true;
m_Parser.reset(ITfLiteParser::CreateRaw(armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options)));
}
@@ -149,7 +150,8 @@
armnn::DataType ArmnnType2>
void RunTest(size_t subgraphId,
const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData,
- const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData);
+ const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData,
+ bool isDynamic = false);
/// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes.
@@ -248,7 +250,8 @@
armnn::DataType armnnType2>
void ParserFlatbuffersFixture::RunTest(size_t subgraphId,
const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData,
- const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData)
+ const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData,
+ bool isDynamic)
{
using DataType2 = armnn::ResolveType<armnnType2>;
@@ -289,8 +292,8 @@
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
- auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second);
- BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first]));
+ auto outputExpected = MakeTensor<DataType2, NumOutputDimensions>(bindingInfo.second, it.second, isDynamic);
+ BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first], false, isDynamic));
}
}
diff --git a/src/armnnTfLiteParser/test/Reshape.cpp b/src/armnnTfLiteParser/test/Reshape.cpp
index 6ed568c..025612f 100644
--- a/src/armnnTfLiteParser/test/Reshape.cpp
+++ b/src/armnnTfLiteParser/test/Reshape.cpp
@@ -138,4 +138,22 @@
== armnn::TensorShape({2,3,3})));
}
+struct DynamicReshapeFixtureWithReshapeDimsFlattenOneDim : ReshapeFixture
+{
+ DynamicReshapeFixtureWithReshapeDimsFlattenOneDim() : ReshapeFixture("[ 2, 9 ]",
+ "[ ]",
+ "[ 2, -1, 3 ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(DynParseReshapeWithReshapeDimsFlattenOneDim, DynamicReshapeFixtureWithReshapeDimsFlattenOneDim)
+{
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ RunTest<3,
+ armnn::DataType::QAsymmU8,
+ armnn::DataType::QAsymmU8>(0,
+ { { "inputTensor", { 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6 } } },
+ { { "outputTensor", { 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6 } } },
+ true);
+}
+
BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnnTfLiteParser/test/Slice.cpp b/src/armnnTfLiteParser/test/Slice.cpp
index 17d1b1a..b94a983 100644
--- a/src/armnnTfLiteParser/test/Slice.cpp
+++ b/src/armnnTfLiteParser/test/Slice.cpp
@@ -173,4 +173,21 @@
== armnn::TensorShape({2,1,3})));
}
+struct DynamicSliceFixtureD213 : SliceFixture
+{
+ DynamicSliceFixtureD213() : SliceFixture("[ 3, 2, 3 ]",
+ "[ ]",
+ "[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]",
+ "[ 2, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0 ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(DynamicSliceD213, DynamicSliceFixtureD213)
+{
+ RunTest<3, armnn::DataType::Float32, armnn::DataType::Float32>(
+ 0,
+ {{"inputTensor", { 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6 }}},
+ {{"outputTensor", { 3, 3, 3, 5, 5, 5 }}},
+ true);
+}
+
BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/armnnTfLiteParser/test/Sub.cpp b/src/armnnTfLiteParser/test/Sub.cpp
index 6a251a5..2854d81 100644
--- a/src/armnnTfLiteParser/test/Sub.cpp
+++ b/src/armnnTfLiteParser/test/Sub.cpp
@@ -104,4 +104,21 @@
{{"outputTensor", { 1, 3, 5, 7 }}});
}
+struct DynamicSubFixture : SubFixture
+{
+ DynamicSubFixture() : SubFixture("[ 1, 4 ]",
+ "[ 1, 4 ]",
+ "[ ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(DynamicSub, DynamicSubFixture)
+{
+ RunTest<2, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(
+ 0,
+ {{"inputTensor1", { 4, 5, 6, 7 }},
+ {"inputTensor2", { 3, 2, 1, 0 }}},
+ {{"outputTensor", { 1, 3, 5, 7 }}},
+ true);
+}
+
BOOST_AUTO_TEST_SUITE_END()