IVGCVSW-1530 Add TfLite slice parser and fix transpose perm vector creation
* TfLite slice parser and relevant tests added
* TfLite transpose parser logic added to translate Tf/np permutation
vector definitions to Armnn definitions
* TfLite transpose parser no permute data test modified to include
data for default permutation vector when none specified
Signed-off-by: josh minor <josh.minor@arm.com>
Change-Id: Iebd30971bd180593dc6b8f0d5be1d1bc61a3a5bf
diff --git a/CMakeLists.txt b/CMakeLists.txt
index f088a21..21d1336 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -718,6 +718,7 @@
src/armnnTfLiteParser/test/ResizeBilinear.cpp
src/armnnTfLiteParser/test/Softmax.cpp
src/armnnTfLiteParser/test/SpaceToBatchND.cpp
+ src/armnnTfLiteParser/test/Slice.cpp
src/armnnTfLiteParser/test/Split.cpp
src/armnnTfLiteParser/test/Squeeze.cpp
src/armnnTfLiteParser/test/StridedSlice.cpp
diff --git a/src/armnnTfLiteParser/TensorFlowLiteSupport.md b/src/armnnTfLiteParser/TensorFlowLiteSupport.md
index 7fa299e..145ca9f 100644
--- a/src/armnnTfLiteParser/TensorFlowLiteSupport.md
+++ b/src/armnnTfLiteParser/TensorFlowLiteSupport.md
@@ -46,6 +46,8 @@
* RESIZE_BILINEAR
+* SLICE
+
* SOFTMAX
* SPACE_TO_BATCH
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 937131c..9a20740 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -459,6 +459,7 @@
m_ParserFunctions[tflite::BuiltinOperator_MEAN] = &TfLiteParser::ParseMean;
m_ParserFunctions[tflite::BuiltinOperator_PACK] = &TfLiteParser::ParsePack;
m_ParserFunctions[tflite::BuiltinOperator_PAD] = &TfLiteParser::ParsePad;
+ m_ParserFunctions[tflite::BuiltinOperator_SLICE] = &TfLiteParser::ParseSlice;
m_ParserFunctions[tflite::BuiltinOperator_SPLIT] = &TfLiteParser::ParseSplit;
m_ParserFunctions[tflite::BuiltinOperator_TANH] = &TfLiteParser::ParseTanH;
m_ParserFunctions[tflite::BuiltinOperator_TRANSPOSE] = &TfLiteParser::ParseTranspose;
@@ -934,17 +935,27 @@
PermuteDescriptor desc;
- if(inputs.size() == 2)
+ if (inputs.size() == 2)
{
armnn::TensorInfo permuteTensorInfo = ToTensorInfo(inputs[1]);
BufferRawPtr permuteBufferPtr = GetBuffer(m_Model, inputs[1]->buffer);
-
- std::vector<unsigned int> permuteShape(permuteTensorInfo.GetNumElements());
+ auto numPermVecElements = permuteTensorInfo.GetNumElements();
+ std::vector<unsigned int> permuteShape(numPermVecElements);
::memcpy(permuteShape.data(), permuteBufferPtr->data.data(), permuteTensorInfo.GetNumBytes());
- PermutationVector permutationVector(permuteShape.data(), permuteTensorInfo.GetNumElements());
+ // permuteShape assumes Tf/Np permute vectors, we must translate to armnn expected form
+ // to do so we find the perm vector which would invert what a tf perm vector would do (ex 3,0,1,2 -> 1,2,3,0)
+ std::vector<unsigned int> armnnPermuteShape(numPermVecElements);
+ std::vector<unsigned int>::iterator it;
+ for (unsigned int i = 0u; i < numPermVecElements; ++i)
+ {
+ it = std::find(permuteShape.begin(), permuteShape.end(), i);
+ armnnPermuteShape[i] = static_cast<unsigned int>(std::distance(permuteShape.begin(), it));
+ }
- desc = PermuteDescriptor(permutationVector);
+ PermutationVector permutationVector(armnnPermuteShape.data(), permuteTensorInfo.GetNumElements());
+
+ desc = PermuteDescriptor(permutationVector);
}
layer = m_Network->AddPermuteLayer(desc, layerName.c_str());
@@ -1254,6 +1265,48 @@
RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
}
+void TfLiteParser::ParseSlice(size_t subgraphIndex, size_t operatorIndex)
+{
+ CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
+
+ auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
+ CHECK_VALID_SIZE(inputs.size(), 3);
+ auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
+ CHECK_VALID_SIZE(outputs.size(), 1);
+
+ SliceDescriptor desc;
+
+ // set begin tensor info for slice descriptor
+ armnn::TensorInfo beginTensorInfo = ToTensorInfo(inputs[1]);
+ BufferRawPtr beginBufferPtr = GetBuffer(m_Model, inputs[1]->buffer);
+
+ std::vector<unsigned int> begin(beginTensorInfo.GetNumElements());
+ ::memcpy(begin.data(), beginBufferPtr->data.data(), beginTensorInfo.GetNumBytes());
+
+ // set size tensor info for slice descriptor
+ armnn::TensorInfo sizeTensorInfo = ToTensorInfo(inputs[2]);
+ BufferRawPtr sizeBufferPtr = GetBuffer(m_Model, inputs[2]->buffer);
+
+ std::vector<unsigned int> size(sizeTensorInfo.GetNumElements());
+ ::memcpy(size.data(), sizeBufferPtr->data.data(), sizeTensorInfo.GetNumBytes());
+ desc = SliceDescriptor(begin, size);
+
+ auto layerName = boost::str(boost::format("Slice:%1%:%2%") % subgraphIndex % operatorIndex);
+ IConnectableLayer* const layer = m_Network->AddSliceLayer(desc, layerName.c_str());
+
+ armnn::TensorInfo outputTensorInfo = ToTensorInfo(outputs[0]);
+ layer->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
+
+ // register the input connection slots for the layer, connections are made after all layers have been created
+ // only the tensors for the inputs are relevant, exclude the const tensors
+ auto inputTensorIndexes = AsUnsignedVector(GetInputTensorIds(m_Model, subgraphIndex, operatorIndex));
+ RegisterInputSlots(subgraphIndex, operatorIndex, layer, {inputTensorIndexes[0]});
+
+ // 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, layer, {outputTensorIndexes[0]});
+}
+
void TfLiteParser::ParseSoftmax(size_t subgraphIndex, size_t operatorIndex)
{
CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
diff --git a/src/armnnTfLiteParser/TfLiteParser.hpp b/src/armnnTfLiteParser/TfLiteParser.hpp
index fb01fe8..5ac6a89 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -116,6 +116,7 @@
void ParseRelu6(size_t subgraphIndex, size_t operatorIndex);
void ParseReshape(size_t subgraphIndex, size_t operatorIndex);
void ParseResizeBilinear(size_t subgraphIndex, size_t operatorIndex);
+ void ParseSlice(size_t subgraphIndex, size_t operatorIndex);
void ParseSoftmax(size_t subgraphIndex, size_t operatorIndex);
void ParseSpaceToBatchND(size_t subgraphIndex, size_t operatorIndex);
void ParseSplit(size_t subgraphIndex, size_t operatorIndex);
diff --git a/src/armnnTfLiteParser/test/Slice.cpp b/src/armnnTfLiteParser/test/Slice.cpp
new file mode 100644
index 0000000..17d1b1a
--- /dev/null
+++ b/src/armnnTfLiteParser/test/Slice.cpp
@@ -0,0 +1,176 @@
+//
+// Copyright © 2019 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include <boost/test/unit_test.hpp>
+#include "ParserFlatbuffersFixture.hpp"
+#include "../TfLiteParser.hpp"
+
+BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
+
+struct SliceFixture : public ParserFlatbuffersFixture
+{
+ explicit SliceFixture(const std::string & inputShape,
+ const std::string & outputShape,
+ const std::string & beginData,
+ const std::string & sizeData)
+ {
+ m_JsonString = R"(
+ {
+ "version": 3,
+ "operator_codes": [
+ {
+ "builtin_code": "SLICE",
+ "version": 1
+ }
+ ],
+ "subgraphs": [
+ {
+ "tensors": [
+ {
+ "shape": )" + inputShape + R"(,
+ "type": "FLOAT32",
+ "buffer": 0,
+ "name": "inputTensor",
+ "quantization": {
+ "min": [
+ 0.0
+ ],
+ "max": [
+ 255.0
+ ],
+ "details_type": 0,
+ "quantized_dimension": 0
+ },
+ "is_variable": false
+ },
+ {
+ "shape": )" + outputShape + R"(,
+ "type": "FLOAT32",
+ "buffer": 1,
+ "name": "outputTensor",
+ "quantization": {
+ "details_type": 0,
+ "quantized_dimension": 0
+ },
+ "is_variable": false
+ })";
+ m_JsonString += R"(,
+ {
+ "shape": [
+ 3
+ ],
+ "type": "INT32",
+ "buffer": 2,
+ "name": "beginTensor",
+ "quantization": {
+ }
+ })";
+ m_JsonString += R"(,
+ {
+ "shape": [
+ 3
+ ],
+ "type": "INT32",
+ "buffer": 3,
+ "name": "sizeTensor",
+ "quantization": {
+ }
+ })";
+ m_JsonString += R"(],
+ "inputs": [
+ 0
+ ],
+ "outputs": [
+ 1
+ ],
+ "operators": [
+ {
+ "opcode_index": 0,
+ "inputs": [
+ 0,
+ 2,
+ 3)";
+ m_JsonString += R"(],
+ "outputs": [
+ 1
+ ],
+ mutating_variable_inputs: [
+ ]
+ }
+ ]
+ }
+ ],
+ "description": "TOCO Converted.",
+ "buffers": [
+ { },
+ { })";
+ m_JsonString += R"(,{"data": )" + beginData + R"( })";
+ m_JsonString += R"(,{"data": )" + sizeData + R"( })";
+ m_JsonString += R"(
+ ]
+ }
+ )";
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ }
+};
+
+struct SliceFixtureSingleDim : SliceFixture
+{
+ SliceFixtureSingleDim() : SliceFixture("[ 3, 2, 3 ]",
+ "[ 1, 1, 3 ]",
+ "[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]",
+ "[ 1, 0, 0, 0, 1, 0, 0, 0, 3, 0, 0, 0 ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(SliceSingleDim, SliceFixtureSingleDim)
+{
+ RunTest<3, 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 }}});
+
+ BOOST_TEST((m_Parser->GetNetworkOutputBindingInfo(0, "outputTensor").second.GetShape()
+ == armnn::TensorShape({1,1,3})));
+}
+
+struct SliceFixtureD123 : SliceFixture
+{
+ SliceFixtureD123() : SliceFixture("[ 3, 2, 3 ]",
+ "[ 1, 2, 3 ]",
+ "[ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]",
+ "[ 1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0 ]") {}
+};
+
+BOOST_FIXTURE_TEST_CASE(SliceD123, SliceFixtureD123)
+{
+ RunTest<3, 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, 4, 4, 4 }}});
+
+ BOOST_TEST((m_Parser->GetNetworkOutputBindingInfo(0, "outputTensor").second.GetShape()
+ == armnn::TensorShape({1,2,3})));
+}
+
+struct SliceFixtureD213 : SliceFixture
+{
+ SliceFixtureD213() : SliceFixture("[ 3, 2, 3 ]",
+ "[ 2, 1, 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(SliceD213, SliceFixtureD213)
+{
+ RunTest<3, 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 }}});
+
+ BOOST_TEST((m_Parser->GetNetworkOutputBindingInfo(0, "outputTensor").second.GetShape()
+ == armnn::TensorShape({2,1,3})));
+}
+
+BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file
diff --git a/src/armnnTfLiteParser/test/Transpose.cpp b/src/armnnTfLiteParser/test/Transpose.cpp
index 2e3190b..b2f953e 100644
--- a/src/armnnTfLiteParser/test/Transpose.cpp
+++ b/src/armnnTfLiteParser/test/Transpose.cpp
@@ -55,24 +55,20 @@
},
"is_variable": false
})";
- if (!permuteData.empty())
- {
- m_JsonString += R"(,
- {
- "shape": [
- 3
- ],
- "type": "INT32",
- "buffer": 2,
- "name": "permuteTensor",
- "quantization": {
- "details_type": 0,
- "quantized_dimension": 0
- },
- "is_variable": false
- })";
- }
-
+ m_JsonString += R"(,
+ {
+ "shape": [
+ 3
+ ],
+ "type": "INT32",
+ "buffer": 2,
+ "name": "permuteTensor",
+ "quantization": {
+ "details_type": 0,
+ "quantized_dimension": 0
+ },
+ "is_variable": false
+ })";
m_JsonString += R"(],
"inputs": [
0
@@ -85,10 +81,7 @@
"opcode_index": 0,
"inputs": [
0)";
- if (!permuteData.empty())
- {
- m_JsonString += R"(,2)";
- }
+ m_JsonString += R"(,2)";
m_JsonString += R"(],
"outputs": [
1
@@ -117,6 +110,7 @@
}
};
+// Note that this assumes the Tensorflow permutation vector implementation as opposed to the armnn implemenation.
struct TransposeFixtureWithPermuteData : TransposeFixture
{
TransposeFixtureWithPermuteData() : TransposeFixture("[ 2, 2, 3 ]",
@@ -128,29 +122,32 @@
{
RunTest<3, armnn::DataType::Float32>(
0,
- {{"inputTensor", { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }}},
- {{"outputTensor", { 1, 4, 2, 5, 3, 6, 7, 10, 8, 11, 9, 12 }}});
+ {{"inputTensor", { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }}},
+ {{"outputTensor", { 1, 4, 2, 5, 3, 6, 7, 10, 8, 11, 9, 12 }}});
BOOST_TEST((m_Parser->GetNetworkOutputBindingInfo(0, "outputTensor").second.GetShape()
== armnn::TensorShape({2,3,2})));
}
+// Tensorflow default permutation behavior assumes no permute argument will create permute vector [n-1...0],
+// where n is the number of dimensions of the input tensor
+// In this case we should get output shape 3,2,2 given default permutation vector 2,1,0
struct TransposeFixtureWithoutPermuteData : TransposeFixture
{
TransposeFixtureWithoutPermuteData() : TransposeFixture("[ 2, 2, 3 ]",
- "",
- "[ 2, 3, 2 ]") {}
+ "[ 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 ]",
+ "[ 3, 2, 2 ]") {}
};
BOOST_FIXTURE_TEST_CASE(TransposeWithoutPermuteDims, TransposeFixtureWithoutPermuteData)
{
RunTest<3, armnn::DataType::Float32>(
0,
- {{"inputTensor", { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }}},
- {{"outputTensor", { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }}});
+ {{"inputTensor", { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 }}},
+ {{"outputTensor", { 1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12 }}});
BOOST_TEST((m_Parser->GetNetworkOutputBindingInfo(0, "outputTensor").second.GetShape()
- == armnn::TensorShape({2,3,2})));
+ == armnn::TensorShape({3,2,2})));
}
BOOST_AUTO_TEST_SUITE_END()
\ No newline at end of file