IVGCVSW-6165 Add Support for Conv3d to TfLiteParser
* Added CONV_3D operator support.
* Fixed TfLiteParser test issue where QAsymmU8 tests were incorrectly
using the Boolean comparison function.
Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I9db03b54b6de63f918717af80c5ac5310fe26183
diff --git a/src/armnnTfLiteParser/TfLiteParser.cpp b/src/armnnTfLiteParser/TfLiteParser.cpp
index 68dbbd1..104a55e 100644
--- a/src/armnnTfLiteParser/TfLiteParser.cpp
+++ b/src/armnnTfLiteParser/TfLiteParser.cpp
@@ -642,6 +642,7 @@
m_ParserFunctions[tflite::BuiltinOperator_CAST] = &TfLiteParserImpl::ParseCast;
m_ParserFunctions[tflite::BuiltinOperator_CONCATENATION] = &TfLiteParserImpl::ParseConcatenation;
m_ParserFunctions[tflite::BuiltinOperator_CONV_2D] = &TfLiteParserImpl::ParseConv2D;
+ m_ParserFunctions[tflite::BuiltinOperator_CONV_3D] = &TfLiteParserImpl::ParseConv3D;
m_ParserFunctions[tflite::BuiltinOperator_CUSTOM] = &TfLiteParserImpl::ParseCustomOperator;
m_ParserFunctions[tflite::BuiltinOperator_DEPTH_TO_SPACE] = &TfLiteParserImpl::ParseDepthToSpace;
m_ParserFunctions[tflite::BuiltinOperator_DEPTHWISE_CONV_2D] = &TfLiteParserImpl::ParseDepthwiseConv2D;
@@ -1048,6 +1049,90 @@
RegisterOutputSlots(subgraphIndex, operatorIndex, layer, {outputTensorIndexes[0]});
}
+void TfLiteParserImpl::ParseConv3D(size_t subgraphIndex, size_t operatorIndex)
+{
+ CHECK_MODEL(m_Model, subgraphIndex, operatorIndex);
+
+ const auto& operatorPtr = m_Model->subgraphs[subgraphIndex]->operators[operatorIndex];
+ const auto* options = operatorPtr->builtin_options.AsConv3DOptions();
+
+ CHECK_SUPPORTED_FUSED_ACTIVATION(options, subgraphIndex, operatorIndex);
+
+ Convolution3dDescriptor desc;
+ desc.m_BiasEnabled = false;
+ desc.m_DataLayout = armnn::DataLayout::NDHWC;
+ desc.m_StrideX = CHECKED_NON_NEGATIVE(options->stride_w);
+ desc.m_StrideY = CHECKED_NON_NEGATIVE(options->stride_h);
+ desc.m_StrideZ = CHECKED_NON_NEGATIVE(options->stride_d);
+ desc.m_DilationX = CHECKED_NON_NEGATIVE(options->dilation_w_factor);
+ desc.m_DilationY = CHECKED_NON_NEGATIVE(options->dilation_h_factor);
+ desc.m_DilationZ = CHECKED_NON_NEGATIVE(options->dilation_d_factor);
+
+ auto inputs = GetInputs(m_Model, subgraphIndex, operatorIndex);
+ CHECK_VALID_SIZE(inputs.size(), 2, 3);
+
+ auto outputs = GetOutputs(m_Model, subgraphIndex, operatorIndex);
+ CHECK_VALID_SIZE(outputs.size(), 1);
+
+ armnn::TensorInfo inputTensorInfo = ToTensorInfo(inputs[0]);
+ armnn::TensorInfo filterTensorInfo = ToTensorInfo(inputs[1]);
+
+ // Assuming input is NDHWC
+ unsigned int inputDepth = inputTensorInfo.GetShape()[1];
+ unsigned int inputHeight = inputTensorInfo.GetShape()[2];
+ unsigned int inputWidth = inputTensorInfo.GetShape()[3];
+
+ // Assuming the filter is DHWIO : Depth, Height, Width, OutputChannels, InputChannels
+ unsigned int filterDepth = filterTensorInfo.GetShape()[0];
+ unsigned int filterHeight = filterTensorInfo.GetShape()[1];
+ unsigned int filterWidth = filterTensorInfo.GetShape()[2];
+
+ CalcPadding(inputDepth, filterDepth, desc.m_StrideZ,
+ desc.m_DilationY, desc.m_PadFront, desc.m_PadBack, options->padding);
+ CalcPadding(inputHeight, filterHeight, desc.m_StrideY,
+ desc.m_DilationY, desc.m_PadTop, desc.m_PadBottom, options->padding);
+ CalcPadding(inputWidth, filterWidth, desc.m_StrideX,
+ desc.m_DilationX, desc.m_PadLeft, desc.m_PadRight, options->padding);
+
+ auto filterTensorAndData = CreateConstTensorNonPermuted(inputs[1], filterTensorInfo);
+
+ armnn::IConnectableLayer* layer = nullptr;
+ auto layerName = fmt::format("Conv3D:{}:{}", subgraphIndex, operatorIndex);
+
+ if (inputs.size() == 3)
+ {
+ desc.m_BiasEnabled = true;
+ armnn::TensorInfo biasTensorInfo = ToTensorInfo(inputs[2]);
+ auto biasTensorAndData = CreateConstTensorNonPermuted(inputs[2], biasTensorInfo);
+ layer = m_Network->AddConvolution3dLayer(desc,
+ filterTensorAndData,
+ Optional<ConstTensor>(biasTensorAndData),
+ layerName.c_str());
+ }
+ else
+ {
+ layer = m_Network->AddConvolution3dLayer(desc,
+ filterTensorAndData,
+ EmptyOptional(),
+ layerName.c_str());
+ }
+
+ ARMNN_ASSERT(layer != nullptr);
+
+ 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
+ // 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]});
+
+ layer = AddFusedActivationLayer(layer, 0, options->fused_activation_function);
+ // 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 TfLiteParserImpl::ParseDepthwiseConv2D(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 5645503..8eb5299 100644
--- a/src/armnnTfLiteParser/TfLiteParser.hpp
+++ b/src/armnnTfLiteParser/TfLiteParser.hpp
@@ -113,6 +113,7 @@
void ParseComparison(size_t subgraphIndex, size_t operatorIndex, armnn::ComparisonOperation comparisonOperation);
void ParseConcatenation(size_t subgraphIndex, size_t operatorIndex);
void ParseConv2D(size_t subgraphIndex, size_t operatorIndex);
+ void ParseConv3D(size_t subgraphIndex, size_t operatorIndex);
void ParseDepthToSpace(size_t subgraphIndex, size_t operatorIndex);
void ParseDepthwiseConv2D(size_t subgraphIndex, size_t operatorIndex);
void ParseDequantize(size_t subgraphIndex, size_t operatorIndex);
diff --git a/src/armnnTfLiteParser/test/Conv3D.cpp b/src/armnnTfLiteParser/test/Conv3D.cpp
new file mode 100644
index 0000000..32cd6fe
--- /dev/null
+++ b/src/armnnTfLiteParser/test/Conv3D.cpp
@@ -0,0 +1,286 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ParserFlatbuffersFixture.hpp"
+#include <sstream>
+
+TEST_SUITE("TensorflowLiteParser_Conv3D")
+{
+struct SimpleConv3DFixture : public ParserFlatbuffersFixture
+{
+ explicit SimpleConv3DFixture()
+ {
+ m_JsonString = R"(
+ {
+ "version": 3,
+ "operator_codes": [ { "builtin_code": "CONV_3D" } ],
+ "subgraphs": [ {
+ "tensors": [
+ {
+ "shape": [ 1, 2, 3, 3, 1 ],
+ "type": "UINT8",
+ "buffer": 0,
+ "name": "inputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": [ 1, 1, 1, 1, 1 ],
+ "type": "UINT8",
+ "buffer": 1,
+ "name": "outputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 511.0 ],
+ "scale": [ 2.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": [ 2, 3, 3, 1, 1 ],
+ "type": "UINT8",
+ "buffer": 2,
+ "name": "filterTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ }
+ ],
+ "inputs": [ 0 ],
+ "outputs": [ 1 ],
+ "operators": [
+ {
+ "opcode_index": 0,
+ "inputs": [ 0, 2 ],
+ "outputs": [ 1 ],
+ "builtin_options_type": "Conv3DOptions",
+ "builtin_options": {
+ "padding": "VALID",
+ "stride_d": 1,
+ "stride_w": 1,
+ "stride_h": 1,
+ "fused_activation_function": "NONE"
+ },
+ "custom_options_format": "FLEXBUFFERS"
+ }
+ ],
+ } ],
+ "buffers" : [
+ { },
+ { },
+ { "data": [ 2,1,0, 6,2,1, 4,1,2,
+ 1,2,1, 2,0,2, 2,1,1 ], },
+ { },
+ ]
+ }
+ )";
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ }
+};
+
+TEST_CASE_FIXTURE(SimpleConv3DFixture, "ParseSimpleConv3D")
+{
+ RunTest<5, armnn::DataType::QAsymmU8>(
+ 0,
+ {
+ 1, 2, 3,
+ 4, 5, 6,
+ 7, 8, 9,
+
+ 10, 11, 12,
+ 13, 14, 15,
+ 16, 17, 18,
+ },
+ // Due to the output scaling we need to half the values.
+ {
+ (1*2 + 2*1 + 3*0 +
+ 4*6 + 5*2 + 6*1 +
+ 7*4 + 8*1 + 9*2 +
+
+ 10*1 + 11*2 + 12*1 +
+ 13*2 + 14*0 + 15*2 +
+ 16*2 + 17*1 + 18*1) /2
+ });
+}
+struct Conv3DWithBiasesFixture : public ParserFlatbuffersFixture
+{
+ explicit Conv3DWithBiasesFixture(const std::string& inputShape,
+ const std::string& outputShape,
+ const std::string& filterShape,
+ const std::string& filterData,
+ const std::string& biasShape,
+ const std::string& biasData,
+ const std::string& strides,
+ const std::string& activation="NONE",
+ const std::string& filterScale="1.0",
+ const std::string& filterZeroPoint="0",
+ const std::string& outputScale="1.0",
+ const std::string& outputZeroPoint="0")
+ {
+ m_JsonString = R"(
+ {
+ "version": 3,
+ "operator_codes": [ { "builtin_code": "CONV_3D" } ],
+ "subgraphs": [ {
+ "tensors": [
+ {
+ "shape": )" + inputShape + R"(,
+ "type": "UINT8",
+ "buffer": 0,
+ "name": "inputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": )" + outputShape + R"(,
+ "type": "UINT8",
+ "buffer": 1,
+ "name": "outputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 511.0 ],
+ "scale": [ )" + outputScale + R"( ],
+ "zero_point": [ )" + outputZeroPoint + R"( ],
+ }
+ },
+ {
+ "shape": )" + filterShape + R"( ,
+ "type": "UINT8",
+ "buffer": 2,
+ "name": "filterTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ )" + filterScale + R"( ],
+ "zero_point": [ )" + filterZeroPoint + R"( ],
+ }
+ },
+ {
+ "shape": )" + biasShape + R"( ,
+ "type": "INT32",
+ "buffer": 3,
+ "name": "biasTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ }
+ ],
+ "inputs": [ 0 ],
+ "outputs": [ 1 ],
+ "operators": [
+ {
+ "opcode_index": 0,
+ "inputs": [ 0, 2, 3 ],
+ "outputs": [ 1 ],
+ "builtin_options_type": "Conv3DOptions",
+ "builtin_options": {
+ "padding": "SAME",
+ "stride_d": )" + strides + R"(,
+ "stride_w": )" + strides + R"(,
+ "stride_h": )" + strides + R"(,
+ "fused_activation_function": )" + activation + R"(
+ },
+ "custom_options_format": "FLEXBUFFERS"
+ }
+ ],
+ } ],
+ "buffers" : [
+ { },
+ { },
+ { "data": )" + filterData + R"(, },
+ { "data": )" + biasData + R"(, },
+ ]
+ }
+ )";
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ }
+};
+
+struct SimpleConv3DWithBiasesFixture : Conv3DWithBiasesFixture
+{
+ SimpleConv3DWithBiasesFixture()
+ : Conv3DWithBiasesFixture("[ 1, 2, 2, 2, 1 ]", // inputShape
+ "[ 1, 2, 2, 2, 1 ]", // outputShape
+ "[ 2, 2, 2, 1, 1 ]", // filterShape
+ "[ 2,1, 1,0, 0,1, 1,1 ]", // filterData
+ "[ 1 ]", // biasShape
+ "[ 5, 0, 0, 0 ]", // biasData
+ "1") // stride d, w and h
+ {}
+};
+
+TEST_CASE_FIXTURE(SimpleConv3DWithBiasesFixture, "ParseConv3DWithBias")
+{
+ RunTest<5,
+ armnn::DataType::QAsymmU8>(0,
+ { 1, 2, 3, 4, 5, 6, 7, 8 },
+ { 33, 21, 23, 13, 28, 25, 27, 21 });
+}
+
+TEST_CASE_FIXTURE(SimpleConv3DWithBiasesFixture, "ParseDynamicConv3DWithBias")
+{
+ RunTest<5,
+ armnn::DataType::QAsymmU8,
+ armnn::DataType::QAsymmU8>(0,
+ { { "inputTensor", { 2, 4, 6, 8, 10, 12, 14, 16 } } },
+ { { "outputTensor", { 61, 37, 41, 21, 51, 45, 49, 37 } } },
+ true);
+}
+
+struct Relu6Conv3DWithBiasesFixture : Conv3DWithBiasesFixture
+{
+ Relu6Conv3DWithBiasesFixture()
+ : Conv3DWithBiasesFixture("[ 1, 2, 2, 2, 1 ]", // inputShape
+ "[ 1, 2, 2, 2, 1 ]", // outputShape
+ "[ 2, 2, 2, 1, 1 ]", // filterShape
+ "[ 2,1, 1,0, 0,1, 1,1 ]", // filterData
+ "[ 1 ]", // biasShape
+ "[ 0, 0, 0, 0 ]", // biasData
+ "1", // stride d, w, and h
+ "RELU6", // activation
+ "1.0", // filter scale
+ "0", // filter zero point
+ "2.0", // output scale
+ "0") // output zero point
+ {}
+};
+
+TEST_CASE_FIXTURE(Relu6Conv3DWithBiasesFixture, "ParseConv3DAndRelu6WithBias")
+{
+ uint8_t relu6Min = 6 / 2; // Divide by output scale
+
+ RunTest<5, armnn::DataType::QAsymmU8>(
+ 0,
+ {
+ 1, 2, 3, 4, 5, 6, 7, 8
+ },
+ // RELU6 cuts output values at +6
+ {
+ std::min(relu6Min, static_cast<uint8_t>((1*2 + 2*1 + 3*1 + 4*0 + 5*0 + 6*1 + 7*1 + 8*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((2*2 + 0*1 + 0*1 + 0*0 + 0*0 + 0*1 + 8*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((3*2 + 0*1 + 0*1 + 0*0 + 0*0 + 8*1 + 0*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((4*2 + 0*1 + 0*1 + 0*0 + 8*0 + 0*1 + 0*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((5*2 + 0*1 + 0*1 + 8*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((6*2 + 0*1 + 8*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((7*2 + 8*1 + 0*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2)),
+ std::min(relu6Min, static_cast<uint8_t>((8*2 + 0*1 + 0*1 + 0*0 + 0*0 + 0*1 + 0*1 + 0*1)/2))
+ });
+}
+
+}
diff --git a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
index b4653cd..a237d2f 100644
--- a/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
+++ b/src/armnnTfLiteParser/test/ParserFlatbuffersFixture.hpp
@@ -376,25 +376,18 @@
m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
+ // Set flag so that the correct comparison function is called if the output is boolean.
+ bool isBoolean = armnnType2 == armnn::DataType::Boolean ? true : false;
+
// Compare each output tensor to the expected values
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
auto outputExpected = it.second;
- if (std::is_same<DataType2, uint8_t>::value)
- {
- auto result = CompareTensors(outputExpected, outputStorage[it.first],
- bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
- true, isDynamic);
- CHECK_MESSAGE(result.m_Result, result.m_Message.str());
- }
- else
- {
- auto result = CompareTensors(outputExpected, outputStorage[it.first],
- bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
- false, isDynamic);
- CHECK_MESSAGE(result.m_Result, result.m_Message.str());
- }
+ auto result = CompareTensors(outputExpected, outputStorage[it.first],
+ bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
+ isBoolean, isDynamic);
+ CHECK_MESSAGE(result.m_Result, result.m_Message.str());
}
if (isDynamic)
@@ -504,22 +497,17 @@
m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors);
+ // Set flag so that the correct comparison function is called if the output is boolean.
+ bool isBoolean = outputType == armnn::DataType::Boolean ? true : false;
+
// Compare each output tensor to the expected values
for (auto&& it : expectedOutputData)
{
armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first);
auto outputExpected = it.second;
- if (std::is_same<DataType2, uint8_t>::value)
- {
- auto result = CompareTensors(outputExpected, outputStorage[it.first],
- bindingInfo.second.GetShape(), bindingInfo.second.GetShape(), true);
- CHECK_MESSAGE(result.m_Result, result.m_Message.str());
- }
- else
- {
- auto result = CompareTensors(outputExpected, outputStorage[it.first],
- bindingInfo.second.GetShape(), bindingInfo.second.GetShape());
- CHECK_MESSAGE(result.m_Result, result.m_Message.str());
- }
+ auto result = CompareTensors(outputExpected, outputStorage[it.first],
+ bindingInfo.second.GetShape(), bindingInfo.second.GetShape(),
+ isBoolean);
+ CHECK_MESSAGE(result.m_Result, result.m_Message.str());
}
}
\ No newline at end of file