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Diffstat (limited to 'src/armnnTfLiteParser/test/Conv2D.cpp')
-rw-r--r-- | src/armnnTfLiteParser/test/Conv2D.cpp | 351 |
1 files changed, 351 insertions, 0 deletions
diff --git a/src/armnnTfLiteParser/test/Conv2D.cpp b/src/armnnTfLiteParser/test/Conv2D.cpp new file mode 100644 index 0000000000..8a17dec47a --- /dev/null +++ b/src/armnnTfLiteParser/test/Conv2D.cpp @@ -0,0 +1,351 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// + +#include <boost/test/unit_test.hpp> +#include "ParserFlatbuffersFixture.hpp" +#include "../TfLiteParser.hpp" +#include <sstream> + +BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) + +struct SimpleConv2DFixture : public ParserFlatbuffersFixture +{ + explicit SimpleConv2DFixture() + { + m_JsonString = R"( + { + "version": 3, + "operator_codes": [ { "builtin_code": "CONV_2D" } ], + "subgraphs": [ { + "tensors": [ + { + "shape": [ 1, 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 ], + "type": "UINT8", + "buffer": 1, + "name": "outputTensor", + "quantization": { + "min": [ 0.0 ], + "max": [ 511.0 ], + "scale": [ 2.0 ], + "zero_point": [ 0 ], + } + }, + { + "shape": [ 1, 3, 3, 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": "Conv2DOptions", + "builtin_options": { + "padding": "VALID", + "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 ], }, + { }, + ] + } + )"; + SetupSingleInputSingleOutput("inputTensor", "outputTensor"); + } +}; + +BOOST_FIXTURE_TEST_CASE( ParseSimpleConv2D, SimpleConv2DFixture ) +{ + RunTest<4, uint8_t>( + 0, + { + 1, 2, 3, + 4, 5, 6, + 7, 8, 9, + }, + // because of the output scaling we need to take half of the values + { + (1*2 + 2*1 + 3*0 + + 4*6 + 5*2 + 6*1 + + 7*4 + 8*1 + 9*2) /2 + }); +} + +struct Conv2DWithBiasesFixture : public ParserFlatbuffersFixture +{ + explicit Conv2DWithBiasesFixture(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="2.0", + const std::string & outputZeroPoint="0") + { + m_JsonString = R"( + { + "version": 3, + "operator_codes": [ { "builtin_code": "CONV_2D" } ], + "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": "Conv2DOptions", + "builtin_options": { + "padding": "SAME", + "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 SimpleConv2DWithBiasesFixture : Conv2DWithBiasesFixture +{ + SimpleConv2DWithBiasesFixture() + : Conv2DWithBiasesFixture("[ 1, 2, 2, 1 ]", // inputShape + "[ 1, 2, 2, 1 ]", // 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( ParseConv2DWithBias, SimpleConv2DWithBiasesFixture ) +{ + RunTest<4, uint8_t>( + 0, + { + 1, 2, + 3, 4, + }, + // because of the output scaling we need to take half of the values + { + (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 + }); +} + +struct Conv2DShapeTestFixture : Conv2DWithBiasesFixture +{ + static std::string GenerateInts(unsigned int n) + { + std::stringstream ss; + ss << " [ "; + for( unsigned int i=0; i<n; ++i ) { + if (i > 0 ) + { + ss << " , "; + } + ss << " " << (i%256); + } + ss << " ] "; + return ss.str(); + } + + Conv2DShapeTestFixture() + : Conv2DWithBiasesFixture("[ 1, 224, 224, 3 ]", // inputShape + "[ 1, 112, 112, 32 ]", // outputShape + "[ 32, 3, 3, 3 ]", // filterShape + GenerateInts(32*3*3*3), // filterData + "[ 32 ]", // biasShape + GenerateInts(32*4), // biasData + "2") // stride w and h + {} +}; + +BOOST_FIXTURE_TEST_CASE( ParseConv2D_112x112_out, Conv2DShapeTestFixture ) +{ +} + +struct ReluConv2DWithBiasesFixture : Conv2DWithBiasesFixture +{ + ReluConv2DWithBiasesFixture() + : Conv2DWithBiasesFixture("[ 1, 2, 2, 1 ]", // inputShape + "[ 1, 2, 2, 1 ]", // outputShape + "[ 1, 2, 2, 1 ]", // filterShape + "[ 2,1, 0,6 ]", // filterData + "[ 1 ]", // biasShape + "[ 16, 0, 0, 0 ]", // biasData + "1", // stride w and h + "RELU", // activation + "1.0", // filter scale + "4", // filter zero point + "2.0", // output scale + "20") // output zero point + {} +}; + +BOOST_FIXTURE_TEST_CASE( ParseConv2DAndReluWithBias, ReluConv2DWithBiasesFixture ) +{ + uint8_t bias = 16; + uint8_t outZero = 20; + uint8_t fz = 4; // filter zero point + + RunTest<4, uint8_t>( + 0, + { + 1, 2, + 4, 8, + }, + // factors to consider: + // - the filter zero point is non zero, hence the (x-fz) + // - the output scale is 2 hence the /2 + // - output zero point is non zero, hence the +outZero + // - RELU cuts negative values and then we add the output zero point + { + std::max(outZero, static_cast<uint8_t>((1*(2-fz) + 2*(1-fz) + 4*(0-fz) + 8*(6-fz) + bias)/2 + outZero)), + std::max(outZero, static_cast<uint8_t>((2*(2-fz) + 0*(1-fz) + 8*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), + std::max(outZero, static_cast<uint8_t>((4*(2-fz) + 8*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), + std::max(outZero, static_cast<uint8_t>((8*(2-fz) + 0*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)) + }); +} + +struct Relu6Conv2DWithBiasesFixture : Conv2DWithBiasesFixture +{ + Relu6Conv2DWithBiasesFixture() + : Conv2DWithBiasesFixture("[ 1, 2, 2, 1 ]", // inputShape + "[ 1, 2, 2, 1 ]", // outputShape + "[ 1, 2, 2, 1 ]", // filterShape + "[ 2,1, 0,6 ]", // filterData + "[ 1 ]", // biasShape + "[ 0, 0, 0, 0 ]", // biasData + "1", // stride w and h + "RELU6", // activation + "1.0", // filter scale + "0", // filter zero point + "2.0", // output scale + "0") // output zero point + {} +}; + +BOOST_FIXTURE_TEST_CASE( ParseConv2DAndRelu6WithBias, Relu6Conv2DWithBiasesFixture ) +{ + uint8_t relu6Min = 6 / 2; // divide by output scale + + RunTest<4, uint8_t>( + 0, + { + 1, 2, + 4, 1, + }, + // factors to consider: + // - the output scale is 2 hence the /2 + // - RELU6 cuts output values at +6 + { + std::min(relu6Min, static_cast<uint8_t>((1*2 + 2*1 + 4*0 + 1*6)/2)), + std::min(relu6Min, static_cast<uint8_t>((2*2 + 0*1 + 1*0 + 0*6)/2)), + std::min(relu6Min, static_cast<uint8_t>((4*2 + 1*1 + 0*0 + 0*6)/2)), + std::min(relu6Min, static_cast<uint8_t>((1*2 + 0*1 + 0*0 + 0*6)/2)) + }); +} + +BOOST_AUTO_TEST_SUITE_END() |