| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #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, armnn::DataType::QuantisedAsymm8>( |
| 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, armnn::DataType::QuantisedAsymm8>( |
| 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, armnn::DataType::QuantisedAsymm8>( |
| 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, armnn::DataType::QuantisedAsymm8>( |
| 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() |
