| // |
| // Copyright © 2017 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ParserFlatbuffersFixture.hpp" |
| #include <sstream> |
| |
| TEST_SUITE("TensorflowLiteParser_Conv2D") |
| { |
| 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"); |
| } |
| }; |
| |
| TEST_CASE_FIXTURE(SimpleConv2DFixture, "ParseSimpleConv2D") |
| { |
| RunTest<4, armnn::DataType::QAsymmU8>( |
| 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", |
| const std::string& dataType = "UINT8", |
| const std::string& filterDataType = "UINT8", |
| const std::string& biasDataType = "INT32") |
| { |
| m_JsonString = R"( |
| { |
| "version": 3, |
| "operator_codes": [ { "builtin_code": "CONV_2D" } ], |
| "subgraphs": [ { |
| "tensors": [ |
| { |
| "shape": )" + inputShape + R"(, |
| "type": )" + dataType + R"(, |
| "buffer": 0, |
| "name": "inputTensor", |
| "quantization": { |
| "min": [ 0.0 ], |
| "max": [ 255.0 ], |
| "scale": [ 1.0 ], |
| "zero_point": [ 0 ], |
| } |
| }, |
| { |
| "shape": )" + outputShape + R"(, |
| "type": )" + dataType + R"(, |
| "buffer": 1, |
| "name": "outputTensor", |
| "quantization": { |
| "min": [ 0.0 ], |
| "max": [ 511.0 ], |
| "scale": [ )" + outputScale + R"( ], |
| "zero_point": [ )" + outputZeroPoint + R"( ], |
| } |
| }, |
| { |
| "shape": )" + filterShape + R"( , |
| "type": )" + filterDataType + R"(, |
| "buffer": 2, |
| "name": "filterTensor", |
| "quantization": { |
| "min": [ 0.0 ], |
| "max": [ 255.0 ], |
| "scale": [ )" + filterScale + R"( ], |
| "zero_point": [ )" + filterZeroPoint + R"( ], |
| } |
| }, |
| { |
| "shape": )" + biasShape + R"( , |
| "type": )" + biasDataType + R"(, |
| "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 |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(SimpleConv2DWithBiasesFixture, "ParseConv2DWithBias") |
| { |
| RunTest<4, armnn::DataType::QAsymmU8>( |
| 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 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 |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(DynamicConv2DWithBiasesFixture, "ParseDynamicConv2DWithBias") |
| { |
| 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) |
| { |
| 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 |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(Conv2DShapeTestFixture, "ParseConv2D_112x112_out") |
| { |
| } |
| |
| 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 |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(ReluConv2DWithBiasesFixture, "ParseConv2DAndReluWithBias") |
| { |
| uint8_t bias = 16; |
| uint8_t outZero = 20; |
| uint8_t fz = 4; // filter zero point |
| |
| RunTest<4, armnn::DataType::QAsymmU8>( |
| 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 |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(Relu6Conv2DWithBiasesFixture, "ParseConv2DAndRelu6WithBias") |
| { |
| uint8_t relu6Min = 6 / 2; // divide by output scale |
| |
| RunTest<4, armnn::DataType::QAsymmU8>( |
| 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)) |
| }); |
| } |
| |
| |
| struct PerChannelConv2DFixture : public ParserFlatbuffersFixture |
| { |
| explicit PerChannelConv2DFixture() |
| { |
| m_JsonString = R"( |
| { |
| "version": 3, |
| "operator_codes": [ |
| { |
| "builtin_code": "CONV_2D", |
| "version": 3 |
| } |
| ], |
| "subgraphs": [ |
| { |
| "tensors": [ |
| { |
| "shape": [ |
| 1, |
| 4, |
| 4, |
| 2 |
| ], |
| "type": "INT8", |
| "buffer": 1, |
| "name": "input", |
| "quantization": { |
| "min": [ |
| -50.0 |
| ], |
| "max": [ |
| 49.0 |
| ], |
| "scale": [ |
| 0.388235 |
| ], |
| "zero_point": [ |
| 1 |
| ], |
| "details_type": "NONE", |
| "quantized_dimension": 0 |
| }, |
| "is_variable": false |
| }, |
| { |
| "shape": [ |
| 4 |
| ], |
| "type": "INT32", |
| "buffer": 2, |
| "name": "model/conv2d/Conv2D", |
| "quantization": { |
| "scale": [ |
| 0.001523, |
| 0.001197, |
| 0.001517, |
| 0.001364 |
| ], |
| "zero_point": [ |
| 0, |
| 0, |
| 0, |
| 0 |
| ], |
| "details_type": "NONE", |
| "quantized_dimension": 0 |
| }, |
| "is_variable": false |
| }, |
| { |
| "shape": [ |
| 4, |
| 2, |
| 2, |
| 2 |
| ], |
| "type": "INT8", |
| "buffer": 3, |
| "name": "model/conv2d/Conv2D1", |
| "quantization": { |
| "min": [ |
| -0.498056, |
| -0.362561, |
| -0.307959, |
| -0.207799 |
| ], |
| "max": [ |
| 0.339136, |
| 0.391629, |
| 0.496193, |
| 0.446191 |
| ], |
| "scale": [ |
| 0.003922, |
| 0.003084, |
| 0.003907, |
| 0.003513 |
| ], |
| "zero_point": [ |
| 0, |
| 0, |
| 0, |
| 0 |
| ], |
| "details_type": "NONE", |
| "quantized_dimension": 0 |
| }, |
| "is_variable": false |
| }, |
| { |
| "shape": [ |
| 1, |
| 4, |
| 4, |
| 4 |
| ], |
| "type": "INT8", |
| "buffer": 4, |
| "name": "Identity", |
| "quantization": { |
| "min": [ |
| -66.578751 |
| ], |
| "max": [ |
| 70.137619 |
| ], |
| "scale": [ |
| 0.536143 |
| ], |
| "zero_point": [ |
| -4 |
| ], |
| "details_type": "NONE", |
| "quantized_dimension": 0 |
| }, |
| "is_variable": false |
| } |
| ], |
| "inputs": [ |
| 0 |
| ], |
| "outputs": [ |
| 3 |
| ], |
| "operators": [ |
| { |
| "opcode_index": 0, |
| "inputs": [ |
| 0, |
| 2, |
| 1 |
| ], |
| "outputs": [ |
| 3 |
| ], |
| "builtin_options_type": "Conv2DOptions", |
| "builtin_options": { |
| "padding": "SAME", |
| "stride_w": 1, |
| "stride_h": 1, |
| "fused_activation_function": "NONE", |
| "dilation_w_factor": 1, |
| "dilation_h_factor": 1 |
| }, |
| "custom_options_format": "FLEXBUFFERS" |
| } |
| ], |
| "name": "main" |
| } |
| ], |
| "description": "MLIR Converted.", |
| "buffers": [ |
| { |
| }, |
| { |
| }, |
| { |
| "data": [ |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0 |
| ] |
| }, |
| { |
| "data": [ |
| 157, |
| 201, |
| 86, |
| 129, |
| 17, |
| 33, |
| 209, |
| 13, |
| 76, |
| 249, |
| 127, |
| 138, |
| 35, |
| 18, |
| 250, |
| 233, |
| 15, |
| 205, |
| 98, |
| 127, |
| 68, |
| 196, |
| 246, |
| 177, |
| 65, |
| 197, |
| 230, |
| 246, |
| 127, |
| 66, |
| 212, |
| 30 |
| ] |
| }, |
| { |
| }, |
| { |
| "data": [ |
| 49, |
| 46, |
| 53, |
| 46, |
| 48, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0, |
| 0 |
| ] |
| } |
| ], |
| "metadata": [ |
| { |
| "name": "min_runtime_version", |
| "buffer": 5 |
| } |
| ] |
| } |
| )"; |
| SetupSingleInputSingleOutput("input", "Identity"); |
| } |
| }; |
| |
| TEST_CASE_FIXTURE(PerChannelConv2DFixture, "ParsePerChannelConv2D") |
| { |
| RunTest<4, armnn::DataType::QAsymmS8>( |
| 0, |
| { |
| -11, 40,-26, 11,-28, 8, 0, -8, |
| -10, 34, 47, 0,-33,-14, 28, 35, |
| 6,-28,-26, 8, 13, 33,-31,-41, |
| 31,-20,-31,-16, 8,-18,-44, 0 |
| }, |
| { |
| -21,-17,-23,-14, -1,-14, 1, 9, |
| 1,-12,-22,-23, 2, -1, -3, 12, |
| 7, 6, 8,-13,-21, -6,-31, 0, |
| 9, -6, 24, 0,-22, -4, -7,-22, |
| -7, -9, 9, 11,-11,-16, 9,-27, |
| -1, 0,-26, 0, 9,-12, -8,-18, |
| -11, -3,-15, 7, 16, -2, -8, -7, |
| -14,-15,-14, 3, 9,-12, -6,-11 |
| }); |
| } |
| |
| struct Conv2FloatWithInt8WeightsAndBiasesFixture : Conv2DWithBiasesFixture |
| { |
| Conv2FloatWithInt8WeightsAndBiasesFixture() |
| : Conv2DWithBiasesFixture("[ 1, 2, 2, 1 ]", // inputShape |
| "[ 1, 2, 2, 1 ]", // outputShape |
| "[ 1, 2, 2, 1 ]", // filterShape |
| "[ 2,1, 0,6 ]", // filterData |
| "[ 1 ]", // biasShape |
| "[ 10 ]", // biasData |
| "1", // stride w and h |
| "NONE", // activation |
| "1.0", // filterScale |
| "0", // filterZeroPoint |
| "2.0", // outputScale |
| "0", // outputZeroPoint |
| "FLOAT32", // dataType |
| "INT8", // filterDataType |
| "INT8") // biasDataType |
| {} |
| }; |
| |
| TEST_CASE_FIXTURE(Conv2FloatWithInt8WeightsAndBiasesFixture, "ParseConv2FloatWithInt8WeightsAndBiasesFixture") |
| { |
| RunTest<4, armnn::DataType::Float32>( |
| 0, |
| { |
| 1, 2, |
| 3, 4, |
| }, |
| { |
| (1*2 + 2*1 + 3*0 + 4*6 + 10), |
| (2*2 + 0*1 + 4*0 + 0*6 + 10), |
| (3*2 + 4*1 + 0*0 + 0*6 + 10), |
| (4*2 + 0*1 + 0*0 + 0*6 + 10) |
| }); |
| } |
| |
| } |