src/armnnTfLiteParser/test/Conv2D.cpp

//
// 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)
            });
}

}