runtime/test/generated/spec_V1_2/vts_V1_2/random_multinomial.model.cpp

// Generated from random_multinomial.mod.py
// DO NOT EDIT
// clang-format off
#include "GeneratedTests.h"

namespace android::hardware::neuralnetworks::V1_2::generated_tests::random_multinomial {

Model createTestModel() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {1, 1024},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {2},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 8},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {1, 128},
            .numberOfConsumers = 0,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::RANDOM_MULTINOMIAL,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      128, 0, 0, 0, 37, 0, 0, 0, 42, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored(int i) {
  static std::set<int> ignore = {0};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::random_multinomial
namespace android::hardware::neuralnetworks::V1_2::generated_tests::random_multinomial {

Model createTestModel_dynamic_output_shape() {
    const std::vector<Operand> operands = {
        {
            .type = OperandType::TENSOR_FLOAT32,
            .dimensions = {1, 1024},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_INPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        },
        {
            .type = OperandType::INT32,
            .dimensions = {},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 0, .length = 4},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {2},
            .numberOfConsumers = 1,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::CONSTANT_COPY,
            .location = {.poolIndex = 0, .offset = 4, .length = 8},
        },
        {
            .type = OperandType::TENSOR_INT32,
            .dimensions = {0, 0},
            .numberOfConsumers = 0,
            .scale = 0.0f,
            .zeroPoint = 0,
            .lifetime = OperandLifeTime::MODEL_OUTPUT,
            .location = {.poolIndex = 0, .offset = 0, .length = 0},
        }
    };

    const std::vector<Operation> operations = {
        {
            .type = OperationType::RANDOM_MULTINOMIAL,
            .inputs = {0, 1, 2},
            .outputs = {3},
        }
    };

    const std::vector<uint32_t> inputIndexes = {0};
    const std::vector<uint32_t> outputIndexes = {3};
    std::vector<uint8_t> operandValues = {
      128, 0, 0, 0, 37, 0, 0, 0, 42, 0, 0, 0
    };
    const std::vector<hidl_memory> pools = {};

    return {
        .operands = operands,
        .operations = operations,
        .inputIndexes = inputIndexes,
        .outputIndexes = outputIndexes,
        .operandValues = operandValues,
        .pools = pools,
    };
}

bool is_ignored_dynamic_output_shape(int i) {
  static std::set<int> ignore = {0};
  return ignore.find(i) != ignore.end();
}

} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::random_multinomial