blob: 46ec24b90198bdfda089ef5484b07acdcbc6ae1d [file] [log] [blame]
// Generated from conv_quant8_large_weights_as_inputs.mod.py
// DO NOT EDIT
// clang-format off
#include "GeneratedTests.h"
namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs {
Model createTestModel() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {3},
.numberOfConsumers = 1,
.scale = 0.25f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 4,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 4, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::CONV_2D,
.inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5},
.outputs = {6},
}
};
const std::vector<uint32_t> inputIndexes = {0, 1, 2};
const std::vector<uint32_t> outputIndexes = {6};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 1, 0, 0, 0, 0, 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 = {};
return ignore.find(i) != ignore.end();
}
} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs
namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs {
Model createTestModel_dynamic_output_shape() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {3},
.numberOfConsumers = 1,
.scale = 0.25f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 4,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 4, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {0, 0, 0, 0},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::CONV_2D,
.inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5},
.outputs = {6},
}
};
const std::vector<uint32_t> inputIndexes = {0, 1, 2};
const std::vector<uint32_t> outputIndexes = {6};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 1, 0, 0, 0, 0, 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 = {};
return ignore.find(i) != ignore.end();
}
} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs
namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs {
Model createTestModel_all_inputs_as_internal() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {3},
.numberOfConsumers = 1,
.scale = 0.25f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 4,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 4, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 12, .length = 1},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 13, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 17, .length = 1},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 18, .length = 4},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::ADD,
.inputs = {7, 8, 9},
.outputs = {0},
},
{
.type = OperationType::ADD,
.inputs = {10, 11, 12},
.outputs = {1},
},
{
.type = OperationType::CONV_2D,
.inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5},
.outputs = {6},
}
};
const std::vector<uint32_t> inputIndexes = {2, 7, 10};
const std::vector<uint32_t> outputIndexes = {6};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_all_inputs_as_internal(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs
namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs {
Model createTestModel_all_inputs_as_internal_dynamic_output_shape() {
const std::vector<Operand> operands = {
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::TEMPORARY_VARIABLE,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_INT32,
.dimensions = {3},
.numberOfConsumers = 1,
.scale = 0.25f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 4,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 0, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 2,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 4, .length = 4},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 8, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {0, 0, 0, 0},
.numberOfConsumers = 0,
.scale = 1.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_OUTPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1, 2, 3, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 12, .length = 1},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 13, .length = 4},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {3, 1, 1, 3},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
},
{
.type = OperandType::TENSOR_QUANT8_ASYMM,
.dimensions = {1},
.numberOfConsumers = 1,
.scale = 0.5f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 17, .length = 1},
},
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 1,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::CONSTANT_COPY,
.location = {.poolIndex = 0, .offset = 18, .length = 4},
}
};
const std::vector<Operation> operations = {
{
.type = OperationType::ADD,
.inputs = {7, 8, 9},
.outputs = {0},
},
{
.type = OperationType::ADD,
.inputs = {10, 11, 12},
.outputs = {1},
},
{
.type = OperationType::CONV_2D,
.inputs = {0, 1, 2, 3, 3, 3, 3, 4, 4, 5},
.outputs = {6},
}
};
const std::vector<uint32_t> inputIndexes = {2, 7, 10};
const std::vector<uint32_t> outputIndexes = {6};
std::vector<uint8_t> operandValues = {
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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_all_inputs_as_internal_dynamic_output_shape(int i) {
static std::set<int> ignore = {};
return ignore.find(i) != ignore.end();
}
} // namespace android::hardware::neuralnetworks::V1_2::generated_tests::conv_quant8_large_weights_as_inputs