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hardware_interfaces/neuralnetworks/1.2/vts/functional/ValidateModel.cpp
Michael Butler 13b0516e56 Consolidate NNAPI VTS utility code 
This CL does additional NNAPI VTS test cleanup, including consolidating
duplicate functionality. Specifically, this CL:
* consolidates the createPreparedModel function, removing the duplicate
* consolidates the std::out ErrorStatus and DeviceStatus code into Utils
* changes non-null constant pointers to constant references
* removes redudant leading namespace specifiers (V1_0::, ::testing, etc.)
* makes the Valdiation tests free functions
* renames device to kDevice and mTestModel to kTestModel

Bug: N/A
Test: mma
Test: VtsHalNeuralnetworksV1_*TargetTest (with sample-all)
Change-Id: Ic401bb1f1760cc10384ac0d30c0c93409b63a9c7
Merged-In: Ic401bb1f1760cc10384ac0d30c0c93409b63a9c7
(cherry picked from commit e16af0a44b)
2019-09-11 11:04:54 -07:00

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30 KiB
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/*
* Copyright (C) 2018 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#define LOG_TAG "neuralnetworks_hidl_hal_test"
#include "1.0/Utils.h"
#include "1.2/Callbacks.h"
#include "GeneratedTestHarness.h"
#include "VtsHalNeuralnetworks.h"
namespace android::hardware::neuralnetworks::V1_2::vts::functional {
using implementation::PreparedModelCallback;
using V1_0::ErrorStatus;
using V1_0::OperandLifeTime;
using V1_1::ExecutionPreference;
using HidlToken = hidl_array<uint8_t, static_cast<uint32_t>(Constant::BYTE_SIZE_OF_CACHE_TOKEN)>;
///////////////////////// UTILITY FUNCTIONS /////////////////////////
static void validateGetSupportedOperations(const sp<IDevice>& device, const std::string& message,
const Model& model) {
SCOPED_TRACE(message + " [getSupportedOperations_1_2]");
Return<void> ret = device->getSupportedOperations_1_2(
model, [&](ErrorStatus status, const hidl_vec<bool>&) {
EXPECT_EQ(ErrorStatus::INVALID_ARGUMENT, status);
});
EXPECT_TRUE(ret.isOk());
}
static void validatePrepareModel(const sp<IDevice>& device, const std::string& message,
const Model& model, ExecutionPreference preference) {
SCOPED_TRACE(message + " [prepareModel_1_2]");
sp<PreparedModelCallback> preparedModelCallback = new PreparedModelCallback();
Return<ErrorStatus> prepareLaunchStatus =
device->prepareModel_1_2(model, preference, hidl_vec<hidl_handle>(),
hidl_vec<hidl_handle>(), HidlToken(), preparedModelCallback);
ASSERT_TRUE(prepareLaunchStatus.isOk());
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, static_cast<ErrorStatus>(prepareLaunchStatus));
preparedModelCallback->wait();
ErrorStatus prepareReturnStatus = preparedModelCallback->getStatus();
ASSERT_EQ(ErrorStatus::INVALID_ARGUMENT, prepareReturnStatus);
sp<IPreparedModel> preparedModel = getPreparedModel_1_2(preparedModelCallback);
ASSERT_EQ(nullptr, preparedModel.get());
}
static bool validExecutionPreference(ExecutionPreference preference) {
return preference == ExecutionPreference::LOW_POWER ||
preference == ExecutionPreference::FAST_SINGLE_ANSWER ||
preference == ExecutionPreference::SUSTAINED_SPEED;
}
// Primary validation function. This function will take a valid model, apply a
// mutation to it to invalidate the model, then pass it to interface calls that
// use the model. Note that the model here is passed by value, and any mutation
// to the model does not leave this function.
static void validate(const sp<IDevice>& device, const std::string& message, Model model,
const std::function<void(Model*)>& mutation,
ExecutionPreference preference = ExecutionPreference::FAST_SINGLE_ANSWER) {
mutation(&model);
if (validExecutionPreference(preference)) {
validateGetSupportedOperations(device, message, model);
}
validatePrepareModel(device, message, model, preference);
}
static uint32_t addOperand(Model* model) {
return hidl_vec_push_back(&model->operands,
{
.type = OperandType::INT32,
.dimensions = {},
.numberOfConsumers = 0,
.scale = 0.0f,
.zeroPoint = 0,
.lifetime = OperandLifeTime::MODEL_INPUT,
.location = {.poolIndex = 0, .offset = 0, .length = 0},
});
}
static uint32_t addOperand(Model* model, OperandLifeTime lifetime) {
uint32_t index = addOperand(model);
model->operands[index].numberOfConsumers = 1;
model->operands[index].lifetime = lifetime;
return index;
}
///////////////////////// VALIDATE MODEL OPERAND TYPE /////////////////////////
static const uint32_t invalidOperandTypes[] = {
static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MIN) - 1,
static_cast<uint32_t>(OperandTypeRange::FUNDAMENTAL_MAX) + 1,
static_cast<uint32_t>(OperandTypeRange::OEM_MIN) - 1,
static_cast<uint32_t>(OperandTypeRange::OEM_MAX) + 1,
};
static void mutateOperandTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
for (uint32_t invalidOperandType : invalidOperandTypes) {
const std::string message = "mutateOperandTypeTest: operand " +
std::to_string(operand) + " set to value " +
std::to_string(invalidOperandType);
validate(device, message, model, [operand, invalidOperandType](Model* model) {
model->operands[operand].type = static_cast<OperandType>(invalidOperandType);
});
}
}
}
///////////////////////// VALIDATE OPERAND RANK /////////////////////////
static uint32_t getInvalidRank(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
return 1;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return 0;
default:
return 0;
}
}
static void mutateOperandRankTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const uint32_t invalidRank = getInvalidRank(model.operands[operand].type);
if (invalidRank == 0) {
continue;
}
const std::string message = "mutateOperandRankTest: operand " + std::to_string(operand) +
" has rank of " + std::to_string(invalidRank);
validate(device, message, model, [operand, invalidRank](Model* model) {
model->operands[operand].dimensions = std::vector<uint32_t>(invalidRank, 0);
});
}
}
///////////////////////// VALIDATE OPERAND SCALE /////////////////////////
static float getInvalidScale(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return 1.0f;
case OperandType::TENSOR_INT32:
return -1.0f;
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
return 0.0f;
default:
return 0.0f;
}
}
static void mutateOperandScaleTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const float invalidScale = getInvalidScale(model.operands[operand].type);
const std::string message = "mutateOperandScaleTest: operand " + std::to_string(operand) +
" has scale of " + std::to_string(invalidScale);
validate(device, message, model, [operand, invalidScale](Model* model) {
model->operands[operand].scale = invalidScale;
});
}
}
///////////////////////// VALIDATE OPERAND ZERO POINT /////////////////////////
static std::vector<int32_t> getInvalidZeroPoints(OperandType type) {
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
case OperandType::TENSOR_INT32:
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL:
return {1};
case OperandType::TENSOR_QUANT8_ASYMM:
return {-1, 256};
case OperandType::TENSOR_QUANT8_SYMM:
return {-129, -1, 1, 128};
case OperandType::TENSOR_QUANT16_ASYMM:
return {-1, 65536};
case OperandType::TENSOR_QUANT16_SYMM:
return {-32769, -1, 1, 32768};
default:
return {};
}
}
static void mutateOperandZeroPointTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
const std::vector<int32_t> invalidZeroPoints =
getInvalidZeroPoints(model.operands[operand].type);
for (int32_t invalidZeroPoint : invalidZeroPoints) {
const std::string message = "mutateOperandZeroPointTest: operand " +
std::to_string(operand) + " has zero point of " +
std::to_string(invalidZeroPoint);
validate(device, message, model, [operand, invalidZeroPoint](Model* model) {
model->operands[operand].zeroPoint = invalidZeroPoint;
});
}
}
}
///////////////////////// VALIDATE EXTRA ??? /////////////////////////
// TODO: Operand::lifetime
// TODO: Operand::location
///////////////////////// VALIDATE OPERATION OPERAND TYPE /////////////////////////
static void mutateOperand(Operand* operand, OperandType type) {
Operand newOperand = *operand;
newOperand.type = type;
switch (type) {
case OperandType::FLOAT16:
case OperandType::FLOAT32:
case OperandType::INT32:
case OperandType::UINT32:
case OperandType::BOOL:
newOperand.dimensions = hidl_vec<uint32_t>();
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_BOOL8:
case OperandType::TENSOR_FLOAT16:
case OperandType::TENSOR_FLOAT32:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_INT32:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.zeroPoint = 0;
break;
case OperandType::TENSOR_QUANT8_ASYMM:
case OperandType::TENSOR_QUANT8_SYMM:
case OperandType::TENSOR_QUANT16_ASYMM:
case OperandType::TENSOR_QUANT16_SYMM:
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = operand->scale != 0.0f ? operand->scale : 1.0f;
break;
case OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL: {
newOperand.dimensions =
operand->dimensions.size() > 0 ? operand->dimensions : hidl_vec<uint32_t>({1});
newOperand.scale = 0.0f;
newOperand.zeroPoint = 0;
SymmPerChannelQuantParams channelQuant;
channelQuant.channelDim = 0;
channelQuant.scales = hidl_vec<float>(
operand->dimensions.size() > 0 ? static_cast<size_t>(operand->dimensions[0])
: 0);
for (size_t i = 0; i < channelQuant.scales.size(); ++i) {
channelQuant.scales[i] = 1.0f;
}
newOperand.extraParams.channelQuant(std::move(channelQuant));
} break;
case OperandType::OEM:
case OperandType::TENSOR_OEM_BYTE:
default:
break;
}
*operand = newOperand;
}
static bool mutateOperationOperandTypeSkip(size_t operand, OperandType type, const Model& model) {
// Do not test OEM types
if (type == model.operands[operand].type || type == OperandType::OEM ||
type == OperandType::TENSOR_OEM_BYTE) {
return true;
}
for (const Operation& operation : model.operations) {
// Skip mutateOperationOperandTypeTest for the following operations.
// - LSH_PROJECTION's second argument is allowed to have any type.
// - ARGMIN and ARGMAX's first argument can be any of
// TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - CAST's argument can be any of TENSOR_(FLOAT16|FLOAT32|INT32|QUANT8_ASYMM).
// - RANDOM_MULTINOMIAL's argument can be either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - DEQUANTIZE input can be any of
// TENSOR_(QUANT8_ASYMM|QUANT8_SYMM|QUANT8_SYMM_PER_CHANNEL), output can
// be of either TENSOR_FLOAT16 or TENSOR_FLOAT32.
// - QUANTIZE input can be either TENSOR_FLOAT16 or TENSOR_FLOAT32
// - CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - DEPTHWISE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - GROUPED_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
// - TRANSPOSE_CONV_2D filter type (arg 1) can be QUANT8_ASYMM or QUANT8_SYMM_PER_CHANNEL
switch (operation.type) {
case OperationType::LSH_PROJECTION: {
if (operand == operation.inputs[1]) {
return true;
}
} break;
case OperationType::CAST:
case OperationType::ARGMAX:
case OperationType::ARGMIN: {
if (type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32 ||
type == OperandType::TENSOR_INT32 || type == OperandType::TENSOR_QUANT8_ASYMM) {
return true;
}
} break;
case OperationType::QUANTIZE:
case OperationType::RANDOM_MULTINOMIAL: {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
case OperationType::DEQUANTIZE: {
if (operand == operation.inputs[0] &&
(type == OperandType::TENSOR_QUANT8_ASYMM ||
type == OperandType::TENSOR_QUANT8_SYMM ||
type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
return true;
}
if (operand == operation.outputs[0] &&
(type == OperandType::TENSOR_FLOAT16 || type == OperandType::TENSOR_FLOAT32)) {
return true;
}
} break;
case OperationType::TRANSPOSE_CONV_2D:
case OperationType::GROUPED_CONV_2D:
case OperationType::DEPTHWISE_CONV_2D:
case OperationType::CONV_2D: {
if (operand == operation.inputs[1] &&
(type == OperandType::TENSOR_QUANT8_ASYMM ||
type == OperandType::TENSOR_QUANT8_SYMM_PER_CHANNEL)) {
return true;
}
} break;
default:
break;
}
}
return false;
}
static void mutateOperationOperandTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
for (OperandType invalidOperandType : hidl_enum_range<OperandType>{}) {
if (mutateOperationOperandTypeSkip(operand, invalidOperandType, model)) {
continue;
}
const std::string message = "mutateOperationOperandTypeTest: operand " +
std::to_string(operand) + " set to type " +
toString(invalidOperandType);
validate(device, message, model, [operand, invalidOperandType](Model* model) {
mutateOperand(&model->operands[operand], invalidOperandType);
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION TYPE /////////////////////////
static const uint32_t invalidOperationTypes[] = {
static_cast<uint32_t>(OperationTypeRange::FUNDAMENTAL_MAX) + 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MIN) - 1,
static_cast<uint32_t>(OperationTypeRange::OEM_MAX) + 1,
};
static void mutateOperationTypeTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (uint32_t invalidOperationType : invalidOperationTypes) {
const std::string message = "mutateOperationTypeTest: operation " +
std::to_string(operation) + " set to value " +
std::to_string(invalidOperationType);
validate(device, message, model, [operation, invalidOperationType](Model* model) {
model->operations[operation].type =
static_cast<OperationType>(invalidOperationType);
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION INPUT OPERAND INDEX /////////////////////////
static void mutateOperationInputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const uint32_t invalidOperand = model.operands.size();
for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
const std::string message = "mutateOperationInputOperandIndexTest: operation " +
std::to_string(operation) + " input " +
std::to_string(input);
validate(device, message, model, [operation, input, invalidOperand](Model* model) {
model->operations[operation].inputs[input] = invalidOperand;
});
}
}
}
///////////////////////// VALIDATE MODEL OPERATION OUTPUT OPERAND INDEX /////////////////////////
static void mutateOperationOutputOperandIndexTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const uint32_t invalidOperand = model.operands.size();
for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
const std::string message = "mutateOperationOutputOperandIndexTest: operation " +
std::to_string(operation) + " output " +
std::to_string(output);
validate(device, message, model, [operation, output, invalidOperand](Model* model) {
model->operations[operation].outputs[output] = invalidOperand;
});
}
}
}
///////////////////////// REMOVE OPERAND FROM EVERYTHING /////////////////////////
static void removeValueAndDecrementGreaterValues(hidl_vec<uint32_t>* vec, uint32_t value) {
if (vec) {
// remove elements matching "value"
auto last = std::remove(vec->begin(), vec->end(), value);
vec->resize(std::distance(vec->begin(), last));
// decrement elements exceeding "value"
std::transform(vec->begin(), vec->end(), vec->begin(),
[value](uint32_t v) { return v > value ? v-- : v; });
}
}
static void removeOperand(Model* model, uint32_t index) {
hidl_vec_removeAt(&model->operands, index);
for (Operation& operation : model->operations) {
removeValueAndDecrementGreaterValues(&operation.inputs, index);
removeValueAndDecrementGreaterValues(&operation.outputs, index);
}
removeValueAndDecrementGreaterValues(&model->inputIndexes, index);
removeValueAndDecrementGreaterValues(&model->outputIndexes, index);
}
static bool removeOperandSkip(size_t operand, const Model& model) {
for (const Operation& operation : model.operations) {
// Skip removeOperandTest for the following operations.
// - SPLIT's outputs are not checked during prepareModel.
if (operation.type == OperationType::SPLIT) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {
return true;
}
}
}
// BIDIRECTIONAL_SEQUENCE_LSTM and BIDIRECTIONAL_SEQUENCE_RNN can have
// either one or two outputs depending on their mergeOutputs parameter.
if (operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_LSTM ||
operation.type == OperationType::BIDIRECTIONAL_SEQUENCE_RNN) {
for (const size_t outOprand : operation.outputs) {
if (operand == outOprand) {
return true;
}
}
}
}
return false;
}
static void removeOperandTest(const sp<IDevice>& device, const Model& model) {
for (size_t operand = 0; operand < model.operands.size(); ++operand) {
if (removeOperandSkip(operand, model)) {
continue;
}
const std::string message = "removeOperandTest: operand " + std::to_string(operand);
validate(device, message, model,
[operand](Model* model) { removeOperand(model, operand); });
}
}
///////////////////////// REMOVE OPERATION /////////////////////////
static void removeOperation(Model* model, uint32_t index) {
for (uint32_t operand : model->operations[index].inputs) {
model->operands[operand].numberOfConsumers--;
}
hidl_vec_removeAt(&model->operations, index);
}
static void removeOperationTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const std::string message = "removeOperationTest: operation " + std::to_string(operation);
validate(device, message, model,
[operation](Model* model) { removeOperation(model, operation); });
}
}
///////////////////////// REMOVE OPERATION INPUT /////////////////////////
static bool removeOperationInputSkip(const Operation& op, size_t input) {
// Skip removeOperationInputTest for the following operations.
// - CONCATENATION has at least 2 inputs, with the last element being INT32.
// - CONV_2D, DEPTHWISE_CONV_2D, MAX_POOL_2D, AVERAGE_POOL_2D, L2_POOL_2D, RESIZE_BILINEAR,
// SPACE_TO_DEPTH, SPACE_TO_DEPTH, SPACE_TO_BATCH_ND, BATCH_TO_SPACE_ND can have an optional
// layout parameter.
// - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional axis
// parameter.
switch (op.type) {
case OperationType::CONCATENATION: {
if (op.inputs.size() > 2 && input != op.inputs.size() - 1) {
return true;
}
} break;
case OperationType::DEPTHWISE_CONV_2D: {
if ((op.inputs.size() == 12 && input == 11) || (op.inputs.size() == 9 && input == 8)) {
return true;
}
} break;
case OperationType::CONV_2D:
case OperationType::AVERAGE_POOL_2D:
case OperationType::MAX_POOL_2D:
case OperationType::L2_POOL_2D: {
if ((op.inputs.size() == 11 && input == 10) || (op.inputs.size() == 8 && input == 7)) {
return true;
}
} break;
case OperationType::RESIZE_BILINEAR: {
if (op.inputs.size() == 4 && input == 3) {
return true;
}
} break;
case OperationType::SPACE_TO_DEPTH:
case OperationType::DEPTH_TO_SPACE:
case OperationType::BATCH_TO_SPACE_ND: {
if (op.inputs.size() == 3 && input == 2) {
return true;
}
} break;
case OperationType::SPACE_TO_BATCH_ND: {
if (op.inputs.size() == 4 && input == 3) {
return true;
}
} break;
case OperationType::L2_NORMALIZATION: {
if (op.inputs.size() == 2 && input == 1) {
return true;
}
} break;
case OperationType::LOCAL_RESPONSE_NORMALIZATION: {
if (op.inputs.size() == 6 && input == 5) {
return true;
}
} break;
case OperationType::SOFTMAX: {
if (op.inputs.size() == 3 && input == 2) {
return true;
}
} break;
default:
break;
}
return false;
}
static void removeOperationInputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (size_t input = 0; input < model.operations[operation].inputs.size(); ++input) {
const Operation& op = model.operations[operation];
if (removeOperationInputSkip(op, input)) {
continue;
}
const std::string message = "removeOperationInputTest: operation " +
std::to_string(operation) + ", input " +
std::to_string(input);
validate(device, message, model, [operation, input](Model* model) {
uint32_t operand = model->operations[operation].inputs[input];
model->operands[operand].numberOfConsumers--;
hidl_vec_removeAt(&model->operations[operation].inputs, input);
});
}
}
}
///////////////////////// REMOVE OPERATION OUTPUT /////////////////////////
static void removeOperationOutputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
for (size_t output = 0; output < model.operations[operation].outputs.size(); ++output) {
const std::string message = "removeOperationOutputTest: operation " +
std::to_string(operation) + ", output " +
std::to_string(output);
validate(device, message, model, [operation, output](Model* model) {
hidl_vec_removeAt(&model->operations[operation].outputs, output);
});
}
}
}
///////////////////////// MODEL VALIDATION /////////////////////////
// TODO: remove model input
// TODO: remove model output
// TODO: add unused operation
///////////////////////// ADD OPERATION INPUT /////////////////////////
static bool addOperationInputSkip(const Operation& op) {
// Skip addOperationInputTest for the following operations.
// - L2_NORMALIZATION, LOCAL_RESPONSE_NORMALIZATION, SOFTMAX can have an optional INT32 axis
// parameter.
if ((op.type == OperationType::L2_NORMALIZATION && op.inputs.size() == 1) ||
(op.type == OperationType::LOCAL_RESPONSE_NORMALIZATION && op.inputs.size() == 5) ||
(op.type == OperationType::SOFTMAX && op.inputs.size() == 2)) {
return true;
}
return false;
}
static void addOperationInputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
if (addOperationInputSkip(model.operations[operation])) {
continue;
}
const std::string message = "addOperationInputTest: operation " + std::to_string(operation);
validate(device, message, model, [operation](Model* model) {
uint32_t index = addOperand(model, OperandLifeTime::MODEL_INPUT);
hidl_vec_push_back(&model->operations[operation].inputs, index);
hidl_vec_push_back(&model->inputIndexes, index);
});
}
}
///////////////////////// ADD OPERATION OUTPUT /////////////////////////
static void addOperationOutputTest(const sp<IDevice>& device, const Model& model) {
for (size_t operation = 0; operation < model.operations.size(); ++operation) {
const std::string message =
"addOperationOutputTest: operation " + std::to_string(operation);
validate(device, message, model, [operation](Model* model) {
uint32_t index = addOperand(model, OperandLifeTime::MODEL_OUTPUT);
hidl_vec_push_back(&model->operations[operation].outputs, index);
hidl_vec_push_back(&model->outputIndexes, index);
});
}
}
///////////////////////// VALIDATE EXECUTION PREFERENCE /////////////////////////
static const int32_t invalidExecutionPreferences[] = {
static_cast<int32_t>(ExecutionPreference::LOW_POWER) - 1, // lower bound
static_cast<int32_t>(ExecutionPreference::SUSTAINED_SPEED) + 1, // upper bound
};
static void mutateExecutionPreferenceTest(const sp<IDevice>& device, const Model& model) {
for (int32_t preference : invalidExecutionPreferences) {
const std::string message =
"mutateExecutionPreferenceTest: preference " + std::to_string(preference);
validate(
device, message, model, [](Model*) {},
static_cast<ExecutionPreference>(preference));
}
}
////////////////////////// ENTRY POINT //////////////////////////////
void validateModel(const sp<IDevice>& device, const Model& model) {
mutateOperandTypeTest(device, model);
mutateOperandRankTest(device, model);
mutateOperandScaleTest(device, model);
mutateOperandZeroPointTest(device, model);
mutateOperationOperandTypeTest(device, model);
mutateOperationTypeTest(device, model);
mutateOperationInputOperandIndexTest(device, model);
mutateOperationOutputOperandIndexTest(device, model);
removeOperandTest(device, model);
removeOperationTest(device, model);
removeOperationInputTest(device, model);
removeOperationOutputTest(device, model);
addOperationInputTest(device, model);
addOperationOutputTest(device, model);
mutateExecutionPreferenceTest(device, model);
}
} // namespace android::hardware::neuralnetworks::V1_2::vts::functional
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