Copy VTS tests from v1.2 to v1.3
So that it's easier to see what actually has changed in VTS tests for
version 1.3
Bug: 139120468
Test: m
Change-Id: I09797f5f3898501a008186a22dd411b00e9e2c67
Merged-In: I09797f5f3898501a008186a22dd411b00e9e2c67
(cherry picked from commit 3b13b55ac1532647ee6f261489d23ca4269c1440)
diff --git a/neuralnetworks/1.3/vts/functional/ValidateModel.cpp b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
new file mode 100644
index 0000000..30530be
--- /dev/null
+++ b/neuralnetworks/1.3/vts/functional/ValidateModel.cpp
@@ -0,0 +1,713 @@
+/*
+ * 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