/* * Copyright (C) 2017 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 "SampleDriverMinimal" #include "SampleDriver.h" #include "HalInterfaces.h" #include "NeuralNetworksOEM.h" #include "Utils.h" #include "ValidateHal.h" #include <android-base/logging.h> #include <hidl/LegacySupport.h> #include <thread> namespace android { namespace nn { namespace sample_driver { class SampleDriverMinimal : public SampleDriver { public: SampleDriverMinimal() : SampleDriver("sample-minimal") {} Return<void> getCapabilities_1_1(getCapabilities_1_1_cb cb) override; Return<void> getSupportedOperations_1_1(const V1_1::Model& model, getSupportedOperations_1_1_cb cb) override; }; Return<void> SampleDriverMinimal::getCapabilities_1_1(getCapabilities_1_1_cb cb) { android::nn::initVLogMask(); VLOG(DRIVER) << "getCapabilities()"; Capabilities capabilities = {.float32Performance = {.execTime = 0.4f, .powerUsage = 0.5f}, .quantized8Performance = {.execTime = 1.0f, .powerUsage = 1.0f}, .relaxedFloat32toFloat16Performance = {.execTime = 0.4f, .powerUsage = 0.5f}}; cb(ErrorStatus::NONE, capabilities); return Void(); } Return<void> SampleDriverMinimal::getSupportedOperations_1_1(const V1_1::Model& model, getSupportedOperations_1_1_cb cb) { VLOG(DRIVER) << "getSupportedOperations()"; if (validateModel(model)) { const size_t count = model.operations.size(); std::vector<bool> supported(count); // Simulate supporting just a few ops for (size_t i = 0; i < count; i++) { supported[i] = false; const Operation& operation = model.operations[i]; switch (operation.type) { case OperationType::ADD: case OperationType::CONCATENATION: case OperationType::CONV_2D: { const Operand& firstOperand = model.operands[operation.inputs[0]]; if (firstOperand.type == OperandType::TENSOR_FLOAT32) { supported[i] = true; } break; } default: break; } } cb(ErrorStatus::NONE, supported); } else { std::vector<bool> supported; cb(ErrorStatus::INVALID_ARGUMENT, supported); } return Void(); } } // namespace sample_driver } // namespace nn } // namespace android using android::nn::sample_driver::SampleDriverMinimal; using android::sp; int main() { sp<SampleDriverMinimal> driver(new SampleDriverMinimal()); return driver->run(); }