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/*
* Copyright 2022 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 "NnapiInfo"
#define CONTINUE_IF_ERR(expr) \
{ \
int _errCode = (expr); \
if (_errCode != ANEURALNETWORKS_NO_ERROR) { \
std::cerr << #expr << " failed at " << __FILE__ << ":" << __LINE__ << std::endl; \
continue; \
} \
}
#include <iostream>
#include <string>
#include "NeuralNetworks.h"
#include "NeuralNetworksTypes.h"
namespace {
std::string featureLevelString(int64_t featureLevel) {
switch (featureLevel) {
case ANEURALNETWORKS_FEATURE_LEVEL_1:
return "Level 1";
case ANEURALNETWORKS_FEATURE_LEVEL_2:
return "Level 2";
case ANEURALNETWORKS_FEATURE_LEVEL_3:
return "Level 3";
case ANEURALNETWORKS_FEATURE_LEVEL_4:
return "Level 4";
case ANEURALNETWORKS_FEATURE_LEVEL_5:
return "Level 5";
case ANEURALNETWORKS_FEATURE_LEVEL_6:
return "Level 6";
case ANEURALNETWORKS_FEATURE_LEVEL_7:
return "Level 7";
case ANEURALNETWORKS_FEATURE_LEVEL_8:
return "Level 8";
default:
return "Undefined feature level code";
}
}
std::string deviceTypeString(int32_t type) {
switch (type) {
case ANEURALNETWORKS_DEVICE_ACCELERATOR:
return "Accelerator";
case ANEURALNETWORKS_DEVICE_CPU:
return "CPU";
case ANEURALNETWORKS_DEVICE_GPU:
return "GPU";
case ANEURALNETWORKS_DEVICE_OTHER:
return "Other";
case ANEURALNETWORKS_DEVICE_UNKNOWN:
default:
return "Unknown";
}
}
} // namespace
int main() {
uint32_t numDevices;
int returnCode = ANeuralNetworks_getDeviceCount(&numDevices);
if (returnCode != ANEURALNETWORKS_NO_ERROR) {
std::cerr << "Error obtaining device count" << std::endl;
return 1;
}
std::cout << "Number of devices: " << numDevices << std::endl << std::endl;
ANeuralNetworksDevice* device = nullptr;
int64_t featureLevel;
const char* name;
int32_t type;
const char* version;
for (uint32_t i = 0; i < numDevices; i++) {
CONTINUE_IF_ERR(ANeuralNetworks_getDevice(i, &device));
CONTINUE_IF_ERR(ANeuralNetworksDevice_getFeatureLevel(device, &featureLevel));
CONTINUE_IF_ERR(ANeuralNetworksDevice_getName(device, &name));
CONTINUE_IF_ERR(ANeuralNetworksDevice_getType(device, &type));
CONTINUE_IF_ERR(ANeuralNetworksDevice_getVersion(device, &version));
std::cout << "Device: " << name << std::endl;
std::cout << "Feature Level: " << featureLevelString(featureLevel) << std::endl;
std::cout << "Type: " << deviceTypeString(type) << std::endl;
std::cout << "Version: " << version << std::endl;
std::cout << std::endl;
}
return 0;
}