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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* 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.
*/
#include "nnfw.h"
#include <vector>
#include <iostream>
uint64_t num_elems(const nnfw_tensorinfo *ti)
{
uint64_t n = 1;
for (uint32_t i = 0; i < ti->rank; ++i)
{
n *= ti->dims[i];
}
return n;
}
int main(const int argc, char **argv)
{
nnfw_session *session = nullptr;
nnfw_create_session(&session);
// Loading nnpackage
nnfw_load_model_from_file(session, argv[1]);
// Use acl_neon backend for CONV_2D and acl_cl for otherwise.
// Note that defalut backend is acl_cl
nnfw_set_op_backend(session, "CONV_2D", "acl_neon");
// Compile model
nnfw_prepare(session);
// Prepare input. Here we just allocate dummy input arrays.
std::vector<float> input;
nnfw_tensorinfo ti;
nnfw_input_tensorinfo(session, 0, &ti); // get first input's info
uint32_t input_elements = num_elems(&ti);
input.resize(input_elements);
// TODO: Please add initialization for your input.
nnfw_set_input(session, 0, ti.dtype, input.data(), sizeof(float) * input_elements);
// Prepare output
std::vector<float> output;
nnfw_output_tensorinfo(session, 0, &ti); // get first output's info
uint32_t output_elements = num_elems(&ti);
output.resize(output_elements);
nnfw_set_output(session, 0, ti.dtype, output.data(), sizeof(float) * output_elements);
// Do inference
nnfw_run(session);
// TODO: Please print or compare the output value in your way.
nnfw_close_session(session);
std::cout << "nnpackage " << argv[1] << " runs successfully." << std::endl;
return 0;
}
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