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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 "nnfw_dev.h"
#include <cassert>
#include <iostream>
#include <vector>
#include <chrono>
#include <cmath>
#define NNPR_ENSURE_STATUS(a) \
do \
{ \
if ((a) != NNFW_STATUS_NO_ERROR) \
{ \
exit(-1); \
} \
} while (0)
extern "C" void FillFromEval(nnfw_custom_kernel_params *params, char *userdata,
size_t userdata_size);
const nnfw_custom_eval custom_func_ptr_list[] = {FillFromEval};
const char *custom_func_name_list[] = {"FillFrom"};
int custom_func_list_size = 1;
void register_custom_operations(nnfw_session *session)
{
for (int i = 0; i < custom_func_list_size; ++i)
{
auto name = custom_func_name_list[i];
custom_kernel_registration_info info;
info.eval_function = custom_func_ptr_list[i];
NNPR_ENSURE_STATUS(nnfw_register_custom_op_info(session, name, &info));
}
}
uint64_t NowMicros()
{
auto time_point = std::chrono::high_resolution_clock::now();
auto since_epoch = time_point.time_since_epoch();
// default precision of high resolution clock is 10e-9 (nanoseconds)
return std::chrono::duration_cast<std::chrono::microseconds>(since_epoch).count();
}
uint64_t num_elems(const nnfw_tensorinfo *ti)
{
uint64_t n = 1;
for (uint32_t i = 0; i < ti->rank; ++i)
{
assert(ti->dims[i] >= 0);
n *= ti->dims[i];
}
return n;
};
// TODO replace with data import
// Valid only for model FillFrom.tflite
// FillFrom(idx=3, val=1.1)
static const float in_data[10] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
static const float ref_data[10] = {0, 0, 0, 4, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1};
std::vector<float> genData(uint64_t size)
{
assert(size == sizeof(in_data) / sizeof(in_data[0]));
std::cout << "Warning: runner uses hardcoded data form in_data" << std::endl;
std::vector<float> vec(size);
for (uint64_t i = 0; i < size; i++)
vec[i] = in_data[i];
return vec;
}
template <typename InIter1, typename InIter2>
static auto findMaxDifference(InIter1 first1, InIter1 last1, InIter2 first2)
-> decltype(*first1 - *first2)
{
auto max_difference = std::abs(*first1 - *first2);
for (; first1 != last1; ++first1, ++first2)
{
auto diff = std::abs(*first1 - *first2);
if (diff > max_difference)
{
max_difference = diff;
}
}
return max_difference;
}
int main(const int argc, char **argv)
{
if (argc < 2)
{
std::cerr << "[ERROR] No model specified\n";
return 1;
}
char *model_path = argv[1];
nnfw_session *session = nullptr;
NNPR_ENSURE_STATUS(nnfw_create_session(&session));
register_custom_operations(session);
NNPR_ENSURE_STATUS(nnfw_load_model_from_file(session, model_path));
uint32_t num_inputs;
NNPR_ENSURE_STATUS(nnfw_input_size(session, &num_inputs));
// verify input and output
if (num_inputs == 0)
{
std::cerr << "[ ERROR ] "
<< "No inputs in model => execution is not possible" << std::endl;
exit(1);
}
auto verifyInputTypes = [session]() {
uint32_t sz;
NNPR_ENSURE_STATUS(nnfw_input_size(session, &sz));
for (uint32_t i = 0; i < sz; ++i)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_input_tensorinfo(session, i, &ti));
if (ti.dtype != NNFW_TYPE_TENSOR_FLOAT32)
{
std::cerr << "Only float 32bit is supported." << std::endl;
exit(-1);
}
}
};
auto verifyOutputTypes = [session]() {
uint32_t sz;
NNPR_ENSURE_STATUS(nnfw_output_size(session, &sz));
for (uint32_t i = 0; i < sz; ++i)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_output_tensorinfo(session, i, &ti));
if (ti.dtype != NNFW_TYPE_TENSOR_FLOAT32)
{
std::cerr << "Only float 32bit is supported." << std::endl;
exit(-1);
}
}
};
verifyInputTypes();
verifyOutputTypes();
// prepare execution
uint64_t prepare_ms = NowMicros();
NNPR_ENSURE_STATUS(nnfw_prepare(session));
prepare_ms = NowMicros() - prepare_ms;
// prepare input
std::vector<std::vector<float>> inputs(num_inputs);
auto generateInputs = [session, num_inputs, &inputs]() {
// generate random data
const int seed = 1;
for (uint32_t i = 0; i < num_inputs; ++i)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_input_tensorinfo(session, i, &ti));
auto input_num_elements = num_elems(&ti);
inputs[i] = genData(input_num_elements);
NNPR_ENSURE_STATUS(nnfw_set_input(session, i, NNFW_TYPE_TENSOR_FLOAT32, inputs[i].data(),
sizeof(float) * input_num_elements));
}
};
generateInputs();
// prepare output
uint32_t num_outputs = 0;
NNPR_ENSURE_STATUS(nnfw_output_size(session, &num_outputs));
std::vector<std::vector<float>> outputs(num_outputs);
for (uint32_t i = 0; i < num_outputs; i++)
{
nnfw_tensorinfo ti;
NNPR_ENSURE_STATUS(nnfw_output_tensorinfo(session, i, &ti));
auto output_num_elements = num_elems(&ti);
outputs[i].resize(output_num_elements);
NNPR_ENSURE_STATUS(nnfw_set_output(session, i, NNFW_TYPE_TENSOR_FLOAT32, outputs[i].data(),
sizeof(float) * output_num_elements));
}
uint64_t run_ms = NowMicros();
NNPR_ENSURE_STATUS(nnfw_run(session));
run_ms = NowMicros() - run_ms;
const float tolerance = 0.01f;
auto max_difference =
findMaxDifference(outputs[0].begin(), outputs[0].end(), std::begin(ref_data));
if (max_difference > tolerance)
{
std::cout << "Max difference is more than tolerance" << std::endl;
std::cout << "Max difference is " << max_difference << std::endl;
}
std::cout << "nnfw_prepare takes " << prepare_ms / 1e3 << " sec" << std::endl;
std::cout << "nnfw_run takes " << run_ms / 1e3 << " sec" << std::endl;
NNPR_ENSURE_STATUS(nnfw_close_session(session));
return 0;
}
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