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/*
* Copyright (c) 2021 Arm Limited. All rights reserved.
* SPDX-License-Identifier: Apache-2.0
*
* 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 <catch.hpp>
#include <random>
#include "AdModel.hpp"
#include "TestData_ad.hpp"
#include "log_macros.h"
#include "TensorFlowLiteMicro.hpp"
#include "BufAttributes.hpp"
#ifndef AD_FEATURE_VEC_DATA_SIZE
#define AD_IN_FEATURE_VEC_DATA_SIZE (1024)
#endif /* AD_FEATURE_VEC_DATA_SIZE */
namespace arm {
namespace app {
static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
namespace ad {
extern uint8_t* GetModelPointer();
extern size_t GetModelLen();
} /* namespace ad */
} /* namespace app */
} /* namespace arm */
using namespace test;
bool RunInference(arm::app::Model& model, const int8_t vec[])
{
TfLiteTensor *inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
const size_t copySz = inputTensor->bytes < AD_IN_FEATURE_VEC_DATA_SIZE ? inputTensor->bytes : AD_IN_FEATURE_VEC_DATA_SIZE;
memcpy(inputTensor->data.data, vec, copySz);
return model.RunInference();
}
bool RunInferenceRandom(arm::app::Model& model)
{
TfLiteTensor *inputTensor = model.GetInputTensor(0);
REQUIRE(inputTensor);
std::random_device rndDevice;
std::mt19937 mersenneGen{rndDevice()};
std::uniform_int_distribution<short> dist{-128, 127};
auto gen = [&dist, &mersenneGen]() {
return dist(mersenneGen);
};
std::vector<int8_t> randomInput(inputTensor->bytes);
std::generate(std::begin(randomInput), std::end(randomInput), gen);
REQUIRE(RunInference(model, randomInput.data()));
return true;
}
template <typename T>
void TestInference(const T *input_goldenFV, const T *output_goldenFV, arm::app::Model& model)
{
REQUIRE(RunInference(model, static_cast<const T*>(input_goldenFV)));
TfLiteTensor *outputTensor = model.GetOutputTensor(0);
REQUIRE(outputTensor);
REQUIRE(outputTensor->bytes == OFM_0_DATA_SIZE);
auto tensorData = tflite::GetTensorData<T>(outputTensor);
REQUIRE(tensorData);
for (size_t i = 0; i < outputTensor->bytes; i++)
{
REQUIRE(static_cast<int>(tensorData[i]) == static_cast<int>(((T)output_goldenFV[i])));
}
}
TEST_CASE("Running random inference with TensorFlow Lite Micro and AdModel Int8", "[AD]")
{
arm::app::AdModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::ad::GetModelPointer(),
arm::app::ad::GetModelLen()));
REQUIRE(model.IsInited());
REQUIRE(RunInferenceRandom(model));
}
TEST_CASE("Running golden vector inference with TensorFlow Lite Micro and AdModel Int8", "[AD]")
{
REQUIRE(NUMBER_OF_IFM_FILES == NUMBER_OF_IFM_FILES);
for (uint32_t i = 0 ; i < NUMBER_OF_IFM_FILES; ++i) {
auto input_goldenFV = get_ifm_data_array(i);;
auto output_goldenFV = get_ofm_data_array(i);
DYNAMIC_SECTION("Executing inference with re-init")
{
arm::app::AdModel model{};
REQUIRE_FALSE(model.IsInited());
REQUIRE(model.Init(arm::app::tensorArena,
sizeof(arm::app::tensorArena),
arm::app::ad::GetModelPointer(),
arm::app::ad::GetModelLen()));
REQUIRE(model.IsInited());
TestInference<int8_t>(input_goldenFV, output_goldenFV, model);
}
}
}
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