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/*
* SPDX-FileCopyrightText: Copyright 2021 Arm Limited and/or its affiliates <open-source-office@arm.com>
* 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 "UseCaseHandler.hpp"
#include "TestModel.hpp"
#include "UseCaseCommonUtils.hpp"
#include "hal.h"
#include "log_macros.h"
#include <cstdlib>
namespace arm {
namespace app {
static void PopulateInputTensor(const Model& model)
{
const size_t numInputs = model.GetNumInputs();
#if defined(DYNAMIC_IFM_BASE) && defined(DYNAMIC_IFM_SIZE)
size_t curInputIdx = 0;
#endif /* defined(DYNAMIC_IFM_BASE) && defined(DYNAMIC_IFM_SIZE) */
/* Populate each input tensor with random data. */
for (size_t inputIndex = 0; inputIndex < numInputs; inputIndex++) {
TfLiteTensor* inputTensor = model.GetInputTensor(inputIndex);
debug("Populating input tensor %zu@%p\n", inputIndex, inputTensor);
debug("Total input size to be populated: %zu\n", inputTensor->bytes);
if (inputTensor->bytes > 0) {
uint8_t* tData = tflite::GetTensorData<uint8_t>(inputTensor);
#if defined(DYNAMIC_IFM_BASE) && defined(DYNAMIC_IFM_SIZE)
if (curInputIdx + inputTensor->bytes > DYNAMIC_IFM_SIZE) {
printf_err("IFM reserved buffer size insufficient\n");
return;
}
memcpy(tData, reinterpret_cast<void *>(DYNAMIC_IFM_BASE + curInputIdx),
inputTensor->bytes);
curInputIdx += inputTensor->bytes;
#else /* defined(DYNAMIC_IFM_BASE) */
/* Create a random input. */
for (size_t j = 0; j < inputTensor->bytes; ++j) {
tData[j] = static_cast<uint8_t>(std::rand() & 0xFF);
}
#endif /* defined(DYNAMIC_IFM_BASE) && defined(DYNAMIC_IFM_SIZE) */
}
}
#if defined(DYNAMIC_IFM_BASE)
info("%d input tensor/s populated with %d bytes with data read from 0x%08x\n",
numInputs, curInputIdx, DYNAMIC_IFM_BASE);
#endif /* defined(DYNAMIC_IFM_BASE) */
}
#if defined (DYNAMIC_OFM_BASE) && defined(DYNAMIC_OFM_SIZE)
static void PopulateDynamicOfm(const Model& model)
{
/* Dump the output to a known memory location */
const size_t numOutputs = model.GetNumOutputs();
size_t curCopyIdx = 0;
uint8_t* const dstPtr = reinterpret_cast<uint8_t *>(DYNAMIC_OFM_BASE);
for (size_t outputIdx = 0; outputIdx < numOutputs; ++outputIdx) {
TfLiteTensor* outputTensor = model.GetOutputTensor(outputIdx);
uint8_t* const tData = tflite::GetTensorData<uint8_t>(outputTensor);
if (tData && outputTensor->bytes > 0) {
if (curCopyIdx + outputTensor->bytes > DYNAMIC_OFM_SIZE) {
printf_err("OFM reserved buffer size insufficient\n");
return;
}
memcpy(dstPtr + curCopyIdx, tData, outputTensor->bytes);
curCopyIdx += outputTensor->bytes;
}
}
info("%d output tensor/s worth %d bytes copied to 0x%08x\n",
numOutputs, curCopyIdx, DYNAMIC_OFM_BASE);
}
#endif /* defined (DYNAMIC_OFM_BASE) && defined(DYNAMIC_OFM_SIZE) */
#if VERIFY_TEST_OUTPUT
static void DumpInputs(const Model& model, const char* message)
{
info("%s\n", message);
for (size_t inputIndex = 0; inputIndex < model.GetNumInputs(); inputIndex++) {
arm::app::DumpTensor(model.GetInputTensor(inputIndex));
}
}
static void DumpOutputs(const Model& model, const char* message)
{
info("%s\n", message);
for (size_t outputIndex = 0; outputIndex < model.GetNumOutputs(); outputIndex++) {
arm::app::DumpTensor(model.GetOutputTensor(outputIndex));
}
}
#endif /* VERIFY_TEST_OUTPUT */
bool RunInferenceHandler(ApplicationContext& ctx)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
auto& model = ctx.Get<Model&>("model");
constexpr uint32_t dataPsnTxtInfStartX = 150;
constexpr uint32_t dataPsnTxtInfStartY = 40;
if (!model.IsInited()) {
printf_err("Model is not initialised! Terminating processing.\n");
return false;
}
#if VERIFY_TEST_OUTPUT
DumpInputs(model, "Initial input tensors values");
DumpOutputs(model, "Initial output tensors values");
#endif /* VERIFY_TEST_OUTPUT */
PopulateInputTensor(model);
#if VERIFY_TEST_OUTPUT
DumpInputs(model, "input tensors populated");
#endif /* VERIFY_TEST_OUTPUT */
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
/* Display message on the LCD - inference running. */
hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
if (!RunInference(model, profiler)) {
return false;
}
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
hal_lcd_display_text(
str_inf.c_str(), str_inf.size(),
dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
info("Final results:\n");
info("Total number of inferences: 1\n");
profiler.PrintProfilingResult();
#if VERIFY_TEST_OUTPUT
DumpOutputs(model, "output tensors post inference");
#endif /* VERIFY_TEST_OUTPUT */
#if defined (DYNAMIC_OFM_BASE) && defined(DYNAMIC_OFM_SIZE)
PopulateDynamicOfm(model);
#endif /* defined (DYNAMIC_OFM_BASE) && defined(DYNAMIC_OFM_SIZE) */
return true;
}
} /* namespace app */
} /* namespace arm */
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