| /* |
| * 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 "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 */ |