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author | Éanna Ó Catháin <eanna.ocathain@arm.com> | 2021-09-15 09:32:30 +0100 |
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committer | Kshitij Sisodia <kshitij.sisodia@arm.com> | 2021-09-16 16:01:23 +0100 |
commit | 8f9588721cbb7356b03a714c97d6b3a9a6e89438 (patch) | |
tree | 1ca19d31958081c09f360d91e15fefb6e38b3992 /source/use_case/vww/src | |
parent | e6588f620c648dd0492f6133152855d77c672568 (diff) | |
download | ml-embedded-evaluation-kit-8f9588721cbb7356b03a714c97d6b3a9a6e89438.tar.gz |
MLECO-2082: Adding visual wake word use case21.08
MLECO-2083: Refactoring img_class and visual wake word
*Added source files for visual wake word
*Added tests
*Added docs
*Added new images for visual wake word demo
*Refactored common functions in img_class, visual wake word and other usecases
Change-Id: Ibd25854e19a5517f940a8d3086a5d4835fab89e9
Signed-off-by: Éanna Ó Catháin <eanna.ocathain@arm.com>
Diffstat (limited to 'source/use_case/vww/src')
-rw-r--r-- | source/use_case/vww/src/MainLoop.cc | 91 | ||||
-rw-r--r-- | source/use_case/vww/src/UseCaseHandler.cc | 182 | ||||
-rw-r--r-- | source/use_case/vww/src/VisualWakeWordModel.cc | 57 |
3 files changed, 330 insertions, 0 deletions
diff --git a/source/use_case/vww/src/MainLoop.cc b/source/use_case/vww/src/MainLoop.cc new file mode 100644 index 0000000..f026cc2 --- /dev/null +++ b/source/use_case/vww/src/MainLoop.cc @@ -0,0 +1,91 @@ +/* + * 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 "hal.h" /* Brings in platform definitions. */ +#include "Classifier.hpp" /* Classifier. */ +#include "InputFiles.hpp" /* For input images. */ +#include "Labels.hpp" /* For label strings. */ +#include "VisualWakeWordModel.hpp" /* Model class for running inference. */ +#include "UseCaseHandler.hpp" /* Handlers for different user options. */ +#include "UseCaseCommonUtils.hpp" /* Utils functions. */ + +using ViusalWakeWordClassifier = arm::app::Classifier; + +void main_loop(hal_platform &platform) +{ + arm::app::VisualWakeWordModel model; /* Model wrapper object. */ + + /* Load the model. */ + if (!model.Init()) { + printf_err("Failed to initialise model\n"); + return; + } + + /* Instantiate application context. */ + arm::app::ApplicationContext caseContext; + + arm::app::Profiler profiler{&platform, "vww"}; + caseContext.Set<arm::app::Profiler&>("profiler", profiler); + caseContext.Set<hal_platform&>("platform", platform); + caseContext.Set<arm::app::Model&>("model", model); + caseContext.Set<uint32_t>("imgIndex", 0); + + ViusalWakeWordClassifier classifier; /* Classifier wrapper object. */ + caseContext.Set<arm::app::Classifier&>("classifier", classifier); + + std::vector <std::string> labels; + GetLabelsVector(labels); + caseContext.Set<const std::vector <std::string>&>("labels", labels); + + /* Loop. */ + bool executionSuccessful = true; + constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false; + do { + int menuOption = common::MENU_OPT_RUN_INF_NEXT; + if (bUseMenu) { + DisplayCommonMenu(); + menuOption = arm::app::ReadUserInputAsInt(platform); + printf("\n"); + } + + switch (menuOption) { + case common::MENU_OPT_RUN_INF_NEXT: + executionSuccessful = ClassifyImageHandler(caseContext, caseContext.Get<uint32_t>("imgIndex"), false); + break; + case common::MENU_OPT_RUN_INF_CHOSEN: { + printf(" Enter the image index [0, %d]: ", NUMBER_OF_FILES-1); + auto imgIndex = static_cast<uint32_t>(arm::app::ReadUserInputAsInt(platform)); + executionSuccessful = ClassifyImageHandler(caseContext, imgIndex, false); + break; + } + case common::MENU_OPT_RUN_INF_ALL: + executionSuccessful = ClassifyImageHandler(caseContext, caseContext.Get<uint32_t>("imgIndex"), true); + break; + case common::MENU_OPT_SHOW_MODEL_INFO: { + executionSuccessful = model.ShowModelInfoHandler(); + break; + } + case common::MENU_OPT_LIST_IFM: + executionSuccessful = ListFilesHandler(caseContext); + break; + default: + printf("Incorrect choice, try again."); + break; + } + } while (executionSuccessful && bUseMenu); + info("Main loop terminated.\n"); + +} diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc new file mode 100644 index 0000000..fb2e837 --- /dev/null +++ b/source/use_case/vww/src/UseCaseHandler.cc @@ -0,0 +1,182 @@ +/* + * 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 "VisualWakeWordModel.hpp" +#include "Classifier.hpp" +#include "InputFiles.hpp" +#include "UseCaseCommonUtils.hpp" +#include "hal.h" + +namespace arm { +namespace app { + + /** + * @brief Helper function to load the current image into the input + * tensor. + * @param[in] imIdx Image index (from the pool of images available + * to the application). + * @param[out] inputTensor Pointer to the input tensor to be populated. + * @return true if tensor is loaded, false otherwise. + **/ + static bool LoadImageIntoTensor(uint32_t imIdx, + TfLiteTensor *inputTensor); + + /* Image inference classification handler. */ + bool ClassifyImageHandler(ApplicationContext &ctx, uint32_t imgIndex, bool runAll) + { + auto& platform = ctx.Get<hal_platform &>("platform"); + auto& profiler = ctx.Get<Profiler&>("profiler"); + + constexpr uint32_t dataPsnImgDownscaleFactor = 1; + constexpr uint32_t dataPsnImgStartX = 10; + constexpr uint32_t dataPsnImgStartY = 35; + + constexpr uint32_t dataPsnTxtInfStartX = 150; + constexpr uint32_t dataPsnTxtInfStartY = 70; + + + platform.data_psn->clear(COLOR_BLACK); + time_t infTimeMs = 0; + + auto& model = ctx.Get<Model&>("model"); + + /* If the request has a valid size, set the image index. */ + if (imgIndex < NUMBER_OF_FILES) { + if (!SetAppCtxIfmIdx(ctx, imgIndex,"imgIndex")) { + return false; + } + } + if (!model.IsInited()) { + printf_err("Model is not initialised! Terminating processing.\n"); + return false; + } + + auto curImIdx = ctx.Get<uint32_t>("imgIndex"); + + TfLiteTensor *outputTensor = model.GetOutputTensor(0); + TfLiteTensor *inputTensor = model.GetInputTensor(0); + + if (!inputTensor->dims) { + printf_err("Invalid input tensor dims\n"); + return false; + } else if (inputTensor->dims->size < 3) { + printf_err("Input tensor dimension should be >= 3\n"); + return false; + } + TfLiteIntArray* inputShape = model.GetInputShape(0); + const uint32_t nCols = inputShape->data[2]; + const uint32_t nRows = inputShape->data[1]; + const uint32_t nChannels = (inputShape->size == 4) ? inputShape->data[3] : 1; + + std::vector<ClassificationResult> results; + + do { + + /* Strings for presentation/logging. */ + std::string str_inf{"Running inference... "}; + + /* Copy over the data. */ + LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor); + + /* Display this image on the LCD. */ + platform.data_psn->present_data_image( + (uint8_t *) inputTensor->data.data, + nCols, nRows, nChannels, + dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor); + + /* If the data is signed. */ + if (model.IsDataSigned()) { + image::ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes); + } + + /* Display message on the LCD - inference running. */ + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + /* Run inference over this image. */ + info("Running inference on image %" PRIu32 " => %s\n", ctx.Get<uint32_t>("imgIndex"), + get_filename(ctx.Get<uint32_t>("imgIndex"))); + + if (!RunInference(model, profiler)) { + return false; + } + + /* Erase. */ + str_inf = std::string(str_inf.size(), ' '); + platform.data_psn->present_data_text( + str_inf.c_str(), str_inf.size(), + dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0); + + auto& classifier = ctx.Get<Classifier&>("classifier"); + classifier.GetClassificationResults(outputTensor, results, + ctx.Get<std::vector <std::string>&>("labels"), 1); + + /* Add results to context for access outside handler. */ + ctx.Set<std::vector<ClassificationResult>>("results", results); + +#if VERIFY_TEST_OUTPUT + arm::app::DumpTensor(outputTensor); +#endif /* VERIFY_TEST_OUTPUT */ + + if (!image::PresentInferenceResult(platform, results, infTimeMs)) { + return false; + } + + profiler.PrintProfilingResult(); + IncrementAppCtxIfmIdx(ctx,"imgIndex"); + + } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx); + + return true; + } + + static bool LoadImageIntoTensor(const uint32_t imIdx, + TfLiteTensor *inputTensor) + { + const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ? + inputTensor->bytes : IMAGE_DATA_SIZE; + if (imIdx >= NUMBER_OF_FILES) { + printf_err("invalid image index %" PRIu32 " (max: %u)\n", imIdx, + NUMBER_OF_FILES - 1); + return false; + } + + const uint32_t nChannels = (inputTensor->dims->size == 4) ? inputTensor->dims->data[3] : 1; + + const uint8_t* srcPtr = get_img_array(imIdx); + auto* dstPtr = (uint8_t*)inputTensor->data.data; + if (1 == nChannels) { + /** + * Visual Wake Word model accepts only one channel => + * Convert image to grayscale here + **/ + for (size_t i = 0; i < copySz; ++i, srcPtr += 3) { + *dstPtr++ = 0.2989*(*srcPtr) + + 0.587*(*(srcPtr+1)) + + 0.114*(*(srcPtr+2)); + } + } else { + memcpy(inputTensor->data.data, srcPtr, copySz); + } + + debug("Image %" PRIu32 " loaded\n", imIdx); + return true; + } + +} /* namespace app */ +} /* namespace arm */
\ No newline at end of file diff --git a/source/use_case/vww/src/VisualWakeWordModel.cc b/source/use_case/vww/src/VisualWakeWordModel.cc new file mode 100644 index 0000000..3067c7a --- /dev/null +++ b/source/use_case/vww/src/VisualWakeWordModel.cc @@ -0,0 +1,57 @@ +/* + * 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 "VisualWakeWordModel.hpp" + +#include "hal.h" + +const tflite::MicroOpResolver& arm::app::VisualWakeWordModel::GetOpResolver() +{ + return this->m_opResolver; +} + +bool arm::app::VisualWakeWordModel::EnlistOperations() +{ + this->m_opResolver.AddDepthwiseConv2D(); + this->m_opResolver.AddConv2D(); + this->m_opResolver.AddAveragePool2D(); + this->m_opResolver.AddReshape(); + this->m_opResolver.AddPad(); + this->m_opResolver.AddAdd(); + +#if defined(ARM_NPU) + if (kTfLiteOk == this->m_opResolver.AddEthosU()) { + info("Added %s support to op resolver\n", + tflite::GetString_ETHOSU()); + } else { + printf_err("Failed to add Arm NPU support to op resolver."); + return false; + } +#endif /* ARM_NPU */ + return true; +} + +extern uint8_t* GetModelPointer(); +const uint8_t* arm::app::VisualWakeWordModel::ModelPointer() +{ + return GetModelPointer(); +} + +extern size_t GetModelLen(); +size_t arm::app::VisualWakeWordModel::ModelSize() +{ + return GetModelLen(); +}
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