Opensource ML embedded evaluation kit
Change-Id: I12e807f19f5cacad7cef82572b6dd48252fd61fd
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
new file mode 100644
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+++ b/source/use_case/img_class/src/UseCaseHandler.cc
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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 "UseCaseHandler.hpp"
+
+#include "Classifier.hpp"
+#include "InputFiles.hpp"
+#include "MobileNetModel.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "hal.h"
+
+using ImgClassClassifier = arm::app::Classifier;
+
+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);
+
+ /**
+ * @brief Helper function to increment current image index.
+ * @param[in,out] ctx Pointer to the application context object.
+ **/
+ static void _IncrementAppCtxImageIdx(ApplicationContext& ctx);
+
+ /**
+ * @brief Helper function to set the image index.
+ * @param[in,out] ctx Pointer to the application context object.
+ * @param[in] idx Value to be set.
+ * @return true if index is set, false otherwise.
+ **/
+ static bool _SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
+
+ /**
+ * @brief Presents inference results using the data presentation
+ * object.
+ * @param[in] platform Reference to the hal platform object.
+ * @param[in] results Vector of classification results to be displayed.
+ * @param[in] infTimeMs Inference time in milliseconds, if available
+ * otherwise, this can be passed in as 0.
+ * @return true if successful, false otherwise.
+ **/
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<ClassificationResult>& results);
+
+ /**
+ * @brief Helper function to convert a UINT8 image to INT8 format.
+ * @param[in,out] data Pointer to the data start.
+ * @param[in] kMaxImageSize Total number of pixels in the image.
+ **/
+ static void ConvertImgToInt8(void* data, size_t kMaxImageSize);
+
+ /* Image inference classification handler. */
+ bool ClassifyImageHandler(ApplicationContext& ctx, uint32_t imgIndex, bool runAll)
+ {
+ auto& platform = ctx.Get<hal_platform&>("platform");
+
+ constexpr uint32_t dataPsnImgDownscaleFactor = 2;
+ constexpr uint32_t dataPsnImgStartX = 10;
+ constexpr uint32_t dataPsnImgStartY = 35;
+
+ constexpr uint32_t dataPsnTxtInfStartX = 150;
+ constexpr uint32_t dataPsnTxtInfStartY = 40;
+
+ platform.data_psn->clear(COLOR_BLACK);
+
+ auto& model = ctx.Get<Model&>("model");
+
+ /* If the request has a valid size, set the image index. */
+ if (imgIndex < NUMBER_OF_FILES) {
+ if (!_SetAppCtxImageIdx(ctx, 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[arm::app::MobileNetModel::ms_inputColsIdx];
+ const uint32_t nRows = inputShape->data[arm::app::MobileNetModel::ms_inputRowsIdx];
+ const uint32_t nChannels = inputShape->data[arm::app::MobileNetModel::ms_inputChannelsIdx];
+
+ 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()) {
+ 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 %u => %s\n", ctx.Get<uint32_t>("imgIndex"),
+ get_filename(ctx.Get<uint32_t>("imgIndex")));
+
+ RunInference(platform, model);
+
+ /* 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<ImgClassClassifier&>("classifier");
+ classifier.GetClassificationResults(outputTensor, results,
+ ctx.Get<std::vector <std::string>&>("labels"),
+ 5);
+
+ /* 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 (!_PresentInferenceResult(platform, results)) {
+ return false;
+ }
+
+ _IncrementAppCtxImageIdx(ctx);
+
+ } 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;
+ const uint8_t* imgSrc = get_img_array(imIdx);
+ if (nullptr == imgSrc) {
+ printf_err("Failed to get image index %u (max: %u)\n", imIdx,
+ NUMBER_OF_FILES - 1);
+ return false;
+ }
+
+ memcpy(inputTensor->data.data, imgSrc, copySz);
+ debug("Image %u loaded\n", imIdx);
+ return true;
+ }
+
+ static void _IncrementAppCtxImageIdx(ApplicationContext& ctx)
+ {
+ auto curImIdx = ctx.Get<uint32_t>("imgIndex");
+
+ if (curImIdx + 1 >= NUMBER_OF_FILES) {
+ ctx.Set<uint32_t>("imgIndex", 0);
+ return;
+ }
+ ++curImIdx;
+ ctx.Set<uint32_t>("imgIndex", curImIdx);
+ }
+
+ static bool _SetAppCtxImageIdx(ApplicationContext& ctx, const uint32_t idx)
+ {
+ if (idx >= NUMBER_OF_FILES) {
+ printf_err("Invalid idx %u (expected less than %u)\n",
+ idx, NUMBER_OF_FILES);
+ return false;
+ }
+ ctx.Set<uint32_t>("imgIndex", idx);
+ return true;
+ }
+
+ static bool _PresentInferenceResult(hal_platform& platform,
+ const std::vector<ClassificationResult>& results)
+ {
+ constexpr uint32_t dataPsnTxtStartX1 = 150;
+ constexpr uint32_t dataPsnTxtStartY1 = 30;
+
+ constexpr uint32_t dataPsnTxtStartX2 = 10;
+ constexpr uint32_t dataPsnTxtStartY2 = 150;
+
+ constexpr uint32_t dataPsnTxtYIncr = 16; /* Row index increment. */
+
+ platform.data_psn->set_text_color(COLOR_GREEN);
+
+ /* Display each result. */
+ uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
+ uint32_t rowIdx2 = dataPsnTxtStartY2;
+
+ for (uint32_t i = 0; i < results.size(); ++i) {
+ std::string resultStr =
+ std::to_string(i + 1) + ") " +
+ std::to_string(results[i].m_labelIdx) +
+ " (" + std::to_string(results[i].m_normalisedVal) + ")";
+
+ platform.data_psn->present_data_text(
+ resultStr.c_str(), resultStr.size(),
+ dataPsnTxtStartX1, rowIdx1, 0);
+ rowIdx1 += dataPsnTxtYIncr;
+
+ resultStr = std::to_string(i + 1) + ") " + results[i].m_label;
+ platform.data_psn->present_data_text(
+ resultStr.c_str(), resultStr.size(),
+ dataPsnTxtStartX2, rowIdx2, 0);
+ rowIdx2 += dataPsnTxtYIncr;
+
+ info("%u) %u (%f) -> %s\n", i, results[i].m_labelIdx,
+ results[i].m_normalisedVal, results[i].m_label.c_str());
+ }
+
+ return true;
+ }
+
+ static void ConvertImgToInt8(void* data, const size_t kMaxImageSize)
+ {
+ auto* tmp_req_data = (uint8_t*) data;
+ auto* tmp_signed_req_data = (int8_t*) data;
+
+ for (size_t i = 0; i < kMaxImageSize; i++) {
+ tmp_signed_req_data[i] = (int8_t) (
+ (int32_t) (tmp_req_data[i]) - 128);
+ }
+ }
+
+} /* namespace app */
+} /* namespace arm */