MLECO-3078: Add VWW use case API
We now expect that img_class and vww use cases model inputs should have 4 dims as dictated by
use case logic
Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I67a57a3a28a7ff2e09c917c40e9fc2c08384a45c
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index 11a1aa8..c68d816 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -43,7 +43,7 @@
return false;
}
}
- auto initialImIdx = ctx.Get<uint32_t>("imgIndex");
+ auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
constexpr uint32_t dataPsnImgDownscaleFactor = 2;
constexpr uint32_t dataPsnImgStartX = 10;
@@ -62,8 +62,8 @@
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");
+ } else if (inputTensor->dims->size < 4) {
+ printf_err("Input tensor dimension should be = 4\n");
return false;
}
@@ -148,7 +148,7 @@
IncrementAppCtxIfmIdx(ctx,"imgIndex");
- } while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImIdx);
+ } while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
return true;
}
diff --git a/source/use_case/vww/include/VisualWakeWordProcessing.hpp b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
new file mode 100644
index 0000000..b1d68ce
--- /dev/null
+++ b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
@@ -0,0 +1,86 @@
+/*
+ * Copyright (c) 2022 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.
+ */
+#ifndef VWW_PROCESSING_HPP
+#define VWW_PROCESSING_HPP
+
+#include "BaseProcessing.hpp"
+#include "Model.hpp"
+#include "Classifier.hpp"
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Pre-processing class for Visual Wake Word use case.
+ * Implements methods declared by BasePreProcess and anything else needed
+ * to populate input tensors ready for inference.
+ */
+ class VisualWakeWordPreProcess : public BasePreProcess {
+
+ public:
+ /**
+ * @brief Constructor
+ * @param[in] model Pointer to the the Image classification Model object.
+ **/
+ explicit VisualWakeWordPreProcess(Model* model);
+
+ /**
+ * @brief Should perform pre-processing of 'raw' input image data and load it into
+ * TFLite Micro input tensors ready for inference
+ * @param[in] input Pointer to the data that pre-processing will work on.
+ * @param[in] inputSize Size of the input data.
+ * @return true if successful, false otherwise.
+ **/
+ bool DoPreProcess(const void* input, size_t inputSize) override;
+ };
+
+ /**
+ * @brief Post-processing class for Visual Wake Word use case.
+ * Implements methods declared by BasePostProcess and anything else needed
+ * to populate result vector.
+ */
+ class VisualWakeWordPostProcess : public BasePostProcess {
+
+ private:
+ Classifier& m_vwwClassifier;
+ const std::vector<std::string>& m_labels;
+ std::vector<ClassificationResult>& m_results;
+
+ public:
+ /**
+ * @brief Constructor
+ * @param[in] classifier Classifier object used to get top N results from classification.
+ * @param[in] model Pointer to the VWW classification Model object.
+ * @param[in] labels Vector of string labels to identify each output of the model.
+ * @param[out] results Vector of classification results to store decoded outputs.
+ **/
+ VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+ const std::vector<std::string>& labels,
+ std::vector<ClassificationResult>& results);
+
+ /**
+ * @brief Should perform post-processing of the result of inference then
+ * populate classification result data for any later use.
+ * @return true if successful, false otherwise.
+ **/
+ bool DoPostProcess() override;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* VWW_PROCESSING_HPP */
\ No newline at end of file
diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc
index 56ba2b5..7681f89 100644
--- a/source/use_case/vww/src/UseCaseHandler.cc
+++ b/source/use_case/vww/src/UseCaseHandler.cc
@@ -22,27 +22,23 @@
#include "UseCaseCommonUtils.hpp"
#include "hal.h"
#include "log_macros.h"
-
-#include <algorithm>
+#include "VisualWakeWordProcessing.hpp"
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. */
+ /* Visual Wake Word inference handler. */
bool ClassifyImageHandler(ApplicationContext &ctx, uint32_t imgIndex, bool runAll)
{
auto& profiler = ctx.Get<Profiler&>("profiler");
+ 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;
+ }
+ }
+ auto initialImgIdx = ctx.Get<uint32_t>("imgIndex");
constexpr uint32_t dataPsnImgDownscaleFactor = 1;
constexpr uint32_t dataPsnImgStartX = 10;
@@ -51,31 +47,22 @@
constexpr uint32_t dataPsnTxtInfStartX = 150;
constexpr uint32_t dataPsnTxtInfStartY = 70;
- 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);
+ 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");
+ } else if (inputTensor->dims->size < 4) {
+ printf_err("Input tensor dimension should be = 4\n");
return false;
}
+
+ /* Get input shape for displaying the image. */
TfLiteIntArray* inputShape = model.GetInputShape(0);
const uint32_t nCols = inputShape->data[arm::app::VisualWakeWordModel::ms_inputColsIdx];
const uint32_t nRows = inputShape->data[arm::app::VisualWakeWordModel::ms_inputRowsIdx];
@@ -83,9 +70,19 @@
printf_err("Invalid channel index.\n");
return false;
}
- const uint32_t nChannels = inputShape->data[arm::app::VisualWakeWordModel::ms_inputChannelsIdx];
+
+ /* We expect RGB images to be provided. */
+ const uint32_t displayChannels = 3;
+
+ /* Set up pre and post-processing. */
+ VisualWakeWordPreProcess preprocess = VisualWakeWordPreProcess(&model);
std::vector<ClassificationResult> results;
+ VisualWakeWordPostProcess postprocess = VisualWakeWordPostProcess(
+ ctx.Get<Classifier&>("classifier"), &model,
+ ctx.Get<std::vector<std::string>&>("labels"), results);
+
+ UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
do {
hal_lcd_clear(COLOR_BLACK);
@@ -93,54 +90,55 @@
/* Strings for presentation/logging. */
std::string str_inf{"Running inference... "};
- /* Copy over the data. */
- LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
+ const uint8_t* imgSrc = get_img_array(ctx.Get<uint32_t>("imgIndex"));
+ if (nullptr == imgSrc) {
+ printf_err("Failed to get image index %" PRIu32 " (max: %u)\n", ctx.Get<uint32_t>("imgIndex"),
+ NUMBER_OF_FILES - 1);
+ return false;
+ }
/* Display this image on the LCD. */
hal_lcd_display_image(
- static_cast<uint8_t *>(inputTensor->data.data),
- nCols, nRows, nChannels,
+ imgSrc,
+ nCols, nRows, displayChannels,
dataPsnImgStartX, dataPsnImgStartY, dataPsnImgDownscaleFactor);
- /* Vww model preprocessing is image conversion from uint8 to [0,1] float values,
- * then quantize them with input quantization info. */
- QuantParams inQuantParams = GetTensorQuantParams(inputTensor);
-
- auto* req_data = static_cast<uint8_t *>(inputTensor->data.data);
- auto* signed_req_data = static_cast<int8_t *>(inputTensor->data.data);
- for (size_t i = 0; i < inputTensor->bytes; i++) {
- auto i_data_int8 = static_cast<int8_t>(((static_cast<float>(req_data[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset);
- signed_req_data[i] = std::min<int8_t>(INT8_MAX, std::max<int8_t>(i_data_int8, INT8_MIN));
- }
-
/* Display message on the LCD - inference running. */
- hal_lcd_display_text(
- str_inf.c_str(), str_inf.size(),
+ hal_lcd_display_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)) {
+ const size_t imgSz = inputTensor->bytes < IMAGE_DATA_SIZE ?
+ inputTensor->bytes : IMAGE_DATA_SIZE;
+
+ /* Run the pre-processing, inference and post-processing. */
+ if (!runner.PreProcess(imgSrc, imgSz)) {
+ return false;
+ }
+
+ profiler.StartProfiling("Inference");
+ if (!runner.RunInference()) {
+ return false;
+ }
+ profiler.StopProfiling();
+
+ if (!runner.PostProcess()) {
return false;
}
/* Erase. */
str_inf = std::string(str_inf.size(), ' ');
- hal_lcd_display_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,
- false);
+ hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
+ dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
/* Add results to context for access outside handler. */
ctx.Set<std::vector<ClassificationResult>>("results", results);
#if VERIFY_TEST_OUTPUT
+ TfLiteTensor* outputTensor = model.GetOutputTensor(0);
arm::app::DumpTensor(outputTensor);
#endif /* VERIFY_TEST_OUTPUT */
@@ -149,45 +147,13 @@
}
profiler.PrintProfilingResult();
+
IncrementAppCtxIfmIdx(ctx,"imgIndex");
- } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
+ } while (runAll && ctx.Get<uint32_t>("imgIndex") != initialImgIdx);
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;
- }
-
- if (arm::app::VisualWakeWordModel::ms_inputChannelsIdx >= static_cast<uint32_t>(inputTensor->dims->size)) {
- printf_err("Invalid channel index.\n");
- return false;
- }
- const uint32_t nChannels = inputTensor->dims->data[arm::app::VisualWakeWordModel::ms_inputChannelsIdx];
-
- const uint8_t* srcPtr = get_img_array(imIdx);
- auto* dstPtr = static_cast<uint8_t *>(inputTensor->data.data);
- if (1 == nChannels) {
- /**
- * Visual Wake Word model accepts only one channel =>
- * Convert image to grayscale here
- **/
- image::RgbToGrayscale(srcPtr, dstPtr, copySz);
- } else {
- memcpy(inputTensor->data.data, srcPtr, copySz);
- }
-
- debug("Image %" PRIu32 " loaded\n", imIdx);
- return true;
- }
-
} /* namespace app */
} /* namespace arm */
diff --git a/source/use_case/vww/src/VisualWakeWordProcessing.cc b/source/use_case/vww/src/VisualWakeWordProcessing.cc
new file mode 100644
index 0000000..94eae28
--- /dev/null
+++ b/source/use_case/vww/src/VisualWakeWordProcessing.cc
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2022 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 "VisualWakeWordProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "VisualWakeWordModel.hpp"
+#include "log_macros.h"
+
+namespace arm {
+namespace app {
+
+ VisualWakeWordPreProcess::VisualWakeWordPreProcess(Model* model)
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ }
+
+ bool VisualWakeWordPreProcess::DoPreProcess(const void* data, size_t inputSize)
+ {
+ if (data == nullptr) {
+ printf_err("Data pointer is null");
+ }
+
+ auto input = static_cast<const uint8_t*>(data);
+ TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
+
+ auto unsignedDstPtr = static_cast<uint8_t*>(inputTensor->data.data);
+
+ /* VWW model has one channel input => Convert image to grayscale here.
+ * We expect images to always be RGB. */
+ image::RgbToGrayscale(input, unsignedDstPtr, inputSize);
+
+ /* VWW model pre-processing is image conversion from uint8 to [0,1] float values,
+ * then quantize them with input quantization info. */
+ QuantParams inQuantParams = GetTensorQuantParams(inputTensor);
+
+ auto signedDstPtr = static_cast<int8_t*>(inputTensor->data.data);
+ for (size_t i = 0; i < inputTensor->bytes; i++) {
+ auto i_data_int8 = static_cast<int8_t>(
+ ((static_cast<float>(unsignedDstPtr[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset
+ );
+ signedDstPtr[i] = std::min<int8_t>(INT8_MAX, std::max<int8_t>(i_data_int8, INT8_MIN));
+ }
+
+ debug("Input tensor populated \n");
+
+ return true;
+ }
+
+ VisualWakeWordPostProcess::VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+ const std::vector<std::string>& labels, std::vector<ClassificationResult>& results)
+ :m_vwwClassifier{classifier},
+ m_labels{labels},
+ m_results{results}
+ {
+ if (!model->IsInited()) {
+ printf_err("Model is not initialised!.\n");
+ }
+ this->m_model = model;
+ }
+
+ bool VisualWakeWordPostProcess::DoPostProcess()
+ {
+ return this->m_vwwClassifier.GetClassificationResults(
+ this->m_model->GetOutputTensor(0), this->m_results,
+ this->m_labels, 1, true);
+ }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file