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/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 */