diff --git a/.gitignore b/.gitignore index e76ba079..b18bbe6e 100644 --- a/.gitignore +++ b/.gitignore @@ -85,6 +85,6 @@ cscope.out # Examples examples/external/list-backends examples/r2i/list_parameters -examples/r2i/ncsdk/googlenet -examples/r2i/ncsdk/tinyyolo -examples/r2i/tensorflow/googlenet +examples/r2i/ncsdk/inception +examples/r2i/ncsdk/tinyyolov2 +examples/r2i/tensorflow/inception diff --git a/COPYING b/COPYING new file mode 100644 index 00000000..fe21c218 --- /dev/null +++ b/COPYING @@ -0,0 +1,479 @@ +R2Inference copyright (C) 2019 RidgeRun LLC + +This machine learning library is free software; you can redistribute it +and/or modify it under the terms of the GNU Lesser General Public +License version 2.1 as published by the Free Software Foundation. + +This library is distributed in the hope that it will be +useful, but WITHOUT ANY WARRANTY; without even the implied +warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. +See the GNU Lesser General Public License below for more details. + +---------------------------------------------------------------------- + + GNU LESSER GENERAL PUBLIC LICENSE + Version 2.1, February 1999 + + Copyright (C) 1991, 1999 Free Software Foundation, Inc. + 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + +[This is the first released version of the Lesser GPL. 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AC_PREREQ([2.69]) -AC_INIT([RidgeRun inference library],[0.2.0],[https://github.com/RidgeRun/r2inference/issues],[r2inference]) +AC_INIT([RidgeRun inference library],[0.3.0],[https://github.com/RidgeRun/r2inference/issues],[r2inference]) # Initialize our build utils RR_INIT diff --git a/examples/r2i/ncsdk/Makefile.am b/examples/r2i/ncsdk/Makefile.am index 2089ffe7..c5be0c3a 100644 --- a/examples/r2i/ncsdk/Makefile.am +++ b/examples/r2i/ncsdk/Makefile.am @@ -1,4 +1,4 @@ -# Copyright (C) 2018 RidgeRun, LLC (http://www.ridgerun.com) +# Copyright (C) 2019 RidgeRun, LLC (http://www.ridgerun.com) # All Rights Reserved. # # The contents of this software are proprietary and confidential to RidgeRun, @@ -13,8 +13,8 @@ AM_DEFAULT_SOURCE_EXT = .cc if ENABLE_EXAMPLES noinst_PROGRAMS = \ - googlenet \ - tinyyolo + inception \ + tinyyolov2 AM_CXXFLAGS = \ $(RR_CXXFLAGS) \ diff --git a/examples/r2i/ncsdk/googlenet.cc b/examples/r2i/ncsdk/inception.cc similarity index 69% rename from examples/r2i/ncsdk/googlenet.cc rename to examples/r2i/ncsdk/inception.cc index d0646dfd..4b8bcf4d 100644 --- a/examples/r2i/ncsdk/googlenet.cc +++ b/examples/r2i/ncsdk/inception.cc @@ -1,4 +1,4 @@ -/* Copyright (C) 2018 RidgeRun, LLC (http://www.ridgerun.com) +/* Copyright (C) 2019 RidgeRun, LLC (http://www.ridgerun.com) * All Rights Reserved. * * The contents of this software are proprietary and confidential to RidgeRun, @@ -13,7 +13,6 @@ #include #include #include - #include #define STB_IMAGE_IMPLEMENTATION @@ -22,19 +21,12 @@ #define STB_IMAGE_RESIZE_IMPLEMENTATION #include "stb_image_resize.h" -#define GOOGLENET_DIM 224 - -const float GoogleNetMean[] = {0.40787054 * 255.0, - 0.45752458 * 255.0, - 0.48109378 * 255.0 - }; - void PrintTopPrediction (std::shared_ptr prediction) { r2i::RuntimeError error; int index = 0; double max = -1; - int num_labels = prediction->GetResultSize(); + int num_labels = prediction->GetResultSize() / sizeof(float); for (int i = 0; i < num_labels; ++i) { double current = prediction->At(i, error); @@ -44,18 +36,23 @@ void PrintTopPrediction (std::shared_ptr prediction) { } } - std::cout << "Highest probability is label " << index << - " (" << max << ")" << std::endl; + std::cout << "Highest probability is label " + << index << " (" << max << ")" << std::endl; } void PrintUsage() { - std::cerr << "Usage: example -i [JPG input_image] -m [GoogLeNet Model]" << - std::endl; + std::cerr << "Required arguments:" + << "-i [JPG input_image] " + << "-m [Inception NCS Model] " + << "-s [Model Input Size] \n" + << " Example:" + << " ./inception -i cat.jpg -m graph_inceptionv2_ncsdk -s 224" + << std::endl; } std::unique_ptr PreProcessImage (const unsigned char *input, - int width, int height, int reqwidth, - int reqheight, const float *mean) { + int width, int height, int reqwidth, int reqheight) { + const int channels = 3; const int scaled_size = channels * reqwidth * reqheight; @@ -66,17 +63,17 @@ std::unique_ptr PreProcessImage (const unsigned char *input, reqheight, 0, channels); for (int i = 0; i < scaled_size; i += channels) { - // BGR = RGB - Mean - adjusted[i + 0] = static_cast(scaled[i + 2]) - mean[0]; - adjusted[i + 1] = static_cast(scaled[i + 1]) - mean[1]; - adjusted[i + 2] = static_cast(scaled[i + 0]) - mean[2]; + /* RGB = (RGB)*StdDev */ + adjusted[i + 0] = (static_cast(scaled[i + 0]) - 128) / 128.0; + adjusted[i + 1] = (static_cast(scaled[i + 1]) - 128) / 128.0; + adjusted[i + 2] = (static_cast(scaled[i + 2]) - 128) / 128.0; } return adjusted; } std::unique_ptr LoadImage(const std::string &path, int reqwidth, - int reqheight, const float *mean) { + int reqheight) { int channels = 3; int width, height, cp; @@ -86,17 +83,17 @@ std::unique_ptr LoadImage(const std::string &path, int reqwidth, return nullptr; } - auto ret = PreProcessImage(img, width, height, reqwidth, reqheight, mean); + auto ret = PreProcessImage(img, width, height, reqwidth, reqheight); free (img); return ret; } bool ParseArgs (int &argc, char *argv[], std::string &image_path, - std::string &model_path, int &index) { - int option = 0; + std::string &model_path, int &index, int &size) { - while ((option = getopt(argc, argv, "i:m:p:")) != -1) { + int option = 0; + while ((option = getopt(argc, argv, "i:m:p:s:")) != -1) { switch (option) { case 'i' : image_path = optarg; @@ -107,22 +104,24 @@ bool ParseArgs (int &argc, char *argv[], std::string &image_path, case 'p' : index = std::stoi (optarg); break; + case 's' : + size = std::stoi (optarg); + break; default: return false; } } - return true; } - int main (int argc, char *argv[]) { r2i::RuntimeError error; std::string model_path; std::string image_path; int Index = 0; + int size = 0; - if (false == ParseArgs (argc, argv, image_path, model_path, Index)) { + if (false == ParseArgs (argc, argv, image_path, model_path, Index, size)) { PrintUsage (); exit (EXIT_FAILURE); } @@ -135,7 +134,7 @@ int main (int argc, char *argv[]) { auto factory = r2i::IFrameworkFactory::MakeFactory(r2i::FrameworkCode::NCSDK, error); - std::cout << "Loading Model: " << model_path << "..." << std::endl; + std::cout << "Loading Model: " << model_path << std::endl; auto loader = factory->MakeLoader (error); auto model = loader->Load (model_path, error); if (error.IsError ()) { @@ -143,20 +142,20 @@ int main (int argc, char *argv[]) { exit(EXIT_FAILURE); } - std::cout << "Setting model to engine..." << std::endl; + std::cout << "Setting model to engine" << std::endl; auto engine = factory->MakeEngine (error); error = engine->SetModel (model); - std::cout << "Loading image: " << image_path << "..." << std::endl; - std::unique_ptr image_data = LoadImage (image_path, GOOGLENET_DIM, - GOOGLENET_DIM, GoogleNetMean); + std::cout << "Loading image: " << image_path << std::endl; + std::unique_ptr image_data = LoadImage (image_path, size, + size); - std::cout << "Configuring frame..." << std::endl; + std::cout << "Configuring frame" << std::endl; std::shared_ptr frame = factory->MakeFrame (error); - error = frame->Configure (image_data.get(), GOOGLENET_DIM, GOOGLENET_DIM, + error = frame->Configure (image_data.get(), size, size, r2i::ImageFormat::Id::RGB); - std::cout << "Starting engine..." << std::endl; + std::cout << "Starting engine" << std::endl; error = engine->Start (); if (error.IsError ()) { std::cerr << "Engine start error: " << error << std::endl; @@ -172,7 +171,7 @@ int main (int argc, char *argv[]) { PrintTopPrediction (prediction); - std::cout << "Stopping engine..." << std::endl; + std::cout << "Stopping engine" << std::endl; error = engine->Stop (); if (error.IsError ()) { std::cerr << "Engine stop error: " << error << std::endl; diff --git a/examples/r2i/ncsdk/tinyyolo.cc b/examples/r2i/ncsdk/tinyyolov2.cc similarity index 67% rename from examples/r2i/ncsdk/tinyyolo.cc rename to examples/r2i/ncsdk/tinyyolov2.cc index b645aeb2..3abac9f0 100644 --- a/examples/r2i/ncsdk/tinyyolo.cc +++ b/examples/r2i/ncsdk/tinyyolov2.cc @@ -1,4 +1,4 @@ -/* Copyright (C) 2018 RidgeRun, LLC (http://www.ridgerun.com) +/* Copyright (C) 2019 RidgeRun, LLC (http://www.ridgerun.com) * All Rights Reserved. * * The contents of this software are proprietary and confidential to RidgeRun, @@ -15,7 +15,7 @@ #include #include #include - +#include #include #define STB_IMAGE_IMPLEMENTATION @@ -24,63 +24,72 @@ #define STB_IMAGE_RESIZE_IMPLEMENTATION #include "stb_image_resize.h" -/* Tiny YOLO oputput parameters */ +/* Tiny YOLOV2 oputput parameters */ /* Input image dim */ -#define DIM 448 +#define DIM 416 /* Grid dim */ -#define GRID_H 7 -#define GRID_W 7 +#define GRID_H 13 +#define GRID_W 13 /* Number of classes */ #define CLASSES 20 /* Number of boxes per cell */ -#define BOXES 2 +#define BOXES 5 /* Box dim */ -#define BOX_DIM 4 +#define BOX_DIM 5 /* Probability threshold */ #define PROB_THRESH 0.08 /* Intersection over union threshold */ #define IOU_THRESH 0.30 +/* Objectness threshold */ +#define OBJ_THRESH 0.08 +/* Grid cell size in pixels */ +#define GRID_SIZE 32 + +const float box_anchors[] = + { 1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52 }; struct box { std::string label; - double x_center; - double y_center; + double x; + double y; double width; double height; double prob; }; -void Box2Pixels (box &normalized_box, int row, int col, int image_width, - int image_height) { +/* sigmoid approximation as a lineal function */ +static double +sigmoid (double x) +{ + return 1.0 / (1.0 + pow (M_E, -1.0 * x)); +} + +void Box2Pixels (box * normalized_box, int row, int col, int box) { /* Convert box coordinates to pixels - * box position (x_center,y_center) is normalized inside each cell from 0 to 1 + * box position (x,y) is normalized inside each cell from 0 to 1 * width and heigh are also normalized, but with image size as reference * box is ordered [x_center,y_center,width,height] */ - /* adjust the box center according to its cell and grid dim */ - normalized_box.x_center += col; - normalized_box.y_center += row; - normalized_box.x_center /= GRID_H; - normalized_box.y_center /= GRID_W; - - /* adjust the lengths and widths */ - normalized_box.width *= normalized_box.width; - normalized_box.height *= normalized_box.height; - - /* scale the boxes to the image size in pixels */ - normalized_box.x_center *= image_width; - normalized_box.y_center *= image_height; - normalized_box.width *= image_width; - normalized_box.height *= image_height; + /* Adjust the box center according to its cell and grid dim */ + + normalized_box->x = (col + sigmoid (normalized_box->x)) * GRID_SIZE; + normalized_box->y = (row + sigmoid (normalized_box->y)) * GRID_SIZE; + + normalized_box->width = + pow (M_E, normalized_box->width) * box_anchors[2 * box] * GRID_SIZE; + normalized_box->height = + pow (M_E, normalized_box->height) * box_anchors[2 * box + 1] * GRID_SIZE; + } void GetBoxesFromPrediction(std::shared_ptr prediction, - int input_image_width, int input_image_height, std::list &boxes) { + int input_image_width, int input_image_height, + std::list &boxes) { /* * Get all the boxes from the prediction and store them in a list * Tiny yolo parameters: - * Grid: 7*7 - * Boxes per grid cell: 2 + * Grid: 13*13 + * Boxes per grid cell: 5 * Number of classes: 20 * Classes: ["aeroplane", "bicycle", "bird", "boat", "bottle", * "bus", "car", "cat", "chair", "cow", "diningtable", @@ -88,23 +97,19 @@ void GetBoxesFromPrediction(std::shared_ptr prediction, * "sheep", "sofa", "train", "tvmonitor"] * * Prediction structure: - * [0:980]: 7*7*20 probability per class per grid cell - * [980:1078]: 7*7*2 probability multiplicator for each box in the grid - * [1078:1470]: 7*7*2*4 [x,y,w,h] for each box in the grid + * 13*13*(5+20) [x,y,w,h,objectness] for each box in the grid */ r2i::RuntimeError error; int i, j, c, b; - int box_probs_start = GRID_H * GRID_W * CLASSES; - int all_boxes_start = GRID_H * GRID_W * CLASSES + GRID_H * GRID_W * BOXES; int index; - double class_prob; - double box_prob; - double prob; - box result; + double max_class_prob; + double cur_class_prob; + double obj_prob; + int max_class_prob_index; /* * This label list is highly dependent on the way the model was trained. - * We are assumig the same labels that are used on the ncappzoo tinyyolo + * We are assumig the same labels that are used on the ncappzoo tinyyolov2 * example. */ std::string labels [CLASSES] = {"aeroplane", "bicycle", "bird", "boat", @@ -116,23 +121,32 @@ void GetBoxesFromPrediction(std::shared_ptr prediction, for (i = 0; i < GRID_H; i++) { /* Iterate rows */ for (j = 0; j < GRID_W; j++) { /* Iterate columns */ - for (c = 0; c < CLASSES; c++) { /* Iterate classes */ - index = (i * GRID_W + j) * CLASSES + c; - class_prob = prediction->At (index, error); - for (b = 0; b < BOXES; b++) { /* Iterate boxes */ - index = (i * GRID_W + j) * BOXES + b; - box_prob = prediction->At (box_probs_start + index, error); - prob = class_prob * box_prob; - /* If the probability is over the threshold add it to the boxes list */ - if (prob > PROB_THRESH) { - index = ((i * GRID_W + j) * BOXES + b ) * BOX_DIM; - result.label = labels[c]; - result.x_center = prediction->At (all_boxes_start + index, error); - result.y_center = prediction->At (all_boxes_start + index + 1, error); - result.width = prediction->At (all_boxes_start + index + 2, error); - result.height = prediction->At (all_boxes_start + index + 3, error); - result.prob = prob; - Box2Pixels(result, i, j, input_image_width, input_image_height); + for (b = 0; b < BOXES; b++) { /* Iterate boxes */ + index = ((i * GRID_W + j) * BOXES + b) * (BOX_DIM + CLASSES); + obj_prob = prediction->At (index + 4, error); + /* If the probability is over the threshold add it to the boxes list */ + if (obj_prob > OBJ_THRESH) { + max_class_prob = 0; + max_class_prob_index = 0; + for (c = 0; c < CLASSES; c++) { + cur_class_prob = prediction->At (index + BOX_DIM + c, error); + if (cur_class_prob > max_class_prob) { + max_class_prob = cur_class_prob; + max_class_prob_index = c; + } + } + + if (max_class_prob > PROB_THRESH) { + box result; + result.label = labels[max_class_prob_index]; + result.x = prediction->At (index, error); + result.y = prediction->At (index + 1, error); + result.width = prediction->At (index + 2, error); + result.height = prediction->At (index + 3, error); + result.prob = max_class_prob; + Box2Pixels(&result, i, j, b); + result.x = result.x - result.width * 0.5; + result.y = result.y - result.height * 0.5; boxes.push_front(result); } } @@ -153,22 +167,22 @@ double IntersectionOverUnion(box box_1, box box_2) { double union_area; /* First diminsion of the intersecting box */ - intersection_dim_1 = std::min(box_1.x_center + 0.5 * box_1.width, - box_2.x_center + 0.5 * box_2.width) - - std::max(box_1.x_center - 0.5 * box_1.width, - box_2.x_center - 0.5 * box_2.width); + intersection_dim_1 = std::min(box_1.x + box_1.width, + box_2.x + box_2.width) - + std::max(box_1.x, box_2.x); /* Second dimension of the intersecting box */ - intersection_dim_2 = std::min(box_1.y_center + 0.5 * box_1.height, - box_2.y_center + 0.5 * box_2.height) - - std::max(box_1.y_center - 0.5 * box_1.height, - box_2.y_center - 0.5 * box_2.height); + intersection_dim_2 = std::min(box_1.y + box_1.height, + box_2.y + box_2.height) - + std::max(box_1.y, box_2.y); if ((intersection_dim_1 < 0) || (intersection_dim_2 < 0)) { intersection_area = 0; - } else { + } + else { intersection_area = intersection_dim_1 * intersection_dim_2; } + union_area = box_1.width * box_1.height + box_2.width * box_2.height - intersection_area; return intersection_area / union_area; @@ -202,8 +216,8 @@ void RemoveDuplicatedBoxes(std::list &boxes) { void PrintBox (box in_box) { std::cout << "Box:" ; std::cout << "[class:'" << in_box.label << "', "; - std::cout << "x_center:" << in_box.x_center << ", "; - std::cout << "y_center:" << in_box.y_center << ", "; + std::cout << "x:" << in_box.x << ", "; + std::cout << "y:" << in_box.y << ", "; std::cout << "width:" << in_box.width << ", "; std::cout << "height:" << in_box.height << ", "; std::cout << "prob:" << in_box.prob << "]" << std::endl; @@ -230,8 +244,9 @@ void PrintTopPredictions (std::shared_ptr prediction, } void PrintUsage() { - std::cerr << "Usage: example -i [JPG input_image] -m [TinyYOLO Model]" << - std::endl; + std::cerr << "Usage: example -i [JPG input_image] -m [TinyYOLOV2 NCSDK Model] \n" + << "Example: ./tinyyolov2 -i dog.jpg -m graph_tinyyolov2_ncsdk" + << std::endl; } std::unique_ptr PreProcessImage (const unsigned char *input, int width, @@ -246,9 +261,9 @@ std::unique_ptr PreProcessImage (const unsigned char *input, int width, reqheight, 0, channels); for (int i = 0; i < scaled_size; i += channels) { - adjusted[i + 2] = static_cast(scaled[i + 2]) / 255.0; - adjusted[i + 1] = static_cast(scaled[i + 1]) / 255.0; - adjusted[i + 0] = static_cast(scaled[i + 0]) / 255.0; + adjusted[i + 0] = static_cast(scaled[i + 0]) / 255; + adjusted[i + 1] = static_cast(scaled[i + 1]) / 255; + adjusted[i + 2] = static_cast(scaled[i + 2]) / 255; } return adjusted; @@ -315,7 +330,7 @@ int main (int argc, char *argv[]) { auto factory = r2i::IFrameworkFactory::MakeFactory(r2i::FrameworkCode::NCSDK, error); - std::cout << "Loading Model: " << model_path << "..." << std::endl; + std::cout << "Loading Model: " << model_path << std::endl; auto loader = factory->MakeLoader (error); auto model = loader->Load (model_path, error); if (error.IsError ()) { @@ -323,27 +338,27 @@ int main (int argc, char *argv[]) { exit(EXIT_FAILURE); } - std::cout << "Setting model to engine..." << std::endl; + std::cout << "Setting model to engine" << std::endl; auto engine = factory->MakeEngine (error); error = engine->SetModel (model); - std::cout << "Loading image: " << image_path << "..." << std::endl; - std::unique_ptr image_data = LoadImage (image_path, DIM, DIM, &width, - &height); + std::cout << "Loading image: " << image_path << std::endl; + std::unique_ptr image_data = LoadImage (image_path, DIM, DIM, + &width, &height); - std::cout << "Configuring frame..." << std::endl; + std::cout << "Configuring frame" << std::endl; std::shared_ptr frame = factory->MakeFrame (error); error = frame->Configure (image_data.get(), DIM, DIM, r2i::ImageFormat::Id::RGB); - std::cout << "Starting engine..." << std::endl; + std::cout << "Starting engine" << std::endl; error = engine->Start (); if (error.IsError ()) { std::cerr << "Engine start error: " << error << std::endl; exit(EXIT_FAILURE); } - std::cout << "Predicting..." << std::endl; + std::cout << "Predicting" << std::endl; auto prediction = engine->Predict (frame, error); if (error.IsError ()) { std::cerr << "Engine prediction error: " << error << std::endl; @@ -352,7 +367,7 @@ int main (int argc, char *argv[]) { PrintTopPredictions (prediction, width, height); - std::cout << "Stopping engine..." << std::endl; + std::cout << "Stopping engine" << std::endl; error = engine->Stop (); if (error.IsError ()) { std::cerr << "Engine stop error: " << error << std::endl; diff --git a/examples/r2i/tensorflow/Makefile.am b/examples/r2i/tensorflow/Makefile.am index 4f10e43d..3be7135d 100644 --- a/examples/r2i/tensorflow/Makefile.am +++ b/examples/r2i/tensorflow/Makefile.am @@ -13,7 +13,7 @@ AM_DEFAULT_SOURCE_EXT = .cc if ENABLE_EXAMPLES noinst_PROGRAMS = \ - googlenet + inception AM_CXXFLAGS = \ $(RR_CXXFLAGS) \ diff --git a/examples/r2i/tensorflow/googlenet.cc b/examples/r2i/tensorflow/inception.cc similarity index 68% rename from examples/r2i/tensorflow/googlenet.cc rename to examples/r2i/tensorflow/inception.cc index e6f7aa51..ea174d6b 100644 --- a/examples/r2i/tensorflow/googlenet.cc +++ b/examples/r2i/tensorflow/inception.cc @@ -1,4 +1,4 @@ -/* Copyright (C) 2018 RidgeRun, LLC (http://www.ridgerun.com) +/* Copyright (C) 2019 RidgeRun, LLC (http://www.ridgerun.com) * All Rights Reserved. * * The contents of this software are proprietary and confidential to RidgeRun, @@ -13,7 +13,6 @@ #include #include #include - #include #define STB_IMAGE_IMPLEMENTATION @@ -22,14 +21,11 @@ #define STB_IMAGE_RESIZE_IMPLEMENTATION #include "stb_image_resize.h" -#define GOOGLENET_DIM 299 - void PrintTopPrediction (std::shared_ptr prediction) { r2i::RuntimeError error; int index = 0; double max = -1; - - int num_labels = prediction->GetResultSize() / sizeof(float) ; + int num_labels = prediction->GetResultSize() / sizeof(float); for (int i = 0; i < num_labels; ++i) { double current = prediction->At(i, error); @@ -39,21 +35,28 @@ void PrintTopPrediction (std::shared_ptr prediction) { } } - std::cout << "Highest probability is label " << index << - " (" << max << ")" << std::endl; + std::cout << "Highest probability is label " + << index << " (" << max << ")" << std::endl; } void PrintUsage() { - std::cerr << "Usage: example -i [JPG input_image] -m [GoogLeNet Model]" << - std::endl; + std::cerr << "Required arguments: " + << "-i [JPG input_image] " + << "-m [Inception TensorFlow Model] " + << "-s [Model Input Size] " + << "-I [Input Node] " + << "-O [Output Node] \n" + << " Example: " + << " ./inception -i cat.jpg -m graph_inceptionv2_tensorflow.pb " + << "-s 224 -I input -O Softmax" + << std::endl; } std::unique_ptr PreProcessImage (const unsigned char *input, - int width, int height, int reqwidth, - int reqheight) { + int width, int height, int reqwidth, int reqheight) { + const int channels = 3; const int scaled_size = channels * reqwidth * reqheight; - std::unique_ptr scaled (new unsigned char[scaled_size]); std::unique_ptr adjusted (new float[scaled_size]); @@ -61,7 +64,7 @@ std::unique_ptr PreProcessImage (const unsigned char *input, reqheight, 0, channels); for (int i = 0; i < scaled_size; i += channels) { - // BGR = (RGB - Mean)*StdDev + /* RGB = (RGB - Mean)*StdDev */ adjusted[i + 0] = (static_cast(scaled[i + 0]) - 128) / 128.0; adjusted[i + 1] = (static_cast(scaled[i + 1]) - 128) / 128.0; adjusted[i + 2] = (static_cast(scaled[i + 2]) - 128) / 128.0; @@ -88,10 +91,11 @@ std::unique_ptr LoadImage(const std::string &path, int reqwidth, } bool ParseArgs (int &argc, char *argv[], std::string &image_path, - std::string &model_path, int &index) { - int option = 0; + std::string &model_path, int &index, int &size, + std::string &in_node, std::string &out_node) { - while ((option = getopt(argc, argv, "i:m:p:")) != -1) { + int option = 0; + while ((option = getopt(argc, argv, "i:m:p:s:I:O:")) != -1) { switch (option) { case 'i' : image_path = optarg; @@ -102,22 +106,34 @@ bool ParseArgs (int &argc, char *argv[], std::string &image_path, case 'p' : index = std::stoi (optarg); break; + case 's' : + size = std::stoi (optarg); + break; + case 'I' : + in_node = optarg; + break; + case 'O' : + out_node = optarg; + break; default: return false; } } - return true; } - int main (int argc, char *argv[]) { + r2i::RuntimeError error; std::string model_path; std::string image_path; + std::string in_node; + std::string out_node; int Index = 0; + int size = 0; - if (false == ParseArgs (argc, argv, image_path, model_path, Index)) { + if (false == ParseArgs (argc, argv, image_path, model_path, Index, + size, in_node, out_node)) { PrintUsage (); exit (EXIT_FAILURE); } @@ -131,7 +147,7 @@ int main (int argc, char *argv[]) { r2i::FrameworkCode::TENSORFLOW, error); - std::cout << "Loading Model: " << model_path << "..." << std::endl; + std::cout << "Loading Model: " << model_path << std::endl; auto loader = factory->MakeLoader (error); std::shared_ptr model = loader->Load (model_path, error); if (error.IsError ()) { @@ -139,27 +155,27 @@ int main (int argc, char *argv[]) { exit(EXIT_FAILURE); } - std::cout << "Setting model to engine..." << std::endl; + std::cout << "Setting model to engine" << std::endl; std::shared_ptr engine = factory->MakeEngine (error); error = engine->SetModel (model); - std::cout << "Configuring input and output layers..." << std::endl; + std::cout << "Configuring input and output layers" << std::endl; auto params = factory->MakeParameters (error); error = params->Configure(engine, model); - params->Set ("input-layer", "input"); - params->Set ("output-layer", "InceptionV4/Logits/Predictions"); + params->Set ("input-layer", in_node); + params->Set ("output-layer", out_node); - std::cout << "Loading image: " << image_path << "..." << std::endl; - std::unique_ptr image_data = LoadImage (image_path, GOOGLENET_DIM, - GOOGLENET_DIM); + std::cout << "Loading image: " << image_path << std::endl; + std::unique_ptr image_data = LoadImage (image_path, size, + size); - std::cout << "Configuring frame..." << std::endl; + std::cout << "Configuring frame" << std::endl; std::shared_ptr frame = factory->MakeFrame (error); - error = frame->Configure (image_data.get(), GOOGLENET_DIM, GOOGLENET_DIM, + error = frame->Configure (image_data.get(), size, size, r2i::ImageFormat::Id::RGB); - std::cout << "Starting engine..." << std::endl; + std::cout << "Starting engine" << std::endl; error = engine->Start (); if (error.IsError ()) { std::cerr << "Engine start error: " << error << std::endl; @@ -175,7 +191,7 @@ int main (int argc, char *argv[]) { PrintTopPrediction (prediction); - std::cout << "Stopping engine..." << std::endl; + std::cout << "Stopping engine" << std::endl; error = engine->Stop (); if (error.IsError ()) { std::cerr << "Engine stop error: " << error << std::endl; @@ -184,3 +200,4 @@ int main (int argc, char *argv[]) { return EXIT_SUCCESS; } + diff --git a/tests/unit/r2i/tensorflow/frame.cc b/tests/unit/r2i/tensorflow/frame.cc index 20b45fdd..d7ad5fd5 100644 --- a/tests/unit/r2i/tensorflow/frame.cc +++ b/tests/unit/r2i/tensorflow/frame.cc @@ -228,9 +228,9 @@ TEST (TensorflowFrame, FrameGetTensorMultipleBatches) { error = frame.Configure(data, width, height, format.GetId()); LONGS_EQUAL (r2i::RuntimeError::Code::EOK, error.GetCode()); + /* R2I will set the tensor size to 1 to allow frame by frame processing */ tensor = frame.GetTensor(pgraph, operation, error); - LONGS_EQUAL (r2i::RuntimeError::Code::INVALID_FRAMEWORK_PARAMETER, - error.GetCode()); + LONGS_EQUAL (r2i::RuntimeError::Code::EOK, error.GetCode()); multiple_batches = false; }