added gluon directory

added latest generator

.gitlab-ci.yml

@@ -40,3 +40,12 @@ TestCAFFE2:
   - ./build.sh
   - RES=$(./build/src/cpp/DigitCalculator resources/images/2.jpg resources/images/3.jpg resources/images/4.png resources/images/6.png resources/images/2.jpg resources/images/3.jpg)
   - "if [[ $RES != *\"SUM: 857\"* ]]; then echo \"Wrong result:\" $RES; exit 1; fi;"
+
+TestMXNET:
+  stage: linux
+  image: registry.git.rwth-aachen.de/monticore/embeddedmontiarc/applications/mnistcalculator/mxnet:v0.0.3
+  script: 
+  - cd gluon
+  - ./build.sh
+  - RES=$(./build/src/cpp/DigitCalculator resources/images/1.png resources/images/2.png resources/images/3.png resources/images/4.png resources/images/5.png resources/images/6.png)
+  - "if [[ $RES != *\"SUM: 579\"* ]]; then echo \"Wrong result:\" $RES; exit 1; fi;"
\ No newline at end of file

gluon/.gitignore

+train.log
+target/
+model/
+build/
\ No newline at end of file

gluon/CMakeLists.txt

+cmake_minimum_required(VERSION 3.5)
+project (default)
+set (CMAKE_CXX_STANDARD 11)
+add_subdirectory(src/cpp)
+add_subdirectory(target)
+

gluon/README.md

+# Calculator MXNET
+
+
+## Prerequisites
+1. Ubuntu Linux 16.04 LTS or 18.04 LTS (experimental)
+2. Deep Learning Framework **MXNet**
+3. Armadillo (at least armadillo version 6.600 must be used) [Official instructions at Armadillo Website](http://arma.sourceforge.net/download.html).
+4. OpenCV
+
+## Prepare the data
+
+To create the data for training, execute:
+
+```
+bash ./prepare.sh
+```
+
+## How to Run
+Generate and build the code for the EMADL model by executing:
+
+```
+bash build.sh
+```
+
+Finally, run the calculator as follows:
+```
+./build/src/cpp/DigitCalculator resources/images/1.png resources/images/2.png resources/images/3.png resources/images/4.png resources/images/5.png resources/images/6.png
+```
+
+You can try different images from the provided `/resources/images` directory or even create your own images for testing. Note that the application program DigitCalculator will automatically rescale the given input image to the size 32x32.
+
+Note: Currently, the mxnet example uses CIFAR-10 data, not MNIST!
+
+
+## Troubleshooting Help
+
+ERROR: HelperA.h:79:28: error: ‘sqrtmat’ was not declared in this scope.
+
+FIX:
+Copy compiled armadillo lib and include files to usr/lib and usr/include respectively. Replace YOUR_ARMADILLO_REPOSITORY and VERSION (e.g. 8.500.1) with your corresponding information:
+```
+cd YOUR_ARMADILLO_REPOSITORY/armadillo-VERSION
+sudo cp libarmadillo* /usr/lib
+cd YOUR_ARMADILLO_REPOSITORY/armadillo-VERSION/include
+sudo cp -r * /usr/include

gluon/build.sh

+MXNET_PATH=$(python -c "import mxnet; print(mxnet.__file__)")
+MXNET_FOLDER=$(dirname $MXNET_PATH)
+echo $MXNET_FOLDER
+
+if [ ! -f $MXNET_FOLDER/libmxnet.so ]; then
+    echo "libmxnet.so not found at default location" $MXNET_FOLDER
+    echo "It should be there if the python mxnet package is installed"
+    echo "Either fix the installation, or adapt the ./build.sh script to locate libmxnet.so correctly"
+    exit 1
+fi
+
+rm -rf target
+java -jar ../embedded-montiarc-emadl-generator-0.3.0-jar-with-dependencies.jar -m src/emadl/models/ -r cNNCalculator.Connector -o target -b GLUON
+
+rm -rf build
+mkdir build && cd build
+
+echo "Building DigitClassifier.."
+cmake -D MXNET_PATH=$MXNET_FOLDER/libmxnet.so ..
+make

gluon/resources/docker/Dockerfile

+FROM registry.git.rwth-aachen.de/monticore/embeddedmontiarc/generators/emadl2cpp/integrationtests/mxnet:v0.0.3
+
+RUN pip install opencv-python
+
+# Install Open CV - Warning, this takes absolutely forever
+RUN apt-get update && apt-get install -y libgdal-dev
+RUN mkdir -p ~/opencv cd ~/opencv && \
+    wget https://github.com/Itseez/opencv/archive/3.0.0.zip && \
+    unzip 3.0.0.zip && \
+    rm 3.0.0.zip && \
+    mv opencv-3.0.0 OpenCV
+RUN apt-get install -y qtbase5-dev
+RUN apt-get install -y build-essential
+ENV PATH="${PATH}:/usr/include/"
+RUN cd OpenCV && \
+    mkdir build && \ 
+    cd build && \
+    cmake \
+    -DWITH_QT=ON \ 
+    -DWITH_OPENGL=ON \ 
+    -DFORCE_VTK=ON \
+    -DWITH_TBB=ON \
+    -DWITH_GDAL=ON \
+    -DWITH_XINE=ON \
+    -DBUILD_EXAMPLES=ON \
+    -D ENABLE_PRECOMPILED_HEADERS=OFF ..
+RUN cd OpenCV/build && make -j4
+RUN cd OpenCV/build && make install
+RUN cd OpenCV/build && ldconfig
+
+RUN apt-get install -y libopenblas-dev
\ No newline at end of file

gluon/resources/imgDir-to-h5.py

+from __future__ import print_function
+from __future__ import division
+import h5py
+import numpy as np
+import cv2
+import os
+import argparse
+import errno
+import random
+import sys
+
+
+def create_img_list(name, data_path):
+    dir_name = data_path + "/" + name
+
+    image_paths = []
+    image_class_indices = []
+    print(dir_name)
+    for class_index_name in os.listdir(dir_name):
+        class_dir_path = dir_name + "/" + class_index_name
+        if os.path.isdir(class_dir_path):
+            for image_name in os.listdir(class_dir_path):
+                image_path = class_dir_path + "/" + image_name
+                image_paths.append(image_path)
+                class_index = float(class_index_name)
+                image_class_indices.append(class_index)
+    return image_paths, image_class_indices
+
+def create_h5_from_list(image_paths, image_class_indices, target_dir, target_file_name, input_port_name, output_port_name, shuffle=True):
+    img = cv2.imread(image_paths[0])
+    t_img = np.transpose(img, (2,0,1)).astype(np.float32)
+    #t_img = t_img[-1:,:,:]
+    #print(t_img)
+    channels = t_img.shape[0]
+    height = t_img.shape[1]
+    width = t_img.shape[2]
+    data_size = len(image_paths)
+    
+    target_file = target_dir + "/" + target_file_name + ".h5"
+    if os.path.isfile(target_file):
+        print("File", target_file, "already exists. Skipping data file creation.")
+        return
+    try:
+        os.makedirs(target_dir)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+
+    if shuffle:
+        combined = list(zip(image_paths, image_class_indices))
+        random.shuffle(combined)
+        image_paths[:], image_class_indices[:] = zip(*combined)
+    
+    print("Creating " + target_file + " (images:" + str(data_size) + ", channels:" + str(channels) + ", height:" + str(height) + ", width:" + str(width) + "):")
+    with h5py.File(target_file, "w") as ofile:
+        in_dset = ofile.create_dataset(input_port_name, (data_size,channels, height, width), dtype=np.float32)
+        out_dset = ofile.create_dataset(output_port_name + "_label", (data_size,), dtype=np.float32)
+        for i in range(data_size):
+            img = cv2.imread(image_paths[i])
+            t_img = np.transpose(img, (2,0,1)).astype(np.float32)
+            #t_img = t_img[-1:,:,:]
+            in_dset[i] = t_img
+            out_dset[i] = image_class_indices[i]
+
+            #print progress
+            if i % 100 == 0:
+                percentage = 100*i / data_size
+                sys.stdout.write("\r{:0.1f}%".format(percentage))
+                sys.stdout.flush()
+    sys.stdout.write("\r100.0%\n")
+    sys.stdout.flush()
+
+
+            
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Translate image directories into hdf5 training sets for EMADL.')
+    parser.add_argument("--in_port", action="store", dest="in_port", default="data")
+    parser.add_argument("--out_port", action="store", dest="out_port", default="softmax")
+    parser.add_argument("--data_path", action="store", dest="data_path", default=".")
+    parser.add_argument("--target_path", action="store", dest="target_path", default=".")
+    args = parser.parse_args()
+    for file_name in os.listdir(args.data_path):
+        if file_name == "train":
+            image_paths, image_class_indices = create_img_list(file_name, args.data_path)
+            create_h5_from_list(image_paths, image_class_indices, args.target_path, file_name, args.in_port, args.out_port)
+        if file_name == "test":
+            image_paths, image_class_indices = create_img_list(file_name, args.data_path)
+            create_h5_from_list(image_paths, image_class_indices, args.target_path, file_name, args.in_port, args.out_port)

gluon/src/cpp/CMakeLists.txt

+cmake_minimum_required(VERSION 3.5)
+project(DigitCalculator CXX)
+set(CMAKE_CXX_STANDARD 11)
+
+option(MXNET_PATH "Path to libmxnet.so")
+
+
+add_executable(DigitCalculator DigitCalculator.cpp)
+
+set_target_properties(DigitCalculator PROPERTIES LINKER_LANGUAGE CXX)
+find_package(OpenCV REQUIRED)
+target_link_libraries(DigitCalculator ${MXNET_PATH})
+set(INCLUDE_DIRS ${INCLUDE_DIRS} ${OpenCV_INCLUDE_DIRS})
+set(LIBS ${LIBS} ${OpenCV_LIBS})
+target_link_libraries(DigitCalculator  cNNCalculator_connector ${LIBS} -lblas -lopenblas)
+target_include_directories(DigitCalculator PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
+export(TARGETS DigitCalculator FILE DigitCalculator.cmake)
\ No newline at end of file

gluon/src/cpp/DigitCalculator.cpp

+#include "CNNTranslator.h"
+#include "cNNCalculator_connector.h"
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include <armadillo>
+#include <string> //Check if it can be removed
+#include <iostream>
+#include <map>
+
+int main(int argc, char* argv[]) {
+    if(argc < 7){ //Note: argc=1 if no arguments are provided
+        std::cout << "Missing argument: Path to 6 test images must be provided " << std::endl;
+        exit(1);
+    }
+    
+
+    cNNCalculator_connector connector;
+    connector.init();
+
+    for(int n = 0; n < 6; n++) {
+        std::string filePath = argv[n+1];
+
+        if (!std::ifstream(filePath).good()) {
+            std::cerr << "Image loading failure, test image '" << filePath << "' does not exist." << std::endl;
+            exit(1);
+        }
+
+        cv::Mat img = cv::imread(filePath);
+        std::cout << "== original image size: " << img.size() << " ==" << std::endl;
+
+        // scale image to fit
+        cv::Size scale(28,28);
+        cv::resize(img, img, scale);
+        std::cout << "== simply resize: " << img.size() << " ==" << std::endl;
+
+        size_t channels = 1;
+        size_t height = img.rows;
+        size_t width = img.cols;
+        vector<float> data(channels*height*width);
+
+        for(size_t j=0; j<height; j++){
+            for(size_t k=0; k<width; k++){
+                cv::Vec3b intensity = img.at<cv::Vec3b>(j, k);
+                for(size_t i=0; i<channels; i++){
+                    data[i*height*width + j*height + k] = (float) intensity[i];
+                }
+            }
+        }
+
+        if(n == 0)
+            connector.image1 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+        if(n == 1)
+            connector.image2 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+        if(n == 2)
+            connector.image3 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+        if(n == 3)
+            connector.image4 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+        if(n == 4)
+            connector.image5 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+        if(n == 5)
+            connector.image6 = conv_to< icube >::from( CNNTranslator::translateToCube(data, vector<size_t> {channels,height,width}) );
+    }
+
+    connector.execute();
+
+    int classIndex = (int)connector.res;
+    std::cout << "== SUM: " << classIndex << std::endl;
+
+    return 0;
+}

gluon/src/emadl/models/cNNCalculator/Add.emadl

+package cNNCalculator;
+
+component Add{
+    ports
+            in Z(0:999) num1,
+            in Z(0:999) num2,
+            out Z(0:1998) sum;
+
+    implementation Math{
+        sum = num1 + num2;
+    }
+}

gluon/src/emadl/models/cNNCalculator/ArgMax.emadl

+package cNNCalculator;
+
+component ArgMax<Z(1:oo) n = 2>{
+    ports in Q^{n} inputVector,
+          out Z(0:oo) maxIndex,
+          out Q maxValue;
+
+    implementation Math{
+         maxIndex = 0;
+         maxValue = inputVector(1);
+
+         for i = 2:n
+            if inputVector(i) > maxValue
+                maxIndex = i - 1;
+                maxValue = inputVector(i);
+            end
+         end
+    }
+}

gluon/src/emadl/models/cNNCalculator/Calculator.emadl

+package cNNCalculator;
+
+component Calculator {
+        ports  
+                in Q(0:1)^10 in1_1,
+                in Q(0:1)^10 in1_2,
+                in Q(0:1)^10 in1_3,
+                in Q(0:1)^10 in2_1,
+                in Q(0:1)^10 in2_2,
+                in Q(0:1)^10 in2_3,
+
+                out Z(0:1998) out1;
+        
+        instance ArgMax<10> number1_ones;
+        instance ArgMax<10> number1_tens;
+        instance ArgMax<10> number1_hundreds;
+        instance ArgMax<10> number2_ones;
+        instance ArgMax<10> number2_tens;
+        instance ArgMax<10> number2_hundreds;
+        instance DigitCombiner number1;
+        instance DigitCombiner number2;
+        instance Add add;
+        
+
+        connect in1_1 -> number1_hundreds.inputVector;
+        connect in1_2 -> number1_tens.inputVector;
+        connect in1_3 -> number1_ones.inputVector;
+        connect in2_1 -> number2_hundreds.inputVector;
+        connect in2_2 -> number2_tens.inputVector;
+        connect in2_3 -> number2_ones.inputVector;
+
+        connect number1_ones.maxIndex -> number1.ones;
+        connect number1_tens.maxIndex -> number1.tens;
+        connect number1_hundreds.maxIndex -> number1.hundreds;
+        connect number2_ones.maxIndex -> number2.ones;
+        connect number2_tens.maxIndex -> number2.tens;
+        connect number2_hundreds.maxIndex -> number2.hundreds;
+
+        connect number1.number -> add.num1;
+        connect number2.number -> add.num2;
+
+        connect add.sum -> out1;
+
+}

gluon/src/emadl/models/cNNCalculator/Connector.emadl

+package cNNCalculator;
+
+component Connector {
+    ports in Z(0:255)^{1, 28, 28} image1,
+          in Z(0:255)^{1, 28, 28} image2,
+          in Z(0:255)^{1, 28, 28} image3,
+          in Z(0:255)^{1, 28, 28} image4,
+          in Z(0:255)^{1, 28, 28} image5,
+          in Z(0:255)^{1, 28, 28} image6,
+          out Z(0:1998) res;
+
+    instance Network<10> predictor1;
+    instance Network<10> predictor2;
+    instance Network<10> predictor3;
+    instance Network<10> predictor4;
+    instance Network<10> predictor5;
+    instance Network<10> predictor6;
+    instance Calculator cal;
+
+    connect image1 -> predictor1.data;
+    connect image2 -> predictor2.data;
+    connect image3 -> predictor3.data;
+    connect image4 -> predictor4.data;
+    connect image5 -> predictor5.data;
+    connect image6 -> predictor6.data;
+
+    connect predictor1.softmax -> cal.in1_1;
+    connect predictor2.softmax -> cal.in1_2;
+    connect predictor3.softmax -> cal.in1_3;
+    connect predictor4.softmax -> cal.in2_1;
+    connect predictor5.softmax -> cal.in2_2;
+    connect predictor6.softmax -> cal.in2_3;
+
+    connect cal.out1 -> res;
+
+}

gluon/src/emadl/models/cNNCalculator/DigitCombiner.emadl

+package cNNCalculator;
+
+component DigitCombiner{
+    ports
+            in Z(0:9) hundreds,
+            in Z(0:9) tens,
+            in Z(0:9) ones,
+            out Z(0:999) number;
+
+    implementation Math{
+        number = ones + 10 * tens + 100 * hundreds;
+    }
+}

gluon/src/emadl/models/cNNCalculator/Network.cnnt

+configuration Network{
+     num_epoch:1
+     batch_size:10
+     normalize:false
+     context:cpu
+     load_checkpoint:false
+     optimizer:sgd{
+         learning_rate:0.1
+         learning_rate_decay:0.85
+         step_size:1000
+         weight_decay:0.0
+     }
+}

gluon/src/emadl/models/cNNCalculator/Network.emadl

+package cNNCalculator;
+
+component Network<Z(2:oo) classes = 10>{
+    ports in Z(0:255)^{1, 28, 28} data,
+         out Q(0:1)^{classes} softmax;
+
+    implementation CNN {
+        def conv(channels, kernel=1, stride=1){
+            Convolution(kernel=(kernel,kernel),channels=channels) ->
+            Relu() ->
+            Pooling(pool_type="max", kernel=(2,2), stride=(stride,stride))
+        }
+
+        data ->
+        conv(kernel=5, channels=20, stride=2) ->
+        conv(kernel=5, channels=50, stride=2) ->
+        FullyConnected(units=500) ->
+        Relu() ->
+        Dropout() ->
+        FullyConnected(units=classes) ->
+        Softmax() ->
+        softmax
+    }
+}

gluon/src/emadl/models/cNNCalculator/VGG16.cnnt

+configuration VGG16{
+    num_epoch:1
+    batch_size:64
+    normalize:true
+    load_checkpoint:false
+    optimizer:adam{
+        learning_rate:0.01
+        learning_rate_decay:0.8
+        step_size:1000
+        weight_decay: 0.01
+    }
+}
+

gluon/src/emadl/models/cNNCalculator/VGG16.emadl

+package cNNCalculator;
+
+component VGG16{
+    ports in Z(0:255)^{1, 28, 28} image,
+         out Q(0:1)^{10} predictions;
+
+         implementation CNN {
+
+             def conv(filter, channels){
+                 Convolution(kernel=(filter,filter), channels=channels) ->
+                 Relu()
+             }
+             def fc(){
+                 FullyConnected(units=100) ->
+                 Relu() ->
+                 Dropout(p=0.5)
+             }
+
+             image ->
+             conv(filter=2, channels=64, ->=2) ->
+             Pooling(pool_type="max", kernel=(2,2), stride=(1,1)) ->
+             fc() ->
+             FullyConnected(units=10) ->
+             Softmax() ->
+             predictions
+         }
+  }

gluon/src/emadl/models/data_paths.txt

+cNNCalculator.Network resources/training_data

mxnet/build.sh

@@ -10,11 +10,11 @@ if [ ! -f $MXNET_FOLDER/libmxnet.so ]; then
 fi
 
 rm -rf target
-java -jar ../embedded-montiarc-emadl-generator-0.2.13-jar-with-dependencies.jar -m src/emadl/models/ -r cNNCalculator.Connector -o target -b MXNET
+java -jar ../embedded-montiarc-emadl-generator-0.3.0-jar-with-dependencies.jar -m src/emadl/models/ -r cNNCalculator.Connector -o target -b MXNET
 
 rm -rf build
 mkdir build && cd build
 
 echo "Building DigitClassifier.."
 cmake -D MXNET_PATH=$MXNET_FOLDER/libmxnet.so ..
-make
\ No newline at end of file
+make

mxnet/prepare.sh

-echo "Creating HDF5 dataset from image files.."
-cd resources/
-wget https://git.rwth-aachen.de/thomas.timmermanns/EMADL-Demo/raw/master/src/resources/data.tar.gz
-tar xf data.tar.gz
-rm data.tar.gz
-python imgDir-to-h5.py --in_port data --out_port softmax --data_path data --target_path training_data
-rm -r data
\ No newline at end of file