Integrate Gluon generator

pom.xml

@@ -19,6 +19,7 @@
     <CNNTrain.version>0.2.6</CNNTrain.version>
     <cnnarch-mxnet-generator.version>0.2.14-SNAPSHOT</cnnarch-mxnet-generator.version>
     <cnnarch-caffe2-generator.version>0.2.11-SNAPSHOT</cnnarch-caffe2-generator.version>
+    <cnnarch-gluon-generator.version>0.1.5</cnnarch-gluon-generator.version>
     <embedded-montiarc-math-opt-generator>0.1.4</embedded-montiarc-math-opt-generator>
 
         <!-- .. Libraries .................................................. -->
@@ -73,6 +74,12 @@
             <version>${cnnarch-mxnet-generator.version}</version>
         </dependency>
 
+        <dependency>
+            <groupId>de.monticore.lang.monticar</groupId>
+            <artifactId>cnnarch-gluon-generator</artifactId>
+            <version>${cnnarch-gluon-generator.version}</version>
+        </dependency>
+
         <dependency>
             <groupId>de.monticore.lang.monticar</groupId>
             <artifactId>cnnarch-caffe2-generator</artifactId>

src/main/java/de/monticore/lang/monticar/emadl/generator/Backend.java

@@ -2,6 +2,8 @@ package de.monticore.lang.monticar.emadl.generator;
 
 
 import de.monticore.lang.monticar.cnnarch.CNNArchGenerator;
+import de.monticore.lang.monticar.cnnarch.gluongenerator.CNNArch2Gluon;
+import de.monticore.lang.monticar.cnnarch.gluongenerator.CNNTrain2Gluon;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNArch2MxNet;
 import de.monticore.lang.monticar.cnnarch.caffe2generator.CNNArch2Caffe2;
 import de.monticore.lang.monticar.cnnarch.mxnetgenerator.CNNTrain2MxNet;
@@ -30,6 +32,16 @@ public enum Backend {
         public CNNTrainGenerator getCNNTrainGenerator() {
             return new CNNTrain2Caffe2();
         }
+    },
+    GLUON{
+        @Override
+        public CNNArchGenerator getCNNArchGenerator() {
+            return new CNNArch2Gluon();
+        }
+        @Override
+        public CNNTrainGenerator getCNNTrainGenerator() {
+            return new CNNTrain2Gluon();
+        }
     };
 
     public abstract CNNArchGenerator getCNNArchGenerator();
@@ -43,6 +55,9 @@ public enum Backend {
             case "CAFFE2":
                 return Optional.of(CAFFE2);
 
+            case "GLUON":
+                return Optional.of(GLUON);
+
             default:
                 return Optional.empty();
         }
@@ -52,8 +67,10 @@ public enum Backend {
         switch (backend){
             case CAFFE2:
                 return "CAFFE2";
+            case GLUON:
+                return "GLUON";
             default:
                 return "MXNET";
         }
     }
-}
+}
\ No newline at end of file

src/main/java/de/monticore/lang/monticar/emadl/generator/EMADLGenerator.java

@@ -160,7 +160,7 @@ public class EMADLGenerator {
 
             printWriter.println("#!/bin/bash");
             printWriter.println("cd " + getGenerationTargetPath());
-            printWriter.println("mkdir --parents build");
+            printWriter.println("mkdir -p build");
             printWriter.println("cd build");
             printWriter.println("cmake ..");
             printWriter.println("make");

src/test/java/de/monticore/lang/monticar/emadl/GenerationTest.java

@@ -160,6 +160,31 @@ public class GenerationTest extends AbstractSymtabTest {
                         "CNNTrainer_mnist_mnistClassifier_net.py"));
     }
 
+    @Test
+    public void testMnistClassifierForGluon() throws IOException, TemplateException {
+        Log.getFindings().clear();
+        String[] args = {"-m", "src/test/resources/models/", "-r", "mnist.MnistClassifier", "-b", "GLUON", "-f", "n", "-c", "n"};
+        EMADLGeneratorCli.main(args);
+        assertTrue(Log.getFindings().isEmpty());
+
+        checkFilesAreEqual(
+                Paths.get("./target/generated-sources-emadl"),
+                Paths.get("./src/test/resources/target_code/gluon"),
+                Arrays.asList(
+                        "CNNBufferFile.h",
+                        "CNNNet_mnist_mnistClassifier_net.py",
+                        "mnist_mnistClassifier.cpp",
+                        "mnist_mnistClassifier.h",
+                        "CNNCreator_mnist_mnistClassifier_net.py",
+                        "CNNPredictor_mnist_mnistClassifier_net.h",
+                        "mnist_mnistClassifier_net.h",
+                        "HelperA.h",
+                        "CNNTranslator.h",
+                        "mnist_mnistClassifier_calculateClass.h",
+                        "CNNTrainer_mnist_mnistClassifier_net.py",
+                        "mnist_mnistClassifier_net.h"));
+    }
+
     @Test
     public void testHashFunction() {
         EMADLGenerator tester = new EMADLGenerator(Backend.MXNET);

src/test/java/de/monticore/lang/monticar/emadl/IntegrationGluonTest.java

+/**
+ *
+ *  ******************************************************************************
+ *  MontiCAR Modeling Family, www.se-rwth.de
+ *  Copyright (c) 2017, Software Engineering Group at RWTH Aachen,
+ *  All rights reserved.
+ *
+ *  This project is free software; you can redistribute it and/or
+ *  modify it under the terms of the GNU Lesser General Public
+ *  License as published by the Free Software Foundation; either
+ *  version 3.0 of the License, or (at your option) any later version.
+ *  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 for more details.
+ *
+ *  You should have received a copy of the GNU Lesser General Public
+ *  License along with this project. If not, see <http://www.gnu.org/licenses/>.
+ * *******************************************************************************
+ */
+package de.monticore.lang.monticar.emadl;
+
+public class IntegrationGluonTest extends IntegrationTest {
+    public IntegrationGluonTest() {
+        super("GLUON", "39253EC049D4A4E5FA0536AD34874B9D#1DBAEE1B1BD83FB7CB5F70AE91B29638#C4C23549E737A759721D6694C75D9771#5AF0CE68E408E8C1F000E49D72AC214A");
+    }
+}

src/test/resources/target_code/gluon/CNNBufferFile.h

+#ifndef CNNBUFFERFILE_H
+#define CNNBUFFERFILE_H
+
+#include <stdio.h>
+#include <iostream>
+#include <fstream>
+
+// Read file to buffer
+class BufferFile {
+ public :
+    std::string file_path_;
+    int length_;
+    char* buffer_;
+
+    explicit BufferFile(std::string file_path)
+    :file_path_(file_path) {
+
+        std::ifstream ifs(file_path.c_str(), std::ios::in | std::ios::binary);
+        if (!ifs) {
+            std::cerr << "Can't open the file. Please check " << file_path << ". \n";
+            length_ = 0;
+            buffer_ = NULL;
+            return;
+        }
+
+        ifs.seekg(0, std::ios::end);
+        length_ = ifs.tellg();
+        ifs.seekg(0, std::ios::beg);
+        std::cout << file_path.c_str() << " ... "<< length_ << " bytes\n";
+
+        buffer_ = new char[sizeof(char) * length_];
+        ifs.read(buffer_, length_);
+        ifs.close();
+    }
+
+    int GetLength() {
+        return length_;
+    }
+    char* GetBuffer() {
+        return buffer_;
+    }
+
+    ~BufferFile() {
+        if (buffer_) {
+          delete[] buffer_;
+          buffer_ = NULL;
+        }
+    }
+};
+
+#endif // CNNBUFFERFILE_H

src/test/resources/target_code/gluon/CNNNet_mnist_mnistClassifier_net.py

+import mxnet as mx
+import numpy as np
+from mxnet import gluon
+
+class Softmax(gluon.HybridBlock):
+    def __init__(self, **kwargs):
+        super(Softmax, self).__init__(**kwargs)
+
+    def hybrid_forward(self, F, x):
+        return F.softmax(x)
+
+
+class Split(gluon.HybridBlock):
+    def __init__(self, num_outputs, axis=1, **kwargs):
+        super(Split, self).__init__(**kwargs)
+        with self.name_scope():
+            self.axis = axis
+            self.num_outputs = num_outputs
+
+    def hybrid_forward(self, F, x):
+        return F.split(data=x, axis=self.axis, num_outputs=self.num_outputs)
+
+
+class Concatenate(gluon.HybridBlock):
+    def __init__(self, dim=1, **kwargs):
+        super(Concatenate, self).__init__(**kwargs)
+        with self.name_scope():
+            self.dim = dim
+
+    def hybrid_forward(self, F, *x):
+        return F.concat(*x, dim=self.dim)
+
+
+class ZScoreNormalization(gluon.HybridBlock):
+    def __init__(self, data_mean, data_std, **kwargs):
+        super(ZScoreNormalization, self).__init__(**kwargs)
+        with self.name_scope():
+            self.data_mean = self.params.get('data_mean', shape=data_mean.shape,
+                init=mx.init.Constant(data_mean.asnumpy().tolist()), differentiable=False)
+            self.data_std = self.params.get('data_std', shape=data_mean.shape,
+                init=mx.init.Constant(data_std.asnumpy().tolist()), differentiable=False)
+
+    def hybrid_forward(self, F, x, data_mean, data_std):
+        x = F.broadcast_sub(x, data_mean)
+        x = F.broadcast_div(x, data_std)
+        return x
+
+
+class Padding(gluon.HybridBlock):
+    def __init__(self, padding, **kwargs):
+        super(Padding, self).__init__(**kwargs)
+        with self.name_scope():
+            self.pad_width = padding
+
+    def hybrid_forward(self, F, x):
+        x = F.pad(data=x,
+            mode='constant',
+            pad_width=self.pad_width,
+            constant_value=0)
+        return x
+
+
+class NoNormalization(gluon.HybridBlock):
+    def __init__(self, **kwargs):
+        super(NoNormalization, self).__init__(**kwargs)
+
+    def hybrid_forward(self, F, x):
+        return x
+
+
+class Net(gluon.HybridBlock):
+    def __init__(self, data_mean=None, data_std=None, **kwargs):
+        super(Net, self).__init__(**kwargs)
+        with self.name_scope():
+            if not data_mean is None:
+                assert(not data_std is None)
+                self.input_normalization = ZScoreNormalization(data_mean=data_mean, data_std=data_std)
+            else:
+                self.input_normalization = NoNormalization()
+
+            self.conv1_ = gluon.nn.Conv2D(channels=20,
+                kernel_size=(5,5),
+                strides=(1,1),
+                use_bias=True)
+            # conv1_, output shape: {[20,24,24]}
+
+            self.pool1_ = gluon.nn.MaxPool2D(
+                pool_size=(2,2),
+                strides=(2,2))
+            # pool1_, output shape: {[20,12,12]}
+
+            self.conv2_ = gluon.nn.Conv2D(channels=50,
+                kernel_size=(5,5),
+                strides=(1,1),
+                use_bias=True)
+            # conv2_, output shape: {[50,8,8]}
+
+            self.pool2_ = gluon.nn.MaxPool2D(
+                pool_size=(2,2),
+                strides=(2,2))
+            # pool2_, output shape: {[50,4,4]}
+
+            self.fc2_flatten = gluon.nn.Flatten()
+            self.fc2_ = gluon.nn.Dense(units=500, use_bias=True)
+            # fc2_, output shape: {[500,1,1]}
+
+            self.relu2_ = gluon.nn.Activation(activation='relu')
+            self.fc3_ = gluon.nn.Dense(units=10, use_bias=True)
+            # fc3_, output shape: {[10,1,1]}
+
+
+        self.last_layer = 'softmax'
+
+
+    def hybrid_forward(self, F, x):
+        image = self.input_normalization(x)
+        conv1_ = self.conv1_(image)
+        pool1_ = self.pool1_(conv1_)
+        conv2_ = self.conv2_(pool1_)
+        pool2_ = self.pool2_(conv2_)
+        fc2_flatten_ = self.fc2_flatten(pool2_)
+        fc2_ = self.fc2_(fc2_flatten_)
+        relu2_ = self.relu2_(fc2_)
+        fc3_ = self.fc3_(relu2_)
+        return fc3_

src/test/resources/target_code/gluon/CNNPredictor_mnist_mnistClassifier_net.h

+#ifndef CNNPREDICTOR_MNIST_MNISTCLASSIFIER_NET
+#define CNNPREDICTOR_MNIST_MNISTCLASSIFIER_NET
+
+#include <mxnet/c_predict_api.h>
+
+#include <cassert>
+#include <string>
+#include <vector>
+
+#include <CNNBufferFile.h>
+
+class CNNPredictor_mnist_mnistClassifier_net{
+public:
+    const std::string json_file = "model/mnist.LeNetNetwork/model_newest-symbol.json";
+    const std::string param_file = "model/mnist.LeNetNetwork/model_newest-0000.params";
+    //const std::vector<std::string> input_keys = {"data"};
+    const std::vector<std::string> input_keys = {"image"};
+    const std::vector<std::vector<mx_uint>> input_shapes = {{1,1,28,28}};
+    const bool use_gpu = false;
+
+    PredictorHandle handle;
+
+    explicit CNNPredictor_mnist_mnistClassifier_net(){
+        init(json_file, param_file, input_keys, input_shapes, use_gpu);
+    }
+
+    ~CNNPredictor_mnist_mnistClassifier_net(){
+        if(handle) MXPredFree(handle);
+    }
+
+    void predict(const std::vector<float> &image,
+                 std::vector<float> &predictions){
+        MXPredSetInput(handle, "data", image.data(), image.size());
+        //MXPredSetInput(handle, "image", image.data(), image.size());
+
+        MXPredForward(handle);
+
+        mx_uint output_index;
+        mx_uint *shape = 0;
+        mx_uint shape_len;
+        size_t size;
+
+        output_index = 0;
+        MXPredGetOutputShape(handle, output_index, &shape, &shape_len);
+        size = 1;
+        for (mx_uint i = 0; i < shape_len; ++i) size *= shape[i];
+        assert(size == predictions.size());
+        MXPredGetOutput(handle, 0, &(predictions[0]), predictions.size());
+
+    }
+
+    void init(const std::string &json_file,
+              const std::string &param_file,
+              const std::vector<std::string> &input_keys,
+              const std::vector<std::vector<mx_uint>> &input_shapes,
+              const bool &use_gpu){
+
+        BufferFile json_data(json_file);
+        BufferFile param_data(param_file);
+
+        int dev_type = use_gpu ? 2 : 1;
+        int dev_id = 0;
+
+        handle = 0;
+
+        if (json_data.GetLength() == 0 ||
+            param_data.GetLength() == 0) {
+            std::exit(-1);
+        }
+
+        const mx_uint num_input_nodes = input_keys.size();
+
+        const char* input_keys_ptr[num_input_nodes];
+        for(mx_uint i = 0; i < num_input_nodes; i++){
+            input_keys_ptr[i] = input_keys[i].c_str();
+        }
+
+        mx_uint shape_data_size = 0;
+        mx_uint input_shape_indptr[input_shapes.size() + 1];
+        input_shape_indptr[0] = 0;
+        for(mx_uint i = 0; i < input_shapes.size(); i++){
+            input_shape_indptr[i+1] = input_shapes[i].size();
+            shape_data_size += input_shapes[i].size();
+        }
+
+        mx_uint input_shape_data[shape_data_size];
+        mx_uint index = 0;
+        for(mx_uint i = 0; i < input_shapes.size(); i++){
+            for(mx_uint j = 0; j < input_shapes[i].size(); j++){
+                input_shape_data[index] = input_shapes[i][j];
+                index++;
+            }
+        }
+
+        MXPredCreate((const char*)json_data.GetBuffer(),
+                     (const char*)param_data.GetBuffer(),
+                     static_cast<size_t>(param_data.GetLength()),
+                     dev_type,
+                     dev_id,
+                     num_input_nodes,
+                     input_keys_ptr,
+                     input_shape_indptr,
+                     input_shape_data,
+                     &handle);
+        assert(handle);
+    }
+};
+
+#endif // CNNPREDICTOR_MNIST_MNISTCLASSIFIER_NET

src/test/resources/target_code/gluon/CNNTrainer_mnist_mnistClassifier_net.py

+import logging
+import mxnet as mx
+import CNNCreator_mnist_mnistClassifier_net
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.DEBUG)
+    logger = logging.getLogger()
+    handler = logging.FileHandler("train.log", "w", encoding=None, delay="true")
+    logger.addHandler(handler)
+
+    mnist_mnistClassifier_net = CNNCreator_mnist_mnistClassifier_net.CNNCreator_mnist_mnistClassifier_net()
+    mnist_mnistClassifier_net.train(
+        batch_size=64,
+        num_epoch=11,
+        context='gpu',
+        eval_metric='accuracy',
+        optimizer='adam',
+        optimizer_params={
+            'epsilon': 1.0E-8,
+            'weight_decay': 0.001,
+            'beta1': 0.9,
+            'beta2': 0.999,
+            'learning_rate_policy': 'fixed',
+            'learning_rate': 0.001}
+    )

src/test/resources/target_code/gluon/CNNTranslator.h

+#ifndef CNNTRANSLATOR_H
+#define CNNTRANSLATOR_H
+#include <armadillo>
+#include <cassert>
+
+using namespace std;
+using namespace arma;
+
+class CNNTranslator{
+public:
+    template<typename T> static void addColToSTDVector(const Col<T> &source, vector<float> &data){
+        for(size_t i = 0; i < source.n_elem; i++){
+            data.push_back((float) source(i));
+        }
+    }
+
+    template<typename T> static void addRowToSTDVector(const subview_row<T> &source, vector<float> &data){
+        for(size_t i = 0; i < source.n_elem; i++){
+            data.push_back((float) source(i));
+        }
+    }
+
+    template<typename T> static void addRowToSTDVector(const Row<T> &source, vector<float> &data){
+        for(size_t i = 0; i < source.n_elem; i++){
+            data.push_back((float) source(i));
+        }
+    }
+
+    template<typename T> static void addMatToSTDVector(const Mat<T> &source, vector<float> &data){
+        for(size_t i = 0; i < source.n_rows; i++){
+            addRowToSTDVector(source.row(i), data);
+        }
+    }
+
+
+    template<typename T> static vector<float> translate(const Col<T> &source){
+        size_t size = source.n_elem;
+        vector<float> data;
+        data.reserve(size);
+        addColToSTDVector(source, data);
+        return data;
+    }
+
+    template<typename T> static vector<float> translate(const Row<T> &source){
+        size_t size = source.n_elem;
+        vector<float> data;
+        data.reserve(size);
+        addRowToSTDVector(source, data);
+        return data;
+    }
+
+    template<typename T> static vector<float> translate(const Mat<T> &source){
+        size_t size = source.n_elem;
+        vector<float> data;
+        data.reserve(size);
+        addMatToSTDVector(source, data);
+        return data;
+    }
+
+    template<typename T> static vector<float> translate(const Cube<T> &source){
+        size_t size = source.n_elem;
+        vector<float> data;
+        data.reserve(size);
+        for(size_t i = 0; i < source.n_slices; i++){
+            addMatToSTDVector(source.slice(i), data);
+        }
+        return data;
+    }
+
+    static vec translateToCol(const vector<float> &source, const vector<size_t> &shape){
+        assert(shape.size() == 1);
+        vec column(shape[0]);
+        for(size_t i = 0; i < source.size(); i++){
+            column(i) = (double) source[i];
+        }
+        return column;
+    }
+
+    static mat translateToMat(const vector<float> &source, const vector<size_t> &shape){
+        assert(shape.size() == 2);
+        mat matrix(shape[1], shape[0]); //create transposed version of the matrix
+        int startPos = 0;
+        int endPos = matrix.n_rows;
+        const vector<size_t> columnShape = {matrix.n_rows};
+        for(size_t i = 0; i < matrix.n_cols; i++){
+            vector<float> colSource(&source[startPos], &source[endPos]);
+            matrix.col(i) = translateToCol(colSource, columnShape);
+            startPos = endPos;
+            endPos += matrix.n_rows;
+        }
+        return matrix.t();
+    }
+
+    static cube translateToCube(const vector<float> &source, const vector<size_t> &shape){
+        assert(shape.size() == 3);
+        cube cubeMatrix(shape[1], shape[2], shape[0]);
+        const int matrixSize = shape[1] * shape[2];
+        const vector<size_t> matrixShape = {shape[1], shape[2]};
+        int startPos = 0;
+        int endPos = matrixSize;
+        for(size_t i = 0; i < cubeMatrix.n_slices; i++){
+            vector<float> matrixSource(&source[startPos], &source[endPos]);
+            cubeMatrix.slice(i) = translateToMat(matrixSource, matrixShape);
+            startPos = endPos;
+            endPos += matrixSize;
+        }
+        return cubeMatrix;
+    }
+
+    template<typename T> static vector<size_t> getShape(const Col<T> &source){
+        return {source.n_elem};
+    }
+
+    template<typename T> static vector<size_t> getShape(const Row<T> &source){
+        return {source.n_elem};
+    }
+
+    template<typename T> static vector<size_t> getShape(const Mat<T> &source){
+        return {source.n_rows, source.n_cols};
+    }
+
+    template<typename T> static vector<size_t> getShape(const Cube<T> &source){
+        return {source.n_slices, source.n_rows, source.n_cols};
+    }
+};
+
+#endif

src/test/resources/target_code/gluon/HelperA.h

+#ifndef HELPERA_H
+#define HELPERA_H
+#include <iostream>
+#include "armadillo"
+#include <stdarg.h>
+#include <initializer_list>
+#include <fstream>
+using namespace arma;
+#ifndef _FILESTRING_CONVERSION___A
+#define _FILESTRING_CONVERSION___A
+void toFileString(std::ofstream& myfile, mat A){
+    myfile << "[";
+    for (int i = 0; i < A.n_rows; i++){
+        for (int j = 0; j < A.n_cols; j++){
+            myfile << A(i,j);
+            if(j + 1 < A.n_cols){
+                myfile << ", ";
+            }
+        }
+        if(i + 1 < A.n_rows){
+            myfile << ";";
+        }
+    }
+    myfile << "]";
+}
+void toFileString(std::ofstream& myfile, double A){
+    myfile << A;
+}
+void toFileString(std::ofstream& myfile, float A){
+    myfile << A;
+}
+void toFileString(std::ofstream& myfile, int A){
+    myfile << A;
+}
+void toFileString(std::ofstream& myfile, bool A){
+    myfile << A;
+}
+bool Is_close(mat& X, mat& Y, double tol)
+{
+    // abs returns a mat type then max checks columns and returns a row_vec
+    // max used again will return the biggest element in the row_vec
+    bool close(false);
+    if(arma::max(arma::max(arma::abs(X-Y))) < tol)
+    {
+        close = true;
+    }