/******************************************************************************/
/*!
 * \file testCNNSimpleProfile.c
 */
/* Copyright (C) 2015 Texas Instruments Incorporated - http://www.ti.com/
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 *    Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *
 *    Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
 *    documentation and/or other materials provided with the
 *    distribution.
 *
 *    Neither the name of Texas Instruments Incorporated nor the names of
 *    its contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
 * OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
 * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
 * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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 ******************************************************************************/


/*******************************************************************************
 *
 * INCLUDES
 *
 ******************************************************************************/

#include "testCNN.h"


/*******************************************************************************
 *
 * DEFINES
 *
 ******************************************************************************/

#define IMAGE_ROW            28
#define IMAGE_COL            28
#define IMAGE_CHANNEL        1
#define ITER                 2
#define BATCH_SIZE           100
#define DATABASE_PATH        "../../database/mnist"


/******************************************************************************/
/*!
 * \ingroup test
 * \brief simple profile function test
 * \return error code
 */
/******************************************************************************/

int testCNNSimpleProfile()
{
   int  dim;
   int  err;
   long mem;
   int  iter;
   int  batchSize;
   timlUtilImageSet      training;
   timlUtilImageSet      testing;
   timlConvNeuralNetwork *cnn;

   iter      = ITER;
   err       = 0;
   dim       = IMAGE_ROW*IMAGE_COL*IMAGE_CHANNEL;
   batchSize = BATCH_SIZE;

   setbuf(stdout, NULL); // do not buffer the console output

   printf("[Test] CNN simple profile\n");
   // build up the CNN
   printf("1. Build CNN\n");
   cnn = timlCNNCreateConvNeuralNetwork(timlCNNTrainingParamsDefault(), 0);
   timlCNNAddInputLayer(cnn, IMAGE_ROW, IMAGE_COL, IMAGE_CHANNEL, timlCNNInputParamsDefault()); // input layer
   timlCNNAddConvLayer(cnn, 5, 5, 1, 1, 6, timlCNNConvParamsDefault());                         // conv layer
   timlCNNAddNonlinearLayer(cnn, Util_Sigmoid);                                                 // sigmoid layer
   timlCNNAddPoolingLayer(cnn, 4, 4, 4, 4, CNN_MaxPooling, timlCNNPoolingParamsDefault());      // max pooling layer
   timlCNNAddNormLayer(cnn, timlCNNNormParamsDefault());                                        // norm layer
   timlCNNAddDropoutLayer(cnn, 0.2);                                                            // dropout layer
   timlCNNAddLinearLayer(cnn, 10, timlCNNLinearParamsDefault());                                // linear layer
   timlCNNAddNonlinearLayer(cnn, Util_Softmax);                                                 // softmax layer
   timlCNNInitialize(cnn);
   timlCNNReset(cnn);

   printf("2. Read MNIST database\n");
   timlUtilReadMNIST(DATABASE_PATH, &training, &testing);

   timlCNNSetMode(cnn, Util_Train);
   printf("3. Start profiling\n");
   timlCNNProfile(cnn, training.data, dim, batchSize, training.label, iter);

   free(training.data);
   free(training.label);
   free(training.mean);
   free(testing.data);
   free(testing.label);

   timlCNNDelete(cnn);

   return err;
}