/******************************************************************************/
/*!
 * \file timlCNNConvReadFromStatesMemory.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
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 *    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
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 *    distribution.
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 *    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
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 * A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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 ******************************************************************************/


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

#include "../api/timl.h"


/******************************************************************************/
/*!
 * \ingroup       cnn
 * \brief         Read the conv layer states from memory
 * \param[out]    layerCopy Copied layer
 * \param[in]     layer     Target layer
 * \return        Error code
 */
/******************************************************************************/

int timlCNNConvReadFromStatesMemory(timlCNNLayer *layerCopy, timlCNNLayer *layer)
{
   int dim;
   int deviceId;
   int threadId;
   deviceId = layerCopy->cnn->deviceId;
   threadId = layerCopy->cnn->threadId;

   // feature map
   dim = layer->maxBatchSize*layer->row*layer->col*layer->channel;
   if (layer->featureMap != NULL && layerCopy->featureMap != NULL) {
      timlUtilMemcpy(layerCopy->featureMap, layer->featureMap, sizeof(float)*dim, deviceId, threadId);
   }
   if (layer->convParams.prevFeatureMapReshape != NULL && layerCopy->convParams.prevFeatureMapReshape != NULL) {
      timlUtilMemcpy(layerCopy->convParams.prevFeatureMapReshape, layer->convParams.prevFeatureMapReshape, sizeof(float)*layer->convParams.inputFeatureMapChannel*layer->convParams.kernelRow*layer->convParams.kernelCol*layer->row*layer->col, deviceId, threadId);
   }
   // level 1 only
   if (layer->allocatorLevel == Util_AllocatorLevel1) {
      // delta
      if (layer->delta != NULL && layerCopy->delta != NULL) {
         timlUtilMemcpy(layerCopy->delta, layer->delta, sizeof(float)*dim, deviceId, threadId);
      }
      dim = layer->convParams.kernelRow*layer->convParams.kernelCol*layer->convParams.inputFeatureMapChannel*layer->convParams.outputFeatureMapChannel;
      // kernel Inc/GradAccum
      if (layer->convParams.kernelInc != NULL && layerCopy->convParams.kernelInc != NULL) {
         timlUtilMemcpy(layerCopy->convParams.kernelInc, layer->convParams.kernelInc, sizeof(float)*dim, deviceId, threadId);
      }
      if (layer->convParams.kernelGradAccum != NULL && layerCopy->convParams.kernelGradAccum != NULL) {
         timlUtilMemcpy(layerCopy->convParams.kernelGradAccum, layer->convParams.kernelGradAccum, sizeof(float)*dim, deviceId, threadId);
      }
      // bias Inc/GradAccum
      if (layer->convParams.biasInc != NULL && layerCopy->convParams.biasInc != NULL) {
         timlUtilMemcpy(layerCopy->convParams.biasInc, layer->convParams.biasInc, sizeof(float)*layer->channel, deviceId, threadId);
      }
      if (layer->convParams.biasGradAccum != NULL && layerCopy->convParams.biasGradAccum != NULL) {
         timlUtilMemcpy(layerCopy->convParams.biasGradAccum, layer->convParams.biasGradAccum, sizeof(float)*layer->channel, deviceId, threadId);
      }
   }

   return 0;
}