[ti-machine-learning/ti-machine-learning.git] / debian / ti-timl / usr / src / timl / src / common / cnn / timlCNNConvInitialize.c
diff --git a/debian/ti-timl/usr/src/timl/src/common/cnn/timlCNNConvInitialize.c b/debian/ti-timl/usr/src/timl/src/common/cnn/timlCNNConvInitialize.c
--- /dev/null
@@ -0,0 +1,168 @@
+/******************************************************************************/
+/*!
+ * \file timlCNNConvInitialize.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,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ ******************************************************************************/
+
+
+/*******************************************************************************
+ *
+ * INCLUDES
+ *
+ ******************************************************************************/
+
+#include "../api/timl.h"
+
+
+/******************************************************************************/
+/*!
+ * \ingroup cnn
+ * \brief Initialize the conv layer
+ * \param[in] layer Conv layer
+ * \return Error code
+ */
+/******************************************************************************/
+
+int timlCNNConvInitialize(timlCNNLayer *layer)
+{
+ timlConvNeuralNetwork *cnn;
+ timlCNNLayer *prev;
+ int dim;
+ int deviceId;
+ int threadId;
+ int err;
+
+ // init
+ err = 0;
+ cnn = layer->cnn;
+ prev = layer->prev;
+ dim = 0;
+ deviceId = layer->cnn->deviceId;
+ threadId = layer->cnn->threadId;
+
+ // common to level 1, 2, 3
+
+ // allocate prev feature map reshape, size = (prev->channel*kernelRow*kernelCol) * (row*col)
+ if (timlUtilMalloc((void**) &(layer->convParams.prevFeatureMapReshape), sizeof(float)*layer->convParams.inputFeatureMapChannel*layer->convParams.kernelRow*layer->convParams.kernelCol*layer->row*layer->col)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate biasMultiplier
+ if (timlUtilMalloc((void**) &(layer->convParams.biasMultiplier), sizeof(float)*layer->row*layer->col)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+ timlUtilVectorResetFloat(layer->convParams.biasMultiplier, layer->row*layer->col, 1.0, deviceId, threadId);
+
+ if (layer->convParams.shared == false) {
+ // allocate prevFeatureMapReshapeIndex, size = (kernelRow*kernelCol) * (row*col)
+ if (timlUtilMalloc((void**) &(layer->convParams.prevFeatureMapReshapeIndex), sizeof(int)*layer->convParams.kernelRow*layer->convParams.kernelCol*layer->row*layer->col)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+ timlUtilConv2ImageReshapeIndex(layer->convParams.prevFeatureMapReshapeIndex, prev->row, prev->col, layer->convParams.kernelRow, layer->convParams.kernelCol, layer->convParams.padUp, layer->convParams.padDown, layer->convParams.padLeft, layer->convParams.padRight, layer->convParams.strideX, layer->convParams.strideY, layer->convParams.type);
+
+ // allocate kernel
+ dim = layer->convParams.inputFeatureMapChannel*layer->convParams.outputFeatureMapChannel*layer->convParams.kernelRow*layer->convParams.kernelCol;
+ if (timlUtilMalloc((void**) &(layer->convParams.kernel), sizeof(float) * dim)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate bias
+ if (timlUtilMalloc((void**) &(layer->convParams.bias), sizeof(float) * layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate connectivity
+// layer->convParams.connectivity =
+// malloc(sizeof(int) * layer->convParams.inputFeatureMapChannel * layer->convParams.outputFeatureMapChannel);
+// if (layer->convParams.connectivity == NULL) {
+// return ERROR_CNN_LAYER_ALLOCATION;
+// }
+ }
+
+ // level 1 only
+ if (layer->allocatorLevel == Util_AllocatorLevel1) {
+ // allocate feature map
+ if (timlUtilMalloc((void**) &(layer->featureMap), sizeof(float)*layer->row*layer->col*layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate feature map delta
+ if (timlUtilMalloc((void**) &(layer->delta), sizeof(float)*layer->row*layer->col*layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ dim = layer->convParams.inputFeatureMapChannel*layer->convParams.outputFeatureMapChannel*layer->convParams.kernelRow*layer->convParams.kernelCol;
+
+ if (layer->convParams.shared == false) {
+ // allocate kernelInc
+ if (timlUtilMalloc((void**) &(layer->convParams.kernelInc), sizeof(float)*dim)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate kernelGradAccum
+ if (timlUtilMalloc((void**) &(layer->convParams.kernelGradAccum), sizeof(float)*dim)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate biasGradAccum
+ if (timlUtilMalloc((void**) &(layer->convParams.biasGradAccum), sizeof(float)*layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+
+ // allocate biasInc
+ if (timlUtilMalloc((void**) &(layer->convParams.biasInc), sizeof(float)*layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+ }
+ }
+
+ // level 2 only
+ if (layer->allocatorLevel == Util_AllocatorLevel2) {
+ // allocate feature map
+ if (timlUtilMalloc((void**) &(layer->featureMap), sizeof(float)*layer->row*layer->col*layer->channel)) {
+ return ERROR_CNN_LAYER_ALLOCATION;
+ }
+ }
+
+ // level 3 only
+ if (layer->allocatorLevel == Util_AllocatorLevel3) {
+ if (layer->id % 2 == 0) { // layer 2, 4, 6 8, ... allocate at the back end
+ layer->featureMap = cnn->memPool + cnn->memPoolSize - layer->channel*layer->row*layer->col;
+ }
+ else { // layer 1, 3, 5, ... allocate at the front end
+ layer->featureMap = cnn->memPool;
+ }
+ }
+
+ return 0;
+}