static Function SetupScale(ScaleParameter param, List <BlobProto> blobs, List <string> bottoms, string name, string[] inputNames, string[] outputNames)
{
//Caffe及びChainerは暗黙的に1次元目をBacthとして利用しているため補正を行う
int axis = param.Axis - 1;
bool biasTerm = param.BiasTerm;
if (bottoms.Count == 1)
{
//Scaleを作成
int[] wShape = new int[blobs[0].Shape.Dims.Length];
for (int i = 0; i < wShape.Length; i++)
{
wShape[i] = (int)blobs[0].Shape.Dims[i];
}
return(new MultiplyScale(axis, wShape, biasTerm, blobs[0].Datas, blobs[1].Datas, name, inputNames, outputNames));
}
else
{
//Biasを作成
int[] shape = new int[blobs[0].Shape.Dims.Length];
for (int i = 0; i < shape.Length; i++)
{
shape[i] = (int)blobs[0].Shape.Dims[i];
}
return(new AddBias(axis, shape, blobs[0].Datas, name));
}
}
////////////////////////////////////////////////////////////////////////////////////////////////////
/// <summary> Sets up the scale. </summary>
///
/// <param name="param"> The parameter. </param>
/// <param name="blobs"> The blobs. </param>
/// <param name="bottoms"> The bottoms. </param>
/// <param name="name"> The name. </param>
/// <param name="inputNames"> List of names of the inputs. </param>
/// <param name="outputNames"> List of names of the outputs. </param>
///
/// <returns> A Function. </returns>
////////////////////////////////////////////////////////////////////////////////////////////////////
static Function SetupScale(ScaleParameter param, List <BlobProto> blobs, List <string> bottoms, string name, string[] inputNames, string[] outputNames)
{
// Caffe and Chainer implicitly use the first dimension as Bacth, so make correction
int axis = param.Axis - 1;
bool biasTerm = param.BiasTerm;
if (bottoms.Count == 1)
{
// Create Scale
int[] wShape = new int[blobs[0].Shape.Dims.Length];
for (int i = 0; i < wShape.Length; i++)
{
wShape[i] = (int)blobs[0].Shape.Dims[i];
}
return(new MultiplyScale(axis, wShape, biasTerm, blobs[0].Datas, blobs[1].Datas, name, inputNames, outputNames));
}
else
{
// Create Bias
int[] shape = new int[blobs[0].Shape.Dims.Length];
for (int i = 0; i < shape.Length; i++)
{
shape[i] = (int)blobs[0].Shape.Dims[i];
}
return(new AddBias(axis, shape, blobs[0].Datas, name));
}
}
/// <summary>
/// Reshape the bottom (input) and top (output) blobs.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void Reshape(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
ScaleParameter p = m_param.scale_param;
Blob <T> blobScale = (colBottom.Count > 1) ? colBottom[1] : m_colBlobs[0];
// Always set axis == 0 in special case where bias is a scalar
// (num_axes == 0. Mathematically eqiuvalent for any choice of axis, os the
// actual setting can be safely ignored; and computation is most efficient
// with axis == 0 and (therefore) outer_dim == 1. (Setting m_nAxis to
// bottom[0].num_axes - 1, giving inner_dim_ == 1, would be equally
// performant.)
m_nAxis = (blobScale.num_axes == 0) ? 0 : colBottom[0].CanonicalAxisIndex(p.axis);
m_log.CHECK_GE(colBottom[0].num_axes, m_nAxis + blobScale.num_axes, "scale blob's shape extends past bottom[0]'s shape when applied starting with bottom[0] axis = " + m_nAxis.ToString());
for (int i = 0; i < blobScale.num_axes; i++)
{
m_log.CHECK_EQ(colBottom[0].shape(m_nAxis + i), blobScale.shape(i), "dimension mismatch between bottom[0]->shape(" + (m_nAxis + i).ToString() + ") and scale->shape(" + i.ToString() + ")");
}
m_nOuterDim = colBottom[0].count(0, m_nAxis);
m_nScaleDim = blobScale.count();
m_nInnerDim = colBottom[0].count(m_nAxis + blobScale.num_axes);
if (colBottom[0] == colTop[0]) // in-place computation
{
m_blobTemp.ReshapeLike(colBottom[0]);
}
else
{
colTop[0].ReshapeLike(colBottom[0]);
}
m_blobSumResult.Reshape(new List <int>()
{
m_nOuterDim *m_nScaleDim
});
int nSumMultSize = Math.Max(m_nOuterDim, m_nInnerDim);
m_blobSumMultiplier.Reshape(new List <int>()
{
nSumMultSize
});
m_blobSumMultiplier.SetData(1.0);
if (m_biasLayer != null)
{
m_colBiasVec[0] = colTop[0];
m_biasLayer.Reshape(m_colBiasVec, colTop);
}
}
/// <summary>
/// Setup the layer.
/// </summary>
/// <param name="colBottom">Specifies the collection of bottom (input) Blobs.</param>
/// <param name="colTop">Specifies the collection of top (output) Blobs.</param>
public override void LayerSetUp(BlobCollection <T> colBottom, BlobCollection <T> colTop)
{
ScaleParameter p = m_param.scale_param;
if (colBottom.Count == 1 && blobs.Count > 0)
{
m_log.WriteLine("Skipping parameter initialization.");
}
else if (colBottom.Count == 1)
{
// scale is a learned parameter; initialize it.
m_nAxis = colBottom[0].CanonicalAxisIndex(p.axis);
int nNumAxes = p.num_axes;
m_log.CHECK_GE(nNumAxes, -1, "num_axes must be non-negative, or -1 to extend to the end of bottom[0].");
if (nNumAxes >= 0)
{
m_log.CHECK_GE(colBottom[0].num_axes, m_nAxis + nNumAxes, "scale blob's shape extends past bottom[0]'s shape when applied starting with bottom[0] axis = " + m_nAxis.ToString());
}
m_colBlobs = new BlobCollection <T>();
List <int> rgShape = new List <int>();
int nStart = m_nAxis;
int nEnd = (nNumAxes == -1) ? colBottom[0].shape().Count : nStart + nNumAxes;
for (int i = nStart; i < nEnd; i++)
{
rgShape.Add(colBottom[0].shape(i));
}
Blob <T> blobScale = new Blob <T>(m_cuda, m_log, rgShape);
blobScale.Name = "scale";
FillerParameter fp = p.filler;
// Default to unit (1) filler for identity operation.
if (fp == null)
{
fp = new FillerParameter("constant", 1.0);
}
Filler <T> filler = Filler <T> .Create(m_cuda, m_log, fp);
filler.Fill(blobScale);
m_colBlobs.Add(blobScale);
}
if (p.bias_term)
{
LayerParameter pb = new LayerParameter(LayerParameter.LayerType.BIAS);
pb.bias_param.axis = p.axis;
pb.bias_param.num_axes = (colBottom.Count > 1) ? colBottom[1].num_axes : p.num_axes;
pb.bias_param.filler = p.bias_filler;
m_colBiasBottomVec = new BlobCollection <T>();
m_colBiasBottomVec.Add(colBottom[0]);
m_biasLayer = new BiasLayer <T>(m_cuda, m_log, pb);
m_biasLayer.Setup(m_colBiasBottomVec, colTop);
m_nBiasParamId = m_colBlobs.Count;
m_colBlobs.Add(m_biasLayer.blobs[0]);
m_rgbBiasPropagateDown = Utility.Create <bool>(1, false);
}
m_rgbParamPropagateDown = new DictionaryMap <bool>(m_colBlobs.Count(), true);
}
