/// <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)
{
base.Reshape(colBottom, colTop);
m_nOuterNum = colBottom[0].shape(0); // batch size
m_nInnerNum = colBottom[0].count(1); // instance size: |output| == |target|
if (colBottom[0].count() != colBottom[1].count())
{
if (colBottom[1].count() != colBottom[0].num)
{
m_log.FAIL("SOFTMAX_CROSS_ENTROPY_LOSS layer inputs must have the same count, or the target must have 'num' items of indexes.");
}
// Set the label at the target index = 1.0
if (m_blobTarget == null)
{
m_blobTarget = new Blob <T>(m_cuda, m_log);
m_blobTarget.Name = "full_label";
}
m_blobTarget.ReshapeLike(colBottom[0]);
}
m_softmaxLayer.Reshape(m_colSoftmaxBottomVec, m_colSoftmaxTopVec);
m_blobLoss.ReshapeLike(colBottom[0]);
}
/// <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)
{
base.Reshape(colBottom, colTop);
m_nOuterNum = colBottom[0].shape(0); // batch size
m_nInnerNum = colBottom[0].count(1); // instance size: |output| == |target|
m_log.CHECK_EQ(colBottom[0].count(), colBottom[1].count(), "SOFTMAX_CROSS_ENTROPY_LOSS layer inputs must have the same count.");
m_softmaxLayer.Reshape(m_colSoftmaxBottomVec, m_colSoftmaxTopVec);
m_blobLoss.ReshapeLike(colBottom[0]);
}
/// <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)
{
base.Reshape(colBottom, colTop);
m_softmaxLayer.Reshape(m_colSoftmaxBottomVec, m_colSoftmaxTopVec);
m_nInfogainAxis = colBottom[0].CanonicalAxisIndex(m_param.infogain_loss_param.axis);
m_nOuterNum = colBottom[0].count(0, m_nInfogainAxis);
m_nInnerNum = colBottom[0].count(m_nInfogainAxis + 1);
m_log.CHECK_EQ(m_nOuterNum * m_nInnerNum, colBottom[1].count(), "Number of labels must match the number of predictions; e.g., if infogain_axis == 1 and predictions shape is (N, C, H, W), label count (number of labels) must be N*H*W, with integer values in {0, 1, ..., C-1}.");
m_nNumLabels = colBottom[0].shape(m_nInfogainAxis);
Blob <T> blobInfoGain = null;
if (colBottom.Count < 3)
{
blobInfoGain = m_blobInfoGain;
}
else
{
blobInfoGain = colBottom[2];
}
m_log.CHECK_EQ(blobInfoGain.count(), m_nNumLabels * m_nNumLabels, "The infogain count must equal 'num_labels' * 'num_labels'.");
m_blobSumRowsOfH.Reshape(new List <int>()
{
m_nNumLabels
});
if (colBottom.Count == 2)
{
// H is provided as a parameter and will not change. Sum rows once.
sum_rows_of_H(blobInfoGain);
}
if (colTop.Count >= 2)
{
// softmax output.
colTop[1].ReshapeLike(colBottom[0]);
}
}
