void NDMask::MaskSection(const std::vector<size_t>& sectionOffset, const NDShape& sectionShape)
{
// TODO: Implement batching of masking operation for masks residing on GPUs to avoid making
// GPU invocations for each MaskSection call.
if (sectionOffset.size() > m_maskShape.NumAxes())
LogicError("NDMask::MaskSection: The sectionOffset cannot have dimensionality higher than the number of axes of 'this' mask");
if (sectionShape.NumAxes() > m_maskShape.NumAxes())
LogicError("NDMask::MaskSection: The section shape cannot have an axes count higher than the number of axes of 'this' mask");
std::vector<size_t> offset(m_maskShape.NumAxes(), 0);
for (size_t i = 0; i < sectionOffset.size(); ++i)
offset[i] = sectionOffset[i];
NDShape shape = sectionShape.AppendShape(NDShape(m_maskShape.NumAxes() - sectionShape.NumAxes(), NDShape::InferredDimension));
auto maskMatrix = GetMatrix();
size_t rowOffset = offset[0];
size_t colOffset = offset[1];
size_t sliceRowLength = (shape[0] != NDShape::InferredDimension) ? shape[0] : (maskMatrix->GetNumRows() - rowOffset);
size_t sliceColLength = (shape[1] != NDShape::InferredDimension) ? shape[1] : (maskMatrix->GetNumCols() - colOffset);
if ((rowOffset == 0) && (sliceRowLength == maskMatrix->GetNumRows()))
maskMatrix->ColumnSlice(colOffset, sliceColLength).SetValue(0);
else
{
// Since Matrix does not support strides in the row dimension, we will need to create separate slices for each column
for (size_t i = colOffset; i < (colOffset + sliceColLength); ++i)
{
auto column = maskMatrix->ColumnSlice(i, 1);
column.Reshape(1, maskMatrix->GetNumRows());
column.ColumnSlice(rowOffset, sliceRowLength).SetValue(0);
}
}
}
// The method describes how input stream is transformed to the output stream. Called once per applied stream.
// Scale transformer transforms the stream so that all samples are of the same size.
StreamInformation ScaleTransformer::Transform(const StreamInformation& inputStream)
{
TransformBase::Transform(inputStream);
auto dims = ImageDimensions(m_imgWidth, m_imgHeight, m_imgChannels).AsTensorShape(HWC).GetDims();
m_outputStream.m_sampleLayout = NDShape(std::vector<size_t>(dims.begin(), dims.end()));
return m_outputStream;
}
// The method describes how input stream is transformed to the output stream. Called once per applied stream.
// Transpose transformer expects the dense input stream with samples as HWC and outputs CHW.
StreamInformation TransposeTransformer::Transform(const StreamInformation& inputStream)
{
m_outputStream = TransformBase::Transform(inputStream);
// Changing from NHWC to NCHW
m_outputStream.m_elementType = m_precision;
if (!m_inputStream.m_sampleLayout.IsUnknown())
{
ImageDimensions dimensions(TensorShape(m_inputStream.m_sampleLayout.Dimensions()), HWC);
auto dims = dimensions.AsTensorShape(CHW).GetDims();
m_outputStream.m_sampleLayout = NDShape(std::vector<size_t>(dims.begin(), dims.end()));
}
return m_outputStream;
}
