NormaliseImage

#include <popops/NormaliseImage.hpp>

Functions for padding and normalising image tensors.

namespace popops

Common functions, such as elementwise and reductions.

Functions

poplar::Tensor createNormaliseImageInput(poplar::Graph &graph, const poplar::Type &type, const poplar::ArrayRef<std::size_t> shape, const poplar::DebugContext &debugContext = {})

Add a tensor for a 3-channel image suitable for padding to 4 channels.

Parameters

  • graph – The graph to which the tensor will be added.

  • type – The type of the elements. Must be UNSIGNED_CHAR, HALF or FLOAT.

  • shape – Required tensor shape. Must have an inner dimension of three.

  • debugContext – Debugging context.

poplar::Tensor normaliseImage(poplar::Graph &graph, poplar::program::Sequence &seq, poplar::Tensor tIn, float inScale, poplar::Tensor offsets, poplar::Tensor scales, const poplar::DebugContext &debugContext = {})

Pad a tensor to have 4 channel dimensions.

Each channel is normalised via: 

 tIn[c] * inScale - offset[c]) * scale[c]

tIn must be mapped with a single region of complete pixels on each tile. createNormaliseImageInput() creates a variable that is suitably mapped.

UINT8 inputs are cast to HALF. Otherwise the output tensor follows the input type.

Parameters

  • graph – The graph containing the tensor.

  • seq – The sequence to which the normalisation programs will be added.

  • tIn – Input image. It must have an inner dimension of 3. and be UNSIGNED_CHAR, HALF or FLOAT.

  • inScale – Input scaling.

  • offsets – Offset for each channel. Must be shape {3} and must match the output type.

  • scales – Scaling factor for each channel. Must be shape {3} and must match the output type.

  • debugContext – Debugging context.