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RleConvLayer.md

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CRleConvLayer Class

The class implements a layer that performs convolution on a set of binary one-channel images in RLE format.

RLE format

An RLE image is represented by the background value (GetNonStrokeValue()) and a set of horizontal strokes filled with GetStrokeValue().

static const int MaxRleConvImageWidth = 64;

// A stroke in RLE format
struct CRleStroke {
	short Start;	// starting coordinate
	short End;		// ending coordinate (the first pixel that does NOT belong to the stroke)

    // A special stroke value for the end of the row
	static CRleStroke Sentinel() { return { SHRT_MAX, -1 }; }
};

static const CRleStroke Sentinel = { SHRT_MAX, -1 };

struct CRleImage {
	int StrokesCount; // the number of strokes in the image
	int Height; // the image height; may be smaller than the blob height,
                // in which case the top (blob.GetHeight() - Height) / 2
                // and the bottom (blob.GetHeight() - Height + 1) / 2 rows will be filled with GetNonStrokeValue()
	int Width; // the image width; may be smaller than the blob width,
               // in which case the left (blob.GetWidth() - Width) / 2
               // and the right (blob.GetWidth() - Width + 1) / 2 columns will be filled with GetNonStrokeValue()
	CRleStroke Stub; // RESERVED
	CRleStroke Lines[1]; // the lines array. As the strokes' coordinates are stored in short variables, it is guaranteed that the size of any RLE image will not exceed the size of a float buffer necessary to store it
};

Sample

// We will encode the following image in RLE format:
// 01110
// 00000
// 01010
// 00110

CPtr<CDnnBlob> imageBlob = CDnnBlob::Create2DImageBlob(GetDefaultCpuMathEngine(), CT_Float, 1, 1, 4, 5, 1);
CArray<float> imageBuff;
imageBuff.Add(0, 4 * 5);
CRleImage* rleImage = reinterpret_cast<CRleImage*>(imageBuff.GetPtr());
rleImage->Height = 4;
rleImage->Width = 3; // the left and the right columns are filled with zeros
rleImage->StrokesCount = 8; // there are 4 strokes on the image + 4 end-of-rows are needed
rleImage->Lines[0] = CRleStroke{ 0, 3 }; // the 3-length stroke in the first row; the stroke coordinates are relative to rleImage->Width and NOT to the blob width
rleImage->Lines[1] = CRleStroke::Sentinel(); // the first row ends
rleImage->Lines[2] = CRleStroke::Sentinel(); // the second row is empty
rleImage->Lines[3] = CRleStroke{ 0, 1 }; // the first one pixel in the third row
rleImage->Lines[4] = CRleStroke{ 2, 3 }; // the second one pixel in the third row
rleImage->Lines[5] = CRleStroke::Sentinel(); // the third row ends
rleImage->Lines[6] = CRleStroke{ 1, 3 };
rleImage->Lines[7] = CRleStroke::Sentinel();
imageBlob->CopyFrom(imageBuff.GetPtr());

// If you want to store several images, place the nth image in the imageBuff array
// shifted by the image size * (n-1)
// CRleImage* nthRleImage = reinterpret_cast<CRleImage*>(imageBuff.GetPtr() + (4 * 5) * (n - 1));

Settings

Filters size

void SetFilterHeight( int filterHeight );
void SetFilterWidth( int filterWidth );
void SetFilterCount( int filterCount );

Convolution stride

void SetStrideHeight( int strideHeight );
void SetStrideWidth( int strideWidth );

Sets the convolution stride. By default, the stride is 1.

Pixel values in RLE format

void SetStrokeValue( float _strokeValue );
void SetNonStrokeValue( float _nonStrokeValue );

The values to be used for the RLE strokes and outside them. See above for details on format.

Using the free terms

void SetZeroFreeTerm(bool isZeroFreeTerm);

Specifies if the free terms should be used. If you set this value to true, the free terms vector will be set to all zeros and won't be trained. By default, this value is set to false.

Trainable parameters

Filters

CPtr<CDnnBlob> GetFilterData() const;

The filters are represented by a blob of the following dimensions:

  • BatchLength * BatchWidth * ListSize is equal to the number of filters used (GetFilterCount()).
  • Height is equal to GetFilterHeight().
  • Width is equal to GetFilterWidth().
  • Depth and Channels are equal to 1.

Free terms

CPtr<CDnnBlob> GetFreeTermData() const;

The free terms are represented by a blob of the total size equal to the number of filters used (GetFilterCount()).

Inputs

Each input accepts a blob with several images in RLE format. The dimensions of all inputs should be the same:

  • BatchLength * BatchWidth * ListSize - the number of images in the set.
  • Height - the images' height.
  • Width - the images' width, not more than 64.
  • Depth - the images' depth, should be equal to 1.
  • Channels - the number of channels, should be equal to 1.

Outputs

For each input the layer has one output. It contains a blob with the result of the convolution, in the same format as the regular CConvLayer.

The output blob dimensions are:

  • BatchLength is equal to the input BatchLength.
  • BatchWidth is equal to the input BatchWidth.
  • ListSize is equal to the input ListSize.
  • Height can be calculated from the input Height as (Height - FilterHeight)/StrideHeight + 1.
  • Width can be calculated from the input Width as (Width - FilterWidth)/StrideWidth + 1.
  • Depth is equal to 1.
  • Channels is equal to GetFilterCount().