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Api redesign #2117

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73fa77a
Simplified the Convolutional Layer's API by eliminating redundant con…
steremma May 16, 2018
a730f72
Simplified the MaxPooling Layer's API by eliminating redundant constr…
steremma May 16, 2018
57a10e2
Modified the inheritance schema: Pooling now inherits from Conv layer…
steremma May 17, 2018
5224f0b
fixed merged conflict
steremma May 30, 2018
0ed1d0b
Merge branch 'master' of https://github.com/root-project/root into ap…
steremma Jun 1, 2018
3cc2f8f
Applied code review corrections
steremma Jun 22, 2018
e5e50f3
Merge branch 'master' of https://github.com/root-project/root into ap…
steremma Jun 22, 2018
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88 changes: 66 additions & 22 deletions tmva/tmva/inc/TMVA/DNN/CNN/ConvLayer.h
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Original file line number Diff line number Diff line change
Expand Up @@ -46,21 +46,35 @@ class TConvLayer : public VGeneralLayer<Architecture_t> {
using Scalar_t = typename Architecture_t::Scalar_t;

private:
bool inline isInteger(Scalar_t x) const { return x == floor(x); }

/* Calculate the output dimension of the convolutional layer */
size_t calculateDimension(size_t imgDim, size_t fltDim, size_t padding, size_t stride);

/* Calculate the number of pixels in a single receptive field */
size_t inline calculateNLocalViewPixels(size_t depth, size_t height, size_t width) { return depth * height * width; }

/* Calculate the number of receptive fields in an image given the filter and image sizes */
size_t calculateNLocalViews(size_t inputHeight, size_t filterHeight, size_t paddingHeight, size_t strideRows,
size_t inputWidth, size_t filterWidth, size_t paddingWidth, size_t strideCols);

protected:
size_t fFilterDepth; ///< The depth of the filter.
size_t fFilterHeight; ///< The height of the filter.
size_t fFilterWidth; ///< The width of the filter.

size_t fStrideRows; ///< The number of row pixels to slid the filter each step.
size_t fStrideCols; ///< The number of column pixels to slid the filter each step.

size_t fPaddingHeight; ///< The number of zero layers added top and bottom of the input.
size_t fPaddingWidth; ///< The number of zero layers left and right of the input.

size_t fNLocalViewPixels; ///< The number of pixels in one local image view.
size_t fNLocalViews; ///< The number of local views in one image.

Scalar_t fDropoutProbability; ///< Probability that an input is active.

private:
size_t fPaddingHeight; ///< The number of zero layers added top and bottom of the input.
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@lmoneta lmoneta Jun 20, 2018

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why now fFilterDepdth, height are now protected and the paddingheight is private ?

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@steremma steremma Jun 20, 2018

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Everything that is needed on MaxPoolingLayer (which is now a subclass of ConvLayer) is now protected to reduce duplication. Since pooling uses 0 padding by definition, this field remains private to ConvLayer

size_t fPaddingWidth; ///< The number of zero layers left and right of the input.

std::vector<Matrix_t> fDerivatives; ///< First fDerivatives of the activations of this layer.

std::vector<int> fForwardIndices; ///< Vector of indices used for a fast Im2Col in forward pass
Expand All @@ -71,11 +85,10 @@ class TConvLayer : public VGeneralLayer<Architecture_t> {
ERegularization fReg; ///< The regularization method.
Scalar_t fWeightDecay; ///< The weight decay.


public:
/*! Constructor. */
TConvLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, size_t Height,
size_t Width, size_t WeightsNRows, size_t WeightsNCols, size_t BiasesNRows, size_t BiasesNCols,
size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, EInitialization Init, size_t FilterDepth,
TConvLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Depth, EInitialization Init,
size_t FilterHeight, size_t FilterWidth, size_t StrideRows, size_t StrideCols, size_t PaddingHeight,
size_t PaddingWidth, Scalar_t DropoutProbability, EActivationFunction f, ERegularization Reg,
Scalar_t WeightDecay);
Expand Down Expand Up @@ -145,23 +158,32 @@ class TConvLayer : public VGeneralLayer<Architecture_t> {
//______________________________________________________________________________
template <typename Architecture_t>
TConvLayer<Architecture_t>::TConvLayer(size_t batchSize, size_t inputDepth, size_t inputHeight, size_t inputWidth,
size_t depth, size_t height, size_t width, size_t weightsNRows,
size_t weightsNCols, size_t biasesNRows, size_t biasesNCols,
size_t outputNSlices, size_t outputNRows, size_t outputNCols,
EInitialization init, size_t filterDepth, size_t filterHeight,
size_t filterWidth, size_t strideRows, size_t strideCols, size_t paddingHeight,
size_t paddingWidth, Scalar_t dropoutProbability, EActivationFunction f,
ERegularization reg, Scalar_t weightDecay)
: VGeneralLayer<Architecture_t>(batchSize, inputDepth, inputHeight, inputWidth, depth, height, width, 1,
weightsNRows, weightsNCols, 1, biasesNRows, biasesNCols, outputNSlices, outputNRows,
outputNCols, init),
fFilterDepth(filterDepth), fFilterHeight(filterHeight), fFilterWidth(filterWidth), fStrideRows(strideRows),
fStrideCols(strideCols), fPaddingHeight(paddingHeight), fPaddingWidth(paddingWidth),
fNLocalViewPixels(filterDepth * filterHeight * filterWidth), fNLocalViews(height * width),
fDropoutProbability(dropoutProbability), fDerivatives(), fF(f), fReg(reg), fWeightDecay(weightDecay)
size_t depth, EInitialization init, size_t filterHeight, size_t filterWidth,
size_t strideRows, size_t strideCols, size_t paddingHeight, size_t paddingWidth,
Scalar_t dropoutProbability, EActivationFunction f, ERegularization reg,
Scalar_t weightDecay)
: VGeneralLayer<Architecture_t>(batchSize, inputDepth, inputHeight, inputWidth, depth,
calculateDimension(inputHeight, filterHeight, paddingHeight, strideRows),
calculateDimension(inputWidth, filterWidth, paddingWidth, strideCols),
1, depth, calculateNLocalViewPixels(inputDepth, filterHeight, filterWidth),
1, depth, 1, batchSize, depth,
calculateNLocalViews(inputHeight, filterHeight, paddingHeight, strideRows,
inputWidth, filterWidth, paddingWidth, strideCols),
init),
fFilterDepth(inputDepth), fFilterHeight(filterHeight), fFilterWidth(filterWidth), fStrideRows(strideRows),
fStrideCols(strideCols), fNLocalViewPixels(calculateNLocalViewPixels(inputDepth, filterHeight, filterWidth)),
fNLocalViews(calculateNLocalViews(inputHeight, filterHeight, paddingHeight, strideRows,
inputWidth, filterWidth, paddingWidth, strideCols)),
fDropoutProbability(dropoutProbability), fPaddingHeight(paddingHeight), fPaddingWidth(paddingWidth),
fDerivatives(), fF(f), fReg(reg), fWeightDecay(weightDecay)
{
for (size_t i = 0; i < outputNSlices; i++) {
fDerivatives.emplace_back(outputNRows, outputNCols);
/** Each element in the vector is a `T_Matrix` representing an event, therefore `vec.size() == batchSize`.
* Cells in these matrices are distributed in the following manner:
* Each row represents a single feature map, therefore we have `nRows == depth`.
* Each column represents a single pixel in that feature map, therefore we have `nCols == nLocalViews`.
**/
for (size_t i = 0; i < batchSize; i++) {
fDerivatives.emplace_back(depth, fNLocalViews);
}
}

Expand Down Expand Up @@ -351,6 +373,28 @@ void TConvLayer<Architecture_t>::ReadWeightsFromXML(void *parent)
this->ReadMatrixXML(parent,"Biases", this -> GetBiasesAt(0));
}

template <typename Architecture_t>
size_t TConvLayer<Architecture_t>::calculateDimension(int imgDim, int fltDim, int padding, int stride)
{
Scalar_t dimension = ((imgDim - fltDim + 2 * padding) / stride) + 1;
if (!isInteger(dimension) || dimension <= 0) {
Fatal("calculateDimension", "Not compatible hyper parameters for layer - (imageDim, filterDim, padding, stride) %d , %d , %d , %d",
imgDim, fltDim, padding, stride);
}

return (size_t)dimension;
}

template <typename Architecture_t>
size_t TConvLayer<Architecture_t>::calculateNLocalViews(int inputHeight, int filterHeight, int paddingHeight,
int strideRows, int inputWidth, int filterWidth,
int paddingWidth, int strideCols)
{
int height = calculateDimension(inputHeight, filterHeight, paddingHeight, strideRows);
int width = calculateDimension(inputWidth, filterWidth, paddingWidth, strideCols);

return height * width;
}

} // namespace CNN
} // namespace DNN
Expand Down
78 changes: 26 additions & 52 deletions tmva/tmva/inc/TMVA/DNN/CNN/MaxPoolLayer.h
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@

#include "TMatrix.h"

#include "TMVA/DNN/GeneralLayer.h"
#include "TMVA/DNN/CNN/ConvLayer.h"
#include "TMVA/DNN/Functions.h"

#include <iostream>
Expand All @@ -43,38 +43,30 @@ namespace CNN {
Generic Max Pooling Layer class.

This generic Max Pooling Layer Class represents a pooling layer of
a CNN. It inherits all of the properties of the generic virtual base class
VGeneralLayer. In addition to that, it contains a matrix of winning units.
a CNN. It inherits all of the properties of the convolutional layer
TConvLayer, but it overrides the propagation methods. In a sense, max pooling
can be seen as non-linear convolution: a filter slides over the input and produces
one element as a function of the the elements within the receptive field.
In addition to that, it contains a matrix of winning units.

The height and width of the weights and biases is set to 0, since this
layer does not contain any weights.

*/
template <typename Architecture_t>
class TMaxPoolLayer : public VGeneralLayer<Architecture_t> {
class TMaxPoolLayer : public TConvLayer<Architecture_t> {

public:
using Matrix_t = typename Architecture_t::Matrix_t;
using Scalar_t = typename Architecture_t::Scalar_t;
using Matrix_t = typename Architecture_t::Matrix_t;
using Scalar_t = typename Architecture_t::Scalar_t;

private:
std::vector<Matrix_t> indexMatrix; ///< Matrix of indices for the backward pass.

size_t fFrameHeight; ///< The height of the frame.
size_t fFrameWidth; ///< The width of the frame.

size_t fStrideRows; ///< The number of row pixels to slid the filter each step.
size_t fStrideCols; ///< The number of column pixels to slid the filter each step.

size_t fNLocalViewPixels; ///< The number of pixels in one local image view.
size_t fNLocalViews; ///< The number of local views in one image.

Scalar_t fDropoutProbability; ///< Probability that an input is active.

public:
/*! Constructor. */
TMaxPoolLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t Height,
size_t Width, size_t OutputNSlices, size_t OutputNRows, size_t OutputNCols, size_t FrameHeight,
size_t FrameWidth, size_t StrideRows, size_t StrideCols, Scalar_t DropoutProbability);
TMaxPoolLayer(size_t BatchSize, size_t InputDepth, size_t InputHeight, size_t InputWidth, size_t FilterHeight,
size_t FilterWidth, size_t StrideRows, size_t StrideCols, Scalar_t DropoutProbability);

/*! Copy the max pooling layer provided as a pointer */
TMaxPoolLayer(TMaxPoolLayer<Architecture_t> *layer);
Expand Down Expand Up @@ -104,37 +96,25 @@ class TMaxPoolLayer : public VGeneralLayer<Architecture_t> {
/*! Read the information and the weights about the layer from XML node. */
virtual void ReadWeightsFromXML(void *parent);


/*! Prints the info about the layer. */
void Print() const;

/*! Getters */
const std::vector<Matrix_t> &GetIndexMatrix() const { return indexMatrix; }
std::vector<Matrix_t> &GetIndexMatrix() { return indexMatrix; }

size_t GetFrameHeight() const { return fFrameHeight; }
size_t GetFrameWidth() const { return fFrameWidth; }

size_t GetStrideRows() const { return fStrideRows; }
size_t GetStrideCols() const { return fStrideCols; }

size_t GetNLocalViewPixels() const { return fNLocalViewPixels; }
size_t GetNLocalViews() const { return fNLocalViews; }

Scalar_t GetDropoutProbability() const { return fDropoutProbability; }
};

//______________________________________________________________________________
template <typename Architecture_t>
TMaxPoolLayer<Architecture_t>::TMaxPoolLayer(size_t batchSize, size_t inputDepth, size_t inputHeight, size_t inputWidth,
size_t height, size_t width, size_t outputNSlices, size_t outputNRows,
size_t outputNCols, size_t frameHeight, size_t frameWidth,
size_t strideRows, size_t strideCols, Scalar_t dropoutProbability)
: VGeneralLayer<Architecture_t>(batchSize, inputDepth, inputHeight, inputWidth, inputDepth, height, width, 0, 0, 0,
0, 0, 0, outputNSlices, outputNRows, outputNCols, EInitialization::kZero),
indexMatrix(), fFrameHeight(frameHeight), fFrameWidth(frameWidth), fStrideRows(strideRows),
fStrideCols(strideCols), fNLocalViewPixels(inputDepth * frameHeight * frameWidth), fNLocalViews(height * width),
fDropoutProbability(dropoutProbability)
size_t filterHeight, size_t filterWidth, size_t strideRows,
size_t strideCols, Scalar_t dropoutProbability)

: TConvLayer<Architecture_t>(batchSize, inputDepth, inputHeight, inputWidth, inputDepth, EInitialization::kZero,
filterHeight, filterWidth, strideRows, strideCols, 0, 0, dropoutProbability,
EActivationFunction::kIdentity, ERegularization::kNone, 0),
indexMatrix()
{
for (size_t i = 0; i < this->GetBatchSize(); i++) {
indexMatrix.emplace_back(this->GetDepth(), this->GetNLocalViews());
Expand All @@ -144,10 +124,7 @@ TMaxPoolLayer<Architecture_t>::TMaxPoolLayer(size_t batchSize, size_t inputDepth
//______________________________________________________________________________
template <typename Architecture_t>
TMaxPoolLayer<Architecture_t>::TMaxPoolLayer(TMaxPoolLayer<Architecture_t> *layer)
: VGeneralLayer<Architecture_t>(layer), indexMatrix(), fFrameHeight(layer->GetFrameHeight()),
fFrameWidth(layer->GetFrameWidth()), fStrideRows(layer->GetStrideRows()), fStrideCols(layer->GetStrideCols()),
fNLocalViewPixels(layer->GetNLocalViewPixels()), fNLocalViews(layer->GetNLocalViews()),
fDropoutProbability(layer->GetDropoutProbability())
: TConvLayer<Architecture_t>(layer), indexMatrix()
{
for (size_t i = 0; i < layer->GetBatchSize(); i++) {
indexMatrix.emplace_back(layer->GetDepth(), layer->GetNLocalViews());
Expand All @@ -157,10 +134,7 @@ TMaxPoolLayer<Architecture_t>::TMaxPoolLayer(TMaxPoolLayer<Architecture_t> *laye
//______________________________________________________________________________
template <typename Architecture_t>
TMaxPoolLayer<Architecture_t>::TMaxPoolLayer(const TMaxPoolLayer &layer)
: VGeneralLayer<Architecture_t>(layer), indexMatrix(), fFrameHeight(layer.fFrameHeight),
fFrameWidth(layer.fFrameWidth), fStrideRows(layer.fStrideRows), fStrideCols(layer.fStrideCols),
fNLocalViewPixels(layer.fNLocalViewPixels), fNLocalViews(layer.fNLocalViews),
fDropoutProbability(layer.fDropoutProbability)
: TConvLayer<Architecture_t>(layer), indexMatrix()
{
for (size_t i = 0; i < layer.fBatchSize; i++) {
indexMatrix.emplace_back(layer.fDepth, layer.fNLocalViews);
Expand All @@ -184,7 +158,7 @@ auto TMaxPoolLayer<Architecture_t>::Forward(std::vector<Matrix_t> &input, bool a
}

Architecture_t::Downsample(this->GetOutputAt(i), indexMatrix[i], input[i], this->GetInputHeight(),
this->GetInputWidth(), this->GetFrameHeight(), this->GetFrameWidth(),
this->GetInputWidth(), this->GetFilterHeight(), this->GetFilterWidth(),
this->GetStrideRows(), this->GetStrideCols());
}
}
Expand All @@ -209,8 +183,8 @@ auto TMaxPoolLayer<Architecture_t>::Print() const -> void
std::cout << " H = " << this->GetHeight() << " , ";
std::cout << " D = " << this->GetDepth() << " ) ";

std::cout << "\t Frame ( W = " << this->GetFrameWidth() << " , ";
std::cout << " H = " << this->GetFrameHeight() << " ) ";
std::cout << "\t Filter ( W = " << this->GetFilterWidth() << " , ";
std::cout << " H = " << this->GetFilterHeight() << " ) ";

if (this->GetOutput().size() > 0) {
std::cout << "\tOutput = ( " << this->GetOutput().size() << " , " << this->GetOutput()[0].GetNrows() << " , " << this->GetOutput()[0].GetNcols() << " ) ";
Expand All @@ -225,8 +199,8 @@ void TMaxPoolLayer<Architecture_t>::AddWeightsXMLTo(void *parent)
auto layerxml = gTools().xmlengine().NewChild(parent, 0, "MaxPoolLayer");

// write maxpool layer info
gTools().xmlengine().NewAttr(layerxml, 0, "FrameHeight", gTools().StringFromInt(this->GetFrameHeight()));
gTools().xmlengine().NewAttr(layerxml, 0, "FrameWidth", gTools().StringFromInt(this->GetFrameWidth()));
gTools().xmlengine().NewAttr(layerxml, 0, "FilterHeight", gTools().StringFromInt(this->GetFilterHeight()));
gTools().xmlengine().NewAttr(layerxml, 0, "FilterWidth", gTools().StringFromInt(this->GetFilterWidth()));
gTools().xmlengine().NewAttr(layerxml, 0, "StrideRows", gTools().StringFromInt(this->GetStrideRows()));
gTools().xmlengine().NewAttr(layerxml, 0, "StrideCols", gTools().StringFromInt(this->GetStrideCols()));

Expand Down
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