ie_layers.h File Reference

a header file for internal Layers structure to describe layers information More...

#include <memory>
#include <string>
#include <vector>
#include <algorithm>
#include <map>
#include <iterator>
#include <limits>
#include <cctype>
#include "ie_common.h"
#include "ie_data.h"
#include "ie_blob.h"
#include "ie_device.hpp"
#include "ie_layers_property.hpp"

Go to the source code of this file.

Data Structures
struct	InferenceEngine::LayerParams
	This is an internal common Layer parameter parsing arguments. More...
class	InferenceEngine::CNNLayer
	This is a base abstraction Layer - all DNN Layers inherit from this class. More...
class	InferenceEngine::WeightableLayer
	This class represents a layer with Weights and/or Biases (e.g. Convolution/Fully Connected, etc.) More...
class	InferenceEngine::ConvolutionLayer
	This class represents a standard 3D Convolution Layer. More...
class	InferenceEngine::DeconvolutionLayer
	This class represents a standard deconvolution layer. More...
class	InferenceEngine::DeformableConvolutionLayer
	This class represents a standard deformable convolution layer. More...
class	InferenceEngine::PoolingLayer
	This class represents a standard pooling layer. More...
class	InferenceEngine::BinaryConvolutionLayer
	This class represents a standard binary convolution layer. More...
class	InferenceEngine::FullyConnectedLayer
	This class represents a fully connected layer. More...
class	InferenceEngine::ConcatLayer
	This class represents concatenation layer Takes as input several data elements and merges them to one using the supplied axis. More...
class	InferenceEngine::SplitLayer
	This class represents a layer that evenly splits the input into the supplied outputs. More...
class	InferenceEngine::NormLayer
	This class represents a Linear Response Normalization (LRN) Layer. More...
class	InferenceEngine::SoftMaxLayer
	This class represents standard softmax Layer. More...
class	InferenceEngine::GRNLayer
	This class represents standard GRN Layer. More...
class	InferenceEngine::MVNLayer
	This class represents standard MVN Layer. More...
class	InferenceEngine::ReLULayer
	This class represents a Rectified Linear activation layer. More...
class	InferenceEngine::ClampLayer
	This class represents a Clamp activation layer Clamps all tensor elements into the range [min_value, max_value]. More...
class	InferenceEngine::ReLU6Layer
	This class represents a ReLU6 activation layer Clamps all tensor elements into the range [0, 6.0]. More...
class	InferenceEngine::EltwiseLayer
	This class represents an element wise operation layer. More...
class	InferenceEngine::CropLayer
	This class represents a standard crop layer. More...
class	InferenceEngine::ReshapeLayer
	This class represents a standard reshape layer. More...
class	InferenceEngine::TileLayer
	This class represents a standard Tile Layer. More...
class	InferenceEngine::ScaleShiftLayer
	This class represents a Layer which performs Scale and Shift. More...
class	InferenceEngine::TensorIterator
	This class represents TensorIterator layer. More...
struct	InferenceEngine::TensorIterator::PortMap
struct	InferenceEngine::TensorIterator::Body
class	InferenceEngine::RNNCellBase
	Base class for recurrent cell layers. More...
class	InferenceEngine::RNNSequenceLayer
	Sequence of recurrent cells. More...
class	InferenceEngine::PReLULayer
	This class represents a Layer which performs Scale and Shift. More...
class	InferenceEngine::PowerLayer
	This class represents a standard Power Layer Formula is: output = (offset + scale * input) ^ power. More...
class	InferenceEngine::BatchNormalizationLayer
	This class represents a Batch Normalization Layer. More...
class	InferenceEngine::GemmLayer
	This class represents a general matrix multiplication operation layer Formula is: dst := alpha*src1*src2 + beta*src3. More...
class	InferenceEngine::PadLayer
	This class represents a standard Pad layer Adds paddings to input tensor. More...
class	InferenceEngine::GatherLayer
	This class represents a standard Gather layer Gather slices from Dictionary according to Indexes. More...
class	InferenceEngine::StridedSliceLayer
	This class represents a standard Strided Slice layer Strided Slice picks from input tensor according parameters. More...
class	InferenceEngine::ShuffleChannelsLayer
	This class represents a standard Shuffle Channels layer Shuffle Channels picks from input tensor according parameters. More...
class	InferenceEngine::DepthToSpaceLayer
	This class represents a standard Depth To Space layer Depth To Space picks from input tensor according parameters. More...
class	InferenceEngine::SpaceToDepthLayer
	This class represents a standard Space To Depth layer Depth To Space picks from input tensor according parameters. More...
class	InferenceEngine::ReverseSequenceLayer
	This class represents a standard Reverse Sequence layer Reverse Sequence modifies input tensor according parameters. More...
class	InferenceEngine::OneHotLayer
	This class represents a OneHot layer Converts input into OneHot representation. More...
class	InferenceEngine::RangeLayer
	This class represents a standard RangeLayer layer RangeLayer modifies input tensor dimensions according parameters. More...
class	InferenceEngine::FillLayer
	This class represents a standard Fill layer RFill modifies input tensor according parameters. More...
class	InferenceEngine::SelectLayer
	This class represents a SelectLayer layer SelectLayer layer takes elements from the second (“then”) or the third (“else”) input based on condition mask (“cond”) provided in the first input. The “cond” tensor is broadcasted to “then” and “else” tensors. The output tensor shape is equal to broadcasted shape of “cond”, “then” and “else”. More...
class	InferenceEngine::BroadcastLayer
	This class represents a standard Broadcast layer Broadcast modifies input tensor dimensions according parameters. More...
class	InferenceEngine::QuantizeLayer
	This class represents a quantization operation layer Element-wise linear quantization of floating point input values into a descrete set of floating point values. More...
class	InferenceEngine::MathLayer
	This class represents a standard Math layers Math modifies input tensor dimensions according parameters. More...
class	InferenceEngine::ReduceLayer
	This class represents a standard Reduce layers Reduce modifies input tensor according parameters. More...
class	InferenceEngine::TopKLayer
	This class represents a standard TopK layer TopK picks top K values from input tensor according parameters. More...

Macros
#define	DEFINE_PROP(prop_name)
	convinenent way to declare property with backward compatibility to 2D members More...

Typedefs
using	InferenceEngine::GenericLayer = class CNNLayer
	Alias for CNNLayer object.
using	InferenceEngine::LSTMCell = RNNCellBase
	LSTM Cell layer. More...
using	InferenceEngine::GRUCell = RNNCellBase
	GRU Cell layer. More...
using	InferenceEngine::RNNCell = RNNCellBase
	RNN Cell layer. More...

Detailed Description

a header file for internal Layers structure to describe layers information

Macro Definition Documentation

DEFINE_PROP

#define DEFINE_PROP

(

prop_name

)

Value:

PropertyVector<unsigned int> prop_name;\
unsigned int &prop_name##_x = prop_name.at(X_AXIS);\
unsigned int &prop_name##_y = prop_name.at(Y_AXIS);\

convinenent way to declare property with backward compatibility to 2D members

Typedef Documentation

GRUCell

using InferenceEngine::GRUCell = typedef RNNCellBase

GRU Cell layer.

G - number of gates (=3) N - batch size S - state size (=hidden_size)

Inputs: [N,D] Xt - input data [N,S] Ht-1 - initial hidden state

Outputs: [N,S] Ht - out hidden state

Weights:

• weights [G,S,D+S]
• biases [G,S] NB! gates order is ZRH {update, reset, output}

activations is {_f, _g} default: {_f=sigm, _g=tanh}

Equations:

• - matrix mult (.) - eltwise mult [,] - concatenation

zt = _f(Wz*[Ht-1, Xt] + Bz)

• rt = _f(Wr*[Ht-1, Xt] + Br)
• ht = _g(Wh*[rt (.) Ht-1, Xt] + Bh)
• Ht = (1 - zt) (.) ht + zt (.) Ht-1

LSTMCell

using InferenceEngine::LSTMCell = typedef RNNCellBase

LSTM Cell layer.

G - number of gates (=4) N - batch size S - state size (=hidden_size)

Inputs: [N,D] Xt - input data [N,S] Ht-1 - initial hidden state [N,S] Ct-1 - initial cell state

Outputs: [N,S] Ht - out hidden state [N,S] Ct - out cell state

Weights:

• weights [G,S,D+S]
• biases [G,S] NB! gates order is FICO {forget, input, candidate, output}

activations is {_f, _g, _h} default: {_f=sigm, _g=tanh, _h=tanh}

Equations:

• - matrix mult (.) - eltwise mult [,] - concatenation

ft = _f(Wf*[Ht-1, Xt] + Bf)

• it = _f(Wi*[Ht-1, Xt] + Bi)
• ct = _g(Wc*[Ht-1, Xt] + Bc)
• ot = _f(Wo*[Ht-1, Xt] + Bo)
• Ct = ft (.) Ct-1 + it (.) ct
• Ht = ot (.) _h(Ct)

RNNCell

using InferenceEngine::RNNCell = typedef RNNCellBase

RNN Cell layer.

G - number of gates (=1) N - batch size S - state size (=hidden_size)

Inputs: [N,D] Xt - input data [N,S] Ht-1 - initial hidden state

Outputs: [N,S] Ht - out hidden state

Weights:

• weights [G,S,D+S]
• biases [G,S]

activations is {_f} default: {_f=tanh}

Equations:

• - matrix mult [,] - concatenation

Ht = _f(Wi*[Ht-1, Xt] + Bi)