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std::string InferenceEngine::name |
| | Layer name.
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std::string InferenceEngine::type |
| | Layer type.
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Precision InferenceEngine::precision |
| | Layer precision.
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| }; | |
| | This is an internal common Layer parameter parsing arguments. More...
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| class { |
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using | Ptr = std::shared_ptr< CNNLayer > |
| | A shared pointer to CNNLayer.
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std::string InferenceEngine::name |
| | Layer name.
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std::string InferenceEngine::type |
| | Layer type.
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Precision InferenceEngine::precision |
| | Layer base operating precision.
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std::vector< DataPtr > InferenceEngine::outData |
| | A vector of pointers to the output data elements of this layer in the di-graph (order matters)
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std::vector< DataWeakPtr > InferenceEngine::insData |
| | A vector of weak pointers to the input data elements of this layer in the di-graph (order matters)
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Ptr InferenceEngine::_fusedWith |
| | If suggested to fuse - a pointer to the layer which needs to be fused with this layer.
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UserValue InferenceEngine::userValue |
| | Convenience user values to store in this object as extra data.
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std::string InferenceEngine::affinity |
| | Layer affinity set by user.
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std::map< std::string, std::string > InferenceEngine::params |
| | Map of pairs: (parameter name, parameter value)
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std::map< std::string, Blob::Ptr > InferenceEngine::blobs |
| | Map of pairs: (name, weights/biases blob)
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std::shared_ptr< ngraph::Node > node |
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| | This is a base abstraction Layer - all DNN Layers inherit from this class. More...
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Blob::Ptr | InferenceEngine::_weights |
| | A pointer to a weights blob.
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Blob::Ptr | InferenceEngine::_biases |
| | A pointer to a biases blob.
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| PropertyVector< unsigned int > | InferenceEngine::_kernel |
| | A convolution kernel array [X, Y, Z, ...]. More...
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unsigned int & | InferenceEngine::_kernel_x = _kernel .at(X_AXIS) |
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unsigned int & | InferenceEngine::_kernel_y = _kernel .at(Y_AXIS) |
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| PropertyVector< unsigned int > | InferenceEngine::_padding |
| | A convolution paddings begin array [X, Y, Z, ...]. More...
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unsigned int & | InferenceEngine::_padding_x = _padding .at(X_AXIS) |
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unsigned int & | InferenceEngine::_padding_y = _padding .at(Y_AXIS) |
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| PropertyVector< unsigned int > | InferenceEngine::_pads_end |
| | A convolution paddings end array [X, Y, Z, ...]. More...
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| PropertyVector< unsigned int > | InferenceEngine::_stride |
| | A convolution strides array [X, Y, Z, ...]. More...
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unsigned int & | InferenceEngine::_stride_x = _stride .at(X_AXIS) |
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unsigned int & | InferenceEngine::_stride_y = _stride .at(Y_AXIS) |
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PropertyVector< unsigned int > | InferenceEngine::_dilation |
| | A convolution dilations array [X, Y, Z, ...].
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unsigned int & | InferenceEngine::_dilation_x = _dilation .at(X_AXIS) |
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unsigned int & | InferenceEngine::_dilation_y = _dilation .at(Y_AXIS) |
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unsigned int | InferenceEngine::_out_depth = 0u |
| | A number of output feature maps (size) generating the 3'rd output dimension.
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unsigned int | InferenceEngine::_group = 1u |
| | Number of groups.
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std::string | InferenceEngine::_auto_pad |
| | Auto padding type.
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unsigned int | InferenceEngine::_deformable_group = 1u |
| | Number of deformable groups.
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PoolType | InferenceEngine::_type = MAX |
| | A pooling type.
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bool | InferenceEngine::_exclude_pad = false |
| | A flag that indicates if padding is excluded or not.
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eBinaryConvolutionMode | InferenceEngine::_mode = xnor_popcount |
| | Mode of binary convolution operation.
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unsigned int | InferenceEngine::_in_depth = 0u |
| | A number of input feature maps (size) generating the 3'rd input dimension.
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float | InferenceEngine::_pad_value = 0.0f |
| | A pad value which is used to fill pad area.
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unsigned int | InferenceEngine::_out_num = 0 |
| | A size of output.
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| unsigned int | InferenceEngine::_axis = 1 |
| | An axis on which concatenation operation is performed. More...
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unsigned int | InferenceEngine::_size = 0 |
| | Response size.
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unsigned int | InferenceEngine::_k = 1 |
| | K.
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float | InferenceEngine::_alpha = 0 |
| | Alpha coefficient.
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float | InferenceEngine::_beta = 0 |
| | Beta coefficient.
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bool | InferenceEngine::_isAcrossMaps = false |
| | Flag to specify normalization across feature maps (true) or across channels.
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| int | InferenceEngine::axis = 1 |
| | Axis number for a softmax operation. More...
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float | InferenceEngine::bias = 0.f |
| | Bias for squares sum.
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int | InferenceEngine::across_channels = 0 |
| | Indicate that mean value is calculated across channels.
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int | InferenceEngine::normalize = 1 |
| | Indicate that the result needs to be normalized.
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float | InferenceEngine::negative_slope = 0.0f |
| | Negative slope is used to takle negative inputs instead of setting them to 0.
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float | InferenceEngine::min_value = 0.0f |
| | A minimum value.
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float | InferenceEngine::max_value = 1.0f |
| | A maximum value.
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eOperation | InferenceEngine::_operation = Sum |
| | A type of the operation to use.
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std::vector< float > | InferenceEngine::coeff |
| | A vector of coefficients to scale the operands.
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std::vector< int > | InferenceEngine::dim |
| | A vector of dimensions to be preserved.
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| std::vector< int > | InferenceEngine::offset = 0.f |
| | A vector of offsets for each dimension. More...
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std::vector< int > | InferenceEngine::shape |
| | A vector of sizes of the shape.
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int | InferenceEngine::num_axes = -1 |
| | A number of first axises to be taken for a reshape.
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int | InferenceEngine::tiles = -1 |
| | A number of copies to be made.
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unsigned int | InferenceEngine::_broadcast = 0 |
| | A flag that indicates if the same value is used for all the features. If false, the value is used pixel wise.
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std::vector< PortMap > | InferenceEngine::input_port_map |
| | Input ports map.
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std::vector< PortMap > | InferenceEngine::output_port_map |
| | Output ports map.
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std::vector< PortMap > | InferenceEngine::back_edges |
| | Back edges map.
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Body | InferenceEngine::body |
| | A Tensor Iterator body.
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CellType | InferenceEngine::cellType = LSTM |
| | Direct type of recurrent cell (including subtypes) Description of particular cell semantics is in LSTMCell, GRUCell, RNNCell.
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| int | InferenceEngine::hidden_size = 0 |
| | Size of hidden state data. More...
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| float | InferenceEngine::clip = 0.0f |
| | Clip data into range [-clip, clip] on input of activations. More...
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| std::vector< std::string > | InferenceEngine::activations |
| | Activations used inside recurrent cell. More...
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| std::vector< float > | InferenceEngine::activation_alpha |
| | Alpha parameters of activations. More...
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| std::vector< float > | InferenceEngine::activation_beta |
| | Beta parameters of activations. More...
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Direction | InferenceEngine::direction = FWD |
| | Direction of iteration through sequence dimension.
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bool | InferenceEngine::_channel_shared = false |
| | A flag that indicates if the same negative_slope value is used for all the features. If false, the value is used pixel wise.
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float | InferenceEngine::power = 1.f |
| | An exponent value.
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float | InferenceEngine::scale = 1.f |
| | A scale factor.
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float | InferenceEngine::epsilon = 1e-3f |
| | A small value to add to the variance estimate to avoid division by zero.
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float | InferenceEngine::alpha = 1.f |
| | A scale factor of src1 matrix.
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float | InferenceEngine::beta = 1.f |
| | A scale factor of src3 matrix.
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bool | InferenceEngine::transpose_a = false |
| | A flag that indicates if the src1 matrix is to be transposed.
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bool | InferenceEngine::transpose_b = false |
| | A flag that indicates if the src2 matrix is to be transposed.
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PropertyVector< unsigned int > | InferenceEngine::pads_begin |
| | Size of padding in the beginning of each axis.
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PropertyVector< unsigned int > | InferenceEngine::pads_end |
| | Size of padding in the end of each axis.
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ePadMode | InferenceEngine::pad_mode = Constant |
| | Mode of pad operation.
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float | InferenceEngine::pad_value = 0.0f |
| | A pad value which is used for filling in Constant mode.
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std::string | InferenceEngine::begin_mask |
| | The begin_mask is a bitmask where bit i being 0 means to ignore the begin value and instead use the default value.
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std::string | InferenceEngine::end_mask |
| | Analogous to begin_mask.
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std::string | InferenceEngine::ellipsis_mask |
| | The ellipsis_mask is a bitmask where bit i being 1 means the i-th is actually an ellipsis.
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std::string | InferenceEngine::new_axis_mask |
| | The new_axis_mask_ is a bitmask where bit i being 1 means the i-th position creates a new 1 dimension shape.
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std::string | InferenceEngine::shrink_axis_mask |
| | The shrink_axis_mask is a bitmask where bit i being 1 means the i-th position shrinks the dimensionality.
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unsigned int | InferenceEngine::group = 1 |
| | The group of output shuffled channels.
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| unsigned int | InferenceEngine::block_size = 1 |
| | The group of output shuffled channels. More...
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std::vector< size_t > | InferenceEngine::_block_shape |
| | Spatial dimensions blocks sizes.
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std::vector< size_t > | InferenceEngine::_pads_begin |
| | Size of padding in the beginning of each axis.
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std::vector< size_t > | InferenceEngine::_crops_begin |
| | It specifies how many elements to crop from the intermediate result across the spatial dimensions.
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std::vector< size_t > | InferenceEngine::_crops_end |
| | It specifies how many elements to crop from the intermediate result across the spatial dimensions.
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bool | InferenceEngine::with_right_bound = false |
| | Indicates whether the intervals include the right or the left bucket edge.
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int | InferenceEngine::seq_axis = 1 |
| | The seq_axis dimension in tensor which is partially reversed.
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int | InferenceEngine::batch_axis = 0 |
| | The batch_axis dimension in tensor along which reversal is performed.
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unsigned int | InferenceEngine::depth = 0 |
| | A depth of representation.
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float | InferenceEngine::on_value = 1.f |
| | The locations represented by indices in input take value on_value.
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float | InferenceEngine::off_value = 0.f |
| | The locations not represented by indices in input take value off_value.
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int | InferenceEngine::levels = 1 |
| | The number of quantization levels.
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bool | InferenceEngine::keep_dims = true |
| | The keep_dims dimension in tensor which is partially reversed.
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std::string | InferenceEngine::mode |
| | The mode could be 'max' or 'min'.
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std::string | InferenceEngine::sort |
| | top K values sort mode could be 'value' or 'index'
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bool | InferenceEngine::sorted |
| | A flag indicating whether to sort unique elements.
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bool | InferenceEngine::return_inverse |
| | A flag indicating whether to return indices of input data elements in the output of uniques.
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bool | InferenceEngine::return_counts |
| | A flag indicating whether to return a number of occurences for each unique element.
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bool | InferenceEngine::center_point_box = false |
| | The 'center_point_box' indicates the format of the box data.
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bool | InferenceEngine::sort_result_descending = true |
| | The 'sort_result_descending' indicates that result will sort descending by score through all batches and classes.
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int | InferenceEngine::flatten = 1 |
| | flatten value
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int | InferenceEngine::grid_w = 0 |
| | Value of grid width.
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int | InferenceEngine::grid_h = 0 |
| | Value of grid height.
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float | InferenceEngine::stride_w = 0.f |
| | Value of width step between grid cells.
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float | InferenceEngine::stride_h = 0.f |
| | Value of height step between grid cells.
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float | InferenceEngine::min_size = 0.f |
| | Minimium width and height for boxes.
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float | InferenceEngine::nms_threshold = 0.7f |
| | Non max suppression threshold.
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int | InferenceEngine::pre_nms_topn = 1000 |
| | Maximum number of anchors selected before nms.
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int | InferenceEngine::post_nms_topn = 1000 |
| | Maximum number of anchors selected after nms.
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