|
using | gpu_handle_param = void * |
| Shortcut for defining a handle parameter.
|
|
using | BlobMap = std::map< std::string, Blob::Ptr > |
| This is a convenient type for working with a map containing pairs(string, pointer to a Blob instance).
|
|
using | SizeVector = std::vector< size_t > |
| Represents tensor size. More...
|
|
using | CNNLayerPtr = std::shared_ptr< CNNLayer > |
| A smart pointer to the CNNLayer.
|
|
using | CNNLayerWeakPtr = std::weak_ptr< CNNLayer > |
| A smart weak pointer to the CNNLayer.
|
|
using | DataPtr = std::shared_ptr< Data > |
| Smart pointer to Data.
|
|
using | CDataPtr = std::shared_ptr< const Data > |
| Smart pointer to constant Data.
|
|
using | DataWeakPtr = std::weak_ptr< Data > |
| Smart weak pointer to Data.
|
|
using | OutputsDataMap = std::map< std::string, DataPtr > |
| A collection that contains string as key, and Data smart pointer as value.
|
|
using | NetworkNodeStatsPtr = std::shared_ptr< NetworkNodeStats > |
| A shared pointer to the NetworkNodeStats object.
|
|
using | NetworkNodeStatsWeakPtr = std::weak_ptr< NetworkNodeStats > |
| A smart pointer to the NetworkNodeStats object.
|
|
using | NetworkStatsMap = std::map< std::string, NetworkNodeStatsPtr > |
| A map of pairs: name of a layer and related statistics.
|
|
using | ConstOutputsDataMap = std::map< std::string, CDataPtr > |
| A collection that contains string as key, and const Data smart pointer as value.
|
|
using | IExtensionPtr = std::shared_ptr< IExtension > |
| A shared pointer to a IExtension interface.
|
|
using | IShapeInferExtensionPtr = std::shared_ptr< IShapeInferExtension > |
| A shared pointer to a IShapeInferExtension interface. More...
|
|
using | InputsDataMap = std::map< std::string, InputInfo::Ptr > |
| A collection that contains string as key, and InputInfo smart pointer as value.
|
|
using | ConstInputsDataMap = std::map< std::string, InputInfo::CPtr > |
| A collection that contains string as key, and const InputInfo smart pointer as value.
|
|
using | GenericLayer = class CNNLayer |
| Alias for CNNLayer object.
|
|
using | idx_t = size_t |
| A type of network objects indexes. More...
|
|
using | InferenceEnginePluginPtr = InferenceEngine::details::SOPointer< IInferencePlugin > |
| A C++ helper to work with objects created by the plugin. More...
|
|
using | ParamMap = std::map< std::string, Parameter > |
| An std::map object containing low-level object parameters of classes that are derived from RemoteBlob or RemoteContext.
|
|
|
template<class T > |
std::shared_ptr< T > | make_so_pointer (const file_name_t &name)=delete |
| Creates a special shared_pointer wrapper for the given type from a specific shared module. More...
|
|
InferenceEngine::IAllocator * | CreateDefaultAllocator () noexcept |
| Creates the default implementation of the Inference Engine allocator per plugin. More...
|
|
template<typename T , typename std::enable_if<!std::is_pointer< T >::value &&!std::is_reference< T >::value, int >::type = 0, typename std::enable_if< std::is_base_of< Blob, T >::value, int >::type = 0> |
std::shared_ptr< T > | as (const Blob::Ptr &blob) noexcept |
| Helper cast function to work with shared Blob objects. More...
|
|
template<typename T , typename std::enable_if<!std::is_pointer< T >::value &&!std::is_reference< T >::value, int >::type = 0, typename std::enable_if< std::is_base_of< Blob, T >::value, int >::type = 0> |
std::shared_ptr< const T > | as (const Blob::CPtr &blob) noexcept |
| Helper cast function to work with shared Blob objects. More...
|
|
template<typename Type > |
InferenceEngine::TBlob< Type >::Ptr | make_shared_blob (const TensorDesc &tensorDesc) |
| Creates a blob with the given tensor descriptor. More...
|
|
template<typename Type > |
InferenceEngine::TBlob< Type >::Ptr | make_shared_blob (const TensorDesc &tensorDesc, Type *ptr, size_t size=0) |
| Creates a blob with the given tensor descriptor from the pointer to the pre-allocated memory. More...
|
|
template<typename Type > |
InferenceEngine::TBlob< Type >::Ptr | make_shared_blob (const TensorDesc &tensorDesc, const std::shared_ptr< InferenceEngine::IAllocator > &alloc) |
| Creates a blob with the given tensor descriptor and allocator. More...
|
|
template<typename TypeTo > |
InferenceEngine::TBlob< TypeTo >::Ptr | make_shared_blob (const TBlob< TypeTo > &arg) |
| Creates a copy of given TBlob instance. More...
|
|
template<typename T , typename... Args, typename std::enable_if< std::is_base_of< Blob, T >::value, int >::type = 0> |
std::shared_ptr< T > | make_shared_blob (Args &&... args) |
| Creates a Blob object of the specified type. More...
|
|
Blob::Ptr | make_shared_blob (const Blob::Ptr &inputBlob, const ROI &roi) |
| Creates a blob describing given ROI object based on the given blob with pre-allocated memory. More...
|
|
std::ostream & | operator<< (std::ostream &out, const Layout &p) |
|
std::ostream & | operator<< (std::ostream &out, const ColorFormat &fmt) |
|
template<> |
std::shared_ptr< IShapeInferExtension > | make_so_pointer (const file_name_t &name) |
| Creates a special shared_pointer wrapper for the given type from a specific shared module. More...
|
|
template<> |
std::shared_ptr< IExtension > | make_so_pointer (const file_name_t &name) |
| Creates a special shared_pointer wrapper for the given type from a specific shared module. More...
|
|
ICNNNetReader * | CreateCNNNetReader () noexcept |
| Creates a CNNNetReader instance. More...
|
|
StatusCode | CreateExtension (IExtension *&ext, ResponseDesc *resp) noexcept |
| Creates the default instance of the extension. More...
|
|
StatusCode | CreateShapeInferExtension (IShapeInferExtension *&ext, ResponseDesc *resp) noexcept |
| Creates the default instance of the shape infer extension. More...
|
|
template<typename F > |
void | parallel_nt (int nthr, const F &func) |
|
template<typename F > |
void | parallel_nt_static (int nthr, const F &func) |
|
template<typename I , typename F > |
void | parallel_sort (I begin, I end, const F &comparator) |
|
template<typename T0 , typename R , typename F > |
R | parallel_sum (const T0 &D0, const R &input, const F &func) |
|
template<typename T0 , typename T1 , typename R , typename F > |
R | parallel_sum2d (const T0 &D0, const T1 &D1, const R &input, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename R , typename F > |
R | parallel_sum3d (const T0 &D0, const T1 &D1, const T2 &D2, const R &input, const F &func) |
|
template<typename T > |
T | parallel_it_init (T start) |
|
template<typename T , typename Q , typename R , typename... Args> |
T | parallel_it_init (T start, Q &x, const R &X, Args &&... tuple) |
|
bool | parallel_it_step () |
|
template<typename Q , typename R , typename... Args> |
bool | parallel_it_step (Q &x, const R &X, Args &&... tuple) |
|
template<typename T , typename Q > |
void | splitter (const T &n, const Q &team, const Q &tid, T &n_start, T &n_end) |
|
template<typename T0 , typename F > |
void | for_1d (const int &ithr, const int &nthr, const T0 &D0, const F &func) |
|
template<typename T0 , typename F > |
void | parallel_for (const T0 &D0, const F &func) |
|
template<typename T0 , typename T1 , typename F > |
void | for_2d (const int &ithr, const int &nthr, const T0 &D0, const T1 &D1, const F &func) |
|
template<typename T0 , typename T1 , typename F > |
void | parallel_for2d (const T0 &D0, const T1 &D1, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename F > |
void | for_3d (const int &ithr, const int &nthr, const T0 &D0, const T1 &D1, const T2 &D2, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename F > |
void | parallel_for3d (const T0 &D0, const T1 &D1, const T2 &D2, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename T3 , typename F > |
void | for_4d (const int &ithr, const int &nthr, const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename T3 , typename F > |
void | parallel_for4d (const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename T3 , typename T4 , typename F > |
void | for_5d (const int &ithr, const int &nthr, const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3, const T4 &D4, const F &func) |
|
template<typename T0 , typename T1 , typename T2 , typename T3 , typename T4 , typename F > |
void | parallel_for5d (const T0 &D0, const T1 &D1, const T2 &D2, const T3 &D3, const T4 &D4, const F &func) |
|
StatusCode | CreatePluginEngine (IInferencePlugin *&plugin, ResponseDesc *resp) noexcept |
| Creates the default instance of the interface (per plugin) More...
|
|
RemoteBlob::Ptr | make_shared_blob (const TensorDesc &desc, RemoteContext::Ptr ctx) |
| A wrapper of CreateBlob method of RemoteContext to keep consistency with plugin-specific wrappers. More...
|
|
std::string | fileNameToString (const file_name_t &str) |
| Conversion from possibly-wide character string to a single-byte chain.
|
|
file_name_t | stringToFileName (const std::string &str) |
| Conversion from single-byte character string to a possibly-wide one.
|
|
const Version * | GetInferenceEngineVersion () noexcept |
| Gets the current Inference Engine version. More...
|
|
template<class T > |
void | TopResults (unsigned int n, TBlob< T > &input, std::vector< unsigned > &output) |
| Gets the top n results from a tblob. More...
|
|
void | TopResults (unsigned int n, Blob &input, std::vector< unsigned > &output) |
| Gets the top n results from a blob. More...
|
|
template<typename data_t > |
void | copyFromRGB8 (uint8_t *RGB8, size_t RGB8_size, InferenceEngine::TBlob< data_t > *blob) |
| Copies a 8-bit RGB image to the blob. More...
|
|
void | ConvertImageToInput (unsigned char *imgBufRGB8, size_t lengthbytesSize, Blob &input) |
| Splits the RGB channels to either I16 Blob or float blob. More...
|
|
template<typename T > |
void | copyToFloat (float *dst, const InferenceEngine::Blob *src) |
| Copies data from a certain precision to float. More...
|
|
|
struct { |
std::string InferenceEngine::name |
| Layer name.
|
|
std::string InferenceEngine::type |
| Layer type.
|
|
Precision InferenceEngine::precision |
| Layer precision.
|
|
}; | |
| This is an internal common Layer parameter parsing arguments. More...
|
|
class { |
using | Ptr = std::shared_ptr< CNNLayer > |
| A shared pointer to CNNLayer.
|
|
std::string InferenceEngine::name |
| Layer name.
|
|
std::string InferenceEngine::type |
| Layer type.
|
|
Precision InferenceEngine::precision |
| Layer base operating precision.
|
|
std::vector< DataPtr > InferenceEngine::outData |
| A vector of pointers to the output data elements of this layer in the di-graph (order matters)
|
|
std::vector< DataWeakPtr > InferenceEngine::insData |
| A vector of weak pointers to the input data elements of this layer in the di-graph (order matters)
|
|
Ptr InferenceEngine::_fusedWith |
| If suggested to fuse - a pointer to the layer which needs to be fused with this layer.
|
|
UserValue InferenceEngine::userValue |
| Convenience user values to store in this object as extra data.
|
|
std::string InferenceEngine::affinity |
| Layer affinity set by user.
|
|
std::map< std::string, std::string > InferenceEngine::params |
| Map of pairs: (parameter name, parameter value)
|
|
std::map< std::string, Blob::Ptr > InferenceEngine::blobs |
| Map of pairs: (name, weights/biases blob)
|
|
std::shared_ptr< ngraph::Node > node |
|
}; | |
| This is a base abstraction Layer - all DNN Layers inherit from this class. More...
|
|
Blob::Ptr | _weights |
| A pointer to a weights blob.
|
|
Blob::Ptr | _biases |
| A pointer to a biases blob.
|
|
PropertyVector< unsigned int > | _kernel |
| A convolution kernel array [X, Y, Z, ...]. More...
|
|
unsigned int & | _kernel_x = _kernel .at(X_AXIS) |
|
unsigned int & | _kernel_y = _kernel .at(Y_AXIS) |
|
PropertyVector< unsigned int > | _padding |
| A convolution paddings begin array [X, Y, Z, ...]. More...
|
|
unsigned int & | _padding_x = _padding .at(X_AXIS) |
|
unsigned int & | _padding_y = _padding .at(Y_AXIS) |
|
PropertyVector< unsigned int > | _pads_end |
| A convolution paddings end array [X, Y, Z, ...]. More...
|
|
PropertyVector< unsigned int > | _stride |
| A convolution strides array [X, Y, Z, ...]. More...
|
|
unsigned int & | _stride_x = _stride .at(X_AXIS) |
|
unsigned int & | _stride_y = _stride .at(Y_AXIS) |
|
PropertyVector< unsigned int > | _dilation |
| A convolution dilations array [X, Y, Z, ...].
|
|
unsigned int & | _dilation_x = _dilation .at(X_AXIS) |
|
unsigned int & | _dilation_y = _dilation .at(Y_AXIS) |
|
unsigned int | _out_depth = 0u |
| A number of output feature maps (size) generating the 3'rd output dimension.
|
|
unsigned int | _group = 1u |
| Number of groups.
|
|
std::string | _auto_pad |
| Auto padding type.
|
|
unsigned int | _deformable_group = 1u |
| Number of deformable groups.
|
|
PoolType | _type = MAX |
| A pooling type.
|
|
bool | _exclude_pad = false |
| A flag that indicates if padding is excluded or not.
|
|
eBinaryConvolutionMode | _mode = xnor_popcount |
| Mode of binary convolution operation.
|
|
unsigned int | _in_depth = 0u |
| A number of input feature maps (size) generating the 3'rd input dimension.
|
|
float | _pad_value = 0.0f |
| A pad value which is used to fill pad area.
|
|
unsigned int | _out_num = 0 |
| A size of output.
|
|
unsigned int | _axis = 1 |
| An axis on which concatenation operation is performed. More...
|
|
unsigned int | _size = 0 |
| Response size.
|
|
unsigned int | _k = 1 |
| K.
|
|
float | _alpha = 0 |
| Alpha coefficient.
|
|
float | _beta = 0 |
| Beta coefficient.
|
|
bool | _isAcrossMaps = false |
| Flag to specify normalization across feature maps (true) or across channels.
|
|
int | axis = 1 |
| Axis number for a softmax operation. More...
|
|
float | bias = 0.f |
| Bias for squares sum.
|
|
int | across_channels = 0 |
| Indicate that mean value is calculated across channels.
|
|
int | normalize = 1 |
| Indicate that the result needs to be normalized.
|
|
float | negative_slope = 0.0f |
| Negative slope is used to takle negative inputs instead of setting them to 0.
|
|
float | min_value = 0.0f |
| A minimum value.
|
|
float | max_value = 1.0f |
| A maximum value.
|
|
eOperation | _operation = Sum |
| A type of the operation to use.
|
|
std::vector< float > | coeff |
| A vector of coefficients to scale the operands.
|
|
std::vector< int > | dim |
| A vector of dimensions to be preserved.
|
|
std::vector< int > | offset = 0.f |
| A vector of offsets for each dimension. More...
|
|
std::vector< int > | shape |
| A vector of sizes of the shape.
|
|
int | num_axes = -1 |
| A number of first axises to be taken for a reshape.
|
|
int | tiles = -1 |
| A number of copies to be made.
|
|
unsigned int | _broadcast = 0 |
| A flag that indicates if the same value is used for all the features. If false, the value is used pixel wise.
|
|
std::vector< PortMap > | input_port_map |
| Input ports map.
|
|
std::vector< PortMap > | output_port_map |
| Output ports map.
|
|
std::vector< PortMap > | back_edges |
| Back edges map.
|
|
Body | body |
| A Tensor Iterator body.
|
|
CellType | cellType = LSTM |
| Direct type of recurrent cell (including subtypes) Description of particular cell semantics is in LSTMCell, GRUCell, RNNCell.
|
|
int | hidden_size = 0 |
| Size of hidden state data. More...
|
|
float | clip = 0.0f |
| Clip data into range [-clip, clip] on input of activations. More...
|
|
std::vector< std::string > | activations |
| Activations used inside recurrent cell. More...
|
|
std::vector< float > | activation_alpha |
| Alpha parameters of activations. More...
|
|
std::vector< float > | activation_beta |
| Beta parameters of activations. More...
|
|
Direction | direction = FWD |
| Direction of iteration through sequence dimension.
|
|
bool | _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.
|
|
float | power = 1.f |
| An exponent value.
|
|
float | scale = 1.f |
| A scale factor.
|
|
float | epsilon = 1e-3f |
| A small value to add to the variance estimate to avoid division by zero.
|
|
float | alpha = 1.f |
| A scale factor of src1 matrix.
|
|
float | beta = 1.f |
| A scale factor of src3 matrix.
|
|
bool | transpose_a = false |
| A flag that indicates if the src1 matrix is to be transposed.
|
|
bool | transpose_b = false |
| A flag that indicates if the src2 matrix is to be transposed.
|
|
PropertyVector< unsigned int > | pads_begin |
| Size of padding in the beginning of each axis.
|
|
PropertyVector< unsigned int > | pads_end |
| Size of padding in the end of each axis.
|
|
ePadMode | pad_mode = Constant |
| Mode of pad operation.
|
|
float | pad_value = 0.0f |
| A pad value which is used for filling in Constant mode.
|
|
std::string | 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.
|
|
std::string | end_mask |
| Analogous to begin_mask.
|
|
std::string | ellipsis_mask |
| The ellipsis_mask is a bitmask where bit i being 1 means the i-th is actually an ellipsis.
|
|
std::string | 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.
|
|
std::string | shrink_axis_mask |
| The shrink_axis_mask is a bitmask where bit i being 1 means the i-th position shrinks the dimensionality.
|
|
unsigned int | group = 1 |
| The group of output shuffled channels.
|
|
unsigned int | block_size = 1 |
| The group of output shuffled channels. More...
|
|
std::vector< size_t > | _block_shape |
| Spatial dimensions blocks sizes.
|
|
std::vector< size_t > | _pads_begin |
| Size of padding in the beginning of each axis.
|
|
std::vector< size_t > | _crops_begin |
| It specifies how many elements to crop from the intermediate result across the spatial dimensions.
|
|
std::vector< size_t > | _crops_end |
| It specifies how many elements to crop from the intermediate result across the spatial dimensions.
|
|
bool | with_right_bound = false |
| Indicates whether the intervals include the right or the left bucket edge.
|
|
int | seq_axis = 1 |
| The seq_axis dimension in tensor which is partially reversed.
|
|
int | batch_axis = 0 |
| The batch_axis dimension in tensor along which reversal is performed.
|
|
unsigned int | depth = 0 |
| A depth of representation.
|
|
float | on_value = 1.f |
| The locations represented by indices in input take value on_value.
|
|
float | off_value = 0.f |
| The locations not represented by indices in input take value off_value.
|
|
int | levels = 1 |
| The number of quantization levels.
|
|
bool | keep_dims = true |
| The keep_dims dimension in tensor which is partially reversed.
|
|
std::string | mode |
| The mode could be 'max' or 'min'.
|
|
std::string | sort |
| top K values sort mode could be 'value' or 'index'
|
|
bool | sorted |
| A flag indicating whether to sort unique elements.
|
|
bool | return_inverse |
| A flag indicating whether to return indices of input data elements in the output of uniques.
|
|
bool | return_counts |
| A flag indicating whether to return a number of occurences for each unique element.
|
|
bool | center_point_box = false |
| The 'center_point_box' indicates the format of the box data.
|
|
bool | sort_result_descending = true |
| The 'sort_result_descending' indicates that result will sort descending by score through all batches and classes.
|
|
int | flatten = 1 |
| flatten value
|
|
int | grid_w = 0 |
| Value of grid width.
|
|
int | grid_h = 0 |
| Value of grid height.
|
|
float | stride_w = 0.f |
| Value of width step between grid cells.
|
|
float | stride_h = 0.f |
| Value of height step between grid cells.
|
|
float | min_size = 0.f |
| Minimium width and height for boxes.
|
|
float | nms_threshold = 0.7f |
| Non max suppression threshold.
|
|
int | pre_nms_topn = 1000 |
| Maximum number of anchors selected before nms.
|
|
int | post_nms_topn = 1000 |
| Maximum number of anchors selected after nms.
|
|
constexpr const int | MAX_DIMS_NUMBER = 12 |
|