dlprim Namespace Reference

Mane namespace. More...

Namespaces
	core
	All Basic Operations on GPU.
	cpu
	CPU related tools and classes.
	gpu
	GPU Related tools and classes.
	json
	This namespace includes all JSON parsing and formatting related classes and functions.
	philox
	CPU implementation of Philox 4-32-10 algorithm.
	solvers
	Namespace that contains various optimizers.

Classes
class	Activation
struct	ActivationConfig
class	AXPBY
class	BatchNorm
struct	BatchNormConfig
class	BuildError
	Thrown if OpenCL kernel compilation failed. More...
class	BWBias
class	Concat
struct	ConcatConfig
class	ConcatSliceBase
class	Context
	This is main object that represent the pair of OpenCL platform and device all other objects use it. More...
class	Convolution2D
class	Convolution2DBase
struct	Convolution2DConfig
struct	Convolution2DConfigBase
	Convolution settings. More...
class	Elementwise
struct	ElementwiseConfig
class	Error
	Base dlprim excetion. More...
class	ExecGuard
class	ExecutionContext
	This class is used to pass cl::Events that the kernel should wait for and/or signal event completion. More...
class	Flatten
struct	FlattenConfig
class	GlobalPooling
struct	GlobalPoolingConfig
class	Hardtanh
struct	HardtanhConfig
class	InnerProduct
struct	InnerProductConfig
class	ModelBase
	Base class used for loading non-native model formats to dlprimitives. More...
class	MSELoss
struct	MSELossConfig
class	Net
	Major object used for inference. More...
class	NLLLoss
struct	NLLLossConfig
class	NotImplementedError
	Thrown if some stuff is not implemented yet. More...
class	ONNXModel
	External model for loading ONNX models for inference with dlprim. More...
class	Operator
	Base class for backward/forward propogation calculations for internal network. More...
class	PointwiseBase
class	Pooling2D
struct	Pooling2DConfig
struct	PoolingBase
class	RandomState
class	Reshape
class	ReshapeBase
struct	ReshapeConfig
class	Scal
class	Shape
	Tensor shape. More...
class	SharedResource
	Resources shared by the entire network. More...
class	Slice
struct	SliceConfig
class	Softmax
class	SoftmaxBase
struct	SoftmaxConfig
class	SoftmaxWithLoss
class	Squeeze
struct	SqueezeConfig
class	Tensor
	Central Data Contrainer - Tensor. More...
struct	TensorAndGradient
	Pair of tensor and its gradient for backpropogation. More...
class	TensorSpecs
	Definition of Tensor without actual memory/object. More...
class	Threshold
struct	ThresholdConfig
class	TimingData
	Class used for benchmarking of the model. More...
class	TransposedConvolution2D
struct	TransposedConvolution2DConfig
struct	TypeTraits
struct	TypeTraits< double >
struct	TypeTraits< float >
struct	TypeTraits< int16_t >
struct	TypeTraits< int32_t >
struct	TypeTraits< int64_t >
struct	TypeTraits< int8_t >
struct	TypeTraits< uint16_t >
struct	TypeTraits< uint32_t >
struct	TypeTraits< uint64_t >
struct	TypeTraits< uint8_t >
class	ValidationError
	Thrown in case of invalid parameters. More...

Enumerations
enum	DataType {   double_data = 4 + (0 << 3), int64_data = 4 + (2 << 3), uint64_data = 4 + (3 << 3), float_data = 3 + (0 << 3),   int32_data = 3 + (2 << 3), uint32_data = 3 + (3 << 3), half_data = 2 + (0 << 3), bfloat16_data = 2 + (1 << 3),   int16_data = 2 + (2 << 3), uint16_data = 2 + (3 << 3), int8_data = 1 + (2 << 3), uint8_data = 1 + (3 << 3) }
	type definition
enum	DataTypeLimit { dt_min_val, dt_max_val }
enum	StandardActivations : int {   identity = 0, relu = 1, tanh = 2, sigmoid = 3,   relu6 = 4 }
	Parameterless Activations that can be embedded to general kernels like inner product or convolution.
enum	CalculationsMode { train, predict }
	Operation mode of layers - inference of training.
enum	GemmOpMode { forward = 1, backward_filter = 2, backward_data = 3 }
	internal GEMM mode

Functions
bool	is_floating_point_data_type (DataType d)
	returns true of data is double, float, half or bfloat16 type
DataType	string_to_data_type (std::string const &s)
std::string	data_type_to_string (DataType dt)
std::string	data_type_to_opencl_numeric_limit (DataType dt, DataTypeLimit lmt)
std::string	data_type_to_opencl_type (DataType dt, bool io_type=false)
constexpr int	size_of_data_type (DataType d)
StandardActivations	activation_from_name (std::string const &name)
char const *	activation_to_name (StandardActivations act)
std::string	activation_equation (StandardActivations act, std::string const &variable)
std::string	activation_backward_equation (StandardActivations act, std::string const &dy, std::string const &y)
std::unique_ptr< Operator >	create_by_name (Context &ctx, std::string const &name, json::value const &parameters)
	Factory - generate operator by its name (type) with parameters needed.
std::ostream &	operator<< (std::ostream &o, Shape const &s)
Shape	broadcast (Shape const &ain, Shape const &bin)
	calculate numpy style broadcast shape
void	shrink_broadcast_ranges (std::vector< Shape > &shapes)
	Broadcast shapes numpy style and remove planes that can be merged. More...
std::ostream &	operator<< (std::ostream &out, TensorSpecs const &ts)
std::ostream &	operator<< (std::ostream &out, Tensor const &ts)
void	set_to_zero (Tensor &t, ExecutionContext const &e)
	Set value of t to zero.
void	set_to_constant (Tensor &t, double value, ExecutionContext const &e)
	Set to constant, value is casted to t.dtype()
void	set_to_urandom (Tensor &t, RandomState &state, float minv, float maxv, ExecutionContext const &e)
	set t values to uniform random values in range [minv,maxv), seed is updated
void	set_to_normal (Tensor &t, RandomState &state, float mean, float sigma, ExecutionContext const &e)
	set t values to normal distribution with mean and sigma), seed is updated
void	set_to_bernoulli (Tensor &t, RandomState &state, float p, ExecutionContext const &e)
	set t values to bernully distribution with mean and sigma), seed is updated

Variables
static constexpr int	max_tensor_dim = 5
	Maximal number of dimensions in tensor.
constexpr int	forward_data = 1
	internal flag
constexpr int	backward_data = 2
	internal flag
constexpr int	backward_param = 3
	internal flag

Detailed Description

Mane namespace.

Function Documentation

void dlprim::shrink_broadcast_ranges

(

std::vector< Shape > &

shapes

)

Broadcast shapes numpy style and remove planes that can be merged.

For example: shrink([2,3,4],[2,1,1]) -> [2,12],[2,1] shrink([2,3,4],[2,3,4]) -> [24],[24] shrink([2,3,4],[1]) -> [24],[1] shrink([2,3,4],[3,1]) -> [2,3,4],[1,3,1] shrink([2,3,4,5],[3,1,1]) -> [2,3,20],[1,3,1] shrink([2,3,4,5],[1,3,4,1]) -> [2,12,5],[1,12,1]

Referenced by dlprim::Shape::split_and_merge_over_axis().