Enumerations
enum	odla_memory_layout { ODLA_CHANNELS_FIRST , ODLA_CHANNELS_LAST , ODLA_SIO , ODLA_SOI , ODLA_OIS , ODLA_IOS }
	Memory layout of data. More...

enum	odla_rnn_direction { ODLA_RNN_FORWARD , ODLA_RNN_REVERSE , ODLA_RNN_BIDIRECTIONAL }
	RNN direction. More...

enum	odla_rnn_outputs { ODLA_RNN_NO_STATE , ODLA_RNN_HIDDEN_STATE , ODLA_RNN_CELL_STATE , ODLA_RNN_HIDDEN_CELL_STATE }
	RNN outputs. More...

enum	odla_reduction_mode { ODLA_REDUCE_NONE , ODLA_REDUCE_MEAN , ODLA_REDUCE_SUM }

enum	odla_rnn_weight_format { ODLA_RNN_LDGOI , ODLA_RNN_LDIGO }
	RNN weight layout. More...

enum	odla_rnn_gate_order { ODLA_RNN_IOFC , ODLA_RNN_IFCO , ODLA_RNN_IFOC , ODLA_RNN_ICOF , ODLA_RNN_URO }
	RNN data layout. More...

enum	odla_tf_idf_mode { ODLA_TFIDF_TF , ODLA_TFIDF_IDF , ODLA_TFIDF_TFIDF }
	TF-IDF computation mode. More...

Functions
ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_AveragePool (odla_value input, odla_memory_layout input_layout, const odla_uint32 window_dims, const odla_uint32 strides, const odla_uint32 paddings_front, const odla_uint32 paddings_back, odla_bool padding_included, odla_value_shape output_dims, const odla_value_id value_id)
	Avgerage pooling. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Hardmax (odla_value input, odla_int32 axis, const odla_value_id value_id)
	Hardmax computation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_LRN (odla_value input, odla_memory_layout input_layout, odla_int32 window_size, odla_float32 alpha, odla_float32 beta, odla_float32 bias, const odla_value_id value_id)

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_BatchNormalization (odla_value input, odla_memory_layout input_layout, odla_value mean, odla_value var, odla_float32 epsilon, odla_value scale, odla_value offset, odla_float32 scalar_scale, odla_float32 scalar_offset, const odla_value_id value_id)
	Compute batch normalization. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Celu (odla_value input, odla_float32 alpha, const odla_value_id value_id)
	Celu activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Conv (odla_value input, odla_memory_layout input_layout, odla_uint32 group, odla_value kernel, odla_memory_layout kernel_layout, const odla_uint32 strides, const odla_uint32 dilations, const odla_uint32 paddings_front, const odla_uint32 paddings_back, odla_value bias, odla_value_shape output_dims, const odla_value_id value_id)
	N-dimensional Convolution. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_DeConv (odla_value input, odla_memory_layout input_layout, odla_uint32 group, odla_value kernel, odla_memory_layout kernel_layout, const odla_uint32 strides, const odla_uint32 dilations, const odla_uint32 paddings_front, const odla_uint32 paddings_back, odla_value bias, odla_value_shape output_dims, const odla_value_id value_id)
	N-dimensional Deconvolution. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Elu (odla_value input, odla_float32 alpha, const odla_value_id value_id)
	Elu activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Gelu (odla_value input, odla_bool use_approx, const odla_value_id value_id)
	Gelu activation. More...

ODLA_API_EXPORT odla_values ODLA_API_CALL	odla_GRU (odla_value input, odla_rnn_weight_format weight_format, odla_rnn_gate_order gate_order, odla_value_shape weight_dims, odla_value W, odla_value R, odla_value B, odla_value sequence_lens, odla_value initial_h, odla_int32 hidden_size, odla_rnn_direction direction, odla_bool linear_before_reset, odla_rnn_outputs outputs, const odla_value_ids value_id)
	Compute a one-layer GRU. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_HardSigmoid (odla_value input, odla_float32 alpha, odla_float32 beta, const odla_value_id value_id)
	HardSigmoid activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_GroupNormalization (odla_value input, odla_memory_layout input_layout, odla_int32 groups, odla_float32 epsilon, odla_value scale, odla_value offset, odla_float32 scalar_scale, odla_float32 scalar_offset, const odla_value_id value_id)
	Group Normalization. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_InstanceNormalization (odla_value input, odla_memory_layout input_layout, odla_float32 epsilon, odla_value scale, odla_value offset, odla_float32 scalar_scale, odla_float32 scalar_offset, const odla_value_id value_id)
	Instance Normalization. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_LpNormalize (odla_value input, odla_int32 p, odla_memory_layout input_layout, odla_size_t axes_size, const odla_int32 *axes, odla_float32 epsilon, odla_value scale, const odla_value_id value_id)
	LpNormalize. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_LeakyRelu (odla_value input, odla_float32 alpha, const odla_value_id value_id)
	LeakyRelu activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_LogSoftmax (odla_value input, odla_int32 axis, const odla_value_id value_id)
	LogSoftmax activation. More...

ODLA_API_EXPORT odla_values ODLA_API_CALL	odla_LSTM (odla_value input, odla_rnn_weight_format weight_format, odla_rnn_gate_order gate_order, odla_value_shape weight_dims, odla_value W, odla_value R, odla_value B, odla_value sequence_lens, odla_value initial_h, odla_value initial_c, odla_value P, odla_int32 hidden_size, odla_rnn_direction direction, odla_rnn_outputs outputs, const odla_value_ids value_ids)
	LSTM. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_MaxPool (odla_value input, odla_memory_layout input_layout, const odla_uint32 window_dims, const odla_uint32 strides, const odla_uint32 paddings_front, const odla_uint32 paddings_back, odla_value_shape output_dims, const odla_value_id value_id)
	Max Pooling. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_NegativeLogLikeliHoodLoss (odla_value input, odla_value gt, odla_int32 ignored, odla_reduction_mode reduction, odla_value weight, odla_value_shape output_shape, odla_value_id value_id)
	Negative Log Likelihood Loss. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_NMS (odla_value boxes, odla_value scores, odla_uint32 max_num_outputs, odla_float32 iou_threshold, odla_float32 score_threshold, odla_value_type output_value_type, const odla_value_id value_id)
	Non-Max Suppression. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_PRelu (odla_value input, odla_value slope, const odla_value_id value_id)
	PRelu activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Relu (odla_value input, const odla_value_id value_id)
	Relu activation. More...

ODLA_API_EXPORT odla_values ODLA_API_CALL	odla_RNN (odla_value input, odla_rnn_weight_format weight_format, odla_value_shape weight_dims, odla_value W, odla_value R, odla_value B, odla_value sequence_lens, odla_value initial_h, odla_int32 hidden_size, odla_rnn_direction direction, odla_rnn_outputs outputs, const odla_value_ids value_id)
	Compute a one-layer RNN. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_ROIAlign (odla_value input, odla_memory_layout input_layout, odla_value rois, odla_int32 sampling_ratio, odla_float32 scale, odla_value_shape output_dim, const odla_value_id value_id)
	Region of Interest align. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_ROIMaxPool (odla_value input, odla_memory_layout input_layout, odla_value rois, odla_int32 win_h, odla_int32 win_w, odla_float32 scale, odla_value_shape output_dim, const odla_value_id value_id)
	ROI max pooling. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Selu (odla_value input, odla_float32 alpha, odla_float32 gamma, const odla_value_id value_id)
	Selu activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Shrink (odla_value input, odla_float32 bias, odla_float32 lambd, const odla_value_id value_id)
	Shrink activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Sigmoid (odla_value input, const odla_value_id value_id)
	Sigmoid activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Softmax (odla_value input, odla_int32 axis, const odla_value_id value_id)
	Softmax activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Softplus (odla_value input, const odla_value_id value_id)
	Softplus activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_Softsign (odla_value input, const odla_value_id value_id)
	Softsign activation. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_TFIDFVectorize (odla_value input, odla_int32 min_gram_length, odla_int32 max_gram_length, odla_int32 max_skip_count, odla_tf_idf_mode mode, odla_value pool, odla_value gram_counts, odla_value output_indices, odla_value weights, odla_value_shape output_shape, odla_value_id value_id)
	TF-IDF vectorization. More...

ODLA_API_EXPORT odla_value ODLA_API_CALL	odla_ThresholdedRelu (odla_value input, odla_float32 alpha, const odla_value_id value_id)
	ThresholdedRelu activation. More...

ODLA_API_EXPORT odla_values ODLA_API_CALL	odla_TopK (odla_value input, odla_uint32 K, odla_bool largest, odla_bool sorted, odla_uint32 axis, odla_value_type output_value_type, odla_value_type output_value_index_type, const odla_value_ids value_ids)
	Find Top-K elements. More...

ODLA_API_EXPORT odla_values ODLA_API_CALL	odla_PostProcess (odla_value orig_img_w, odla_value orig_img_h, odla_value bb13, odla_value bb26, odla_value bb52, const odla_value_id value_id)
	Yolov3 Post Process. More...

Detailed Description

This file defines the ODLA neural network related operators.

Definition in file odla_ops_nn.h.

Enumeration Type Documentation

◆ odla_memory_layout

enum odla_memory_layout

Memory layout of data.

Enumerator
ODLA_CHANNELS_FIRST	layout channel dimension prior to spatial dimension
ODLA_CHANNELS_LAST	layout channel dimension prior to spatial dimension
ODLA_SIO	spatial dimension, input and output channels
ODLA_SOI	spatial first, output and input channels
ODLA_OIS	output channls, input channels and spatial dimensions
ODLA_IOS	input channles, output channels and spatial dimensions

Definition at line 33 of file odla_ops_nn.h.

             {
  ODLA_CHANNELS_FIRST, 
  ODLA_CHANNELS_LAST,  
  ODLA_SIO,            
  ODLA_SOI,            
  ODLA_OIS, 
  ODLA_IOS, 
} odla_memory_layout;

◆ odla_reduction_mode

enum odla_reduction_mode

Definition at line 59 of file odla_ops_nn.h.

             {
  ODLA_REDUCE_NONE,
  ODLA_REDUCE_MEAN,
  ODLA_REDUCE_SUM,
} odla_reduction_mode;

◆ odla_rnn_direction

enum odla_rnn_direction

RNN direction.

Enumerator
ODLA_RNN_FORWARD	forward
ODLA_RNN_REVERSE	reverse
ODLA_RNN_BIDIRECTIONAL	both

Definition at line 45 of file odla_ops_nn.h.

             {
  ODLA_RNN_FORWARD,       
  ODLA_RNN_REVERSE,       
  ODLA_RNN_BIDIRECTIONAL, 
} odla_rnn_direction;

◆ odla_rnn_gate_order

enum odla_rnn_gate_order

RNN data layout.

Enumerator
ODLA_RNN_IOFC	input gate, output gate, forget gate, cell gate
ODLA_RNN_IFCO	input gate, forget gate, cell gate, output gate
ODLA_RNN_IFOC	input gate, forget gate, output gate, cell gate
ODLA_RNN_ICOF	input gate, cell gate, output gate, forget gate
ODLA_RNN_URO	update gate, reset gate, hidden gate

Definition at line 75 of file odla_ops_nn.h.

             {
  ODLA_RNN_IOFC, 
  ODLA_RNN_IFCO, 
  ODLA_RNN_IFOC, 
  ODLA_RNN_ICOF, 
  ODLA_RNN_URO,  
} odla_rnn_gate_order;

◆ odla_rnn_outputs

enum odla_rnn_outputs

RNN outputs.

Enumerator
ODLA_RNN_NO_STATE	default output
ODLA_RNN_HIDDEN_STATE	output hidden state
ODLA_RNN_CELL_STATE	output cell state
ODLA_RNN_HIDDEN_CELL_STATE	output both hidden and cell states

Definition at line 52 of file odla_ops_nn.h.

             {
  ODLA_RNN_NO_STATE,          
  ODLA_RNN_HIDDEN_STATE,      
  ODLA_RNN_CELL_STATE,        
  ODLA_RNN_HIDDEN_CELL_STATE, 
} odla_rnn_outputs;

◆ odla_rnn_weight_format

enum odla_rnn_weight_format

RNN weight layout.

Enumerator
ODLA_RNN_LDGOI	layers, directions, num gates, output channels, input channels
ODLA_RNN_LDIGO	layers, directions, input_channels, num gates, output channels,

Definition at line 66 of file odla_ops_nn.h.

             {
  ODLA_RNN_LDGOI, 
  ODLA_RNN_LDIGO, 
} odla_rnn_weight_format;

◆ odla_tf_idf_mode

enum odla_tf_idf_mode

TF-IDF computation mode.

Enumerator
ODLA_TFIDF_TF	term frequency
ODLA_TFIDF_IDF	inverse document frequency
ODLA_TFIDF_TFIDF	the combination of TF and IDF

Definition at line 84 of file odla_ops_nn.h.

             {
  ODLA_TFIDF_TF,    
  ODLA_TFIDF_IDF,   
  ODLA_TFIDF_TFIDF, 
} odla_tf_idf_mode;

Function Documentation

◆ odla_AveragePool()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_AveragePool	(	odla_value	input,
		odla_memory_layout	input_layout,
		const odla_uint32 *	window_dims,
		const odla_uint32 *	strides,
		const odla_uint32 *	paddings_front,
		const odla_uint32 *	paddings_back,
		odla_bool	padding_included,
		odla_value_shape	output_dims,
		const odla_value_id	value_id
	)

Avgerage pooling.

AveragePool computes the average pooling across the input according to the window size, strides and paddings.

Parameters

input	the input value
input_layout	the memory layout of input
window_dims	the spatial dimension of window
strides	the stride along each spatial axis
paddings_front	paddings applied to the start of each spatial dimension
paddings_back	paddings applied to the end of each spatial dimension
padding_included	padded values included when computing average
output_dims	the optional output shape (can be undefined)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_BatchNormalization()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_BatchNormalization	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_value	mean,
		odla_value	var,
		odla_float32	epsilon,
		odla_value	scale,
		odla_value	offset,
		odla_float32	scalar_scale,
		odla_float32	scalar_offset,
		const odla_value_id	value_id
	)

Compute batch normalization.

BatchNormalization computes the batch normalization on input as y = (x - mean) / sqrt(var + epsilon). The result can then be scaled and biased. If scale is not null, it will be applied. Otherwise, scalar value scalar_scale will be applied. Similar to offset and scalar_offset.

Parameters

input	the input value
input_layout	the memory layout of input
mean	the mean value
var	the variance value
epsilon	the epsilon
scale	optional scale vector (can be NULL). If not NULL, it must be a vector with the same size as number of channels.
offset	optional offset vector (can be NULL). If not NULL, it must be a vector with the same size as number of channels.
scalar_scale	If `scale` is NULL, this value will be used.
scalar_offset	if `offset` is NULL, this value will be used.
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Celu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Celu	(	odla_value	input,
		odla_float32	alpha,
		const odla_value_id	value_id
	)

Celu activation.

Celu computes the celu activaion as y = x < 0 ? alpha * (exp(x / alpha) - 1) : x

Parameters

input	the input value
alpha	the coefficient
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Conv()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Conv	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_uint32	group,
		odla_value	kernel,
		odla_memory_layout	kernel_layout,
		const odla_uint32 *	strides,
		const odla_uint32 *	dilations,
		const odla_uint32 *	paddings_front,
		const odla_uint32 *	paddings_back,
		odla_value	bias,
		odla_value_shape	output_dims,
		const odla_value_id	value_id
	)

N-dimensional Convolution.

Conv computes convolution of input and kernel based on the strides, dialiations, padding and group. The result can be added with bias optionally.

Parameters

input	the input value
input_layout	the memory layout of input
group	number of groups input and output channels are divided into
kernel	the kernel value
kernel_layout	the memory layout of kernel
strides	the stride along each spatial axis
dilations	dilations (same number of values as `strides`)
paddings_front	paddings applied to the start of each spatial dim
paddings_back	paddings applied to the end of each spatial dim
bias	optional bias. NULL if not needed.
output_dims	the optional output shape (can be undefined)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_DeConv()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_DeConv	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_uint32	group,
		odla_value	kernel,
		odla_memory_layout	kernel_layout,
		const odla_uint32 *	strides,
		const odla_uint32 *	dilations,
		const odla_uint32 *	paddings_front,
		const odla_uint32 *	paddings_back,
		odla_value	bias,
		odla_value_shape	output_dims,
		const odla_value_id	value_id
	)

N-dimensional Deconvolution.

DeConv computes the deconvolution (transposed convolution) based on the strides, dialiations, padding and group. The result can be added with bias optionally.

Parameters

input	the input value
input_layout	the memory layout of input
group	number of groups input and output channels are divided into
kernel	the kernel value
kernel_layout	the storage layout of kernel in memory
strides	the stride along each spatial axis
dilations	dilations (same number of values as `strides`)
paddings_front	paddings applied to the start of each spatial dim
paddings_back	paddings applied to the end of each spatial dim
bias	optional bias. NULL if not needed.
output_dims	the optional output shape (can be undefined)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Elu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Elu	(	odla_value	input,
		odla_float32	alpha,
		const odla_value_id	value_id
	)

Elu activation.

Elu computes the elu activaion as y = x < 0 ? alpha * (exp(x) - 1) : x

Parameters

input	the input value
alpha	the coefficient
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Gelu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Gelu	(	odla_value	input,
		odla_bool	use_approx,
		const odla_value_id	value_id
	)

Gelu activation.

Gelu computes gaussian error linear unit activation as 0.5 * x *(1 + erf(x / sqrt(2)))

Parameters

input	the input value
use_approx	use the approximated version or not
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_GroupNormalization()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_GroupNormalization	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_int32	groups,
		odla_float32	epsilon,
		odla_value	scale,
		odla_value	offset,
		odla_float32	scalar_scale,
		odla_float32	scalar_offset,
		const odla_value_id	value_id
	)

Group Normalization.

GroupNormalization normalizes the features within channel groups.

Parameters

input	the input value
input_layout	the memory layout of input
groups	the number of groups
epsilon	the epsilon
scale	optional scale value (can be NULL)
offset	optional offset value (Default is NULL)
scalar_scale	If `scale` is NULL, this value will be used.
scalar_offset	if `offset` is NULL, this value will be used.
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_GRU()

ODLA_API_EXPORT odla_values ODLA_API_CALL odla_GRU	(	odla_value	input,
		odla_rnn_weight_format	weight_format,
		odla_rnn_gate_order	gate_order,
		odla_value_shape	weight_dims,
		odla_value	W,
		odla_value	R,
		odla_value	B,
		odla_value	sequence_lens,
		odla_value	initial_h,
		odla_int32	hidden_size,
		odla_rnn_direction	direction,
		odla_bool	linear_before_reset,
		odla_rnn_outputs	outputs,
		const odla_value_ids	value_id
	)

Compute a one-layer GRU.

GRU computes one-layer GRU. The output values are determined by outputs.

Parameters

input	the input value
weight_format	the data layout of weights
gate_order	the order of gates in weights
weight_dims	the dims of weights
W	the weights for gates. Assuming layout of [in, out, forget, cell]
R	the recurrence weight
B	the optional bias
sequence_lens	the sequence length
initial_h	the initial value of the hidden(can be NULL)
hidden_size	the size of hidden neurons
direction	the directon of network
linear_before_reset	Whether to apply linear transformation before multiplying by the output of reset gate
outputs	speicify needed option outputs
value_ids	an array of values ids (can be NULL)

Returns: odla_value

◆ odla_Hardmax()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Hardmax	(	odla_value	input,
		odla_int32	axis,
		const odla_value_id	value_id
	)

Hardmax computation.

Hardmax computes one-hot tensor for the location of the highest value alone with specified axis.

Parameters

input	the input value
axis	the dimension alone with hardmax will perform on
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_HardSigmoid()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_HardSigmoid	(	odla_value	input,
		odla_float32	alpha,
		odla_float32	beta,
		const odla_value_id	value_id
	)

HardSigmoid activation.

HardSigmoid computes the hard sigmoid function as y = max(0, min( 1, alpha * x

beta)).

Parameters

input	the input value
alpha	value of alpha
beta	value of beta
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_InstanceNormalization()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_InstanceNormalization	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_float32	epsilon,
		odla_value	scale,
		odla_value	offset,
		odla_float32	scalar_scale,
		odla_float32	scalar_offset,
		const odla_value_id	value_id
	)

Instance Normalization.

InstanceNormalization computes the instance normalization as
y = scale * (x - mean) / sqrt(variance + epsilon) + bias
mean and var are per channel per instance.

Parameters

input	the input value
input_layout	the memory layout of input
epsilon	the epsilon
scale	optional scale value (can be NULL)
offset	optional offset value (Default is NULL)
scalar_scale	If `scale` is NULL, this value will be used.
scalar_offset	if `offset` is NULL, this value will be used.
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_LeakyRelu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_LeakyRelu	(	odla_value	input,
		odla_float32	alpha,
		const odla_value_id	value_id
	)

LeakyRelu activation.

LeakyRelu computes the LeakyRelu activation as y = x < 0 ? alpha * x : x

Parameters

input	the input value
alpha	the alpha value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_LogSoftmax()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_LogSoftmax	(	odla_value	input,
		odla_int32	axis,
		const odla_value_id	value_id
	)

LogSoftmax activation.

LogSoftmax computes the log softmax activation as y = x - log(reduce_sum(exp(x), axis))

Parameters

input	the input value
axis	the dimension it will perform on
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_LpNormalize()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_LpNormalize	(	odla_value	input,
		odla_int32	p,
		odla_memory_layout	input_layout,
		odla_size_t	axes_size,
		const odla_int32 *	axes,
		odla_float32	epsilon,
		odla_value	scale,
		const odla_value_id	value_id
	)

LpNormalize.

LpNormalize currently supports L1Normalize and L2Normalize L1Normalize computes the L1 normalization as
yi = scale * ( xi / sum(abs(xi) + epsilon) )
. L2Normalize computes the L2 normalization as
yi = scale * ( xi / sqrt(sum(xi^2) + epsilon) )
.

Parameters

input	the input value
p	the exponent value in the norm formulation
input_layout	the memory layout of input
axes_size	nubmer of axes
axes	the axes along which to normalize
epsilon	the epsilon
scale	optional scale value (can be NULL)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_LRN()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_LRN	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_int32	window_size,
		odla_float32	alpha,
		odla_float32	beta,
		odla_float32	bias,
		const odla_value_id	value_id
	)

Local Response Normalization normalizes over local regions on input The result can then be scaled and biased.

Parameters

input	the input value.
input_layout	the memory layout of input.
window_size	the size of the local region.
alpha	the scalaing factor.
beta	the exponent.
bias	the offset.
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_LSTM()

ODLA_API_EXPORT odla_values ODLA_API_CALL odla_LSTM	(	odla_value	input,
		odla_rnn_weight_format	weight_format,
		odla_rnn_gate_order	gate_order,
		odla_value_shape	weight_dims,
		odla_value	W,
		odla_value	R,
		odla_value	B,
		odla_value	sequence_lens,
		odla_value	initial_h,
		odla_value	initial_c,
		odla_value	P,
		odla_int32	hidden_size,
		odla_rnn_direction	direction,
		odla_rnn_outputs	outputs,
		const odla_value_ids	value_ids
	)

LSTM.

LSTM computes one-layer LSTM.

Parameters

input	the input value
weight_format	the data layout of weights
gate_order	the order of gates in weights
weight_dims	the dims of weights
W	the weights for gates. Assuming layout of [in, out, forget, cell]
R	the recurrence weight
B	the optional bias
sequence_lens	the lengths of the sequences in a batch
initial_h	the initial value of the hidden(can be NULL)
initial_c	the initial value of the cell(can be NULL)
P	the weight tensor for peepholes(can be NULL).
hidden_size	the size of hidden neurons
direction	the directon of network
outputs	speicify needed option outputs
value_ids	an array of value ids (can be NULL)

Returns: odla_values

◆ odla_MaxPool()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_MaxPool	(	odla_value	input,
		odla_memory_layout	input_layout,
		const odla_uint32 *	window_dims,
		const odla_uint32 *	strides,
		const odla_uint32 *	paddings_front,
		const odla_uint32 *	paddings_back,
		odla_value_shape	output_dims,
		const odla_value_id	value_id
	)

Max Pooling.

MaxPool computes the max pooling across the input according to the window size, strides and paddings.

Parameters

input	the input value
input_layout	the memory layout of input
window_dims	the spatial dimension of window
strides	the stride along each spatial axis
paddings_front	paddings applied to the start of each spatial dim
paddings_back	paddings applied to the end of each spatial dim
output_dims	the optional output shape (can be undefined)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_NegativeLogLikeliHoodLoss()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_NegativeLogLikeliHoodLoss	(	odla_value	input,
		odla_value	gt,
		odla_int32	ignored,
		odla_reduction_mode	reduction,
		odla_value	weight,
		odla_value_shape	output_shape,
		odla_value_id	value_id
	)

Negative Log Likelihood Loss.

NegativeLogLikeliHoodLoss computes negative log likelihood loss.

Parameters

input	the input value
gt	the ground truth (label) value
ignored	the ignored label value
reduction	optional reduction
weight	optional weight
output_shape	output shape
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_NMS()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_NMS	(	odla_value	boxes,
		odla_value	scores,
		odla_uint32	max_num_outputs,
		odla_float32	iou_threshold,
		odla_float32	score_threshold,
		odla_value_type	output_value_type,
		const odla_value_id	value_id
	)

Non-Max Suppression.

NMS applies the Non-max suppression on boxes that have overlap above iou_threshold.

Parameters

boxes	the coordinates of boxes
scores	the scores
max_num_outputs	the maximum number of outputs
iou_threshold	the threshold of IoU
score_threhold	the threshold of scores
output_value_type	the output value type (an integer value type)
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_PostProcess()

ODLA_API_EXPORT odla_values ODLA_API_CALL odla_PostProcess	(	odla_value	orig_img_w,
		odla_value	orig_img_h,
		odla_value	bb13,
		odla_value	bb26,
		odla_value	bb52,
		const odla_value_id	value_id
	)

Yolov3 Post Process.

PostProcess Return Selected Info (cx, cy, w, h, pred_cls) of Each Class

Parameters

orig_img_w	the width of original image
orig_img_h	the height of original image
bb13	BBoxes 13 x 13
bb26	BBoxes 26 x 26
bb52	BBoxes 52 x 52
value_id	a unique value id (can be NULL)

Returns: odla_values

◆ odla_PRelu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_PRelu	(	odla_value	input,
		odla_value	slope,
		const odla_value_id	value_id
	)

PRelu activation.

PRelu computes the PRelu activation as y = x < 0 ? slope * x : x

Parameters

input	the input value
slope	the slope value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Relu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Relu	(	odla_value	input,
		const odla_value_id	value_id
	)

Relu activation.

Relu computes the relu activation as y = x < 0 : 0 ? x

Parameters

input	the input value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_RNN()

ODLA_API_EXPORT odla_values ODLA_API_CALL odla_RNN	(	odla_value	input,
		odla_rnn_weight_format	weight_format,
		odla_value_shape	weight_dims,
		odla_value	W,
		odla_value	R,
		odla_value	B,
		odla_value	sequence_lens,
		odla_value	initial_h,
		odla_int32	hidden_size,
		odla_rnn_direction	direction,
		odla_rnn_outputs	outputs,
		const odla_value_ids	value_id
	)

Compute a one-layer RNN.

RNN computes one-layer Recurrent Neuron Network. The output values are determined by outputs.

Parameters

input	the input value
weight_format	the data layout of weights
weight_dims	the dims of weights
W	the weights for gates. Assuming layout of [in, out, forget, cell]
R	the recurrence weight
B	the optional bias
sequence_lens	the sequence length
initial_h	the initial value of the hidden(can be NULL)
hidden_size	the size of hidden neurons
direction	the directon of network
outputs	speicify needed option outputs
value_ids	an array of values ids (can be NULL)

Returns: odla_value

◆ odla_ROIAlign()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_ROIAlign	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_value	rois,
		odla_int32	sampling_ratio,
		odla_float32	scale,
		odla_value_shape	output_dim,
		const odla_value_id	value_id
	)

Region of Interest align.

ROIAlign computes the corresponding data from input using rois via bilinear interpolation.

Parameters

input	the input value
input_layout	the memory layout of input
rois	the ROIs to pool. Dims of [num of ROIs, 4]
sampling_ratio	number of sampling points in the interpolation grid
scale	the scale factor to translate the ROI coordinates when pooling
output_dim	output dimension
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_ROIMaxPool()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_ROIMaxPool	(	odla_value	input,
		odla_memory_layout	input_layout,
		odla_value	rois,
		odla_int32	win_h,
		odla_int32	win_w,
		odla_float32	scale,
		odla_value_shape	output_dim,
		const odla_value_id	value_id
	)

ROI max pooling.

ROIMaxPool Computes corresponding data from input using rois via max pooling.

Parameters

input	the input value
input_layout	the memory layout of input
rois	the ROIs to pool. Dims of [num of ROIs, 4]
win_h	the height of the pooling window
win_w	the width of the pooling window
scale	the scale factor to translate the ROI coordinates when pooling
output_dim	output dimension
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Selu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Selu	(	odla_value	input,
		odla_float32	alpha,
		odla_float32	gamma,
		const odla_value_id	value_id
	)

Selu activation.

Selu computes the selu activation as y = x <= 0 ? gamma * (alpha * e^x - alpha) : gamma * x

Parameters

input	the input value
alpha	the alpha value
gamma	the gamma value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Shrink()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Shrink	(	odla_value	input,
		odla_float32	bias,
		odla_float32	lambd,
		const odla_value_id	value_id
	)

Shrink activation.

Shrink computes the function if x < -lambd, y = x + bias; if x > lambd, y = x - bias; Otherwise, y = 0.

Parameters

input	the input value
bias	value of bias
lambd	value of lambd
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Sigmoid()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Sigmoid	(	odla_value	input,
		const odla_value_id	value_id
	)

Sigmoid activation.

Sigmoid computes the sigmoid activation as y = 1 / (1 + exp(-x))

Parameters

input	the input value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Softmax()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Softmax	(	odla_value	input,
		odla_int32	axis,
		const odla_value_id	value_id
	)

Softmax activation.

Softmax computes the softmax activation as y = exp(x) / reduce_sum(exp(x), axis)

Parameters

input	the input value
axis	the dimension it will perform on
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Softplus()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Softplus	(	odla_value	input,
		const odla_value_id	value_id
	)

Softplus activation.

Softplus computes the Softplus activation as y = ln(exp(x) + 1)

Parameters

input	the input value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_Softsign()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_Softsign	(	odla_value	input,
		const odla_value_id	value_id
	)

Softsign activation.

Softsign computes the Softsign activation as y = x / (1 + |x|)

Parameters

input	the input value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_TFIDFVectorize()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_TFIDFVectorize	(	odla_value	input,
		odla_int32	min_gram_length,
		odla_int32	max_gram_length,
		odla_int32	max_skip_count,
		odla_tf_idf_mode	mode,
		odla_value	pool,
		odla_value	gram_counts,
		odla_value	output_indices,
		odla_value	weights,
		odla_value_shape	output_shape,
		odla_value_id	value_id
	)

TF-IDF vectorization.

TFIDF vectorizes input sequence into a vector.

Parameters

input	the input value
min_gram_length	the minimum gram length to extract
max_gram_length	the maximum gram length to extract
max_skip_count	the maximum items to be skipped
mode	the weighting metric to be used
pool	the grams pool
gram_counts	zero-based starting indices of n-grams in the pool
output_indices	the mapping of gram indices to output tensor
weights	optional weights used when mode is ODLA_TFIDF_IDF or ODLA_TFIDF_TFIDF
output_shape	shape of output
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_ThresholdedRelu()

ODLA_API_EXPORT odla_value ODLA_API_CALL odla_ThresholdedRelu	(	odla_value	input,
		odla_float32	alpha,
		const odla_value_id	value_id
	)

ThresholdedRelu activation.

ThresholdedRelu computes the ThresholdedRelu activation as y = x > alpha ? x : 0

Parameters

input	the input value
alpha	the alpha value
value_id	a unique value id (can be NULL)

Returns: odla_value

◆ odla_TopK()

ODLA_API_EXPORT odla_values ODLA_API_CALL odla_TopK	(	odla_value	input,
		odla_uint32	K,
		odla_bool	largest,
		odla_bool	sorted,
		odla_uint32	axis,
		odla_value_type	output_value_type,
		odla_value_type	output_value_index_type,
		const odla_value_ids	value_ids
	)

Find Top-K elements.

TopK returns the top-K largest or smallest elements and index

Parameters

input	the input value
K	the number of top values to be returned
largest	indicates largest (true) or smallest values to retrieve
sorted	indicates if the results are sorted or in original order
dimension	on which to do the sort
output_value_type	the output 0 type
output_value_index_type	the output 1 type
value_ids	an array of value ids (can be NULL)

Returns: odla_values

Enumerations

Functions

Detailed Description

Enumeration Type Documentation

◆ odla_memory_layout

◆ odla_reduction_mode

◆ odla_rnn_direction

◆ odla_rnn_gate_order

◆ odla_rnn_outputs

◆ odla_rnn_weight_format

◆ odla_tf_idf_mode

Function Documentation

◆ odla_AveragePool()

◆ odla_BatchNormalization()

◆ odla_Celu()

◆ odla_Conv()

◆ odla_DeConv()

◆ odla_Elu()

◆ odla_Gelu()

◆ odla_GroupNormalization()

◆ odla_GRU()

◆ odla_Hardmax()

◆ odla_HardSigmoid()

◆ odla_InstanceNormalization()

◆ odla_LeakyRelu()

◆ odla_LogSoftmax()

◆ odla_LpNormalize()

◆ odla_LRN()

◆ odla_LSTM()

◆ odla_MaxPool()

◆ odla_NegativeLogLikeliHoodLoss()

◆ odla_NMS()

◆ odla_PostProcess()

◆ odla_PRelu()

◆ odla_Relu()

◆ odla_RNN()

◆ odla_ROIAlign()

◆ odla_ROIMaxPool()

◆ odla_Selu()

◆ odla_Shrink()

◆ odla_Sigmoid()

◆ odla_Softmax()

◆ odla_Softplus()

◆ odla_Softsign()

◆ odla_TFIDFVectorize()

◆ odla_ThresholdedRelu()

◆ odla_TopK()