GstAnalytics.Tensor¶

Fields¶

Name	Type	Access	Description
data	`Gst.Buffer`	r/w	`Gst.Buffer` holding tensor data
data_type	`GstAnalytics.TensorDataType`	r/w	`GstAnalytics.TensorDataType` of tensor data
dims	[`int`]	r/w	number of tensor dimensions
dims_order	`GstAnalytics.TensorDimOrder`	r/w	Indicate tensor elements layout in memory.
id	`int`	r/w	semantically identify the contents of the tensor
layout	`GstAnalytics.TensorLayout`	r/w	Indicate tensor layout
num_dims	`int`	r/w	number of tensor dimensions

Methods¶

class	`alloc` (num_dims)
class	`data_type_get_name` (data_type)
class	`new_simple` (id, data_type, data, dims_order, dims)
	`check_type` (data_type, order, dims)
	`copy` ()
	`free` ()
	`get_dims` ()
	`set_simple` (id, data_type, data, dims_order, dims)

Details¶

class GstAnalytics.Tensor¶

Hold tensor data

New in version 1.26.

classmethod alloc(num_dims)¶

Parameters:: num_dims (int) – Number of dimension of the tensors
Returns:: tensor allocated
Return type:: GstAnalytics.Tensor

Allocate a tensor with num_dims dimensions.

New in version 1.26.

classmethod data_type_get_name(data_type)¶

Parameters:: data_type (GstAnalytics.TensorDataType) – a GstAnalytics.TensorDataType
Returns:: a constant string with the name of the data type
Return type:: str

Get a string version of the data type

New in version 1.28.

classmethod new_simple(id, data_type, data, dims_order, dims)¶

Parameters:

id (int) – semantically identify the contents of the tensor
data_type (GstAnalytics.TensorDataType) – GstAnalytics.TensorDataType of tensor data
data (Gst.Buffer) – Gst.Buffer holding tensor data
dims_order (GstAnalytics.TensorDimOrder) – Indicate tensor dimension indexing order
dims ([int]) – size of tensor in each dimension. A value of 0 means the dimension is dynamic.

Returns:

A newly allocated GstAnalytics.Tensor

Return type:

GstAnalytics.Tensor

Allocates a new GstAnalytics.Tensor of dims_order ROW_MAJOR or COLUMN_MAJOR and with an interleaved layout.

For example, a two-dimensional tensor with 32 rows and 4 columns, dims would be the two element array [32, 4].

New in version 1.26.

check_type(data_type, order, dims)¶

Parameters:

data_type (GstAnalytics.TensorDataType) – The data type of the tensor
order (GstAnalytics.TensorDimOrder) – The order of the tensor to read from the memory
dims ([int] or None) – An optional array of dimensions, where GObject.G_MAXSIZE means ANY.

Returns:

True if the GstAnalytics.Tensor has the reading order from the memory matching order, dimensions matching num_dims, data type matching data_type Otherwise False will be returned.

Return type:

bool

Validate the tensor whether it mathces the reading order, dimensions and the data type. Validate whether the Gst.Buffer has enough size to hold the tensor data.

New in version 1.28.

copy()¶

Returns:: a new GstAnalytics.Tensor
Return type:: GstAnalytics.Tensor or None

Create a copy of self.

New in version 1.26.

free()¶: Free tensor

New in version 1.26.

get_dims()¶

Returns:: The dims array form the tensor
Return type:: [int]

Gets the dimensions of the tensor.

New in version 1.26.

set_simple(id, data_type, data, dims_order, dims)¶

Parameters:

id (int) – semantically identify the contents of the tensor
data_type (GstAnalytics.TensorDataType) – GstAnalytics.TensorDataType of tensor data
data (Gst.Buffer) – Gst.Buffer holding tensor data
dims_order (GstAnalytics.TensorDimOrder) – Indicate tensor dimension indexing order
dims ([int]) – size of tensor in each dimension. A value of 0 means the dimension is dynamic.

Returns:

True if it coudl be set correctly

Return type:

bool

Sets the content of a GstAnalytics.Tensor of dims_order ROW_MAJOR or COLUMN_MAJOR and with an interleaved layout. The GstAnalytics.Tensor must have exactly num_dims.

For example, a two-dimensional tensor with 32 rows and 4 columns, dims would be the two element array [32, 4].

New in version 1.28.