create_dl_layer_depth_to_spaceT_create_dl_layer_depth_to_spaceCreateDlLayerDepthToSpaceCreateDlLayerDepthToSpacecreate_dl_layer_depth_to_space (Operator)

Name

create_dl_layer_depth_to_spaceT_create_dl_layer_depth_to_spaceCreateDlLayerDepthToSpaceCreateDlLayerDepthToSpacecreate_dl_layer_depth_to_space — Create a depth to space layer.

Signature

create_dl_layer_depth_to_space( : : DLLayerInput, LayerName, BlockSize, Mode, GenParamName, GenParamValue : DLLayerDepthToSpace)

Description

The operator create_dl_layer_depth_to_spacecreate_dl_layer_depth_to_spaceCreateDlLayerDepthToSpaceCreateDlLayerDepthToSpacecreate_dl_layer_depth_to_space creates a depth to space layer whose handle is returned in DLLayerDepthToSpaceDLLayerDepthToSpaceDLLayerDepthToSpaceDLLayerDepthToSpacedllayer_depth_to_space.

The parameter DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input determines the feeding input layer and expects the layer handle as value.

The parameter LayerNameLayerNameLayerNamelayerNamelayer_name sets an individual layer name. Note that if creating a model using create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model each layer of the created network must have a unique name.

This layer rearranges the elements of the feeding tensor of shape to a tensor of shape . Thereby can be considered an upscale factor, which is set with BlockSizeBlockSizeBlockSizeblockSizeblock_size.

The output element is mapped from the input element .

With ModeModeModemodemode the ordering in the output tensor is set. Currently only the 'column_row_depth'"column_row_depth""column_row_depth""column_row_depth""column_row_depth" order described above is available.

Certain parameters of layers created using this operator create_dl_layer_depth_to_spacecreate_dl_layer_depth_to_spaceCreateDlLayerDepthToSpaceCreateDlLayerDepthToSpacecreate_dl_layer_depth_to_space can be set and retrieved using further operators. The following tables give an overview, which parameters can be set using set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and which ones can be retrieved using get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param or get_dl_layer_paramget_dl_layer_paramGetDlLayerParamGetDlLayerParamget_dl_layer_param. Note, the operators set_dl_model_layer_paramset_dl_model_layer_paramSetDlModelLayerParamSetDlModelLayerParamset_dl_model_layer_param and get_dl_model_layer_paramget_dl_model_layer_paramGetDlModelLayerParamGetDlModelLayerParamget_dl_model_layer_param require a model created by create_dl_modelcreate_dl_modelCreateDlModelCreateDlModelcreate_dl_model.

Layer Parameters	`set`	`get`
'input_layer'"input_layer""input_layer""input_layer""input_layer" (`DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input`)		`x`
'name'"name""name""name""name" (`LayerNameLayerNameLayerNamelayerNamelayer_name`)	`x`	`x`
'block_size'"block_size""block_size""block_size""block_size" (`BlockSizeBlockSizeBlockSizeblockSizeblock_size`)		`x`
'shape'"shape""shape""shape""shape"		`x`
'type'"type""type""type""type"		`x`

Generic Layer Parameters	`set`	`get`
'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output"	`x`	`x`
'num_trainable_params'"num_trainable_params""num_trainable_params""num_trainable_params""num_trainable_params"		`x`

Execution Information

Multithreading type: reentrant (runs in parallel with non-exclusive operators).
Multithreading scope: global (may be called from any thread).
Processed without parallelization.

Parameters

DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input (input_control) dl_layer → (handle)

Feeding layer.

LayerNameLayerNameLayerNamelayerNamelayer_name (input_control) string → (string)

Name of the output layer.

BlockSizeBlockSizeBlockSizeblockSizeblock_size (input_control) number → (integer)

Block size (i.e., upscale factor).

Default: 3

ModeModeModemodemode (input_control) string → (string)

Ordering mode.

Default: 'column_row_depth' "column_row_depth" "column_row_depth" "column_row_depth" "column_row_depth"

GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name (input_control) attribute.name(-array) → (string)

Generic input parameter names.

Default: []

List of values: 'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output"

GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value (input_control) attribute.value(-array) → (string / integer / real)

Generic input parameter values.

Default: []

Suggested values: 'true'"true""true""true""true", 'false'"false""false""false""false"

DLLayerDepthToSpaceDLLayerDepthToSpaceDLLayerDepthToSpaceDLLayerDepthToSpacedllayer_depth_to_space (output_control) dl_layer → (handle)

Depth to space layer.

Example (HDevelop)

InputShape := [16, 16, 3]
Upscale := 2
*
create_dl_layer_input ('input', InputShape, [], [], DLLayerInput)
* Create a convolutional layer, that generates Upscale^2*NumChannel feature maps.
create_dl_layer_convolution (DLLayerInput, 'conv1', 3, 1, 1,\
                             Upscale * Upscale * InputShape[2],\
                             1, 'half_kernel_size', 'none',\
                             [],  [], DLLayerConvolution)
* Use a depth to space layer to combine Upscale^2 feature maps to upscale.
create_dl_layer_depth_to_space (DLLayerConvolution, 'upscaled', Upscale,\
                                'column_row_depth',[], [],\
                                DLLayerDepthToSpace)
* The output shape of DLLayerDepthToSpace is now [16*Upscale, 16*Upscale, 3].
create_dl_model (DLLayerDepthToSpace, DLModel)