create_dl_layer_permutationT_create_dl_layer_permutationCreateDlLayerPermutationCreateDlLayerPermutationcreate_dl_layer_permutation (Operator)

Name

create_dl_layer_permutationT_create_dl_layer_permutationCreateDlLayerPermutationCreateDlLayerPermutationcreate_dl_layer_permutation — Create a permutation layer.

Signature

Description

The operator create_dl_layer_permutationcreate_dl_layer_permutationCreateDlLayerPermutationCreateDlLayerPermutationcreate_dl_layer_permutation creates a permutation layer whose handle is returned in DLLayerPermutationDLLayerPermutationDLLayerPermutationDLLayerPermutationdllayer_permutation.

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.

The parameter PermutationPermutationPermutationpermutationpermutation determines the new order of the axes of DLLayerInputDLLayerInputDLLayerInputDLLayerInputdllayer_input, to which the input axes should be permuted.

PermutationPermutationPermutationpermutationpermutation has the form [index width, index height, index depth, index batch], where the indices are corresponding to the dimensions of the input. For example, [0, 1, 3, 2] leads to swapping the depth and the batch axes. Therefore, each index must be unique and be taken from the set .

Using a CPU device, for some values of PermutationPermutationPermutationpermutationpermutation the internal code can not be optimized which can lead to an increased runtime. In this case, the layer parameter 'fall_back_to_baseline'"fall_back_to_baseline""fall_back_to_baseline""fall_back_to_baseline""fall_back_to_baseline" is set to 'true'"true""true""true""true".

The following generic parameters GenParamNameGenParamNameGenParamNamegenParamNamegen_param_name and the corresponding values GenParamValueGenParamValueGenParamValuegenParamValuegen_param_value are supported:

'is_inference_output'"is_inference_output""is_inference_output""is_inference_output""is_inference_output":

Determines whether apply_dl_modelapply_dl_modelApplyDlModelApplyDlModelapply_dl_model will include the output of this layer in the dictionary DLResultBatchDLResultBatchDLResultBatchDLResultBatchdlresult_batch even without specifying this layer in OutputsOutputsOutputsoutputsoutputs ('true'"true""true""true""true") or not ('false'"false""false""false""false").

Default: 'false'"false""false""false""false"

Certain parameters of layers created using this operator create_dl_layer_permutationcreate_dl_layer_permutationCreateDlLayerPermutationCreateDlLayerPermutationcreate_dl_layer_permutation 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.

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

Example (HDevelop)

* Swap the batch and depth axes with a permutation layer.
create_dl_layer_input ('input_a', [1, 1, 4], ['input_type', 'const_val'], \
                       ['constant', 1.0], DLLayerInputA)
create_dl_layer_input ('input_b', [1, 1, 4], ['input_type', 'const_val'], \
                       ['constant', 2.0], DLLayerInputB)
create_dl_layer_concat ([DLLayerInputA, DLLayerInputB], 'concat', 'batch', \
                        [], [], DLLayerConcat)
create_dl_layer_permutation (DLLayerConcat, 'permute', [0,1,3,2], \
                             [], [], DLLayerPermute)
create_dl_layer_depth_max (DLLayerPermute, 'depth_max', 'value', \
                           [], [], _, DLLayerDepthMaxValue)
create_dl_model (DLLayerDepthMaxValue, DLModel)
* The expected output values in DLResultBatch.depth_max are [2.0,2.0,2.0,2.0]
query_available_dl_devices (['runtime'], ['cpu'], DLDeviceHandles)
set_dl_model_param (DLModel, 'device', DLDeviceHandles[0])
apply_dl_model (DLModel, dict{}, [], DLResultBatch)

Possible Predecessors

create_dl_layer_inputcreate_dl_layer_inputCreateDlLayerInputCreateDlLayerInputcreate_dl_layer_input, create_dl_layer_concatcreate_dl_layer_concatCreateDlLayerConcatCreateDlLayerConcatcreate_dl_layer_concat, create_dl_layer_reshapecreate_dl_layer_reshapeCreateDlLayerReshapeCreateDlLayerReshapecreate_dl_layer_reshape

Possible Successors

create_dl_layer_convolutioncreate_dl_layer_convolutionCreateDlLayerConvolutionCreateDlLayerConvolutioncreate_dl_layer_convolution, create_dl_layer_densecreate_dl_layer_denseCreateDlLayerDenseCreateDlLayerDensecreate_dl_layer_dense, create_dl_layer_reshapecreate_dl_layer_reshapeCreateDlLayerReshapeCreateDlLayerReshapecreate_dl_layer_reshape

See also

create_dl_layer_reshapecreate_dl_layer_reshapeCreateDlLayerReshapeCreateDlLayerReshapecreate_dl_layer_reshape

Module

Deep Learning Professional