license import { add , fill , FusedBatchNorm , FusedBatchNormAttrs , FusedBatchNormInputs , KernelConfig , mul , Rank , rsqrt , scalar , sub , Tensor , Tensor4D , tidy } from '@tensorflow/tfjs' ; import { createTensorsTypeOpAttr , NodeJSKernelBackend } from '../nodejs_kernel_backend' ; export const fusedBatchNormConfig = { kernelName : FusedBatchNorm , backendName : 'tensorflow' , kernelFunc : ( args ) => { const { x , mean , variance } = args . inputs as FusedBatchNormInputs ; let { scale , offset } = args . inputs as FusedBatchNormInputs ; const backend = args . backend as NodeJSKernelBackend ; const { varianceEpsilon } = args . attrs as { } as FusedBatchNormAttrs ; return tidy ( ( ) => { if ( ( mean as Tensor ) . rank > 1 ) { let inv = rsqrt ( add ( variance as Tensor , scalar ( varianceEpsilon ) ) ) ; if ( scale != null ) { inv = mul ( inv , scale as Tensor ) ; } const xNorm = mul ( sub ( x as Tensor , mean as Tensor ) , inv ) ; return offset != null ? add ( xNorm , offset as Tensor ) : xNorm ; } const dataFormat = 'NHWC' ; const depth = x . shape [ 3 ] ; const opAttrs = [ createTensorsTypeOpAttr ( 'T' , x . dtype ) , { name : 'epsilon' , type : backend . binding . TF_ATTR_FLOAT , value : varianceEpsilon } , { name : 'data_format' , type : backend . binding . TF_ATTR_STRING , value : dataFormat } , { name : 'is_training' , type : backend . binding . TF_ATTR_BOOL , value : false } , ] ; const numOutputs = 5 ; if ( scale == null ) { scale = fill < Rank . R1 > ( [ depth ] , 1 ) ; } if ( offset == null ) { offset = fill < Rank . R1 > ( [ depth ] , 0 ) ; } return backend . executeMultipleOutputs ( FusedBatchNorm , opAttrs , [ x , scale , offset , mean , variance ] , numOutputs ) [ 0 ] ; } ) ; } } ;