license import { ENGINE } from '../engine' ; import { FusedBatchNorm , FusedBatchNormAttrs , FusedBatchNormInputs } from '../kernel_names' ; import { NamedAttrMap } from '../kernel_registry' ; import { Tensor , Tensor1D , Tensor4D } from '../tensor' ; import { NamedTensorMap } from '../tensor_types' ; import { convertToTensor } from '../tensor_util_env' ; import { Rank , TensorLike } from '../types' ; import * as util from '../util' ; import { xAs4D } from './batchnorm_util' ; import { op } from './operation' ; import { reshape } from './reshape' ; param x param mean param variance param offset param scale param varianceEpsilon doc function batchNorm_ < R extends Rank > ( x , mean , variance , offset ? , scale ? , varianceEpsilon ? ) { if ( varianceEpsilon == null ) { varianceEpsilon = 0.001 ; } const $x = convertToTensor ( x , 'x' , 'batchNorm' ) ; const $mean = convertToTensor ( mean , 'mean' , 'batchNorm' ) ; const $variance = convertToTensor ( variance , 'variance' , 'batchNorm' ) ; let $scale ; if ( scale != null ) { $scale = convertToTensor ( scale , 'scale' , 'batchNorm' ) ; } let $offset ; if ( offset != null ) { $offset = convertToTensor ( offset , 'offset' , 'batchNorm' ) ; } util . assert ( $mean . rank === $variance . rank , ( ) => 'Batch normalization gradient requires mean and variance to have ' + 'equal ranks.' ) ; util . assert ( $offset == null || $mean . rank === $offset . rank , ( ) => 'Batch normalization gradient requires mean and offset to have ' + 'equal ranks.' ) ; util . assert ( $scale == null || $mean . rank === $scale . rank , ( ) => 'Batch normalization gradient requires mean and scale to have ' + 'equal ranks.' ) ; const x4D = xAs4D ( $x ) ; const inputs = { x : x4D , scale : $scale , offset : $offset , mean : $mean , variance : $variance } ; const attrs = { varianceEpsilon } ; const res = ENGINE . runKernel ( FusedBatchNorm , inputs as { } as NamedTensorMap , attrs as { } as NamedAttrMap ) as Tensor < R > ; return reshape ( res , $x . shape ) ; } export const batchNorm = op ( { batchNorm_ } ) ;