license import { FusedBatchNorm , FusedBatchNormAttrs , FusedBatchNormInputs , KernelConfig , KernelFunc , TensorInfo , TypedArray , util } from '@tensorflow/tfjs-core' ; import { MathBackendCPU } from '../backend_cpu' ; import { assertNotComplex } from '../cpu_util' ; export function batchNorm ( args ) { const { inputs , backend , attrs } = args ; const { x , scale , offset , mean , variance } = inputs ; util . assert ( mean . shape . length === variance . shape . length , ( ) => 'Batch normalization gradient requires mean and variance to have ' + 'equal ranks.' ) ; util . assert ( offset == null || mean . shape . length === offset . shape . length , ( ) => 'Batch normalization gradient requires mean and offset to have ' + 'equal ranks.' ) ; util . assert ( scale == null || mean . shape . length === scale . shape . length , ( ) => 'Batch normalization gradient requires mean and scale to have ' + 'equal ranks.' ) ; assertNotComplex ( [ x , mean , variance , scale , offset ] , 'batchNorm' ) ; let { varianceEpsilon } = attrs ; if ( varianceEpsilon == null ) { varianceEpsilon = 0.001 ; } const xVals = backend . data . get ( x . dataId ) . values as TypedArray ; const mVals = backend . data . get ( mean . dataId ) . values as TypedArray ; const varVals = backend . data . get ( variance . dataId ) . values as TypedArray ; const sVals = scale ? backend . data . get ( scale . dataId ) . values as TypedArray : new Float32Array ( [ 1 ] ) ; const offVals = offset ? backend . data . get ( offset . dataId ) . values as TypedArray : new Float32Array ( [ 0 ] ) ; const outVals = new Float32Array ( xVals . length ) ; const offValsLength = offVals . length ; const sValsLength = sVals . length ; const varValsLength = varVals . length ; const mValsLength = mVals . length ; let offi = 0 ; let mi = 0 ; let si = 0 ; let vi = 0 ; for ( let i = 0 ; i < xVals . length ; ++ i ) { outVals [ i ] = offVals [ offi ++ ] + ( xVals [ i ] - mVals [ mi ++ ] ) * sVals [ si ++ ] / Math . sqrt ( varVals [ vi ++ ] + varianceEpsilon ) ; if ( offi >= offValsLength ) { offi = 0 ; } if ( mi >= mValsLength ) { mi = 0 ; } if ( si >= sValsLength ) { si = 0 ; } if ( vi >= varValsLength ) { vi = 0 ; } } return backend . makeTensorInfo ( x . shape , x . dtype , outVals ) ; } export const batchNormConfig = { kernelName : FusedBatchNorm , backendName : 'cpu' , kernelFunc : batchNorm as { } as KernelFunc , } ;