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BatchNorm.ts
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BatchNorm.ts
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/**
* @license
* Copyright 2020 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the 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: {
inputs: FusedBatchNormInputs,
backend: MathBackendCPU,
attrs: FusedBatchNormAttrs
}): TensorInfo {
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: KernelConfig = {
kernelName: FusedBatchNorm,
backendName: 'cpu',
kernelFunc: batchNorm as unknown as KernelFunc,
};