gx

chenyc

2025-06-12 7b72ac13a83764a662159d4a49b7fffb90476ecb

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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 { LRNGrad, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function lRNGrad(args) {
    const { inputs, backend, attrs } = args;
    const { x, y, dy } = inputs;
    const { depthRadius, bias, alpha, beta } = attrs;
    assertNotComplex(dy, 'LRNGrad');
    const dySize = util.sizeFromShape(dy.shape);
    const channels = dy.shape[3];
    const dyValues = backend.data.get(dy.dataId).values;
    const xValues = backend.data.get(x.dataId).values;
    const yValues = backend.data.get(y.dataId).values;
    const result = new Float32Array(dySize);
    const size = dySize;
    for (let offset = 0; offset < size; offset++) {
        const currentChannel = offset % channels;
        const depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);
        const depthEnd = (offset - currentChannel) +
            Math.min(channels, currentChannel + depthRadius + 1);
        let norm = 0;
        for (let k = depthBegin; k < depthEnd; k++) {
            norm += Math.pow(xValues[k], 2);
        }
        norm = alpha * norm + bias;
        for (let k = depthBegin; k < depthEnd; k++) {
            let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm;
            if (offset === k) {
                dyi += Math.pow(norm, -beta);
            }
            dyi *= dyValues[offset];
            result[k] += dyi;
        }
    }
    return backend.makeTensorInfo(dy.shape, x.dtype, result);
}
// tslint:disable-next-line: variable-name
export const LRNGradConfig = {
    kernelName: LRNGrad,
    backendName: 'cpu',
    kernelFunc: lRNGrad
};
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