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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 { ArgMax, backend_util, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
import { transpose } from './Transpose';
export function argMax(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis } = attrs;
    assertNotComplex(x, 'argMax');
    let axes = util.parseAxisParam(axis, x.shape);
    const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    const intermediateTensorInfos = [];
    if (permutedAxes != null) {
        $x = transpose({ inputs: { x }, backend, attrs: { perm: permutedAxes } });
        intermediateTensorInfos.push($x);
        axes = backend_util.getInnerMostAxes(axes.length, $x.shape.length);
    }
    axes = [axes[0]];
    backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes($x.shape, axes);
    const outSize = util.sizeFromShape(outShape);
    const vals = util.makeZerosTypedArray(outSize, 'int32');
    const reduceSize = util.sizeFromShape(reduceShape);
    const aVals = backend.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
        const offset = i * reduceSize;
        let max = aVals[offset];
        let maxIndex = 0;
        for (let j = 0; j < reduceSize; ++j) {
            const value = aVals[offset + j];
            if (value > max) {
                max = value;
                maxIndex = j;
            }
        }
        vals[i] = maxIndex;
    }
    intermediateTensorInfos.forEach(t => backend.disposeIntermediateTensorInfo(t));
    return backend.makeTensorInfo(outShape, 'int32', vals);
}
export const argMaxConfig = {
    kernelName: ArgMax,
    backendName: 'cpu',
    kernelFunc: argMax
};
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