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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 { backend_util, Min, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
import { reshape } from './Reshape';
import { transpose } from './Transpose';
export function min(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { axis, keepDims } = attrs;
    assertNotComplex(x, 'min');
    const origAxes = util.parseAxisParam(axis, x.shape);
    let axes = origAxes;
    const permutedAxes = backend_util.getAxesPermutation(axes, x.shape.length);
    let $x = x;
    if (permutedAxes != null) {
        $x = transpose({ inputs: { x }, backend, attrs: { perm: permutedAxes } });
        axes = backend_util.getInnerMostAxes(axes.length, x.shape.length);
    }
    backend_util.assertAxesAreInnerMostDims('min', axes, $x.shape.length);
    const [outShape, reduceShape] = backend_util.computeOutAndReduceShapes($x.shape, axes);
    const reduceSize = util.sizeFromShape(reduceShape);
    const vals = util.makeZerosTypedArray(util.sizeFromShape(outShape), $x.dtype);
    const aVals = backend.data.get($x.dataId).values;
    for (let i = 0; i < vals.length; ++i) {
        const offset = i * reduceSize;
        let min = aVals[offset];
        for (let j = 0; j < reduceSize; ++j) {
            const value = aVals[offset + j];
            if (Number.isNaN(value) ||
                value < min) { // comparison with NaN always return false
                min = value;
            }
        }
        vals[i] = min;
    }
    if (permutedAxes != null) {
        backend.disposeIntermediateTensorInfo($x);
    }
    const result = backend.makeTensorInfo(outShape, $x.dtype, vals);
    if (keepDims) {
        const expandedShape = backend_util.expandShapeToKeepDim(outShape, origAxes);
        const reshapedResult = reshape({ inputs: { x: result }, backend, attrs: { shape: expandedShape } });
        backend.disposeIntermediateTensorInfo(result);
        return reshapedResult;
    }
    return result;
}
export const minConfig = {
    kernelName: Min,
    backendName: 'cpu',
    kernelFunc: min
};
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