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 { MirrorPad, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function mirrorPad(args) {
    const { inputs, backend, attrs } = args;
    const { x } = inputs;
    const { paddings, mode } = attrs;
    assertNotComplex(x, 'mirrorPad');
    const outShape = paddings.map((p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);
    const start = paddings.map(p => p[0]);
    const end = paddings.map((p, i) => p[0] + x.shape[i]);
    const offset = mode === 'reflect' ? 0 : 1;
    const xVals = backend.data.get(x.dataId).values;
    const xRank = x.shape.length;
    const xStrides = util.computeStrides(x.shape);
    const resultSize = util.sizeFromShape(outShape);
    const resultRank = outShape.length;
    const resultStrides = util.computeStrides(outShape);
    const resVals = util.getTypedArrayFromDType(x.dtype, resultSize);
    for (let i = 0; i < resultSize; i++) {
        let coords = util.indexToLoc(i, resultRank, resultStrides);
        for (let i = 0; i < resultRank; i++) {
            if (coords[i] < start[i]) {
                coords[i] = start[i] * 2 - coords[i] - offset;
            }
            else if (coords[i] >= end[i]) {
                coords[i] = (end[i] - 1) * 2 - coords[i] + offset;
            }
        }
        coords = coords.map((c, i) => c - start[i]);
        const inIndex = util.locToIndex(coords, xRank, xStrides);
        resVals[i] = xVals[inIndex];
    }
    const outId = backend.write(resVals, outShape, x.dtype);
    return { dataId: outId, shape: outShape, dtype: x.dtype };
}
export const mirrorPadConfig = {
    kernelName: MirrorPad,
    backendName: 'cpu',
    kernelFunc: mirrorPad
};
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