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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 { ResizeNearestNeighbor, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function resizeNearestNeighbor(args) {
    const { inputs, backend, attrs } = args;
    const { images } = inputs;
    const { alignCorners, halfPixelCenters, size } = attrs;
    assertNotComplex(images, 'resizeNearestNeighbor');
    const imagesStrides = util.computeStrides(images.shape);
    const [newHeight, newWidth] = size;
    const [batch, oldHeight, oldWidth, numChannels] = images.shape;
    const xValues = backend.data.get(images.dataId).values;
    const output = new Float32Array(batch * newHeight * newWidth * numChannels);
    const effectiveInputSize = [
        (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
        (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
    ];
    const effectiveOutputSize = [
        (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
        (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
    ];
    const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
    const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
    let outputOffset = 0;
    for (let b = 0; b < batch; b++) {
        const batchOffset = b * imagesStrides[0];
        for (let r = 0; r < newHeight; r++) {
            const sourceFracRow = halfPixelCenters ?
                effectiveRowSizeRatio * (r + 0.5) :
                effectiveRowSizeRatio * r;
            let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
            if (halfPixelCenters) {
                sourceNearestRow = Math.max(0, sourceNearestRow);
            }
            const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
            for (let c = 0; c < newWidth; c++) {
                const sourceFracCol = halfPixelCenters ?
                    effectiveColSizeRatio * (c + 0.5) :
                    effectiveColSizeRatio * c;
                let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :
                    Math.floor(sourceFracCol));
                if (halfPixelCenters) {
                    sourceNearestCol = Math.max(0, sourceNearestCol);
                }
                const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
                for (let d = 0; d < numChannels; d++) {
                    // Begin shader.
                    // Compute the fractional index of the source.
                    const newVal = xValues[colOffset + d];
                    output[outputOffset++] = newVal;
                }
            }
        }
    }
    return backend.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
}
export const resizeNearestNeighborConfig = {
    kernelName: ResizeNearestNeighbor,
    backendName: 'cpu',
    kernelFunc: resizeNearestNeighbor
};
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {KernelConfig, KernelFunc, ResizeNearestNeighbor, ResizeNearestNeighborAttrs, ResizeNearestNeighborInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeNearestNeighbor(args: {\n  inputs: ResizeNearestNeighborInputs,\n  backend: MathBackendCPU,\n  attrs: ResizeNearestNeighborAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images} = inputs;\n  const {alignCorners, halfPixelCenters, size} = attrs;\n\n  assertNotComplex(images, 'resizeNearestNeighbor');\n\n  const imagesStrides = util.computeStrides(images.shape);\n  const [newHeight, newWidth] = size;\n\n  const [batch, oldHeight, oldWidth, numChannels] = images.shape;\n  const xValues = backend.data.get(images.dataId).values as TypedArray;\n  const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n  const effectiveInputSize: [number, number] = [\n    (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n    (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n  ];\n\n  const effectiveOutputSize: [number, number] = [\n    (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n    (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n  ];\n\n  const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];\n  const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];\n\n  let outputOffset = 0;\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r = 0; r < newHeight; r++) {\n      const sourceFracRow = halfPixelCenters ?\n          effectiveRowSizeRatio * (r + 0.5) :\n          effectiveRowSizeRatio * r;\n      let sourceNearestRow = Math.min(\n          oldHeight - 1,\n          alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));\n      if (halfPixelCenters) {\n        sourceNearestRow = Math.max(0, sourceNearestRow);\n      }\n      const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];\n      for (let c = 0; c < newWidth; c++) {\n        const sourceFracCol = halfPixelCenters ?\n            effectiveColSizeRatio * (c + 0.5) :\n            effectiveColSizeRatio * c;\n        let sourceNearestCol = Math.min(\n            oldWidth - 1,\n            alignCorners ? Math.round(sourceFracCol) :\n                           Math.floor(sourceFracCol));\n        if (halfPixelCenters) {\n          sourceNearestCol = Math.max(0, sourceNearestCol);\n        }\n        const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];\n        for (let d = 0; d < numChannels; d++) {\n          // Begin shader.\n          // Compute the fractional index of the source.\n          const newVal = xValues[colOffset + d];\n          output[outputOffset++] = newVal;\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(\n      [batch, newHeight, newWidth, numChannels], images.dtype, output);\n}\n\nexport const resizeNearestNeighborConfig: KernelConfig = {\n  kernelName: ResizeNearestNeighbor,\n  backendName: 'cpu',\n  kernelFunc: resizeNearestNeighbor as unknown as KernelFunc\n};\n"]}