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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 { ResizeNearestNeighborGrad, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function resizeNearestNeighborGrad(args) {
    const { inputs, backend, attrs } = args;
    const { images, dy } = inputs;
    const { alignCorners } = attrs;
    assertNotComplex([dy, images], 'resizeNearestNeighborGrad');
    const imagesStrides = util.computeStrides(images.shape);
    const dyStrides = util.computeStrides(dy.shape);
    const [batch, xHeight, xWidth, depth] = images.shape;
    const [, yHeight, yWidth] = dy.shape;
    const output = new Float32Array(batch * xHeight * xWidth * depth);
    const dyValues = backend.data.get(dy.dataId).values;
    // In the backwards pass, we want to find the pixels that were generated
    // for each pixel in the input image the forward pass
    const effectiveXSize = [
        (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
        (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
    ];
    const effectiveYSize = [
        (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
        (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
    ];
    const heightScale = effectiveXSize[0] / effectiveYSize[0];
    const widthScale = effectiveXSize[1] / effectiveYSize[1];
    const invHeightScale = 1 / heightScale;
    const invWidthScale = 1 / widthScale;
    // This defines the size of the window of values around a particular
    // index in dy that we want to search for contributions to dx.
    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;
    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;
    // Loop over the output space.
    for (let b = 0; b < batch; b++) {
        const batchOffset = b * imagesStrides[0];
        for (let r = 0; r < xHeight; r++) {
            const rowOffset = batchOffset + r * imagesStrides[1];
            // Compute bounds for where in dy we will look
            const startRLerp = Math.floor(r * invHeightScale);
            const startDyR = Math.floor(startRLerp - (winHeight / 2));
            for (let c = 0; c < xWidth; c++) {
                const colOffset = rowOffset + c * imagesStrides[2];
                // Compute bounds for where in dy we will look
                const startCLerp = Math.floor(c * invWidthScale);
                const startDyC = Math.floor(startCLerp - (winWidth / 2));
                for (let d = 0; d < depth; d++) {
                    let accum = 0;
                    // loop over dy
                    for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
                        const dyR = dyRIndex + startDyR;
                        // Guard against the window exceeding the bounds of dy
                        if (dyR < 0 || dyR >= yHeight) {
                            continue;
                        }
                        const dyROffset = batchOffset + dyR * dyStrides[1];
                        const sourceFracRow = dyR * heightScale;
                        const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :
                            Math.floor(sourceFracRow));
                        if (r !== sourceNearestRow) {
                            continue;
                        }
                        for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
                            const dyC = dyCIndex + startDyC;
                            // Guard against the window exceeding the bounds of dy
                            if (dyC < 0 || dyC >= yWidth) {
                                continue;
                            }
                            const dyCOffset = dyROffset + dyC * dyStrides[2];
                            const sourceFracCol = dyC * widthScale;
                            const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :
                                Math.floor(sourceFracCol));
                            if (c === sourceNearestCol) {
                                accum += dyValues[dyCOffset + d];
                            }
                        }
                    }
                    output[colOffset + d] = accum;
                }
            }
        }
    }
    return backend.makeTensorInfo(images.shape, images.dtype, output);
}
export const resizeNearestNeighborGradConfig = {
    kernelName: ResizeNearestNeighborGrad,
    backendName: 'cpu',
    kernelFunc: resizeNearestNeighborGrad
};
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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, ResizeNearestNeighborGrad, ResizeNearestNeighborGradAttrs, ResizeNearestNeighborGradInputs, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function resizeNearestNeighborGrad(args: {\n  inputs: ResizeNearestNeighborGradInputs,\n  backend: MathBackendCPU,\n  attrs: ResizeNearestNeighborGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {images, dy} = inputs;\n  const {alignCorners} = attrs;\n\n  assertNotComplex([dy, images], 'resizeNearestNeighborGrad');\n\n  const imagesStrides = util.computeStrides(images.shape);\n  const dyStrides = util.computeStrides(dy.shape);\n  const [batch, xHeight, xWidth, depth] = images.shape;\n  const [, yHeight, yWidth] = dy.shape;\n\n  const output = new Float32Array(batch * xHeight * xWidth * depth);\n  const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n\n  // In the backwards pass, we want to find the pixels that were generated\n  // for each pixel in the input image the forward pass\n\n  const effectiveXSize: [number, number] = [\n    (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n    (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n  ];\n\n  const effectiveYSize: [number, number] = [\n    (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n    (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n  ];\n\n  const heightScale = effectiveXSize[0] / effectiveYSize[0];\n  const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n  const invHeightScale = 1 / heightScale;\n  const invWidthScale = 1 / widthScale;\n\n  // This defines the size of the window of values around a particular\n  // index in dy that we want to search for contributions to dx.\n  const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n  const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n  // Loop over the output space.\n  for (let b = 0; b < batch; b++) {\n    const batchOffset = b * imagesStrides[0];\n    for (let r = 0; r < xHeight; r++) {\n      const rowOffset = batchOffset + r * imagesStrides[1];\n\n      // Compute bounds for where in dy we will look\n      const startRLerp = Math.floor(r * invHeightScale);\n      const startDyR = Math.floor(startRLerp - (winHeight / 2));\n      for (let c = 0; c < xWidth; c++) {\n        const colOffset = rowOffset + c * imagesStrides[2];\n\n        // Compute bounds for where in dy we will look\n        const startCLerp = Math.floor(c * invWidthScale);\n        const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n        for (let d = 0; d < depth; d++) {\n          let accum = 0;\n          // loop over dy\n\n          for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n            const dyR = dyRIndex + startDyR;\n            // Guard against the window exceeding the bounds of dy\n            if (dyR < 0 || dyR >= yHeight) {\n              continue;\n            }\n\n            const dyROffset = batchOffset + dyR * dyStrides[1];\n            const sourceFracRow = dyR * heightScale;\n            const sourceNearestRow = Math.min(\n                xHeight - 1,\n                alignCorners ? Math.round(sourceFracRow) :\n                               Math.floor(sourceFracRow));\n            if (r !== sourceNearestRow) {\n              continue;\n            }\n            for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n              const dyC = dyCIndex + startDyC;\n              // Guard against the window exceeding the bounds of dy\n              if (dyC < 0 || dyC >= yWidth) {\n                continue;\n              }\n\n              const dyCOffset = dyROffset + dyC * dyStrides[2];\n              const sourceFracCol = dyC * widthScale;\n              const sourceNearestCol = Math.min(\n                  xWidth - 1,\n                  alignCorners ? Math.round(sourceFracCol) :\n                                 Math.floor(sourceFracCol));\n\n              if (c === sourceNearestCol) {\n                accum += dyValues[dyCOffset + d];\n              }\n            }\n          }\n          output[colOffset + d] = accum;\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(images.shape, images.dtype, output);\n}\n\nexport const resizeNearestNeighborGradConfig: KernelConfig = {\n  kernelName: ResizeNearestNeighborGrad,\n  backendName: 'cpu',\n  kernelFunc: resizeNearestNeighborGrad as unknown as KernelFunc\n};\n"]}