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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 { backend_util, Dilation2DBackpropInput, util } from '@tensorflow/tfjs-core';
export const dilation2DBackpropInputConfig = {
    kernelName: Dilation2DBackpropInput,
    backendName: 'cpu',
    kernelFunc: ({ inputs, backend, attrs }) => {
        const { x, filter, dy } = inputs;
        const { strides, pad, dilations } = attrs;
        const cpuBackend = backend;
        const $x = util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
        const $filter = util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
        const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations);
        util.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy ` +
            `must have the same rank as output ${outShape.length}, but got ` +
            `${dy.rank}`);
        const $dy = util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
        // The computed gradients has the same dimensions as the input:
        // [batch, inputHeight, inputCols, inChannel]
        const gradients = util.makeZerosNestedTypedArray(x.shape, x.dtype);
        // In the case of multiple argmax branches, we only back-propagate along the
        // last branch, i.e., the one with largest value of `h * filter_cols + w`,
        // similarly to the max-pooling backward routines.
        // This implementation follows the TF c++ implementation:
        // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc
        for (let b = 0; b < batchSize; ++b) {
            for (let hOut = 0; hOut < outHeight; ++hOut) {
                const hBeg = hOut * strideHeight - padInfo.top;
                for (let wOut = 0; wOut < outWidth; ++wOut) {
                    const wBeg = wOut * strideWidth - padInfo.left;
                    for (let d = 0; d < inChannels; ++d) {
                        let curVal = Number.MIN_SAFE_INTEGER;
                        let hInMax = (hBeg < 0) ? 0 : hBeg;
                        let wInMax = (wBeg < 0) ? 0 : wBeg;
                        for (let h = 0; h < filterHeight; ++h) {
                            const hIn = hBeg + h * dilationHeight;
                            if (hIn >= 0 && hIn < inHeight) {
                                for (let w = 0; w < filterWidth; ++w) {
                                    const wIn = wBeg + w * dilationWidth;
                                    if (wIn >= 0 && wIn < inWidth) {
                                        const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
                                        if (val > curVal) {
                                            curVal = val;
                                            hInMax = hIn;
                                            wInMax = wIn;
                                        }
                                    }
                                }
                            }
                        }
                        gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
                    }
                }
            }
        }
        const dataId = cpuBackend.write(util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
        return { dataId, shape: x.shape, dtype: x.dtype };
    }
};
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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 {backend_util, Dilation2DAttrs, Dilation2DBackpropInput, Tensor3D, Tensor4D, TypedArray, util} from '@tensorflow/tfjs-core';\nimport {KernelConfig} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const dilation2DBackpropInputConfig: KernelConfig = {\n  kernelName: Dilation2DBackpropInput,\n  backendName: 'cpu',\n  kernelFunc: ({inputs, backend, attrs}) => {\n    const {x, filter, dy} =\n        inputs as {x: Tensor4D, filter: Tensor3D, dy: Tensor4D};\n    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;\n    const cpuBackend = backend as MathBackendCPU;\n\n    const $x =\n        util.toNestedArray(\n            x.shape, cpuBackend.data.get(x.dataId).values as TypedArray) as\n        number[][][][];\n\n    const $filter = util.toNestedArray(\n                        filter.shape,\n                        cpuBackend.data.get(filter.dataId).values as\n                            TypedArray) as number[][][];\n\n    const {\n      batchSize,\n      inHeight,\n      inWidth,\n      inChannels,\n      outHeight,\n      outWidth,\n      padInfo,\n      strideHeight,\n      strideWidth,\n      filterHeight,\n      filterWidth,\n      dilationHeight,\n      dilationWidth,\n      outShape\n    } =\n        backend_util.computeDilation2DInfo(\n            x.shape as [number, number, number, number],\n            filter.shape as [number, number, number], strides, pad,\n            'NHWC' /* dataFormat */, dilations);\n\n    util.assert(\n        dy.rank === outShape.length,\n        () => `Error in ${Dilation2DBackpropInput}, dy ` +\n            `must have the same rank as output ${outShape.length}, but got ` +\n            `${dy.rank}`);\n\n    const $dy =\n        util.toNestedArray(\n            outShape, cpuBackend.data.get(dy.dataId).values as TypedArray) as\n        number[][][][];\n\n    // The computed gradients has the same dimensions as the input:\n    // [batch, inputHeight, inputCols, inChannel]\n    const gradients =\n        util.makeZerosNestedTypedArray(x.shape, x.dtype) as number[][][][];\n\n    // In the case of multiple argmax branches, we only back-propagate along the\n    // last branch, i.e., the one with largest value of `h * filter_cols + w`,\n    // similarly to the max-pooling backward routines.\n    // This implementation follows the TF c++ implementation:\n    // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc\n    for (let b = 0; b < batchSize; ++b) {\n      for (let hOut = 0; hOut < outHeight; ++hOut) {\n        const hBeg = hOut * strideHeight - padInfo.top;\n        for (let wOut = 0; wOut < outWidth; ++wOut) {\n          const wBeg = wOut * strideWidth - padInfo.left;\n          for (let d = 0; d < inChannels; ++d) {\n            let curVal = Number.MIN_SAFE_INTEGER;\n            let hInMax = (hBeg < 0) ? 0 : hBeg;\n            let wInMax = (wBeg < 0) ? 0 : wBeg;\n            for (let h = 0; h < filterHeight; ++h) {\n              const hIn = hBeg + h * dilationHeight;\n              if (hIn >= 0 && hIn < inHeight) {\n                for (let w = 0; w < filterWidth; ++w) {\n                  const wIn = wBeg + w * dilationWidth;\n                  if (wIn >= 0 && wIn < inWidth) {\n                    const val = $x[b][hIn][wIn][d] + $filter[h][w][d];\n                    if (val > curVal) {\n                      curVal = val;\n                      hInMax = hIn;\n                      wInMax = wIn;\n                    }\n                  }\n                }\n              }\n            }\n            gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];\n          }\n        }\n      }\n    }\n\n    const dataId = cpuBackend.write(\n        util.toTypedArray(gradients, x.dtype), x.shape, x.dtype);\n\n    return {dataId, shape: x.shape, dtype: x.dtype};\n  }\n};\n"]}