/**
 * @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, Dilation2D, util } from '@tensorflow/tfjs-core';
export const dilation2DConfig = {
    kernelName: Dilation2D,
    backendName: 'cpu',
    kernelFunc: ({ inputs, backend, attrs }) => {
        const { x, filter } = inputs;
        const { strides, pad, dilations } = attrs;
        const cpuBackend = backend;
        const xVals = cpuBackend.data.get(x.dataId).values;
        const xRank = x.shape.length;
        const filterVals = cpuBackend.data.get(filter.dataId).values;
        const filterRank = filter.shape.length;
        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);
        const outSize = util.sizeFromShape(outShape);
        const outRank = outShape.length;
        const outputVals = util.getArrayFromDType(x.dtype, outSize);
        // Upsampling the input by fill in `dilation size - 1` values between each
        // input value.
        // 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;
                        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 xIndex = util.locToIndex([b, hIn, wIn, d], xRank, util.computeStrides(x.shape));
                                        const filterIndex = util.locToIndex([h, w, d], filterRank, util.computeStrides(filter.shape));
                                        const val = xVals[xIndex] + filterVals[filterIndex];
                                        if (val > curVal) {
                                            curVal = val;
                                        }
                                    }
                                }
                            }
                        }
                        const outputIndex = util.locToIndex([b, hOut, wOut, d], outRank, util.computeStrides(outShape));
                        outputVals[outputIndex] = curVal;
                    }
                }
            }
        }
        const dataId = cpuBackend.write(util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
        return { dataId, shape: outShape, 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, Dilation2D, Dilation2DAttrs, Dilation2DInputs, KernelConfig, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\n\nexport const dilation2DConfig: KernelConfig = {\n  kernelName: Dilation2D,\n  backendName: 'cpu',\n  kernelFunc: ({inputs, backend, attrs}) => {\n    const {x, filter} = inputs as Dilation2DInputs;\n    const {strides, pad, dilations} = attrs as unknown as Dilation2DAttrs;\n    const cpuBackend = backend as MathBackendCPU;\n\n    const xVals = cpuBackend.data.get(x.dataId).values as TypedArray;\n    const xRank = x.shape.length;\n\n    const filterVals = cpuBackend.data.get(filter.dataId).values as TypedArray;\n    const filterRank = filter.shape.length;\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    const outSize = util.sizeFromShape(outShape);\n    const outRank = outShape.length;\n    const outputVals = util.getArrayFromDType(x.dtype, outSize);\n\n    // Upsampling the input by fill in `dilation size - 1` values between each\n    // input value.\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            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 xIndex = util.locToIndex(\n                        [b, hIn, wIn, d], xRank, util.computeStrides(x.shape));\n                    const filterIndex = util.locToIndex(\n                        [h, w, d], filterRank,\n                        util.computeStrides(filter.shape));\n                    const val = xVals[xIndex] + filterVals[filterIndex];\n                    if (val > curVal) {\n                      curVal = val;\n                    }\n                  }\n                }\n              }\n            }\n            const outputIndex = util.locToIndex(\n                [b, hOut, wOut, d], outRank, util.computeStrides(outShape));\n            outputVals[outputIndex] = curVal;\n          }\n        }\n      }\n    }\n\n    const dataId = cpuBackend.write(\n        util.toTypedArray(outputVals, x.dtype), outShape, x.dtype);\n\n    return {dataId, shape: outShape, dtype: x.dtype};\n  }\n};\n"]}