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2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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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, Conv3D, TensorBuffer, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function conv3D(args) {
    const { inputs, backend, attrs } = args;
    const { x, filter } = inputs;
    const { strides, pad, dilations } = attrs;
    assertNotComplex([x, filter], 'conv3d');
    const convInfo = backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad);
    const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;
    const padFront = padInfo.front;
    const padLeft = padInfo.left;
    const padTop = padInfo.top;
    const y = new TensorBuffer(convInfo.outShape, x.dtype);
    const xVals = backend.data.get(x.dataId).values;
    const wVals = backend.data.get(filter.dataId).values;
    const yVals = y.values;
    const xStrides = util.computeStrides(x.shape);
    const filterStrides = util.computeStrides(filter.shape);
    for (let b = 0; b < convInfo.batchSize; ++b) {
        const xOffset1 = b * xStrides[0];
        const yOffset1 = b * y.strides[0];
        for (let yF = 0; yF < convInfo.outDepth; ++yF) {
            const yOffset2 = yOffset1 + yF * y.strides[1];
            const xFCorner = yF * convInfo.strideDepth - padFront;
            for (let wF = 0; wF < filterDepth; ++wF) {
                const xF = xFCorner + wF * dilationDepth;
                if (xF < 0 || xF >= convInfo.inDepth) {
                    continue;
                }
                const wOffset1 = wF * filterStrides[0];
                const xOffset2 = xOffset1 + xF * xStrides[1];
                for (let yR = 0; yR < convInfo.outHeight; ++yR) {
                    const yOffset3 = yOffset2 + yR * y.strides[2];
                    const xRCorner = yR * convInfo.strideHeight - padTop;
                    for (let wR = 0; wR < filterHeight; ++wR) {
                        const xR = xRCorner + wR * dilationHeight;
                        if (xR < 0 || xR >= convInfo.inHeight) {
                            continue;
                        }
                        const wOffset2 = wOffset1 + wR * filterStrides[1];
                        const xOffset3 = xOffset2 + xR * xStrides[2];
                        for (let yC = 0; yC < convInfo.outWidth; ++yC) {
                            const yOffset4 = yOffset3 + yC * convInfo.outChannels;
                            const xCCorner = yC * convInfo.strideWidth - padLeft;
                            for (let wC = 0; wC < filterWidth; ++wC) {
                                const xC = xCCorner + wC * dilationWidth;
                                if (xC < 0 || xC >= convInfo.inWidth) {
                                    continue;
                                }
                                const wOffset3 = wOffset2 + wC * filterStrides[2];
                                const xOffset4 = xOffset3 + xC * convInfo.inChannels;
                                let wOffset4 = wOffset3;
                                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
                                    const xVal = xVals[xOffset4 + d1];
                                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
                                        yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
                                    }
                                    wOffset4 += convInfo.outChannels;
                                }
                            }
                        }
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(y.shape, y.dtype, y.values);
}
export const conv3DConfig = {
    kernelName: Conv3D,
    backendName: 'cpu',
    kernelFunc: conv3D
};
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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, Conv3D, Conv3DAttrs, Conv3DInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3D(\n    args: {inputs: Conv3DInputs, backend: MathBackendCPU, attrs: Conv3DAttrs}):\n    TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, filter} = inputs;\n  const {strides, pad, dilations} = attrs;\n\n  assertNotComplex([x, filter], 'conv3d');\n\n  const convInfo = backend_util.computeConv3DInfo(\n      x.shape as [number, number, number, number, number],\n      filter.shape as [number, number, number, number, number], strides,\n      dilations, pad);\n\n  const {\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    padInfo\n  } = convInfo;\n  const padFront = padInfo.front;\n  const padLeft = padInfo.left;\n  const padTop = padInfo.top;\n  const y = new TensorBuffer(convInfo.outShape, x.dtype as 'float32');\n\n  const xVals = backend.data.get(x.dataId).values as TypedArray;\n  const wVals = backend.data.get(filter.dataId).values as TypedArray;\n  const yVals = y.values;\n\n  const xStrides = util.computeStrides(x.shape);\n  const filterStrides = util.computeStrides(filter.shape);\n\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    const xOffset1 = b * xStrides[0];\n    const yOffset1 = b * y.strides[0];\n    for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n      const yOffset2 = yOffset1 + yF * y.strides[1];\n      const xFCorner = yF * convInfo.strideDepth - padFront;\n      for (let wF = 0; wF < filterDepth; ++wF) {\n        const xF = xFCorner + wF * dilationDepth;\n        if (xF < 0 || xF >= convInfo.inDepth) {\n          continue;\n        }\n        const wOffset1 = wF * filterStrides[0];\n        const xOffset2 = xOffset1 + xF * xStrides[1];\n\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const yOffset3 = yOffset2 + yR * y.strides[2];\n          const xRCorner = yR * convInfo.strideHeight - padTop;\n          for (let wR = 0; wR < filterHeight; ++wR) {\n            const xR = xRCorner + wR * dilationHeight;\n            if (xR < 0 || xR >= convInfo.inHeight) {\n              continue;\n            }\n            const wOffset2 = wOffset1 + wR * filterStrides[1];\n            const xOffset3 = xOffset2 + xR * xStrides[2];\n            for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n              const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n              const xCCorner = yC * convInfo.strideWidth - padLeft;\n              for (let wC = 0; wC < filterWidth; ++wC) {\n                const xC = xCCorner + wC * dilationWidth;\n                if (xC < 0 || xC >= convInfo.inWidth) {\n                  continue;\n                }\n                const wOffset3 = wOffset2 + wC * filterStrides[2];\n                const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                let wOffset4 = wOffset3;\n                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                  const xVal = xVals[xOffset4 + d1];\n                  for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                    yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                  }\n                  wOffset4 += convInfo.outChannels;\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(y.shape, y.dtype, y.values);\n}\n\nexport const conv3DConfig: KernelConfig = {\n  kernelName: Conv3D,\n  backendName: 'cpu',\n  kernelFunc: conv3D as unknown as KernelFunc\n};\n"]}