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chenyc

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, Conv2DBackpropInput, TensorBuffer, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function conv2DBackpropInput(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { inputShape, strides, pad, dataFormat, dimRoundingMode } = attrs;
    assertNotComplex([dy, filter], 'conv2dBackpropInput');
    const filterStrides = util.computeStrides(filter.shape);
    const dyStrides = util.computeStrides(dy.shape);
    let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);
    const convInfo = backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);
    const dx = new TensorBuffer(convInfo.inShape, 'float32');
    const dxValues = dx.values;
    const dyValues = backend.data.get(dy.dataId).values;
    const fltValues = backend.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2] = filterStrides;
    const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
    $dataFormat = convInfo.dataFormat;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    const isChannelsLast = $dataFormat === 'channelsLast';
    const xBatchStride = dx.strides[0];
    const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
    const xColStride = isChannelsLast ? dx.strides[2] : 1;
    const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
    const yBatchStride = dyStrides[0];
    const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
    const yColStride = isChannelsLast ? dyStrides[2] : 1;
    const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
    for (let b = 0; b < batchSize; ++b) {
        for (let d1 = 0; d1 < inChannels; ++d1) {
            for (let xR = 0; xR < inHeight; ++xR) {
                const xRCorner = xR - topPad;
                const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
                const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
                for (let xC = 0; xC < inWidth; ++xC) {
                    const xCCorner = xC - leftPad;
                    const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
                    const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
                    let dotProd = 0;
                    for (let yR = xRMin; yR < yRMax; ++yR) {
                        const wR = yR * strideHeight - xRCorner;
                        for (let yC = xCMin; yC < yCMax; ++yC) {
                            const wC = yC * strideWidth - xCCorner;
                            const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
                            const fltOffset = fltS0 * (filterHeight - 1 - wR) +
                                fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
                            for (let d2 = 0; d2 < outChannels; ++d2) {
                                const pixel = dyValues[dyOffset + yChannelStride * d2];
                                const weight = fltValues[fltOffset + d2];
                                dotProd += pixel * weight;
                            }
                        }
                    }
                    const dxOffset = xBatchStride * b + xRowStride * xR +
                        xColStride * xC + xChannelStride * d1;
                    dxValues[dxOffset] = dotProd;
                }
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
export const conv2DBackpropInputConfig = {
    kernelName: Conv2DBackpropInput,
    backendName: 'cpu',
    kernelFunc: conv2DBackpropInput
};
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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, Conv2DBackpropInput, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv2DBackpropInput(args: {\n  inputs: Conv2DBackpropInputInputs,\n  backend: MathBackendCPU,\n  attrs: Conv2DBackpropInputAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, filter} = inputs;\n  const {inputShape, strides, pad, dataFormat, dimRoundingMode} = attrs;\n\n  assertNotComplex([dy, filter], 'conv2dBackpropInput');\n\n  const filterStrides = util.computeStrides(filter.shape);\n  const dyStrides = util.computeStrides(dy.shape);\n\n  let $dataFormat = backend_util.convertConv2DDataFormat(dataFormat);\n  const convInfo = backend_util.computeConv2DInfo(\n      inputShape, filter.shape as [number, number, number, number], strides,\n      1 /* dilations */, pad, dimRoundingMode, false, $dataFormat);\n\n  const dx = new TensorBuffer(convInfo.inShape, 'float32');\n  const dxValues = dx.values;\n  const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n  const fltValues = backend.data.get(filter.dataId).values as TypedArray;\n  const [fltS0, fltS1, fltS2] = filterStrides;\n  const {\n    batchSize,\n    filterHeight,\n    filterWidth,\n    inChannels,\n    inHeight,\n    inWidth,\n    outChannels,\n    outHeight,\n    outWidth,\n    strideHeight,\n    strideWidth\n  } = convInfo;\n  $dataFormat = convInfo.dataFormat;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n  const isChannelsLast = $dataFormat === 'channelsLast';\n  const xBatchStride = dx.strides[0];\n  const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];\n  const xColStride = isChannelsLast ? dx.strides[2] : 1;\n  const xChannelStride = isChannelsLast ? 1 : dx.strides[1];\n  const yBatchStride = dyStrides[0];\n  const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];\n  const yColStride = isChannelsLast ? dyStrides[2] : 1;\n  const yChannelStride = isChannelsLast ? 1 : dyStrides[1];\n\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      for (let xR = 0; xR < inHeight; ++xR) {\n        const xRCorner = xR - topPad;\n        const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n        const yRMax =\n            Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n        for (let xC = 0; xC < inWidth; ++xC) {\n          const xCCorner = xC - leftPad;\n          const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n          const yCMax =\n              Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n          let dotProd = 0;\n          for (let yR = xRMin; yR < yRMax; ++yR) {\n            const wR = yR * strideHeight - xRCorner;\n\n            for (let yC = xCMin; yC < yCMax; ++yC) {\n              const wC = yC * strideWidth - xCCorner;\n              const dyOffset =\n                  yBatchStride * b + yRowStride * yR + yColStride * yC;\n              const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                  fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n              for (let d2 = 0; d2 < outChannels; ++d2) {\n                const pixel = dyValues[dyOffset + yChannelStride * d2];\n                const weight = fltValues[fltOffset + d2];\n                dotProd += pixel * weight;\n              }\n            }\n          }\n          const dxOffset = xBatchStride * b + xRowStride * xR +\n              xColStride * xC + xChannelStride * d1;\n          dxValues[dxOffset] = dotProd;\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const conv2DBackpropInputConfig: KernelConfig = {\n  kernelName: Conv2DBackpropInput,\n  backendName: 'cpu',\n  kernelFunc: conv2DBackpropInput as unknown as KernelFunc\n};\n"]}