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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, Conv3DBackpropFilterV2, TensorBuffer, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function conv3DBackpropFilterV2(args) {
    const { inputs, backend, attrs } = args;
    const { x, dy } = inputs;
    const { strides, pad, filterShape } = attrs;
    assertNotComplex([x, dy], 'conv3dBackpropFilterV2');
    const xStrides = util.computeStrides(x.shape);
    const dyStrides = util.computeStrides(dy.shape);
    const convInfo = backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad);
    const strideDepth = convInfo.strideDepth;
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const filterDepth = convInfo.filterDepth;
    const filterHeight = convInfo.filterHeight;
    const filterWidth = convInfo.filterWidth;
    const dw = new TensorBuffer(convInfo.filterShape, 'float32');
    const dwValues = dw.values;
    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
    const dyValues = backend.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const xValues = backend.data.get(x.dataId).values;
    const [xS0, xS1, xS2, xS3] = xStrides;
    const frontPad = convInfo.padInfo.front;
    const leftPad = convInfo.padInfo.left;
    const topPad = convInfo.padInfo.top;
    for (let wF = 0; wF < filterDepth; ++wF) {
        const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
        const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
        const wOffset1 = wF * dwS0;
        for (let wR = 0; wR < filterHeight; ++wR) {
            const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
            const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
            const wOffset2 = wR * dwS1 + wOffset1;
            for (let wC = 0; wC < filterWidth; ++wC) {
                const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
                const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
                const wOffset3 = wC * dwS2 + wOffset2;
                for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
                    const wOffset4 = d1 * dwS3 + wOffset3;
                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
                        let dotProd = 0;
                        for (let b = 0; b < convInfo.batchSize; ++b) {
                            const xOffset1 = b * xS0;
                            const yOffset1 = b * dyS0;
                            for (let yF = yFMin; yF < yFMax; ++yF) {
                                const xF = wF + yF * strideDepth - frontPad;
                                const xOffset2 = xF * xS1 + xOffset1;
                                const yOffset2 = yF * dyS1 + yOffset1;
                                for (let yR = yRMin; yR < yRMax; ++yR) {
                                    const xR = wR + yR * strideHeight - topPad;
                                    const xOffset3 = xR * xS2 + xOffset2;
                                    const yOffset3 = yR * dyS2 + yOffset2;
                                    for (let yC = yCMin; yC < yCMax; ++yC) {
                                        const xC = wC + yC * strideWidth - leftPad;
                                        const xOffset4 = xC * xS3 + xOffset3;
                                        const yOffset4 = yC * dyS3 + yOffset3;
                                        dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
                                    }
                                }
                            }
                        }
                        dwValues[wOffset4 + d2] = dotProd;
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);
}
export const conv3DBackpropFilterV2Config = {
    kernelName: Conv3DBackpropFilterV2,
    backendName: 'cpu',
    kernelFunc: conv3DBackpropFilterV2
};
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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, Conv3DBackpropFilterV2, Conv3DBackpropFilterV2Attrs, Conv3DBackpropFilterV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3DBackpropFilterV2(args: {\n  inputs: Conv3DBackpropFilterV2Inputs,\n  backend: MathBackendCPU,\n  attrs: Conv3DBackpropFilterV2Attrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {x, dy} = inputs;\n  const {strides, pad, filterShape} = attrs;\n\n  assertNotComplex([x, dy], 'conv3dBackpropFilterV2');\n\n  const xStrides = util.computeStrides(x.shape);\n  const dyStrides = util.computeStrides(dy.shape);\n\n  const convInfo = backend_util.computeConv3DInfo(\n      x.shape as [number, number, number, number, number], filterShape, strides,\n      1 /* dilations */, pad);\n\n  const strideDepth = convInfo.strideDepth;\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const filterDepth = convInfo.filterDepth;\n  const filterHeight = convInfo.filterHeight;\n  const filterWidth = convInfo.filterWidth;\n\n  const dw = new TensorBuffer(convInfo.filterShape, 'float32');\n  const dwValues = dw.values;\n  const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n  const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const xValues = backend.data.get(x.dataId).values as TypedArray;\n  const [xS0, xS1, xS2, xS3] = xStrides;\n\n  const frontPad = convInfo.padInfo.front;\n  const leftPad = convInfo.padInfo.left;\n  const topPad = convInfo.padInfo.top;\n\n  for (let wF = 0; wF < filterDepth; ++wF) {\n    const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n    const yFMax = Math.min(\n        convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n    const wOffset1 = wF * dwS0;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n      const wOffset2 = wR * dwS1 + wOffset1;\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n        const wOffset3 = wC * dwS2 + wOffset2;\n\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          const wOffset4 = d1 * dwS3 + wOffset3;\n\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              const xOffset1 = b * xS0;\n              const yOffset1 = b * dyS0;\n\n              for (let yF = yFMin; yF < yFMax; ++yF) {\n                const xF = wF + yF * strideDepth - frontPad;\n                const xOffset2 = xF * xS1 + xOffset1;\n                const yOffset2 = yF * dyS1 + yOffset1;\n\n                for (let yR = yRMin; yR < yRMax; ++yR) {\n                  const xR = wR + yR * strideHeight - topPad;\n                  const xOffset3 = xR * xS2 + xOffset2;\n                  const yOffset3 = yR * dyS2 + yOffset2;\n\n                  for (let yC = yCMin; yC < yCMax; ++yC) {\n                    const xC = wC + yC * strideWidth - leftPad;\n                    const xOffset4 = xC * xS3 + xOffset3;\n                    const yOffset4 = yC * dyS3 + yOffset3;\n\n                    dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                  }\n                }\n              }\n            }\n            dwValues[wOffset4 + d2] = dotProd;\n          }\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values);\n}\n\nexport const conv3DBackpropFilterV2Config: KernelConfig = {\n  kernelName: Conv3DBackpropFilterV2,\n  backendName: 'cpu',\n  kernelFunc: conv3DBackpropFilterV2 as unknown as KernelFunc\n};\n"]}