/**
 * @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, Conv3DBackpropInputV2, TensorBuffer, util } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
export function conv3DBackpropInputV2(args) {
    const { inputs, backend, attrs } = args;
    const { dy, filter } = inputs;
    const { pad, strides, inputShape } = attrs;
    assertNotComplex([dy], 'conv3dBackpropInputV2');
    const dyStrides = util.computeStrides(dy.shape);
    const filterStrides = util.computeStrides(filter.shape);
    const convInfo = backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad);
    const dx = new TensorBuffer(convInfo.inShape, 'float32');
    const dxValues = dx.values;
    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
    const dyValues = backend.data.get(dy.dataId).values;
    const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
    const fltValues = backend.data.get(filter.dataId).values;
    const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
    const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;
    const frontPad = filterDepth - 1 - convInfo.padInfo.front;
    const topPad = filterHeight - 1 - convInfo.padInfo.top;
    const leftPad = filterWidth - 1 - convInfo.padInfo.left;
    for (let b = 0; b < batchSize; ++b) {
        for (let d1 = 0; d1 < inChannels; ++d1) {
            // Frames of depth
            for (let xF = 0; xF < inDepth; ++xF) {
                const xFCorner = xF - frontPad;
                const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
                const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
                // Rows as per standard 2d matrix notation
                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);
                    // Columns as per standard 2d matrix notation
                    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 yF = xFMin; yF < yFMax; ++yF) {
                            const wF = yF * strideDepth - xFCorner;
                            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 = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
                                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +
                                        fltS1 * (filterHeight - 1 - wR) +
                                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
                                    for (let d2 = 0; d2 < outChannels; ++d2) {
                                        const pixel = dyValues[dyOffset + d2];
                                        const weight = fltValues[fltOffset + d2];
                                        dotProd += pixel * weight;
                                    }
                                }
                            }
                        }
                        dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =
                            dotProd;
                    }
                }
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
export const conv3DBackpropInputV2Config = {
    kernelName: Conv3DBackpropInputV2,
    backendName: 'cpu',
    kernelFunc: conv3DBackpropInputV2
};
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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, Conv3DBackpropInputV2, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs, KernelConfig, KernelFunc, TensorBuffer, TensorInfo, TypedArray, util} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\n\nexport function conv3DBackpropInputV2(args: {\n  inputs: Conv3DBackpropInputV2Inputs,\n  backend: MathBackendCPU,\n  attrs: Conv3DBackpropInputV2Attrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, filter} = inputs;\n  const {pad, strides, inputShape} = attrs;\n\n  assertNotComplex([dy], 'conv3dBackpropInputV2');\n\n  const dyStrides = util.computeStrides(dy.shape);\n  const filterStrides = util.computeStrides(filter.shape);\n\n  const convInfo = backend_util.computeConv3DInfo(\n      inputShape, filter.shape as [number, number, number, number, number],\n      strides, 1 /* dilations */, pad);\n\n  const dx = new TensorBuffer(convInfo.inShape, 'float32');\n  const dxValues = dx.values;\n  const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n  const dyValues = backend.data.get(dy.dataId).values as TypedArray;\n  const [dyS0, dyS1, dyS2, dyS3] = dyStrides;\n  const fltValues = backend.data.get(filter.dataId).values as TypedArray;\n  const [fltS0, fltS1, fltS2, fltS3] = filterStrides;\n  const {\n    batchSize,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    inChannels,\n    inDepth,\n    inHeight,\n    inWidth,\n    outChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    strideDepth,\n    strideHeight,\n    strideWidth\n  } = convInfo;\n  const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n  const topPad = filterHeight - 1 - convInfo.padInfo.top;\n  const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n  for (let b = 0; b < batchSize; ++b) {\n    for (let d1 = 0; d1 < inChannels; ++d1) {\n      // Frames of depth\n      for (let xF = 0; xF < inDepth; ++xF) {\n        const xFCorner = xF - frontPad;\n        const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n        const yFMax =\n            Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n        // Rows as per standard 2d matrix notation\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          // Columns as per standard 2d matrix notation\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 yF = xFMin; yF < yFMax; ++yF) {\n              const wF = yF * strideDepth - xFCorner;\n\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 = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                  const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                      fltS1 * (filterHeight - 1 - wR) +\n                      fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n                  for (let d2 = 0; d2 < outChannels; ++d2) {\n                    const pixel = dyValues[dyOffset + d2];\n                    const weight = fltValues[fltOffset + d2];\n                    dotProd += pixel * weight;\n                  }\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                dotProd;\n          }\n        }\n      }\n    }\n  }\n\n  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const conv3DBackpropInputV2Config: KernelConfig = {\n  kernelName: Conv3DBackpropInputV2,\n  backendName: 'cpu',\n  kernelFunc: conv3DBackpropInputV2 as unknown as KernelFunc\n};\n"]}