/**
 * @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, buffer, MaxPoolGrad } from '@tensorflow/tfjs-core';
import { assertNotComplex } from '../cpu_util';
import { maxPoolPositions } from '../utils/pool_utils';
export function maxPoolGrad(args) {
    const { inputs, backend, attrs } = args;
    const { dy, input, output } = inputs;
    const x = input;
    assertNotComplex([input, output], 'maxPoolGrad');
    const { filterSize, strides, pad, dimRoundingMode } = attrs;
    const convInfo = backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode);
    const xValues = backend.data.get(x.dataId).values;
    const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
    const strideHeight = convInfo.strideHeight;
    const strideWidth = convInfo.strideWidth;
    const dilationHeight = convInfo.dilationHeight;
    const dilationWidth = convInfo.dilationWidth;
    const effectiveFilterHeight = convInfo.effectiveFilterHeight;
    const effectiveFilterWidth = convInfo.effectiveFilterWidth;
    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
    const dx = buffer(x.shape, 'float32');
    const dyData = backend.data.get(dy.dataId).values;
    const dyBuf = buffer(dy.shape, 'float32', dyData);
    for (let b = 0; b < convInfo.batchSize; ++b) {
        for (let d = 0; d < convInfo.inChannels; ++d) {
            for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
                for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
                    // Shader code begins.
                    const dyRCorner = dxR - padTop;
                    const dyCCorner = dxC - padLeft;
                    let dotProd = 0;
                    for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
                        const dyR = (dyRCorner + wR) / strideHeight;
                        if (dyR < 0 || dyR >= convInfo.outHeight ||
                            Math.floor(dyR) !== dyR) {
                            continue;
                        }
                        for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
                            const dyC = (dyCCorner + wC) / strideWidth;
                            if (dyC < 0 || dyC >= convInfo.outWidth ||
                                Math.floor(dyC) !== dyC) {
                                continue;
                            }
                            const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -
                                maxPosBuf.get(b, dyR, dyC, d);
                            const curPos = wR * effectiveFilterWidth + wC;
                            const mask = maxPos === curPos ? 1 : 0;
                            if (mask === 0) {
                                continue;
                            }
                            const pixel = dyBuf.get(b, dyR, dyC, d);
                            dotProd += pixel * mask;
                        }
                    }
                    dx.set(dotProd, b, dxR, dxC, d);
                }
            }
        }
    }
    return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
export const maxPoolGradConfig = {
    kernelName: MaxPoolGrad,
    backendName: 'cpu',
    kernelFunc: maxPoolGrad
};
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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 */\nimport {backend_util, buffer, KernelConfig, KernelFunc, MaxPoolGrad, MaxPoolGradAttrs, MaxPoolGradInputs, Rank, TensorInfo, TypedArray} from '@tensorflow/tfjs-core';\n\nimport {MathBackendCPU} from '../backend_cpu';\nimport {assertNotComplex} from '../cpu_util';\nimport {maxPoolPositions} from '../utils/pool_utils';\n\nexport function maxPoolGrad(args: {\n  inputs: MaxPoolGradInputs,\n  backend: MathBackendCPU,\n  attrs: MaxPoolGradAttrs\n}): TensorInfo {\n  const {inputs, backend, attrs} = args;\n  const {dy, input, output} = inputs;\n  const x = input;\n  assertNotComplex([input, output], 'maxPoolGrad');\n  const {filterSize, strides, pad, dimRoundingMode} = attrs;\n\n  const convInfo = backend_util.computePool2DInfo(\n      x.shape as [number, number, number, number], filterSize, strides,\n      1 /* dilations */, pad, dimRoundingMode);\n  const xValues = backend.data.get(x.dataId).values as TypedArray;\n  const maxPosBuf = buffer(\n      convInfo.outShape, x.dtype,\n      maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);\n  const strideHeight = convInfo.strideHeight;\n  const strideWidth = convInfo.strideWidth;\n  const dilationHeight = convInfo.dilationHeight;\n  const dilationWidth = convInfo.dilationWidth;\n  const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n  const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n  const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n  const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n  const dx =\n      buffer<Rank.R4>(x.shape as [number, number, number, number], 'float32');\n\n  const dyData = backend.data.get(dy.dataId).values as Float32Array;\n  const dyBuf = buffer<Rank.R4>(\n      dy.shape as [number, number, number, number], 'float32', dyData);\n\n  for (let b = 0; b < convInfo.batchSize; ++b) {\n    for (let d = 0; d < convInfo.inChannels; ++d) {\n      for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n        for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n          // Shader code begins.\n          const dyRCorner = dxR - padTop;\n          const dyCCorner = dxC - padLeft;\n          let dotProd = 0;\n          for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n            const dyR = (dyRCorner + wR) / strideHeight;\n            if (dyR < 0 || dyR >= convInfo.outHeight ||\n                Math.floor(dyR) !== dyR) {\n              continue;\n            }\n            for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n              const dyC = (dyCCorner + wC) / strideWidth;\n              if (dyC < 0 || dyC >= convInfo.outWidth ||\n                  Math.floor(dyC) !== dyC) {\n                continue;\n              }\n              const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 -\n                  (maxPosBuf.get(b, dyR, dyC, d) as number);\n              const curPos = wR * effectiveFilterWidth + wC;\n\n              const mask = maxPos === curPos ? 1 : 0;\n              if (mask === 0) {\n                continue;\n              }\n\n              const pixel = dyBuf.get(b, dyR, dyC, d);\n              dotProd += pixel * mask;\n            }\n          }\n          dx.set(dotProd, b, dxR, dxC, d);\n        }\n      }\n    }\n  }\n  return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values);\n}\n\nexport const maxPoolGradConfig: KernelConfig = {\n  kernelName: MaxPoolGrad,\n  backendName: 'cpu',\n  kernelFunc: maxPoolGrad as unknown as KernelFunc\n};\n"]}