/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/// <amd-module name="@tensorflow/tfjs-core/dist/ops/conv_util" />
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type PadType = 'SAME' | 'VALID' | 'NUMBER' | 'EXPLICIT';
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export type ExplicitPadding = [
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[number, number],
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[number, number],
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[number, number],
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[number, number]
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];
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export type PadInfo = {
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top: number;
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left: number;
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right: number;
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bottom: number;
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type: PadType;
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};
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export type PadInfo3D = {
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top: number;
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left: number;
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right: number;
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bottom: number;
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front: number;
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back: number;
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type: PadType;
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};
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/**
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* Information about the forward pass of a convolution/pooling operation.
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* It includes input and output shape, strides, filter size and padding
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* information.
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*/
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export type Conv2DInfo = {
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batchSize: number;
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inHeight: number;
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inWidth: number;
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inChannels: number;
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outHeight: number;
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outWidth: number;
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outChannels: number;
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dataFormat: 'channelsFirst' | 'channelsLast';
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strideHeight: number;
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strideWidth: number;
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dilationHeight: number;
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dilationWidth: number;
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filterHeight: number;
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filterWidth: number;
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effectiveFilterHeight: number;
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effectiveFilterWidth: number;
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padInfo: PadInfo;
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inShape: [number, number, number, number];
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outShape: [number, number, number, number];
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filterShape: [number, number, number, number];
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};
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/**
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*
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* @param inputShape Input tensor shape is of the following dimensions:
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* `[batch, height, width, inChannels]`.
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* @param filterShape The filter shape is of the following dimensions:
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* `[filterHeight, filterWidth, depth]`.
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* @param strides The strides of the sliding window for each dimension of the
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* input tensor: `[strideHeight, strideWidth]`.
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* If `strides` is a single number,
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* then `strideHeight == strideWidth`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1*1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
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* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
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* @param dataFormat The data format of the input and output data.
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* Defaults to 'NHWC'.
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.
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* Defaults to `[1, 1]`. If `dilations` is a single number, then
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* `dilationHeight == dilationWidth`.
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*/
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export declare function computeDilation2DInfo(inputShape: [number, number, number, number], filterShape: [number, number, number], strides: number | [number, number], pad: 'same' | 'valid' | number, dataFormat: 'NHWC', dilations: number | [number, number]): Conv2DInfo;
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export declare function computePool2DInfo(inShape: [number, number, number, number], filterSize: [number, number] | number, strides: number | [number, number], dilations: number | [number, number], pad: 'same' | 'valid' | number | ExplicitPadding, roundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'channelsFirst' | 'channelsLast'): Conv2DInfo;
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/**
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* Computes the information for a forward pass of a pooling3D operation.
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*/
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export declare function computePool3DInfo(inShape: [number, number, number, number, number], filterSize: number | [number, number, number], strides: number | [number, number, number], dilations: number | [number, number, number], pad: 'same' | 'valid' | number, roundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'NDHWC' | 'NCDHW'): Conv3DInfo;
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/**
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* Computes the information for a forward pass of a convolution/pooling
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* operation.
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*/
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export declare function computeConv2DInfo(inShape: [number, number, number, number], filterShape: [number, number, number, number], strides: number | [number, number], dilations: number | [number, number], pad: 'same' | 'valid' | number | ExplicitPadding, roundingMode?: 'floor' | 'round' | 'ceil', depthwise?: boolean, dataFormat?: 'channelsFirst' | 'channelsLast'): Conv2DInfo;
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/**
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* Information about the forward pass of a 3D convolution/pooling operation.
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* It includes input and output shape, strides, filter size and padding
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* information.
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*/
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export type Conv3DInfo = {
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batchSize: number;
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inDepth: number;
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inHeight: number;
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inWidth: number;
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inChannels: number;
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outDepth: number;
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outHeight: number;
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outWidth: number;
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outChannels: number;
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dataFormat: 'channelsFirst' | 'channelsLast';
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strideDepth: number;
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strideHeight: number;
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strideWidth: number;
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dilationDepth: number;
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dilationHeight: number;
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dilationWidth: number;
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filterDepth: number;
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filterHeight: number;
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filterWidth: number;
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effectiveFilterDepth: number;
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effectiveFilterHeight: number;
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effectiveFilterWidth: number;
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padInfo: PadInfo3D;
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inShape: [number, number, number, number, number];
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outShape: [number, number, number, number, number];
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filterShape: [number, number, number, number, number];
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};
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/**
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* Computes the information for a forward pass of a 3D convolution/pooling
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* operation.
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*/
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export declare function computeConv3DInfo(inShape: [number, number, number, number, number], filterShape: [number, number, number, number, number], strides: number | [number, number, number], dilations: number | [number, number, number], pad: 'same' | 'valid' | number, depthwise?: boolean, dataFormat?: 'channelsFirst' | 'channelsLast', roundingMode?: 'floor' | 'round' | 'ceil'): Conv3DInfo;
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export declare function computeDefaultPad(inputShape: [number, number] | [number, number, number, number], fieldSize: number, stride: number, dilation?: number): number;
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export declare function tupleValuesAreOne(param: number | number[]): boolean;
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export declare function eitherStridesOrDilationsAreOne(strides: number | number[], dilations: number | number[]): boolean;
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export declare function stridesOrDilationsArePositive(values: number | number[]): boolean;
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/**
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* Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
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* 'channelsLast'|'channelsFirst'
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* @param dataFormat in 'NHWC'|'NCHW' mode
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* @return dataFormat in 'channelsLast'|'channelsFirst' mode
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* @throws unknown dataFormat
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*/
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export declare function convertConv2DDataFormat(dataFormat: 'NHWC' | 'NCHW'): 'channelsLast' | 'channelsFirst';
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/**
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* Check validity of pad when using dimRoundingMode.
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* @param opDesc A string of op description
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid` output will be smaller than input if filter is larger
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* than 1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
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* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
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* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
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* provided, it will default to truncate.
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* @throws unknown padding parameter
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*/
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export declare function checkPadOnDimRoundingMode(opDesc: string, pad: 'valid' | 'same' | number | ExplicitPadding, dimRoundingMode?: 'floor' | 'round' | 'ceil'): void;
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export {};
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