@tensorflow/tfjs-core/dist/ops/conv_util.d.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/conv_util" />
type PadType = 'SAME' | 'VALID' | 'NUMBER' | 'EXPLICIT';
export type ExplicitPadding = [
    [number, number],
    [number, number],
    [number, number],
    [number, number]
];
export type PadInfo = {
    top: number;
    left: number;
    right: number;
    bottom: number;
    type: PadType;
};
export type PadInfo3D = {
    top: number;
    left: number;
    right: number;
    bottom: number;
    front: number;
    back: number;
    type: PadType;
};
/**
 * Information about the forward pass of a convolution/pooling operation.
 * It includes input and output shape, strides, filter size and padding
 * information.
 */
export type Conv2DInfo = {
    batchSize: number;
    inHeight: number;
    inWidth: number;
    inChannels: number;
    outHeight: number;
    outWidth: number;
    outChannels: number;
    dataFormat: 'channelsFirst' | 'channelsLast';
    strideHeight: number;
    strideWidth: number;
    dilationHeight: number;
    dilationWidth: number;
    filterHeight: number;
    filterWidth: number;
    effectiveFilterHeight: number;
    effectiveFilterWidth: number;
    padInfo: PadInfo;
    inShape: [number, number, number, number];
    outShape: [number, number, number, number];
    filterShape: [number, number, number, number];
};
/**
 *
 * @param inputShape Input tensor shape is of the following dimensions:
 *     `[batch, height, width, inChannels]`.
 * @param filterShape The filter shape is of the following dimensions:
 *     `[filterHeight, filterWidth, depth]`.
 * @param strides The strides of the sliding window for each dimension of the
 *     input tensor: `[strideHeight, strideWidth]`.
 *     If `strides` is a single number,
 *     then `strideHeight == strideWidth`.
 * @param pad The type of padding algorithm.
 *    - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *    - `valid`: output will be smaller than input if filter is larger
 *       than 1*1x1.
 *    - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dataFormat The data format of the input and output data.
 *     Defaults to 'NHWC'.
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`.
 *     Defaults to `[1, 1]`. If `dilations` is a single number, then
 *     `dilationHeight == dilationWidth`.
 */
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;
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;
/**
 * Computes the information for a forward pass of a pooling3D operation.
 */
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;
/**
 * Computes the information for a forward pass of a convolution/pooling
 * operation.
 */
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;
/**
 * Information about the forward pass of a 3D convolution/pooling operation.
 * It includes input and output shape, strides, filter size and padding
 * information.
 */
export type Conv3DInfo = {
    batchSize: number;
    inDepth: number;
    inHeight: number;
    inWidth: number;
    inChannels: number;
    outDepth: number;
    outHeight: number;
    outWidth: number;
    outChannels: number;
    dataFormat: 'channelsFirst' | 'channelsLast';
    strideDepth: number;
    strideHeight: number;
    strideWidth: number;
    dilationDepth: number;
    dilationHeight: number;
    dilationWidth: number;
    filterDepth: number;
    filterHeight: number;
    filterWidth: number;
    effectiveFilterDepth: number;
    effectiveFilterHeight: number;
    effectiveFilterWidth: number;
    padInfo: PadInfo3D;
    inShape: [number, number, number, number, number];
    outShape: [number, number, number, number, number];
    filterShape: [number, number, number, number, number];
};
/**
 * Computes the information for a forward pass of a 3D convolution/pooling
 * operation.
 */
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;
export declare function computeDefaultPad(inputShape: [number, number] | [number, number, number, number], fieldSize: number, stride: number, dilation?: number): number;
export declare function tupleValuesAreOne(param: number | number[]): boolean;
export declare function eitherStridesOrDilationsAreOne(strides: number | number[], dilations: number | number[]): boolean;
export declare function stridesOrDilationsArePositive(values: number | number[]): boolean;
/**
 * Convert Conv2D dataFormat from 'NHWC'|'NCHW' to
 *    'channelsLast'|'channelsFirst'
 * @param dataFormat in 'NHWC'|'NCHW' mode
 * @return dataFormat in 'channelsLast'|'channelsFirst' mode
 * @throws unknown dataFormat
 */
export declare function convertConv2DDataFormat(dataFormat: 'NHWC' | 'NCHW'): 'channelsLast' | 'channelsFirst';
/**
 * Check validity of pad when using dimRoundingMode.
 * @param opDesc A string of op description
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid` output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @throws unknown padding parameter
 */
export declare function checkPadOnDimRoundingMode(opDesc: string, pad: 'valid' | 'same' | number | ExplicitPadding, dimRoundingMode?: 'floor' | 'round' | 'ceil'): void;
export {};