/**
|
* @license
|
* Copyright 2018 Google Inc. 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 { Tensor3D, Tensor4D, Tensor5D } from '../tensor';
|
import { TensorLike } from '../types';
|
/**
|
* Computes the 2D max pooling of an image.
|
*
|
* @param x The input tensor, of rank 4 or rank 3 of shape
|
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
|
* @param filterSize The filter size: `[filterHeight, filterWidth]`. If
|
* `filterSize` is a single number, then `filterHeight == filterWidth`.
|
* @param strides The strides of the pooling: `[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 1x1.
|
* - For more info, see this guide:
|
* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dimRoundingMode The rounding mode used when computing output
|
* dimensions if pad is a number. If none is provided, it will not round
|
* and error if the output is of fractional size.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
declare function maxPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
/**
|
* Computes the 2D average pooling of an image.
|
*
|
* @param x The input tensor, of rank 4 or rank 3 of shape
|
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
|
* @param filterSize The filter size: `[filterHeight, filterWidth]`. If
|
* `filterSize` is a single number, then `filterHeight == filterWidth`.
|
* @param strides The strides of the pooling: `[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 1x1.
|
* - For more info, see this guide:
|
* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dimRoundingMode The rounding mode used when computing output
|
* dimensions if pad is a number. If none is provided, it will not round
|
* and error if the output is of fractional size.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
declare function avgPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
|
/**
|
* Performs an N-D pooling operation
|
*
|
* @param input The input tensor, of rank 4 or rank 3 of shape
|
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
|
* @param windowShape The filter size: `[filterHeight, filterWidth]`. If
|
* `filterSize` is a single number, then `filterHeight == filterWidth`.
|
* @param poolingType The type of pooling, either 'max' or 'avg'.
|
* @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_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
|
* in which we sample input values across the height and width dimensions
|
* in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single
|
* number, then `dilationHeight == dilationWidth`. If it is greater than
|
* 1, then all values of `strides` must be 1.
|
* @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
|
* `strides` is a single number, then `strideHeight == strideWidth`.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
declare function pool_<T extends Tensor3D | Tensor4D>(input: T | TensorLike, windowShape: [number, number] | number, poolingType: 'avg' | 'max', pad: 'valid' | 'same' | number, dilations?: [number, number] | number, strides?: [number, number] | number): T;
|
/**
|
* Computes the 3D average pooling.
|
*
|
* ```js
|
* const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
|
* const result = tf.avgPool3d(x, 2, 1, 'valid');
|
* result.print();
|
* ```
|
*
|
* @param x The input tensor, of rank 5 or rank 4 of shape
|
* `[batch, depth, height, width, inChannels]`.
|
* @param filterSize The filter size:
|
* `[filterDepth, filterHeight, filterWidth]`.
|
* If `filterSize` is a single number,
|
* then `filterDepth == filterHeight == filterWidth`.
|
* @param strides The strides of the pooling:
|
* `[strideDepth, strideHeight, strideWidth]`.
|
* If `strides` is a single number,
|
* then `strideDepth == 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_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dimRoundingMode The rounding mode used when computing output
|
* dimensions if pad is a number. If none is provided, it will not round
|
* and error if the output is of fractional size.
|
* @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
|
* "NDHWC". Specify the data format of the input and output data. With the
|
* default format "NDHWC", the data is stored in the order of: [batch,
|
* depth, height, width, channels]. Only "NDHWC" is currently supported.
|
* @param dilations The dilation rates:
|
* `[dilationDepth, dilationHeight, dilationWidth]`
|
* in which we sample input values across the depth, height and width
|
* dimensions in dilated pooling.
|
* Defaults to `[1, 1, 1]`. If `dilations` is a single number,
|
* then `dilationDepth == dilationHeight == dilationWidth`.
|
* If it is greater than 1, then all values of `strides` must be 1.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
declare function avgPool3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filterSize: [number, number, number] | number, strides: [number, number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'NDHWC' | 'NCDHW', dilations?: [number, number, number] | number): T;
|
/**
|
* Computes the 3D max pooling.
|
*
|
* ```js
|
* const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]);
|
* const result = tf.maxPool3d(x, 2, 1, 'valid');
|
* result.print();
|
* ```
|
*
|
* @param x The input tensor, of rank 5 or rank 4 of shape
|
* `[batch, depth, height, width, inChannels]`.
|
* @param filterSize The filter size:
|
* `[filterDepth, filterHeight, filterWidth]`.
|
* If `filterSize` is a single number,
|
* then `filterDepth == filterHeight == filterWidth`.
|
* @param strides The strides of the pooling:
|
* `[strideDepth, strideHeight, strideWidth]`.
|
* If `strides` is a single number,
|
* then `strideDepth == 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_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dimRoundingMode The rounding mode used when computing output
|
* dimensions if pad is a number. If none is provided, it will not round
|
* and error if the output is of fractional size.
|
* @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to
|
* "NDHWC". Specify the data format of the input and output data. With the
|
* default format "NDHWC", the data is stored in the order of: [batch,
|
* depth, height, width, channels]. Only "NDHWC" is currently supported.
|
* @param dilations The dilation rates:
|
* `[dilationDepth, dilationHeight, dilationWidth]`
|
* in which we sample input values across the depth, height and width
|
* dimensions in dilated pooling.
|
* Defaults to `[1, 1, 1]`. If `dilations` is a single number,
|
* then `dilationDepth == dilationHeight == dilationWidth`.
|
* If it is greater than 1, then all values of `strides` must be 1.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
declare function maxPool3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filterSize: [number, number, number] | number, strides: [number, number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'NDHWC' | 'NCDHW', dilations?: [number, number, number] | number): T;
|
export declare const maxPool: typeof maxPool_;
|
export declare const avgPool: typeof avgPool_;
|
export declare const pool: typeof pool_;
|
export declare const maxPool3d: typeof maxPool3d_;
|
export declare const avgPool3d: typeof avgPool3d_;
|
export {};
|
