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pool.ts
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pool.ts
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/**
* @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 {ENV} from '../environment';
import {Tensor, Tensor3D, Tensor4D} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import * as util from '../util';
import {batchToSpaceND, spaceToBatchND} from './array_ops';
import * as conv_util from './conv_util';
import {op} from './operation';
/**
* 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 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 `dilations` is a single
* number, then `dilationHeight == dilationWidth`. If it is greater than
* 1, then all values of `strides` must be 1.
* @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.
*/
function maxPoolImpl_<T extends Tensor3D|Tensor4D>(
x: T|TensorLike, filterSize: [number, number]|number,
strides: [number, number]|number, dilations: [number, number]|number,
pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {
const $x = convertToTensor(x, 'x', 'maxPool');
let x4D = $x as Tensor4D;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
if (dilations == null) {
dilations = [1, 1];
}
util.assert(
x4D.rank === 4,
`Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);
util.assert(
conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
'Error in maxPool: Either strides or dilations must be 1. ' +
`Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
util.assert(
util.isInt(pad as number),
`Error in maxPool: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const convInfo = conv_util.computePool2DInfo(
x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
const grad = (dy: Tensor4D, saved: Tensor[]) => {
const [y4D] = saved;
return {
x: () => maxPoolBackprop(
dy, x4D, y4D as Tensor4D, filterSize, strides, dilations, pad)
};
};
const res = ENV.engine.runKernel(
(backend, save) => save(backend.maxPool(x4D, convInfo)), {x: x4D}, grad);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
}
return res as T;
}
/**
* 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'} */
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 {
return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
}
/**
* 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 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 `dilations` is a single
* number, then `dilationHeight == dilationWidth`. If it is greater than
* 1, then all values of `strides` must be 1.
* @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.
*/
function avgPoolImpl_<T extends Tensor3D|Tensor4D>(
x: T|TensorLike, filterSize: [number, number]|number,
strides: [number, number]|number, dilations: [number, number]|number,
pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {
const $x = convertToTensor(x, 'x', 'avgPool', 'float32');
if (dilations == null) {
dilations = [1, 1];
}
util.assert(
conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
'Error in avgPool: Either strides or dilations must be 1. ' +
`Got strides ${strides} and dilations '${dilations}'`);
let x4D = $x as Tensor4D;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
util.assert(
x4D.rank === 4,
`Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);
if (dimRoundingMode != null) {
util.assert(
util.isInt(pad as number),
`Error in avgPool: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const convInfo = conv_util.computePool2DInfo(
x4D.shape, filterSize, strides, dilations, pad);
const grad = (dy: Tensor4D) => {
return {
x: () => avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad)
};
};
let res = ENV.engine.runKernel(
backend => backend.avgPool(x4D, convInfo), {x: x4D}, grad);
res = res.cast($x.dtype);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
}
return res as 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'} */
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 {
return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
}
/**
* 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'} */
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) {
if (dilations == null) {
dilations = [1, 1];
}
if (strides == null) {
strides = 1;
}
if (pad === 0) {
pad = 'valid';
}
const $x = convertToTensor(input, 'x', 'maxPool');
let x4D = $x as Tensor4D;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
util.assert(
conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
'Error in pool: Either strides or dilations must be 1. ' +
`Got strides ${strides} and dilations '${dilations}'`);
const convInfo = conv_util.computePool2DInfo(
x4D.shape, windowShape, strides, dilations, pad);
const dilation: [number, number] =
[convInfo.dilationHeight, convInfo.dilationWidth];
// The following implementation does batchToSpace(pool(spaceToBatch(x)))
// whenever dilation > 1 since the TF kernels do not support dilation > 1.
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037
let basePadding: number[][];
if (pad === 'same') {
basePadding = withSpaceToBatchBasePaddings(
[convInfo.filterHeight, convInfo.filterWidth], dilation);
} else {
basePadding = [[0, 0], [0, 0]];
}
const isDilationOne = dilation[0] === 1 && dilation[1] === 1;
const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(
[convInfo.inHeight, convInfo.inWidth], dilation, basePadding);
const convertedPad = isDilationOne ? pad : 'valid';
const convertedX =
isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
const forwardOp = poolingType === 'avg' ?
() => avgPoolImpl_(
convertedX, windowShape, strides, 1 /* dilation */, convertedPad) :
() => maxPoolImpl_(
convertedX, windowShape, strides, 1 /* dilation */, convertedPad);
const y = forwardOp();
const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
}
return res as T;
}
/**
* Computes the backprop of a max pool.
*
* @param dy The dy error, of rank 4 or rank 3 of shape
* [batchSize, height, width, channels]. If rank 3, batch of 1 is
* assumed.
* @param input The original input image, of rank 4, of shape
* [batchSize, height, width, channels].
* @param output The original output image, of rank 4, of shape
* [batchSize, outHeight, outWidth, channels].
* @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 A string from: 'same', 'valid'. The type of padding algorithm
* used in the forward prop of the op.
* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. 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.
*/
function maxPoolBackprop(
dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,
output: Tensor4D|TensorLike, filterSize: [number, number]|number,
strides: [number, number]|number, dilations: [number, number]|number,
pad: 'valid'|'same'|number,
dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {
const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');
const $input = convertToTensor(input, 'input', 'maxPoolBackprop');
const $output = convertToTensor(output, 'output', 'maxPoolBackprop');
util.assert(
$input.rank === $dy.rank,
`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
if (dilations == null) {
dilations = [1, 1];
}
util.assert(
conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +
`Got strides ${strides} and dilations '${dilations}'`);
util.assert(
$dy.rank === 4,
`Error in maxPoolBackprop: dy must be rank 4 but got rank ` +
`${$dy.rank}.`);
util.assert(
$input.rank === 4,
`Error in maxPoolBackprop: input must be rank 4 but got rank ` +
`${$input.rank}.`);
if (dimRoundingMode != null) {
util.assert(
util.isInt(pad as number),
`Error in maxPoolBackprop: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const convInfo = conv_util.computePool2DInfo(
$input.shape, filterSize, strides, dilations, pad, dimRoundingMode);
const res = ENV.engine.runKernel(
backend => backend.maxPoolBackprop($dy, $input, $output, convInfo),
{$dy, $input});
return res;
}
/**
* Computes the backprop of an avg pool.
*
* @param dy The dy error, of rank 4 or rank 3 of shape
* [batchSize, height, width, channels]. If rank 3, batch of 1 is
* assumed.
* @param input The input image, of rank 4 or rank 3 of shape
* [batchSize, height, width, channels]. 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 A string from: 'same', 'valid'. The type of padding algorithm
* used in the forward prop of the op.
*/
function avgPoolBackprop<T extends Tensor3D|Tensor4D>(
dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,
strides: [number, number]|number, dilations: [number, number]|number,
pad: 'valid'|'same'|number): T {
const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');
const $input = convertToTensor(input, 'input', 'avgPoolBackprop');
util.assert(
$input.rank === $dy.rank,
`Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
if (dilations == null) {
dilations = [1, 1];
}
util.assert(
conv_util.eitherStridesOrDilationsAreOne(strides, dilations),
'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +
`Got strides ${strides} and dilations '${dilations}'`);
let input4D = $input as Tensor4D;
let dy4D = $dy as Tensor4D;
let reshapedTo4D = false;
if ($input.rank === 3) {
reshapedTo4D = true;
input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);
dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);
}
util.assert(
dy4D.rank === 4,
`Error in avgPoolBackprop: dy must be rank 4 but got rank ` +
`${dy4D.rank}.`);
util.assert(
input4D.rank === 4,
`Error in avgPoolBackprop: input must be rank 4 but got rank ` +
`${input4D.rank}.`);
const convInfo = conv_util.computePool2DInfo(
input4D.shape, filterSize, strides, dilations, pad);
const res = ENV.engine.runKernel(
backend => backend.avgPoolBackprop(dy4D, input4D, convInfo),
{dy4D, input4D});
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;
}
return res as T;
}
// Helper function to compute crops and paddings for pool with dilation > 1.
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184
function requiredSpaceToBatchPaddings(
inputShape: [number, number], blockShape: [number, number],
basePadding: number[][]) {
const padStart = basePadding.map(b => b[0]);
const origPadEnd = basePadding.map(b => b[1]);
const fullInputShape = inputShape.concat(padStart, origPadEnd);
const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);
const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);
const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);
const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);
return [paddings, crops];
}
// Helper function to compute base paddings for pool with dilation > 1.
// tslint:disable-next-line:max-line-length
// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524
function withSpaceToBatchBasePaddings(
filterShape: [number, number], dilation: [number, number]) {
// Spatial dimensions of the filters and the upsampled filters in which we
// introduce (rate - 1) zeros between consecutive filter values.
const dilatedFilterShape = filterShape.map((s, i) => {
return s + (s - 1) * (dilation[i] - 1);
});
const padExtraShape = dilatedFilterShape.map(s => s - 1);
// When padding is odd, we pad more at end, following the same
// convention as conv2d.
const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));
const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);
return padExtraShape.map((_, i) => {
return [padExtraStart[i], padExtraEnd[i]];
});
}
export const maxPool = op({maxPool_});
export const avgPool = op({avgPool_});
export const pool = op({pool_});