@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @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 { ENGINE } from '../engine';
import { AvgPool3D } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { cast } from './cast';
import { op } from './operation';
import { reshape } from './reshape';
/**
* 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 A string from: 'ceil', 'round', 'floor'. If none is
* provided, it will default to truncate.
* @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.
*
* @doc {heading: 'Operations', subheading: 'Convolution'}
*/
function avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat = 'NDHWC') {
const $x = convertToTensor(x, 'x', 'avgPool3d', 'float32');
let x5D = $x;
let reshapedTo5D = false;
if ($x.rank === 4) {
reshapedTo5D = true;
x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]);
}
util.assert(x5D.rank === 5, () => `Error in avgPool3d: x must be rank 5 but got rank ${x5D.rank}.`);
util.assert(dataFormat === 'NDHWC', () => `Error in avgPool3d: Only NDHWC is currently supported, ` +
`but got dataFormat of ${dataFormat}`);
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), () => `Error in avgPool3d: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const inputs = { x: x5D };
const attrs = { filterSize, strides, pad, dimRoundingMode, dataFormat };
// tslint:disable-next-line: no-unnecessary-type-assertion
let res = ENGINE.runKernel(AvgPool3D, inputs, attrs);
res = cast(res, x5D.dtype);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
export const avgPool3d = op({ avgPool3d_ });
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