@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 { AvgPoolGrad } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { op } from './operation';
import { reshape } from './reshape';
/**
* Computes the backprop of an 2D 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 avgPoolGrad_(dy, input, filterSize, strides, pad) {
const $dy = convertToTensor(dy, 'dy', 'avgPoolGrad');
const $input = convertToTensor(input, 'input', 'avgPoolGrad');
util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
let input4D = $input;
let dy4D = $dy;
let reshapedTo4D = false;
if ($input.rank === 3) {
reshapedTo4D = true;
input4D =
reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
}
util.assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ` +
`${dy4D.rank}.`);
util.assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ` +
`${input4D.rank}.`);
const inputs = { dy: dy4D, input: input4D };
const attrs = { filterSize, strides, pad };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
export const avgPoolGrad = op({ avgPoolGrad_ });
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