@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 { MaxPoolGrad } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { op } from './operation';
/**
* Computes the backprop of a 2D 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'. If none is
* provided, it will default to truncate.
*/
function maxPoolGrad_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {
const $dy = convertToTensor(dy, 'dy', 'maxPoolGrad');
const $input = convertToTensor(input, 'input', 'maxPoolGrad');
const $output = convertToTensor(output, 'output', 'maxPoolGrad');
util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy ` +
`(${$dy.rank})`);
util.assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ` +
`${$dy.rank}.`);
util.assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ` +
`${$input.rank}.`);
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), () => `Error in maxPoolGrad: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const inputs = { dy: $dy, input: $input, output: $output };
const attrs = { filterSize, strides, pad, dimRoundingMode };
// tslint:disable-next-line: no-unnecessary-type-assertion
return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);
}
export const maxPoolGrad = op({ maxPoolGrad_ });
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