@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 { MaxPool3DGrad } 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 a 3d max pool.
*
* @param dy The dy error, of rank 5 of shape
* [batchSize, depth, height, width, channels].
* assumed.
* @param input The original input image, of rank 5 or rank 4 of shape
* [batchSize, depth, height, width, channels].
* @param output The original output image, of rank 5 of shape
* [batchSize, outDepth, outHeight, outWidth, channels].
* @param filterSize The filter size:
* `[filterDepth, filterHeight, filterWidth]`.
* `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 `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 maxPool3dGrad_(dy, input, output, filterSize, strides, pad, dimRoundingMode) {
const $dy = convertToTensor(dy, 'dy', 'maxPool3dGrad');
const $input = convertToTensor(input, 'input', 'maxPool3dGrad');
const $output = convertToTensor(output, 'output', 'maxPool3dGrad');
let dy5D = $dy;
let input5D = $input;
let output5D = $output;
let reshapedTo5D = false;
if ($input.rank === 4) {
reshapedTo5D = true;
dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
input5D = reshape($input, [
1, $input.shape[0], $input.shape[1], $input.shape[2], $input.shape[3]
]);
output5D = reshape($output, [
1, $output.shape[0], $output.shape[1], $output.shape[2], $output.shape[3]
]);
}
util.assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ` +
`${dy5D.rank}.`);
util.assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ` +
`${input5D.rank}.`);
util.assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ` +
`${output5D.rank}.`);
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), () => `Error in maxPool3dGrad: pad must be an integer when ` +
`using, dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const inputs = { dy: dy5D, input: input5D, output: output5D };
const attrs = { filterSize, strides, pad, dimRoundingMode };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
export const maxPool3dGrad = op({ maxPool3dGrad_ });
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