| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2020 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | import { ENGINE } from '../engine';
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| 18 | import { AvgPoolGrad } from '../kernel_names';
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| 19 | import { convertToTensor } from '../tensor_util_env';
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| 20 | import * as util from '../util';
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| 21 | import { op } from './operation';
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| 22 | import { reshape } from './reshape';
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| 23 | /**
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| 24 | * Computes the backprop of an 2D avg pool.
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| 25 | *
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| 26 | * @param dy The dy error, of rank 4 or rank 3 of shape
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| 27 | * [batchSize, height, width, channels]. If rank 3, batch of 1 is
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| 28 | * assumed.
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| 29 | * @param input The input image, of rank 4 or rank 3 of shape
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| 30 | * [batchSize, height, width, channels]. If rank 3, batch of 1 is
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| 31 | * assumed.
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| 32 | * @param filterSize The filter size: `[filterHeight, filterWidth]`. If
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| 33 | * `filterSize` is a single number, then `filterHeight == filterWidth`.
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| 34 | * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If
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| 35 | * `strides` is a single number, then `strideHeight == strideWidth`.
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| 36 | * @param pad A string from: 'same', 'valid'. The type of padding algorithm
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| 37 | * used in the forward prop of the op.
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| 38 | */
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| 39 | function avgPoolGrad_(dy, input, filterSize, strides, pad) {
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| 40 | const $dy = convertToTensor(dy, 'dy', 'avgPoolGrad');
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| 41 | const $input = convertToTensor(input, 'input', 'avgPoolGrad');
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| 42 | util.assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
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| 43 | let input4D = $input;
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| 44 | let dy4D = $dy;
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| 45 | let reshapedTo4D = false;
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| 46 | if ($input.rank === 3) {
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| 47 | reshapedTo4D = true;
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| 48 | input4D =
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| 49 | reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
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| 50 | dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
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| 51 | }
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| 52 | util.assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ` +
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| 53 | `${dy4D.rank}.`);
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| 54 | util.assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ` +
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| 55 | `${input4D.rank}.`);
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| 56 | const inputs = { dy: dy4D, input: input4D };
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| 57 | const attrs = { filterSize, strides, pad };
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| 58 | // tslint:disable-next-line: no-unnecessary-type-assertion
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| 59 | const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
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| 60 | if (reshapedTo4D) {
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| 61 | return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
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| 62 | }
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| 63 | return res;
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| 64 | }
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| 65 | export const avgPoolGrad = op({ avgPoolGrad_ });
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| 66 | //# sourceMappingURL=avg_pool_grad.js.map |
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