@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 { Dilation2D } from '../kernel_names';
import { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { op } from './operation';
import { reshape } from './reshape';
/**
* Computes the grayscale dilation over the input `x`.
*
* @param x The input tensor, rank 3 or rank 4 of shape
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.
* @param filter The filter tensor, rank 3, of shape
* `[filterHeight, filterWidth, depth]`.
* @param strides The strides of the sliding window for each dimension of the
* input tensor: `[strideHeight, strideWidth]`.
* If `strides` is a single number,
* then `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 dataFormat Specify the data format of the input and output data.
* Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the
* default format "NHWC", the data is stored in the order of: [batch,
* height, width, channels].
* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
* in which we sample input values across the height and width dimensions
* for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations`
* is a single number, then `dilationHeight == dilationWidth`. If it is
* greater than 1, then all values of `strides` must be 1.
*
* @doc {heading: 'Operations', subheading: 'Convolution'}
*/
function dilation2d_(x, filter, strides, pad, dilations = [1, 1], dataFormat = 'NHWC') {
const $x = convertToTensor(x, 'x', 'dilation2d');
const $filter = convertToTensor(filter, 'filter', 'dilation2d');
util.assert($x.rank === 3 || $x.rank === 4, () => `Error in dilation2d: input must be rank 3 or 4, but got rank ` +
`${$x.rank}.`);
util.assert($filter.rank === 3, () => `Error in dilation2d: filter must be rank 3, but got rank ` +
`${$filter.rank}.`);
util.assert(dataFormat === 'NHWC', () => `Error in dilation2d: Only NHWC is currently supported, ` +
`but got dataFormat of ${dataFormat}`);
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
reshapedTo4D = true;
}
const inputs = { x: x4D, filter: $filter };
const attrs = { strides, pad, dilations };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
export const dilation2d = op({ dilation2d_ });
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