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separable_conv2d.ts
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separable_conv2d.ts
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/**
* @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 {Tensor3D, Tensor4D} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
import * as util from '../util';
import {conv2d} from './conv2d';
import {depthwiseConv2d} from './depthwise_conv2d';
import {op} from './operation';
import {reshape} from './reshape';
/**
* 2-D convolution with separable filters.
*
* Performs a depthwise convolution that acts separately on channels followed
* by a pointwise convolution that mixes channels. Note that this is
* separability between dimensions [1, 2] and 3, not spatial separability
* between dimensions 1 and 2.
*
* See
* [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
* https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
* for more details.
*
* @param x The input tensor, of rank 4 or rank 3, of shape
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
* assumed.
* @param depthwiseFilter The depthwise filter tensor, rank 4, of shape
* `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
* the filter used in the first step.
* @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
* `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
* the filter used in the second step.
* @param strides The strides of the convolution: `[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 1x1.
* - For more info, see this guide:
* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
* in which we sample input values across the height and width dimensions
* in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
* number, then `dilationHeight == dilationWidth`. If it is greater than
* 1, then all values of `strides` must be 1.
* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
* "NHWC". Specify the data format of the input and output data. With the
* default format "NHWC", the data is stored in the order of: [batch,
* height, width, channels]. Only "NHWC" is currently supported.
*
* @doc {heading: 'Operations', subheading: 'Convolution'}
*/
function separableConv2d_<T extends Tensor3D|Tensor4D>(
x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,
pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,
pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],
dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {
const $x = convertToTensor(x, 'x', 'separableConv2d');
const $depthwiseFilter =
convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
const $pointwiseFilter =
convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
let x4D = $x as Tensor4D;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
if (dataFormat === 'NCHW') {
throw new Error(
'separableConv2d currently does not support dataFormat NCHW; only ' +
'NHWC is supported');
}
util.assert(
x4D.rank === 4,
() => `Error in separableConv2d: input must be rank 4, but got ` +
`rank ${x4D.rank}.`);
util.assert(
$depthwiseFilter.rank === 4,
() => `Error in separableConv2d: depthwise filter must be rank 4, but ` +
`got rank ${$depthwiseFilter.rank}.`);
util.assert(
$pointwiseFilter.rank === 4,
() => `Error in separableConv2d: pointwise filter must be rank 4, but ` +
`got rank ${$depthwiseFilter.rank}.`);
util.assert(
$pointwiseFilter.shape[0] === 1,
() =>
`Error in separableConv2d: the first dimension of pointwise filter ` +
` must be 1, but got ${$pointwiseFilter.shape[0]}.`);
util.assert(
$pointwiseFilter.shape[1] === 1,
() => `Error in separableConv2d: the second dimension of pointwise ` +
`filter must be 1, but got ${$pointwiseFilter.shape[1]}.`);
const inChannels = $depthwiseFilter.shape[2];
const channelMultiplier = $depthwiseFilter.shape[3];
util.assert(
$pointwiseFilter.shape[2] === inChannels * channelMultiplier,
() =>
`Error in separableConv2d: the third dimension of pointwise filter ` +
`must be ${inChannels * channelMultiplier}, ` +
`but got ${$pointwiseFilter.shape[2]}.`);
const depthwise = depthwiseConv2d(
x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
const pointwiseStride = 1;
const res =
conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
}
return res as T;
}
export const separableConv2d = /* @__PURE__ */ op({separableConv2d_});