@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 { Conv2DBackpropInput } from '../kernel_names';
import * as util from '../util';
import { op } from './operation';
import { reshape } from './reshape';
/**
* Computes the derivative of the input of a 2D convolution.
*
* @param xShape The shape of the input: [batch, height, width, inDepth].
* If length of 3, batch of 1 is assumed.
* @param dy The derivative of the output, of rank 4 or rank 3 of shape
* `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
* assumed.
* @param filter The filter, rank 4, of shape
* `[filterHeight, filterWidth, inDepth, outDepth]`.
* @param strides The strides of the convolution: `[strideHeight,
* strideWidth]`.
* @param pad The type of padding algorithm used:
* - `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.
* @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].
* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
* provided, it will default to truncate.
*/
function conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat = 'NHWC', dimRoundingMode) {
util.assert(xShape.length === dy.rank, () => `Length of inShape ` +
`(${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape4D = xShape;
let dy4D = dy;
let reshapedTo4D = false;
if (dy.rank === 3) {
reshapedTo4D = true;
dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]);
xShape4D = [1, xShape[0], xShape[1], xShape[2]];
}
util.assert(xShape4D.length === 4, () => `Error in conv2dDerInput: inShape must be length 4, but got length ` +
`${xShape4D.length}.`);
util.assert(dy4D.rank === 4, () => `Error in conv2dDerInput: dy must be rank 4, but got ` +
`rank ${dy4D.rank}`);
util.assert(filter.rank === 4, () => `Error in conv2dDerInput: filter must be rank 4, but got ` +
`rank ${filter.rank}`);
const inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];
const outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
util.assert(inDepth === filter.shape[2], () => `Error in conv2dDerInput: depth of input (${inDepth}) must ` +
`match input depth for filter ${filter.shape[2]}.`);
util.assert(outDepth === filter.shape[3], () => `Error in conv2dDerInput: depth of output (${outDepth}) must ` +
`match output depth for filter ${filter.shape[3]}.`);
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), () => `Error in conv2dDerInput: pad must be an integer when using, ` +
`dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const inputs = { dy: dy4D, filter };
const attrs = { strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
export const conv2DBackpropInput = op({ conv2DBackpropInput_ });
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