@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 { Conv3DBackpropInputV2 } from '../kernel_names';
import * as util from '../util';
import { op } from './operation';
import { reshape } from './reshape';
/**
* Computes the derivative of the input of a 3D convolution.
*
* @param xShape The shape of the input: [batch, depth, height, width,
* in_channels]. If length of 4, batch of 1 is assumed.
* @param dy The derivative of the output, of rank 5 or rank 4 of shape
* `[batch, outDepth, outHeight, outWidth, in_channels]`.
* If rank 4, batch of 1 is assumed.
* @param filter The filter, rank 5, of shape
* `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.
* @param strides The strides of the convolution: `[strideDepth, 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.
*/
function conv3DBackpropInput_(xShape, dy, filter, strides, pad) {
util.assert(xShape.length === dy.rank, () => `Length of inShape ` +
`(${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape5D = xShape;
let dy5D = dy;
let reshapedTo5D = false;
if (dy.rank === 4) {
reshapedTo5D = true;
dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
}
const inDepth = xShape5D[4];
const outDepth = dy5D.shape[4];
util.assert(xShape5D.length === 5, () => `Error in conv3dDerInput: inShape must be length 5, but got length ` +
`${xShape5D.length}.`);
util.assert(dy5D.rank === 5, () => `Error in conv3dDerInput: dy must be rank 5, but got ` +
`rank ${dy5D.rank}`);
util.assert(filter.rank === 5, () => `Error in conv3dDerInput: filter must be rank 5, but got ` +
`rank ${filter.rank}`);
util.assert(inDepth === filter.shape[3], () => `Error in conv3dDerInput: depth of input (${inDepth}) must ` +
`match input depth for filter ${filter.shape[3]}.`);
util.assert(outDepth === filter.shape[4], () => `Error in conv3dDerInput: depth of output (${outDepth}) must ` +
`match output depth for filter ${filter.shape[4]}.`);
const inputs = { dy: dy5D, filter };
const attrs = { pad, strides, inputShape: xShape5D };
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
export const conv3DBackpropInput = op({ conv3DBackpropInput_ });
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