@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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text/typescript
/**
* @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, Conv3DBackpropInputV2Attrs, Conv3DBackpropInputV2Inputs} from '../kernel_names';
import {NamedAttrMap} from '../kernel_registry';
import {Tensor4D, Tensor5D} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
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_<T extends Tensor4D|Tensor5D>(
xShape:
[number, number, number, number,
number]|[number, number, number, number],
dy: T, filter: Tensor5D, strides: [number, number, number]|number,
pad: 'valid'|'same'): T {
util.assert(
xShape.length === dy.rank,
() => `Length of inShape ` +
`(${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape5D = xShape as [number, number, number, number, number];
let dy5D = dy as Tensor5D;
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: Conv3DBackpropInputV2Inputs = {dy: dy5D, filter};
const attrs:
Conv3DBackpropInputV2Attrs = {pad, strides, inputShape: xShape5D};
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(
Conv3DBackpropInputV2, inputs as {} as NamedTensorMap,
attrs as {} as NamedAttrMap) as T;
if (reshapedTo5D) {
return reshape(
res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]) as
T;
}
return res;
}
export const conv3DBackpropInput = op({conv3DBackpropInput_});