-
Notifications
You must be signed in to change notification settings - Fork 1.9k
/
conv2d_backprop_input.ts
111 lines (104 loc) · 4.63 KB
/
conv2d_backprop_input.ts
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
/**
* @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, Conv2DBackpropInputAttrs, Conv2DBackpropInputInputs} from '../kernel_names';
import {NamedAttrMap} from '../kernel_registry';
import {Tensor3D, Tensor4D} from '../tensor';
import {NamedTensorMap} from '../tensor_types';
import * as util from '../util';
import * as conv_util from './conv_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_<T extends Tensor3D|Tensor4D>(
xShape: [number, number, number, number]|[number, number, number], dy: T,
filter: Tensor4D, strides: [number, number]|number,
pad: 'valid'|'same'|number|conv_util.ExplicitPadding,
dataFormat: 'NHWC'|'NCHW' = 'NHWC',
dimRoundingMode?: 'floor'|'round'|'ceil'): T {
util.assert(
xShape.length === dy.rank,
() => `Length of inShape ` +
`(${xShape.length}) and rank of dy (${dy.rank}) must match`);
let xShape4D = xShape as [number, number, number, number];
let dy4D = dy as Tensor4D;
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]}.`);
conv_util.checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode);
const inputs: Conv2DBackpropInputInputs = {dy: dy4D, filter};
const attrs: Conv2DBackpropInputAttrs =
{strides, pad, dataFormat, dimRoundingMode, inputShape: xShape4D};
// tslint:disable-next-line: no-unnecessary-type-assertion
const res = ENGINE.runKernel(
Conv2DBackpropInput, inputs as unknown as NamedTensorMap,
attrs as unknown as NamedAttrMap) as T;
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as T;
}
return res;
}
export const conv2DBackpropInput = /* @__PURE__ */ op({conv2DBackpropInput_});