/
averagepool.js
159 lines (152 loc) · 4.56 KB
/
averagepool.js
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
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
import Layer, { NeuralnetworkLayerException } from './base.js'
import Tensor from '../../../util/tensor.js'
/**
* Average pool layer
*/
export default class AveragePoolLayer extends Layer {
/**
* @param {object} config object
* @param {number | number[]} config.kernel Size of kernel
* @param {number | number[]} [config.stride] Step of stride
* @param {number | number[]} [config.padding] size of padding
* @param {number} [config.channel_dim] Dimension of the channel
*/
constructor({ kernel, stride = null, padding = null, channel_dim = -1, ...rest }) {
super(rest)
this._kernel = kernel
this._stride = stride || kernel
this._padding = padding || 0
this._channel_dim = channel_dim
if (this._channel_dim !== -1 && this._channel_dim !== 1) {
throw new NeuralnetworkLayerException('Invalid channel dimension.')
}
}
_index(i, c, k) {
return this._channel_dim === -1 ? [i, ...k, c] : [i, c, ...k]
}
calc(x) {
if (!Array.isArray(this._kernel)) {
this._kernel = Array(x.dimension - 2).fill(this._kernel)
}
if (x.dimension !== this._kernel.length + 2) {
throw new NeuralnetworkLayerException('Invalid kernel size', [this, x])
}
if (!Array.isArray(this._stride)) {
this._stride = Array(x.dimension - 2).fill(this._stride)
}
if (x.dimension !== this._stride.length + 2) {
throw new NeuralnetworkLayerException('Invalid stride size', [this, x])
}
if (!Array.isArray(this._padding)) {
this._padding = Array.from({ length: x.dimension - 2 }, () => [this._padding, this._padding])
} else if (!Array.isArray(this._padding[0])) {
this._padding = this._padding.map(p => [p, p])
}
if (x.dimension !== this._padding.length + 2) {
throw new NeuralnetworkLayerException('Invalid padding size', [this, x])
}
this._i = x
const koff = this._channel_dim === -1 ? 1 : 2
const outSize = [
x.sizes[0],
...this._kernel.map(
(k, d) =>
Math.ceil(
Math.max(0, x.sizes[d + koff] + this._padding[d][0] + this._padding[d][1] - k) / this._stride[d]
) + 1
),
]
if (this._channel_dim === -1) {
outSize.push(x.sizes[x.dimension - 1])
} else if (this._channel_dim === 1) {
outSize.splice(1, 0, x.sizes[1])
}
const channels = this._channel_dim === -1 ? x.sizes[x.dimension - 1] : x.sizes[1]
this._o = new Tensor(outSize)
for (let i = 0; i < x.sizes[0]; i++) {
for (let c = 0; c < channels; c++) {
const idx = Array(x.dimension - 2).fill(0)
do {
const offset = Array(x.dimension - 2).fill(0)
let sumval = 0
let count = 0
do {
const p = idx.map((v, i) => v * this._stride[i] - this._padding[i][0] + offset[i])
if (p.every((v, i) => 0 <= v && v < x.sizes[i + koff])) {
sumval += x.at(this._index(i, c, p))
count++
}
for (let k = 0; k < offset.length; k++) {
offset[k]++
if (offset[k] < this._kernel[k]) {
break
}
offset[k] = 0
}
} while (offset.some(v => v > 0))
this._o.set(this._index(i, c, idx), sumval / count)
for (let k = 0; k < idx.length; k++) {
idx[k]++
if (idx[k] < outSize[k + koff]) {
break
}
idx[k] = 0
}
} while (idx.some(v => v > 0))
}
}
return this._o
}
grad(bo) {
this._bo = bo
this._bi = new Tensor(this._i.sizes)
const koff = this._channel_dim === -1 ? 1 : 2
const channels = this._channel_dim === -1 ? this._i.sizes[this._i.dimension - 1] : this._i.sizes[1]
for (let i = 0; i < this._i.sizes[0]; i++) {
for (let c = 0; c < channels; c++) {
const idx = Array(this._i.dimension - 2).fill(0)
do {
const offset = Array(this._i.dimension - 2).fill(0)
const ps = []
do {
const p = idx.map((v, i) => v * this._stride[i] - this._padding[i][0] + offset[i])
if (p.every((v, i) => 0 <= v && v < this._i.sizes[i + koff])) {
ps.push(p)
}
for (let k = 0; k < offset.length; k++) {
offset[k]++
if (offset[k] < this._kernel[k]) {
break
}
offset[k] = 0
}
} while (offset.some(v => v > 0))
for (const p of ps) {
this._bi.operateAt(
this._index(i, c, p),
v => v + this._bo.at(this._index(i, c, idx)) / ps.length
)
}
for (let k = 0; k < idx.length; k++) {
idx[k]++
if (idx[k] < this._o.sizes[k + koff]) {
break
}
idx[k] = 0
}
} while (idx.some(v => v > 0))
}
}
return this._bi
}
toObject() {
return {
type: 'average_pool',
kernel: this._kernel,
stride: this._stride,
padding: this._padding,
channel_dim: this._channel_dim,
}
}
}
AveragePoolLayer.registLayer()