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
|
//! Convolution Layers.
use candle::{Result, Tensor};
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv1dConfig {
pub padding: usize,
pub stride: usize,
}
impl Default for Conv1dConfig {
fn default() -> Self {
Self {
padding: 0,
stride: 1,
}
}
}
#[derive(Debug)]
pub struct Conv1d {
weight: Tensor,
bias: Option<Tensor>,
config: Conv1dConfig,
}
impl Conv1d {
pub fn new(weight: Tensor, bias: Option<Tensor>, config: Conv1dConfig) -> Self {
Self {
weight,
bias,
config,
}
}
pub fn config(&self) -> &Conv1dConfig {
&self.config
}
}
impl crate::Module for Conv1d {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = x.conv1d(&self.weight, self.config.padding, self.config.stride)?;
match &self.bias {
None => Ok(x),
Some(bias) => {
let b = bias.dims1()?;
let bias = bias.reshape((1, b, 1))?;
Ok(x.broadcast_add(&bias)?)
}
}
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub struct Conv2dConfig {
pub padding: usize,
pub stride: usize,
}
impl Default for Conv2dConfig {
fn default() -> Self {
Self {
padding: 0,
stride: 1,
}
}
}
#[allow(dead_code)]
#[derive(Debug)]
pub struct Conv2d {
weight: Tensor,
bias: Option<Tensor>,
config: Conv2dConfig,
}
impl Conv2d {
pub fn new(weight: Tensor, bias: Option<Tensor>, config: Conv2dConfig) -> Self {
Self {
weight,
bias,
config,
}
}
pub fn config(&self) -> &Conv2dConfig {
&self.config
}
}
impl crate::Module for Conv2d {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = x.conv2d(&self.weight, self.config.padding, self.config.stride)?;
match &self.bias {
None => Ok(x),
Some(bias) => {
let b = bias.dims1()?;
let bias = bias.reshape((1, b, 1, 1))?;
Ok(x.broadcast_add(&bias)?)
}
}
}
}
pub fn conv1d(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
cfg: Conv1dConfig,
vs: crate::VarBuilder,
) -> Result<Conv1d> {
let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
let ws = vs.get_or_init((out_channels, in_channels, kernel_size), "weight", init_ws)?;
let bound = 1. / (in_channels as f64).sqrt();
let init_bs = crate::Init::Uniform {
lo: -bound,
up: bound,
};
let bs = vs.get_or_init(out_channels, "bias", init_bs)?;
Ok(Conv1d::new(ws, Some(bs), cfg))
}
pub fn conv2d(
in_channels: usize,
out_channels: usize,
kernel_size: usize,
cfg: Conv2dConfig,
vs: crate::VarBuilder,
) -> Result<Conv2d> {
let init_ws = crate::init::DEFAULT_KAIMING_NORMAL;
let ws = vs.get_or_init(
(out_channels, in_channels, kernel_size, kernel_size),
"weight",
init_ws,
)?;
let bound = 1. / (in_channels as f64).sqrt();
let init_bs = crate::Init::Uniform {
lo: -bound,
up: bound,
};
let bs = vs.get_or_init(out_channels, "bias", init_bs)?;
Ok(Conv2d::new(ws, Some(bs), cfg))
}
|
