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
160
161
162
163
164
165
166

use crate as burn;

use crate::config::Config;
use crate::module::Module;
use crate::module::Param;
use crate::nn::Initializer;
use crate::tensor::backend::Backend;
use crate::tensor::Tensor;
use burn_tensor::module::conv_transpose1d;
use burn_tensor::ops::ConvTransposeOptions;
use libm::sqrt;

use super::checks;

/// Configuration to create an [1D transposed convolution](ConvTranspose1d) layer.
#[derive(Config, Debug)]
pub struct ConvTranspose1dConfig {
    /// The number of channels.
    pub channels: [usize; 2],
    /// The size of the kernel.
    pub kernel_size: usize,
    /// The stride of the convolution.
    #[config(default = "1")]
    pub stride: usize,
    /// Spacing between kernel elements.
    #[config(default = "1")]
    pub dilation: usize,
    /// Controls the connections between input and output channels.
    #[config(default = "1")]
    pub groups: usize,
    /// The padding configuration.
    #[config(default = "0")]
    pub padding: usize,
    /// The padding output configuration.
    #[config(default = "0")]
    pub padding_out: usize,
    /// If bias should be added to the output.
    #[config(default = true)]
    pub bias: bool,
    /// The type of function used to initialize neural network parameters
    #[config(default = "Initializer::KaimingUniform{gain:1.0/sqrt(3.0),fan_out_only:false}")]
    pub initializer: Initializer,
}

/// Applies a 1D transposed convolution over input tensors.
///
/// # Params
///
/// - weight: Tensor of shape `[channels_in, channels_out / groups, kernel_size]`
///
/// - bias:   Tensor of shape `[channels_out]`
#[derive(Module, Debug)]
pub struct ConvTranspose1d<B: Backend> {
    weight: Param<Tensor<B, 3>>,
    bias: Option<Param<Tensor<B, 1>>>,
    stride: usize,
    kernel_size: usize,
    dilation: usize,
    groups: usize,
    padding: usize,
    padding_out: usize,
}

impl ConvTranspose1dConfig {
    /// Initialize a new [conv transpose 1d](ConvTranspose1d) module.
    pub fn init<B: Backend>(&self) -> ConvTranspose1d<B> {
        checks::checks_channels_div_groups(self.channels[0], self.channels[1], self.groups);

        let shape = [
            self.channels[0],
            self.channels[1] / self.groups,
            self.kernel_size,
        ];

        let fan_in = self.channels[1] / self.groups * self.kernel_size;
        let weight = self.initializer.init_with(shape, Some(fan_in), None);
        let mut bias = None;

        if self.bias {
            bias = Some(
                self.initializer
                    .init_with([self.channels[1]], Some(fan_in), None),
            );
        }

        ConvTranspose1d {
            weight: Param::from(weight),
            bias: bias.map(Param::from),
            stride: self.stride,
            kernel_size: self.kernel_size,
            dilation: self.dilation,
            groups: self.groups,
            padding: self.padding,
            padding_out: self.padding_out,
        }
    }

    /// Initialize a new [conv transpose 1d](ConvTranspose1d) module with a [record](ConvTranspose1dRecord).
    pub fn init_with<B: Backend>(&self, record: ConvTranspose1dRecord<B>) -> ConvTranspose1d<B> {
        ConvTranspose1d {
            weight: record.weight,
            bias: record.bias,
            stride: self.stride,
            dilation: self.dilation,
            kernel_size: self.kernel_size,
            groups: self.groups,
            padding: self.padding,
            padding_out: self.padding_out,
        }
    }
}

impl<B: Backend> ConvTranspose1d<B> {
    /// Applies the forward pass on the input tensor.
    ///
    /// # Shapes
    ///
    /// - input: [batch_size, channels_in, length_in],
    /// - output: [batch_size, channels_out, length_out],
    pub fn forward(&self, input: Tensor<B, 3>) -> Tensor<B, 3> {
        conv_transpose1d(
            input,
            self.weight.val(),
            self.bias.as_ref().map(|bias| bias.val()),
            ConvTransposeOptions::new(
                [self.stride],
                [self.padding],
                [self.padding_out],
                [self.dilation],
                self.groups,
            ),
        )
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::TestBackend;
    use burn_tensor::Data;

    #[test]
    fn initializer_default() {
        TestBackend::seed(0);

        let config = ConvTranspose1dConfig::new([5, 1], 5);
        let k = (config.channels[1] * config.kernel_size) as f64;
        let k = sqrt(config.groups as f64 / k) as f32;
        let conv = config.init::<TestBackend>();

        conv.weight.to_data().assert_within_range(-k..k);
    }

    #[test]
    fn initializer_zeros() {
        TestBackend::seed(0);

        let config = ConvTranspose1dConfig::new([5, 2], 5).with_initializer(Initializer::Zeros);
        let conv = config.init::<TestBackend>();

        assert_eq!(config.initializer, Initializer::Zeros);
        conv.weight
            .to_data()
            .assert_approx_eq(&Data::zeros(conv.weight.shape()), 3);
    }
}