Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feature(norm): Add GroupNorm #963

Merged
merged 5 commits into from
Nov 21, 2023
Merged

feature(norm): Add GroupNorm #963

Changes from 1 commit
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
296fc63
Add GroupNorm
dcvz Nov 16, 2023
7adeefe
Fix implemenation and add tests
dcvz Nov 16, 2023
34b24f6
Address PR comments
dcvz Nov 19, 2023
dc3f9de
Fix formatting
dcvz Nov 20, 2023
423b1bb
Update burn book
dcvz Nov 20, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
Address PR comments
  • Loading branch information
@dcvz
dcvz committed Nov 19, 2023
commit 34b24f6941b459a19aadd1fba888cdc4604a4013
99 changes: 86 additions & 13 deletions burn-core/src/nn/norm/group.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,8 +30,8 @@ pub struct GroupNormConfig {
pub struct GroupNorm<B: Backend> {
num_groups: usize,
num_channels: usize,
gamma: Param<Tensor<B, 1>>,
beta: Param<Tensor<B, 1>>,
gamma: Option<Param<Tensor<B, 1>>>,
beta: Option<Param<Tensor<B, 1>>>,
epsilon: f64,
affine: bool,
}
Expand All @@ -45,8 +45,14 @@ impl GroupNormConfig {
"The number of channels must be divisible by the number of groups"
);

let gamma = Tensor::ones([self.num_channels]).into();
let beta = Tensor::zeros([self.num_channels]).into();
let (gamma, beta) = if self.affine {
let gamma = Tensor::ones([self.num_channels]).into();
let beta = Tensor::zeros([self.num_channels]).into();

(Some(gamma), Some(beta))
} else {
(None, None)
};

GroupNorm {
num_groups: self.num_groups,
Expand Down Expand Up @@ -111,8 +117,8 @@ impl<B: Backend> GroupNorm<B> {

input_normalized
.reshape(shape)
.mul(self.gamma.val().reshape(affine_shape))
.add(self.beta.val().reshape(affine_shape))
.mul(self.gamma.clone().unwrap().val().reshape(affine_shape))
.add(self.beta.clone().unwrap().val().reshape(affine_shape))
} else {
input_normalized.reshape(shape)
}
Expand All @@ -123,16 +129,17 @@ impl<B: Backend> GroupNorm<B> {
mod tests {
use super::*;
use burn_tensor::Data;

#[cfg(feature = "std")]
use crate::TestBackend;

#[cfg(not(feature = "std"))]
use crate::TestBackend;

#[test]
fn group_norm_forward() {
let module = GroupNormConfig::new(2, 6).init::<TestBackend>();
fn group_norm_forward_affine_false() {
let module = GroupNormConfig::new(2, 6)
.with_affine(false)
.init::<TestBackend>();

assert!(module.gamma.is_none());
assert!(module.beta.is_none());

let input = Tensor::from_data(Data::from([
[
[-0.3034f32, 0.2726, -0.9659],
Expand Down Expand Up @@ -176,4 +183,70 @@ mod tests {
3,
);
}

#[test]
fn group_norm_forward_affine_true() {
let module = GroupNormConfig::new(3, 6)
.with_affine(true)
.init::<TestBackend>();

module
.gamma
.as_ref()
.expect("Gamma is None")
.val()
.to_data()
.assert_approx_eq(&Data::ones([6].into()), 3);

module
.beta
.as_ref()
.expect("beta is None")
.val()
.to_data()
.assert_approx_eq(&Data::zeros([6]), 3);

let input = Tensor::from_data(Data::from([
[
[-0.3034f32, 0.2726, -0.9659],
[-1.1845, -1.3236, 0.0172],
[1.9507, 1.2554, -0.8625],
[1.0682, 0.3604, 0.3985],
[-0.4957, -0.4461, -0.9721],
[1.5157, -0.1546, -0.5596],
],
[
[-1.6698, -0.4040, -0.7927],
[0.3736, -0.0975, -0.1351],
[-0.9461, 0.5461, -0.6334],
[-1.0919, -0.1158, 0.1213],
[-0.9535, 0.1281, 0.4372],
[-0.2845, 0.3488, 0.5641],
],
]));

let output = module.forward(input);

output.to_data().assert_approx_eq(
&Data::from([
[
[0.4560, 1.4014, -0.6313],
[-0.9901, -1.2184, 0.9822],
[1.4254, 0.6360, -1.7682],
[0.4235, -0.3800, -0.3367],
[-0.3890, -0.3268, -0.9862],
[2.1325, 0.0386, -0.4691]
],
[
[-1.8797, 0.0777, -0.5234],
[1.2802, 0.5517, 0.4935],
[-1.0102, 1.5327, -0.4773],
[-1.2587, 0.4047, 0.8088],
[-1.9074, 0.1691, 0.7625],
[-0.6230, 0.5928, 1.0061]
]
]),
3,
);
}
}
Loading