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src/model/groupnorm/mod.rs

@@ -0,0 +1,72 @@
+pub mod load;
+
+use burn::{
+    config::Config, 
+    module::{Module, Param},
+    tensor::{
+        backend::Backend,
+        Tensor,
+    },
+};
+
+#[derive(Config)]
+pub struct GroupNormConfig {
+    n_group: usize, 
+    n_channel: usize, 
+    #[config(default = 1e-5)]
+    eps: f64, 
+}
+
+impl GroupNormConfig {
+    pub fn init<B: Backend>(&self) -> GroupNorm<B> {
+        assert!(self.n_channel % self.n_group == 0, "The number of channels {} must be divisible by the number of groups {}", self.n_channel, self.n_group);
+
+        let n_per_group = self.n_channel / self.n_group;
+
+        let gamma = Tensor::ones([self.n_channel]).into();
+        let beta = Tensor::zeros([self.n_channel]).into();
+
+        let eps = self.eps;
+
+        GroupNorm {
+            n_group: self.n_group, 
+            n_channel: self.n_channel, 
+            gamma, 
+            beta, 
+            eps, 
+        }
+    }
+}
+
+#[derive(Module, Debug)]
+pub struct GroupNorm<B: Backend> {
+    n_group: usize, 
+    n_channel: usize,  
+    gamma: Param<Tensor<B, 1>>, 
+    beta: Param<Tensor<B, 1>>, 
+    eps: f64, 
+}
+
+impl<B: Backend> GroupNorm<B> {
+    pub fn forward<const D: usize>(&self, x: Tensor<B, D>) -> Tensor<B, D> {
+        let shape = x.shape();
+        let n_batch = shape.dims[0];
+        let num_elements = shape.num_elements();
+
+        let mut affine_shape = [1; D];
+        affine_shape[1] = self.n_channel;
+
+        layernorm( x.reshape([n_batch, self.n_group, num_elements / (n_batch * self.n_group) ]), self.eps )
+            .reshape(shape)
+            .mul(self.gamma.val().reshape(affine_shape))
+            .add(self.beta.val().reshape(affine_shape))
+    }
+}
+
+pub fn layernorm<B: Backend, const D: usize>(x: Tensor<B, D>, eps: f64) -> Tensor<B, D> {
+    //let (var, mean) = x.clone().var_mean_bias(D - 1);
+    //x.sub(mean).div(var.sqrt().add_scalar(eps))
+
+    let u = x.clone() - x.mean_dim(D - 1);
+    u.clone().div( (u.clone() * u).mean_dim(D - 1).add_scalar(eps).sqrt() )
+}