Add conv attention, up and downsampling to vqvae module

1 files changed, 20 insertions, 0 deletions

diff --git a/text_recognizer/networks/vqvae/norm.py b/text_recognizer/networks/vqvae/norm.py
new file mode 100644
index 0000000..df66efc
--- /dev/null
+++ b/text_recognizer/networks/vqvae/norm.py
@@ -0,0 +1,20 @@
+"""Normalizer block."""
+import attr
+from torch import nn, Tensor
+
+
+@attr.s
+class Normalize(nn.Module):
+    num_channels: int = attr.ib()
+    norm: nn.GroupNorm = attr.ib(init=False)
+
+    def __attrs_post_init__(self) -> None:
+        """Post init configuration."""
+        super().__init__()
+        self.norm = nn.GroupNorm(
+            num_groups=32, num_channels=self.num_channels, eps=1.0e-6, affine=True
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Applies group normalization."""
+        return self.norm(x)