remove group norms https://arxiv.org/abs/2312.02696

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'x-unet',
   packages = find_packages(exclude=[]),
-  version = '0.3.1',
+  version = '0.4.0',
   license='MIT',
   description = 'X-Unet',
   long_description_content_type = 'text/markdown',

x_unet/x_unet.py

@@ -57,6 +57,15 @@ def __init__(self, fn):
     def forward(self, x):
         return self.fn(x) + x
 
+class RMSNorm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.scale = dim ** 0.5
+        self.gamma = nn.Parameter(torch.ones(dim, 1, 1, 1))
+
+    def forward(self, x):
+        return F.normalize(x, dim = 1) * self.scale * self.gamma
+
 class LayerNorm(nn.Module):
     def __init__(self, dim):
         super().__init__()
@@ -96,7 +105,7 @@ def __init__(
         conv = nn.Conv3d if not weight_standardize else WeightStandardizedConv3d
 
         self.proj = conv(dim, dim_out, **kernel_conv_kwargs(3, 3))
-        self.norm = nn.GroupNorm(groups, dim_out)
+        self.norm = RMSNorm(dim_out)
         self.act = nn.SiLU()
 
     def forward(self, x):
@@ -109,19 +118,18 @@ def __init__(
         self,
         dim,
         dim_out,
-        groups = 8,
         frame_kernel_size = 1,
         nested_unet_depth = 0,
         nested_unet_dim = 32,
         weight_standardize = False
     ):
         super().__init__()
-        self.block1 = Block(dim, dim_out, groups = groups, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
+        self.block1 = Block(dim, dim_out, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
 
         if nested_unet_depth > 0:
             self.block2 = NestedResidualUnet(dim_out, depth = nested_unet_depth, M = nested_unet_dim, frame_kernel_size = frame_kernel_size, weight_standardize = weight_standardize, add_residual = True)
         else:
-            self.block2 = Block(dim_out, dim_out, groups = groups, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
+            self.block2 = Block(dim_out, dim_out, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
 
         self.res_conv = nn.Conv3d(dim, dim_out, 1) if dim != dim_out else nn.Identity()
 
@@ -317,7 +325,6 @@ def __init__(
         nested_unet_dim = 32,
         channels = 3,
         use_convnext = False,
-        resnet_groups = 8,
         consolidate_upsample_fmaps = True,
         skip_scale = 2 ** -0.5,
         weight_standardize = False,
@@ -344,7 +351,7 @@ def __init__(
 
         # resnet or convnext
 
-        blocks = partial(ConvNextBlock, frame_kernel_size = frame_kernel_size) if use_convnext else partial(ResnetBlock, groups = resnet_groups, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
+        blocks = partial(ConvNextBlock, frame_kernel_size = frame_kernel_size) if use_convnext else partial(ResnetBlock, weight_standardize = weight_standardize, frame_kernel_size = frame_kernel_size)
 
         # whether to use nested unet, as in unet squared paper
 
@@ -557,7 +564,6 @@ def __init__(
         M = 32,
         frame_kernel_size = 1,
         add_residual = False,
-        groups = 4,
         skip_scale = 2 ** -0.5,
         weight_standardize = False
     ):
@@ -575,13 +581,13 @@ def __init__(
 
             down = nn.Sequential(
                 conv(dim_in, M, (1, 4, 4), stride = (1, 2, 2), padding = (0, 1, 1)),
-                nn.GroupNorm(groups, M),
+                RMSNorm(M),
                 nn.SiLU()
             )
 
             up = nn.Sequential(
                 PixelShuffleUpsample(2 * M, dim_in),
-                nn.GroupNorm(groups, dim_in),
+                RMSNorm(dim_in),
                 nn.SiLU()
             )
 
@@ -590,7 +596,7 @@ def __init__(
 
         self.mid = nn.Sequential(
             conv(M, M, **kernel_and_same_pad(frame_kernel_size, 3, 3)),
-            nn.GroupNorm(groups, M),
+            RMSNorm(M),
             nn.SiLU()
         )