double down on dual patch norm, fix MAE and Simmim to be compatible with dual patchnorm

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.0.0',
+  version = '1.0.2',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description_content_type = 'text/markdown',

vit_pytorch/ats_vit.py

@@ -230,7 +230,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/cait.py

@@ -150,7 +150,9 @@ class CaiT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))

vit_pytorch/cross_vit.py

@@ -186,7 +186,9 @@ class ImageEmbedder(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/efficient.py

@@ -17,7 +17,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/learnable_memory_vit.py

@@ -118,7 +118,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/mae.py

@@ -24,8 +24,11 @@ class MAE(nn.Module):
 
         self.encoder = encoder
         num_patches, encoder_dim = encoder.pos_embedding.shape[-2:]
-        self.to_patch, self.patch_to_emb = encoder.to_patch_embedding[:2]
-        pixel_values_per_patch = self.patch_to_emb.weight.shape[-1]
+
+        self.to_patch = encoder.to_patch_embedding[0]
+        self.patch_to_emb = nn.Sequential(*encoder.to_patch_embedding[1:])
+
+        pixel_values_per_patch = encoder.to_patch_embedding[2].weight.shape[-1]
 
         # decoder parameters
         self.decoder_dim = decoder_dim

vit_pytorch/simmim.py

@@ -18,8 +18,11 @@ class SimMIM(nn.Module):
 
         self.encoder = encoder
         num_patches, encoder_dim = encoder.pos_embedding.shape[-2:]
-        self.to_patch, self.patch_to_emb = encoder.to_patch_embedding[:2]
-        pixel_values_per_patch = self.patch_to_emb.weight.shape[-1]
+
+        self.to_patch = encoder.to_patch_embedding[0]
+        self.patch_to_emb = nn.Sequential(*encoder.to_patch_embedding[1:])
+
+        pixel_values_per_patch = encoder.to_patch_embedding[2].weight.shape[-1]
 
         # simple linear head
 

vit_pytorch/twins_svt.py

@@ -71,7 +71,12 @@ class PatchEmbedding(nn.Module):
         self.dim = dim
         self.dim_out = dim_out
         self.patch_size = patch_size
-        self.proj = nn.Conv2d(patch_size ** 2 * dim, dim_out, 1)
+
+        self.proj = nn.Sequential(
+            LayerNorm(patch_size ** 2 * dim),
+            nn.Conv2d(patch_size ** 2 * dim, dim_out, 1),
+            LayerNorm(dim_out)
+        )
 
     def forward(self, fmap):
         p = self.patch_size

vit_pytorch/vit_with_patch_merger.py

@@ -121,7 +121,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))

vit_pytorch/vivit.py

@@ -120,7 +120,9 @@ class ViT(nn.Module):
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (f pf) (h p1) (w p2) -> b f (h w) (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
+            nn.LayerNorm(patch_dim),
             nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim)
         )
 
         self.pos_embedding = nn.Parameter(torch.randn(1, num_frame_patches, num_image_patches, dim))