make value residual learned

setup.py

@@ -6,10 +6,10 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.8.8',
+  version = '1.8.9',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
-  long_description=long_description,
+  long_description = long_description,
   long_description_content_type = 'text/markdown',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',

vit_pytorch/simple_vit_with_value_residual.py

@@ -38,7 +38,7 @@ def FeedForward(dim, hidden_dim):
     )
 
 class Attention(Module):
-    def __init__(self, dim, heads = 8, dim_head = 64):
+    def __init__(self, dim, heads = 8, dim_head = 64, learned_value_residual_mix = False):
         super().__init__()
         inner_dim = dim_head *  heads
         self.heads = heads
@@ -50,6 +50,12 @@ class Attention(Module):
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
         self.to_out = nn.Linear(inner_dim, dim, bias = False)
 
+        self.to_residual_mix = nn.Sequential(
+            nn.Linear(dim, heads),
+            nn.Sigmoid(),
+            Rearrange('b n h -> b h n 1')
+        ) if learned_value_residual_mix else (lambda _: 0.5)
+
     def forward(self, x, value_residual = None):
         x = self.norm(x)
 
@@ -57,7 +63,8 @@ class Attention(Module):
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
 
         if exists(value_residual):
-            v = 0.5 * (v + value_residual)
+            mix = self.to_residual_mix(x)
+            v = v * mix + value_residual * (1. - mix)
 
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
 
@@ -73,9 +80,10 @@ class Transformer(Module):
         super().__init__()
         self.norm = nn.LayerNorm(dim)
         self.layers = ModuleList([])
-        for _ in range(depth):
+        for i in range(depth):
+            is_first = i == 0
             self.layers.append(ModuleList([
-                Attention(dim, heads = heads, dim_head = dim_head),
+                Attention(dim, heads = heads, dim_head = dim_head, learned_value_residual_mix = not is_first),
                 FeedForward(dim, mlp_dim)
             ]))
     def forward(self, x):