add ability to turn off rotary, for ablation

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.16.6',
+  version = '0.16.7',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/rvt.py

@@ -94,10 +94,10 @@ class FeedForward(nn.Module):
         return self.net(x)
 
 class Attention(nn.Module):
-    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., conv_query_kernel = 5):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0., use_rotary = True, conv_query_kernel = 5):
         super().__init__()
         inner_dim = dim_head *  heads
-
+        self.use_rotary = use_rotary
         self.heads = heads
         self.scale = dim_head ** -0.5
 
@@ -120,16 +120,17 @@ class Attention(nn.Module):
 
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h = h), qkv)
 
-        # apply 2d rotary embeddings to queries and keys, excluding CLS tokens
+        if self.use_rotary:
+            # apply 2d rotary embeddings to queries and keys, excluding CLS tokens
 
-        sin, cos = pos_emb
-        (q_cls, q), (k_cls, k) = map(lambda t: (t[:, :1], t[:, 1:]), (q, k))
-        q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
+            sin, cos = pos_emb
+            (q_cls, q), (k_cls, k) = map(lambda t: (t[:, :1], t[:, 1:]), (q, k))
+            q, k = map(lambda t: (t * cos) + (rotate_every_two(t) * sin), (q, k))
 
-        # concat back the CLS tokens
+            # concat back the CLS tokens
 
-        q = torch.cat((q_cls, q), dim = 1)
-        k = torch.cat((k_cls, k), dim = 1)
+            q = torch.cat((q_cls, q), dim = 1)
+            k = torch.cat((k_cls, k), dim = 1)
 
         dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
@@ -140,13 +141,13 @@ class Attention(nn.Module):
         return self.to_out(out)
 
 class Transformer(nn.Module):
-    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., use_rotary = True):
         super().__init__()
         self.layers = nn.ModuleList([])
         self.pos_emb = AxialRotaryEmbedding(dim_head)
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
-                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout, use_rotary = use_rotary)),
                 PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
             ]))
     def forward(self, x, fmap_dims):
@@ -160,7 +161,7 @@ class Transformer(nn.Module):
 # Rotary Vision Transformer
 
 class RvT(nn.Module):
-    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0., use_rotary = True):
         super().__init__()
         assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
         num_patches = (image_size // patch_size) ** 2
@@ -173,7 +174,7 @@ class RvT(nn.Module):
         )
 
         self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
-        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, use_rotary)
 
         self.mlp_head = nn.Sequential(
             nn.LayerNorm(dim),