dropouts are more specific and aggressive in the paper, thanks for letting me know @hila-chefer

README.md

@@ -24,8 +24,8 @@ v = ViT(
     depth = 6,
     heads = 8,
     mlp_dim = 2048,
-    attn_dropout = 0.1,
-    ff_dropout = 0.1
+    dropout = 0.1,
+    emb_dropout = 0.1
 )
 
 img = torch.randn(1, 3, 256, 256)

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.2.1',
+  version = '0.2.2',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/vit_pytorch.py

@@ -25,7 +25,8 @@ class FeedForward(nn.Module):
             nn.Linear(dim, hidden_dim),
             nn.GELU(),
             nn.Dropout(dropout),
-            nn.Linear(hidden_dim, dim)
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
         )
     def forward(self, x):
         return self.net(x)
@@ -37,8 +38,11 @@ class Attention(nn.Module):
         self.scale = dim ** -0.5
 
         self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
-        self.to_out = nn.Linear(dim, dim)
-        self.dropout = nn.Dropout(dropout)
+        self.to_out = nn.Sequential(
+            nn.Linear(dim, dim),
+            nn.Dropout(dropout)
+        )
+
     def forward(self, x, mask = None):
         b, n, _, h = *x.shape, self.heads
         qkv = self.to_qkv(x)
@@ -54,7 +58,6 @@ class Attention(nn.Module):
             del mask
 
         attn = dots.softmax(dim=-1)
-        attn = self.dropout(attn)
 
         out = torch.einsum('bhij,bhjd->bhid', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
@@ -62,13 +65,13 @@ class Attention(nn.Module):
         return out
 
 class Transformer(nn.Module):
-    def __init__(self, dim, depth, heads, mlp_dim, attn_dropout, ff_dropout):
+    def __init__(self, dim, depth, heads, mlp_dim, dropout):
         super().__init__()
         self.layers = nn.ModuleList([])
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
-                Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = attn_dropout))),
-                Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = ff_dropout)))
+                Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = dropout))),
+                Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
             ]))
     def forward(self, x, mask = None):
         for attn, ff in self.layers:
@@ -77,7 +80,7 @@ class Transformer(nn.Module):
         return x
 
 class ViT(nn.Module):
-    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, attn_dropout = 0., ff_dropout = 0.):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dropout = 0., emb_dropout = 0.):
         super().__init__()
         assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
         num_patches = (image_size // patch_size) ** 2
@@ -88,7 +91,9 @@ class ViT(nn.Module):
         self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
         self.patch_to_embedding = nn.Linear(patch_dim, dim)
         self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
-        self.transformer = Transformer(dim, depth, heads, mlp_dim, attn_dropout, ff_dropout)
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout)
 
         self.to_cls_token = nn.Identity()
 
@@ -96,7 +101,9 @@ class ViT(nn.Module):
             nn.LayerNorm(dim),
             nn.Linear(dim, mlp_dim),
             nn.GELU(),
-            nn.Linear(mlp_dim, num_classes)
+            nn.Dropout(dropout),
+            nn.Linear(mlp_dim, num_classes),
+            nn.Dropout(dropout)
         )
 
     def forward(self, img, mask = None):
@@ -108,6 +115,8 @@ class ViT(nn.Module):
         cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
         x = torch.cat((cls_tokens, x), dim=1)
         x += self.pos_embedding
+        x = self.dropout(x)
+
         x = self.transformer(x, mask)
 
         x = self.to_cls_token(x[:, 0])