remove masking, as it complicates with little benefit

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.9.1',
+  version = '0.9.3',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/distill.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn.functional as F
 from torch import nn
-from vit_pytorch.vit_pytorch import ViT
+from vit_pytorch.vit import ViT
 from vit_pytorch.t2t import T2TViT
 from vit_pytorch.efficient import ViT as EfficientViT
 
@@ -15,7 +15,7 @@ def exists(val):
 # classes
 
 class DistillMixin:
-    def forward(self, img, distill_token = None, mask = None):
+    def forward(self, img, distill_token = None):
         distilling = exists(distill_token)
         x = self.to_patch_embedding(img)
         b, n, _ = x.shape
@@ -28,7 +28,7 @@ class DistillMixin:
             distill_tokens = repeat(distill_token, '() n d -> b n d', b = b)
             x = torch.cat((x, distill_tokens), dim = 1)
 
-        x = self._attend(x, mask)
+        x = self._attend(x)
 
         if distilling:
             x, distill_tokens = x[:, :-1], x[:, -1]
@@ -56,9 +56,9 @@ class DistillableViT(DistillMixin, ViT):
         v.load_state_dict(self.state_dict())
         return v
 
-    def _attend(self, x, mask):
+    def _attend(self, x):
         x = self.dropout(x)
-        x = self.transformer(x, mask)
+        x = self.transformer(x)
         return x
 
 class DistillableT2TViT(DistillMixin, T2TViT):
@@ -74,7 +74,7 @@ class DistillableT2TViT(DistillMixin, T2TViT):
         v.load_state_dict(self.state_dict())
         return v
 
-    def _attend(self, x, mask):
+    def _attend(self, x):
         x = self.dropout(x)
         x = self.transformer(x)
         return x
@@ -92,7 +92,7 @@ class DistillableEfficientViT(DistillMixin, EfficientViT):
         v.load_state_dict(self.state_dict())
         return v
 
-    def _attend(self, x, mask):
+    def _attend(self, x):
         return self.transformer(x)
 
 # knowledge distillation wrapper

vit_pytorch/t2t.py

@@ -2,7 +2,7 @@ import math
 import torch
 from torch import nn
 
-from vit_pytorch.vit_pytorch import Transformer
+from vit_pytorch.vit import Transformer
 
 from einops import rearrange, repeat
 from einops.layers.torch import Rearrange

vit_pytorch/vit.py

@@ -0,0 +1,122 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) + x
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, dim),
+            nn.Dropout(dropout)
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        project_out = not (heads == 1 and dim_head == dim)
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        ) if project_out else nn.Identity()
+
+    def forward(self, x):
+        b, n, _, h = *x.shape, self.heads
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        attn = dots.softmax(dim=-1)
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out =  self.to_out(out)
+        return out
+
+class Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
+                Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout)))
+            ]))
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x)
+            x = ff(x)
+        return x
+
+class ViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, 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
+        patch_dim = channels * patch_size ** 2
+        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
+
+        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.Linear(patch_dim, dim),
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        self.pool = pool
+        self.to_latent = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patch_embedding(img)
+        b, n, _ = x.shape
+
+        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding[:, :(n + 1)]
+        x = self.dropout(x)
+
+        x = self.transformer(x)
+
+        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
+
+        x = self.to_latent(x)
+        return self.mlp_head(x)