allow for training on different image sizes, provided images are smaller than what was passed as image_size keyword on init

Image Classification Example

README.md

@@ -23,14 +23,20 @@ v = ViT(
     dim = 1024,
     depth = 6,
     heads = 8,
-    mlp_dim = 2048
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
 )
 
 img = torch.randn(1, 3, 256, 256)
-preds = v(img) # (1, 1000)
+mask = torch.ones(1, 8, 8).bool() # optional mask, designating which patch to attend to
+
+preds = v(img, mask = mask) # (1, 1000)
 ```
 
-## Suggestion
+## Research Ideas
+
+### Self Supervised Training
 
 You can train this with a near SOTA self-supervised learning technique, <a href="https://github.com/lucidrains/byol-pytorch">BYOL</a>, with the following code.
 
@@ -80,6 +86,43 @@ torch.save(model.state_dict(), './pretrained-net.pt')
 
 A pytorch-lightning script is ready for you to use at the repository link above.
 
+### Efficient Attention
+
+There may be some coming from computer vision who think attention still suffers from quadratic costs. Fortunately, we have a lot of new techniques that may help. This repository offers a way for you to plugin your own sparse attention transformer.
+
+An example with <a href="https://arxiv.org/abs/2006.04768">Linformer</a>
+
+```bash
+$ pip install linformer
+```
+
+```python
+import torch
+from vit_pytorch.efficient import ViT
+from linformer import Linformer
+
+efficient_transformer = Linformer(
+    dim = 512,
+    seq_len = 4096 + 1,  # 64 x 64 patches + 1 cls token
+    depth = 12,
+    heads = 8,
+    k = 256
+)
+
+v = ViT(
+    dim = 512,
+    image_size = 2048,
+    patch_size = 32,
+    num_classes = 1000,
+    transformer = efficient_transformer
+)
+
+img = torch.randn(1, 3, 2048, 2048) # your high resolution picture
+v(img) # (1, 1000)
+```
+
+Other sparse attention frameworks I would highly recommend is <a href="https://github.com/lucidrains/routing-transformer">Routing Transformer</a> or <a href="https://github.com/lucidrains/sinkhorn-transformer">Sinkhorn Transformer</a>
+
 ## Citations
 
 ```bibtex

setup.py

@@ -2,8 +2,8 @@ from setuptools import setup, find_packages
 
 setup(
   name = 'vit-pytorch',
-  packages = find_packages(),
-  version = '0.0.3',
+  packages = find_packages(exclude=['examples']),
+  version = '0.2.6',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',
@@ -25,4 +25,4 @@ setup(
     'License :: OSI Approved :: MIT License',
     'Programming Language :: Python :: 3.6',
   ],
-)
+)

vit_pytorch/efficient.py

@@ -0,0 +1,41 @@
+import torch
+from einops import rearrange
+from torch import nn
+
+class ViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, channels = 3):
+        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
+
+        self.patch_size = patch_size
+
+        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
+
+        self.to_cls_token = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, dim * 4),
+            nn.GELU(),
+            nn.Linear(dim * 4, num_classes)
+        )
+
+    def forward(self, img):
+        p = self.patch_size
+
+        x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
+        x = self.patch_to_embedding(x)
+        b, n, _ = x.shape
+
+        cls_tokens = self.cls_token.expand(b, -1, -1)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding[:, :(n + 1)]
+        x = self.transformer(x)
+
+        x = self.to_cls_token(x[:, 0])
+        return self.mlp_head(x)

vit_pytorch/vit_pytorch.py

@@ -1,48 +1,64 @@
 import torch
-from einops import rearrange
 import torch.nn.functional as F
+from einops import rearrange
 from torch import nn
 
+MIN_NUM_PATCHES = 16
+
 class Residual(nn.Module):
     def __init__(self, fn):
         super().__init__()
         self.fn = fn
-    def forward(self, x):
-        return self.fn(x) + x
+    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):
-        return self.fn(self.norm(x))
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
 
 class FeedForward(nn.Module):
-    def __init__(self, dim, hidden_dim):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
         super().__init__()
         self.net = nn.Sequential(
             nn.Linear(dim, hidden_dim),
             nn.GELU(),
-            nn.Linear(hidden_dim, dim)
+            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):
+    def __init__(self, dim, heads = 8, dropout = 0.):
         super().__init__()
         self.heads = heads
         self.scale = dim ** -0.5
 
         self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
-        self.to_out = nn.Linear(dim, dim)
-    def forward(self, x):
+        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)
-        q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv = 3, h = h)
+        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 = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
+
+        if mask is not None:
+            mask = F.pad(mask.flatten(1), (1, 0), value = True)
+            assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions'
+            mask = mask[:, None, :] * mask[:, :, None]
+            dots.masked_fill_(~mask, float('-inf'))
+            del mask
+
         attn = dots.softmax(dim=-1)
 
         out = torch.einsum('bhij,bhjd->bhid', attn, v)
@@ -51,50 +67,60 @@ class Attention(nn.Module):
         return out
 
 class Transformer(nn.Module):
-    def __init__(self, dim, depth, heads, mlp_dim):
+    def __init__(self, dim, depth, heads, mlp_dim, dropout):
         super().__init__()
-        layers = []
+        self.layers = nn.ModuleList([])
         for _ in range(depth):
-            layers.extend([
-                Residual(PreNorm(dim, Attention(dim, heads = heads))),
-                Residual(PreNorm(dim, FeedForward(dim, mlp_dim)))
-            ])
-        self.net = nn.Sequential(*layers)
-    def forward(self, x):
-        return self.net(x)
+            self.layers.append(nn.ModuleList([
+                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:
+            x = attn(x, mask = mask)
+            x = ff(x)
+        return x
 
 class ViT(nn.Module):
-    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3):
+    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
         patch_dim = channels * patch_size ** 2
+        assert num_patches > MIN_NUM_PATCHES, f'your number of patches ({num_patches}) is way too small for attention to be effective. try decreasing your patch size'
 
         self.patch_size = patch_size
 
         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)
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout)
 
         self.to_cls_token = nn.Identity()
 
         self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
             nn.Linear(dim, mlp_dim),
             nn.GELU(),
+            nn.Dropout(dropout),
             nn.Linear(mlp_dim, num_classes)
         )
 
-    def forward(self, img):
+    def forward(self, img, mask = None):
         p = self.patch_size
 
         x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
         x = self.patch_to_embedding(x)
+        b, n, _ = x.shape
 
-        cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
+        cls_tokens = self.cls_token.expand(b, -1, -1)
         x = torch.cat((cls_tokens, x), dim=1)
-        x += self.pos_embedding
-        x = self.transformer(x)
+        x += self.pos_embedding[:, :(n + 1)]
+        x = self.dropout(x)
+
+        x = self.transformer(x, mask)
 
         x = self.to_cls_token(x[:, 0])
         return self.mlp_head(x)