bump package

Image Classification Example

README.md

@@ -23,7 +23,9 @@ v = ViT(
     dim = 1024,
     depth = 6,
     heads = 8,
-    mlp_dim = 2048
+    mlp_dim = 2048,
+    attn_dropout = 0.1,
+    ff_dropout = 0.1
 )
 
 img = torch.randn(1, 3, 256, 256)
@@ -32,7 +34,9 @@ mask = torch.ones(1, 8, 8).bool() # optional mask, designating which patch to at
 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.
 
@@ -82,6 +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 = '0.0.5',
+  packages = find_packages(exclude=['examples']),
+  version = '0.2.1',
   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 +1,40 @@
+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)
+
+        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.transformer(x)
+
+        x = self.to_cls_token(x[:, 0])
+        return self.mlp_head(x)

vit_pytorch/vit_pytorch.py

@@ -19,24 +19,26 @@ class PreNorm(nn.Module):
         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.Dropout(dropout),
             nn.Linear(hidden_dim, dim)
         )
     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)
+        self.dropout = nn.Dropout(dropout)
     def forward(self, x, mask = None):
         b, n, _, h = *x.shape, self.heads
         qkv = self.to_qkv(x)
@@ -52,6 +54,7 @@ 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)')
@@ -59,13 +62,13 @@ 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, attn_dropout, ff_dropout):
         super().__init__()
         self.layers = nn.ModuleList([])
         for _ in range(depth):
             self.layers.append(nn.ModuleList([
-                Residual(PreNorm(dim, Attention(dim, heads = heads))),
-                Residual(PreNorm(dim, FeedForward(dim, mlp_dim)))
+                Residual(PreNorm(dim, Attention(dim, heads = heads, dropout = attn_dropout))),
+                Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = ff_dropout)))
             ]))
     def forward(self, x, mask = None):
         for attn, ff in self.layers:
@@ -74,7 +77,7 @@ 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):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, attn_dropout = 0., ff_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
@@ -85,11 +88,12 @@ 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)
+        self.transformer = Transformer(dim, depth, heads, mlp_dim, attn_dropout, ff_dropout)
 
         self.to_cls_token = nn.Identity()
 
         self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
             nn.Linear(dim, mlp_dim),
             nn.GELU(),
             nn.Linear(mlp_dim, num_classes)