norm cls token before sending to mlp head

README.md

@@ -32,7 +32,9 @@ 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 +84,43 @@ 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

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(),
-  version = '0.0.5',
+  version = '0.2.0',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

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

@@ -90,6 +90,7 @@ class ViT(nn.Module):
         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)