add patch merger

README.md

@@ -24,6 +24,7 @@
 - [Simple Masked Image Modeling](#simple-masked-image-modeling)
 - [Masked Patch Prediction](#masked-patch-prediction)
 - [Adaptive Token Sampling](#adaptive-token-sampling)
+- [Patch Merger](#patch-merger)
 - [Vision Transformer for Small Datasets](#vision-transformer-for-small-datasets)
 - [Dino](#dino)
 - [Accessing Attention](#accessing-attention)
@@ -732,12 +733,58 @@ v = ViT(
 
 img = torch.randn(4, 3, 256, 256)
 
-preds = v(img) # (1, 1000)
+preds = v(img) # (4, 1000)
 
 # you can also get a list of the final sampled patch ids
 # a value of -1 denotes padding
 
-preds, token_ids = v(img, return_sampled_token_ids = True) # (1, 1000), (1, <=8)
+preds, token_ids = v(img, return_sampled_token_ids = True) # (4, 1000), (4, <=8)
+```
+
+## Patch Merger
+
+
+<img src="./images/patch_merger.png" width="400px"></img>
+
+This <a href="https://arxiv.org/abs/2202.12015">paper</a> proposes a simple module (Patch Merger) for reducing the number of tokens at any layer of a vision transformer without sacrificing performance.
+
+```python
+import torch
+from vit_pytorch.vit_with_patch_merger import ViT
+
+v = ViT(
+    image_size = 256,
+    patch_size = 16,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 12,
+    heads = 8,
+    patch_merge_layer = 6,        # at which transformer layer to do patch merging
+    patch_merge_num_tokens = 8,   # the output number of tokens from the patch merge
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
+)
+
+img = torch.randn(4, 3, 256, 256)
+
+preds = v(img) # (4, 1000)
+```
+
+One can also use the `PatchMerger` module by itself
+
+```python
+import torch
+from vit_pytorch.vit_with_patch_merger import PatchMerger
+
+merger = PatchMerger(
+    dim = 1024,
+    num_tokens_out = 8   # output number of tokens
+)
+
+features = torch.randn(4, 256, 1024) # (batch, num tokens, dimension)
+
+out = merger(features) # (4, 8, 1024)
 ```
 
 ## Vision Transformer for Small Datasets
@@ -1294,6 +1341,17 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@misc{renggli2022learning,
+    title   = {Learning to Merge Tokens in Vision Transformers},
+    author  = {Cedric Renggli and André Susano Pinto and Neil Houlsby and Basil Mustafa and Joan Puigcerver and Carlos Riquelme},
+    year    = {2022},
+    eprint  = {2202.12015},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
 ```bibtex
 @misc{vaswani2017attention,
     title   = {Attention Is All You Need},

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.26.7',
+  version = '0.27.0',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',

vit_pytorch/vit_with_patch_merger.py

@@ -0,0 +1,144 @@
+import torch
+from torch import nn
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange, Reduce
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val ,d):
+    return val if exists(val) else d
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# patch merger class
+
+class PatchMerger(nn.Module):
+    def __init__(self, dim, num_tokens_out):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.norm = nn.LayerNorm(dim)
+        self.queries = nn.Parameter(torch.randn(num_tokens_out, dim))
+
+    def forward(self, x):
+        x = self.norm(x)
+        sim = torch.matmul(self.queries, x.transpose(-1, -2)) * self.scale
+        attn = sim.softmax(dim = -1)
+        return torch.matmul(attn, x)
+
+# classes
+
+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.attend = nn.Softmax(dim = -1)
+        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):
+        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 = self.heads), qkv)
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        attn = self.attend(dots)
+
+        out = torch.matmul(attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., patch_merge_layer = None, patch_merge_num_tokens = 8):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+
+        self.patch_merge_layer_index = default(patch_merge_layer, depth // 2) - 1 # default to mid-way through transformer, as shown in paper
+        self.patch_merger = PatchMerger(dim = dim, num_tokens_out = patch_merge_num_tokens)
+
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
+            ]))
+    def forward(self, x):
+        for index, (attn, ff) in enumerate(self.layers):
+            x = attn(x) + x
+            x = ff(x) + x
+
+            if index == self.patch_merge_layer_index:
+                x = self.patch_merger(x)
+
+        return x
+
+class ViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, patch_merge_layer = None, patch_merge_num_tokens = 8, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
+
+        num_patches = (image_height // patch_height) * (image_width // patch_width)
+        patch_dim = channels * patch_height * patch_width
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
+            nn.Linear(patch_dim, dim),
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, patch_merge_layer, patch_merge_num_tokens)
+
+        self.mlp_head = nn.Sequential(
+            Reduce('b n d -> b d', 'mean'),
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patch_embedding(img)
+        b, n, _ = x.shape
+
+        x += self.pos_embedding[:, :n]
+        x = self.dropout(x)
+
+        x = self.transformer(x)
+
+        return self.mlp_head(x)