follow @arquolo feedback and advice for MaxViT

README.md

@@ -6,6 +6,7 @@
 - [Install](#install)
 - [Usage](#usage)
 - [Parameters](#parameters)
+- [Simple ViT](#simple-vit)
 - [Distillation](#distillation)
 - [Deep ViT](#deep-vit)
 - [CaiT](#cait)
@@ -106,6 +107,33 @@ Embedding dropout rate.
 - `pool`: string, either `cls` token pooling or `mean` pooling
 
 
+## Simple ViT
+
+<a href="https://arxiv.org/abs/2205.01580">An update</a> from some of the same authors of the original paper proposes simplifications to `ViT` that allows it to train faster and better.
+
+Among these simplifications include 2d sinusoidal positional embedding, global average pooling (no CLS token), no dropout, batch sizes of 1024 rather than 4096, and use of RandAugment and MixUp augmentations. They also show that a simple linear at the end is not significantly worse than the original MLP head
+
+You can use it by importing the `SimpleViT` as shown below
+
+```python
+import torch
+from vit_pytorch import SimpleViT
+
+v = SimpleViT(
+    image_size = 256,
+    patch_size = 32,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 16,
+    mlp_dim = 2048
+)
+
+img = torch.randn(1, 3, 256, 256)
+
+preds = v(img) # (1, 1000)
+```
+
 ## Distillation
 
 <img src="./images/distill.png" width="300px"></img>
@@ -1227,6 +1255,47 @@ logits, embeddings = v(img)
 embeddings # (1, 65, 1024) - (batch x patches x model dim)
 ```
 
+Or say for `CrossViT`, which has a multi-scale encoder that outputs two sets of embeddings for 'large' and 'small' scales
+
+```python
+import torch
+from vit_pytorch.cross_vit import CrossViT
+
+v = CrossViT(
+    image_size = 256,
+    num_classes = 1000,
+    depth = 4,
+    sm_dim = 192,
+    sm_patch_size = 16,
+    sm_enc_depth = 2,
+    sm_enc_heads = 8,
+    sm_enc_mlp_dim = 2048,
+    lg_dim = 384,
+    lg_patch_size = 64,
+    lg_enc_depth = 3,
+    lg_enc_heads = 8,
+    lg_enc_mlp_dim = 2048,
+    cross_attn_depth = 2,
+    cross_attn_heads = 8,
+    dropout = 0.1,
+    emb_dropout = 0.1
+)
+
+# wrap the CrossViT
+
+from vit_pytorch.extractor import Extractor
+v = Extractor(v, layer_name = 'multi_scale_encoder') # take embedding coming from the output of multi-scale-encoder
+
+# forward pass now returns predictions and the attention maps
+
+img = torch.randn(1, 3, 256, 256)
+logits, embeddings = v(img)
+
+# there is one extra token due to the CLS token
+
+embeddings # ((1, 257, 192), (1, 17, 384)) - (batch x patches x dimension) <- large and small scales respectively
+```
+
 ## Research Ideas
 
 ### Efficient Attention
@@ -1669,6 +1738,16 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@misc{Beyer2022BetterPlainViT
+    title     = {Better plain ViT baselines for ImageNet-1k},
+    author    = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
+    publisher = {arXiv},
+    year      = {2022}
+}
+
+```
+
 ```bibtex
 @misc{vaswani2017attention,
     title   = {Attention Is All You Need},

setup.py

@@ -3,9 +3,10 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.34.1',
+  version = '0.35.6',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
+  long_description_content_type = 'text/markdown',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',
   url = 'https://github.com/lucidrains/vit-pytorch',

vit_pytorch/__init__.py

@@ -1,3 +1,5 @@
 from vit_pytorch.vit import ViT
+from vit_pytorch.simple_vit import SimpleViT
+
 from vit_pytorch.mae import MAE
 from vit_pytorch.dino import Dino

vit_pytorch/extractor.py

@@ -4,11 +4,17 @@ from torch import nn
 def exists(val):
     return val is not None
 
+def apply_tuple_or_single(fn, val):
+    if isinstance(val, tuple):
+        return tuple(map(fn, val))
+    return fn(val)
+
 class Extractor(nn.Module):
     def __init__(
         self,
         vit,
         device = None,
+        layer = None,
         layer_name = 'transformer',
         layer_save_input = False,
         return_embeddings_only = False
@@ -23,17 +29,22 @@ class Extractor(nn.Module):
         self.ejected = False
         self.device = device
 
+        self.layer = layer
         self.layer_name = layer_name
         self.layer_save_input = layer_save_input # whether to save input or output of layer
         self.return_embeddings_only = return_embeddings_only
 
     def _hook(self, _, inputs, output):
-        tensor_to_save = inputs if self.layer_save_input else output
-        self.latents = tensor_to_save.clone().detach()
+        layer_output = inputs if self.layer_save_input else output
+        self.latents = apply_tuple_or_single(lambda t: t.clone().detach(), layer_output)
 
     def _register_hook(self):
-        assert hasattr(self.vit, self.layer_name), 'layer whose output to take as embedding not found in vision transformer'
-        layer = getattr(self.vit, self.layer_name)
+        if not exists(self.layer):
+            assert hasattr(self.vit, self.layer_name), 'layer whose output to take as embedding not found in vision transformer'
+            layer = getattr(self.vit, self.layer_name)
+        else:
+            layer = self.layer
+
         handle = layer.register_forward_hook(self._hook)
         self.hooks.append(handle)
         self.hook_registered = True
@@ -62,7 +73,7 @@ class Extractor(nn.Module):
         pred = self.vit(img)
 
         target_device = self.device if exists(self.device) else img.device
-        latents = self.latents.to(target_device)
+        latents = apply_tuple_or_single(lambda t: t.to(target_device), self.latents)
 
         if return_embeddings_only or self.return_embeddings_only:
             return latents

vit_pytorch/max_vit.py

@@ -100,12 +100,14 @@ def MBConv(
     stride = 2 if downsample else 1
 
     net = nn.Sequential(
-        nn.Conv2d(dim_in, dim_out, 1),
-        nn.BatchNorm2d(dim_out),
-        nn.SiLU(),
-        nn.Conv2d(dim_out, dim_out, 3, stride = stride, padding = 1, groups = dim_out),
-        SqueezeExcitation(dim_out, shrinkage_rate = shrinkage_rate),
-        nn.Conv2d(dim_out, dim_out, 1),
+        nn.Conv2d(dim_in, hidden_dim, 1),
+        nn.BatchNorm2d(hidden_dim),
+        nn.GELU(),
+        nn.Conv2d(hidden_dim, hidden_dim, 3, stride = stride, padding = 1, groups = hidden_dim),
+        nn.BatchNorm2d(hidden_dim),
+        nn.GELU(),
+        SqueezeExcitation(hidden_dim, shrinkage_rate = shrinkage_rate),
+        nn.Conv2d(hidden_dim, dim_out, 1),
         nn.BatchNorm2d(dim_out)
     )
 

vit_pytorch/simple_vit.py

@@ -0,0 +1,116 @@
+import torch
+from torch import nn
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def posemb_sincos_2d(patches, temperature = 10000, dtype = torch.float32):
+    _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
+
+    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
+    assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb'
+    omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1)
+    omega = 1. / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :] 
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
+    return pe.type(dtype)
+
+# classes
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, dim),
+        )
+    def forward(self, x):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+    def forward(self, x):
+        x = self.norm(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):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head),
+                FeedForward(dim, mlp_dim)
+            ]))
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
+
+class SimpleViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64):
+        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.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
+
+        self.to_latent = nn.Identity()
+        self.linear_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        *_, h, w, dtype = *img.shape, img.dtype
+
+        x = self.to_patch_embedding(img)
+        pe = posemb_sincos_2d(x)
+        x = rearrange(x, 'b ... d -> b (...) d') + pe
+
+        x = self.transformer(x)
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)