some better defaults for scalable vit

Update MobileViT

README.md

@@ -18,12 +18,15 @@
 - [Twins SVT](#twins-svt)
 - [CrossFormer](#crossformer)
 - [RegionViT](#regionvit)
+- [ScalableViT](#scalablevit)
 - [NesT](#nest)
 - [MobileViT](#mobilevit)
 - [Masked Autoencoder](#masked-autoencoder)
 - [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)
 - [Research Ideas](#research-ideas)
@@ -523,6 +526,38 @@ img = torch.randn(1, 3, 224, 224)
 pred = model(img) # (1, 1000)
 ```
 
+## ScalableViT
+
+<img src="./images/scalable-vit-1.png" width="400px"></img>
+
+<img src="./images/scalable-vit-2.png" width="400px"></img>
+
+This Bytedance AI <a href="https://arxiv.org/abs/2203.10790">paper</a> proposes the Scalable Self Attention (SSA) and the Interactive Windowed Self Attention (IWSA) modules. The SSA alleviates the computation needed at earlier stages by reducing the key / value feature map by some factor (`reduction_factor`), while modulating the dimension of the queries and keys (`ssa_dim_key`). The IWSA performs self attention within local windows, similar to other vision transformer papers. However, they add a residual of the values, passed through a convolution of kernel size 3, which they named Local Interactive Module (LIM).
+
+They make the claim in this paper that this scheme outperforms Swin Transformer, and also demonstrate competitive performance against Crossformer.
+
+You can use it as follows (ex. ScalableViT-S)
+
+```python
+import torch
+from vit_pytorch.scalable_vit import ScalableViT
+
+model = ScalableViT(
+    num_classes = 1000,
+    dim = 64,                               # starting model dimension. at every stage, dimension is doubled
+    heads = (2, 4, 8, 16),                  # number of attention heads at each stage
+    depth = (2, 2, 20, 2),                  # number of transformer blocks at each stage
+    ssa_dim_key = (40, 40, 40, 32),         # the dimension of the attention keys (and queries) for SSA. in the paper, they represented this as a scale factor on the base dimension per key (ssa_dim_key / dim_key)
+    reduction_factor = (8, 4, 2, 1),        # downsampling of the key / values in SSA. in the paper, this was represented as (reduction_factor ** -2)
+    window_size = (64, 32, None, None),     # window size of the IWSA at each stage. None means no windowing needed
+    dropout = 0.1,                          # attention and feedforward dropout
+).cuda()
+
+img = torch.randn(1, 3, 256, 256).cuda()
+
+preds = model(img) # (1, 1000)
+```
+
 ## NesT
 
 <img src="./images/nest.png" width="400px"></img>
@@ -541,7 +576,7 @@ nest = NesT(
     dim = 96,
     heads = 3,
     num_hierarchies = 3,        # number of hierarchies
-    block_repeats = (8, 4, 1),  # the number of transformer blocks at each heirarchy, starting from the bottom
+    block_repeats = (2, 2, 8),  # the number of transformer blocks at each heirarchy, starting from the bottom
     num_classes = 1000
 )
 
@@ -731,12 +766,104 @@ 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
+
+<img src="./images/vit_for_small_datasets.png" width="400px"></img>
+
+This <a href="https://arxiv.org/abs/2112.13492">paper</a> proposes a new image to patch function that incorporates shifts of the image, before normalizing and dividing the image into patches. I have found shifting to be extremely helpful in some other transformers work, so decided to include this for further explorations. It also includes the `LSA` with the learned temperature and masking out of a token's attention to itself.
+
+You can use as follows:
+
+```python
+import torch
+from vit_pytorch.vit_for_small_dataset import ViT
+
+v = ViT(
+    image_size = 256,
+    patch_size = 16,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 16,
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
+)
+
+img = torch.randn(4, 3, 256, 256)
+
+preds = v(img) # (1, 1000)
+```
+
+You can also use the `SPT` from this paper as a standalone module
+
+```python
+import torch
+from vit_pytorch.vit_for_small_dataset import SPT
+
+spt = SPT(
+    dim = 1024,
+    patch_size = 16,
+    channels = 3
+)
+
+img = torch.randn(4, 3, 256, 256)
+
+tokens = spt(img) # (4, 256, 1024)
 ```
 
 ## Dino
@@ -1236,6 +1363,39 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@misc{lee2021vision,
+    title   = {Vision Transformer for Small-Size Datasets}, 
+    author  = {Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song},
+    year    = {2021},
+    eprint  = {2112.13492},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
+```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{yang2022scalablevit,
+    title   = {ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer}, 
+    author  = {Rui Yang and Hailong Ma and Jie Wu and Yansong Tang and Xuefeng Xiao and Min Zheng and Xiu Li},
+    year    = {2022},
+    eprint  = {2203.10790},
+    archivePrefix = {arXiv},
+    primaryClass = {cs.CV}
+}
+```
+
 ```bibtex
 @misc{vaswani2017attention,
     title   = {Attention Is All You Need},

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '0.25.5',
+  version = '0.28.1',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',
@@ -15,7 +15,7 @@ setup(
     'image recognition'
   ],
   install_requires=[
-    'einops>=0.3',
+    'einops>=0.4.1',
     'torch>=1.6',
     'torchvision'
   ],

vit_pytorch/crossformer.py

@@ -62,9 +62,9 @@ class LayerNorm(nn.Module):
         self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
 
     def forward(self, x):
-        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (std + self.eps) * self.g + self.b
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
 
 def FeedForward(dim, mult = 4, dropout = 0.):
     return nn.Sequential(

vit_pytorch/cvt.py

@@ -30,9 +30,9 @@ class LayerNorm(nn.Module): # layernorm, but done in the channel dimension #1
         self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
 
     def forward(self, x):
-        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (std + self.eps) * self.g + self.b
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
 
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):

vit_pytorch/efficient.py

@@ -3,12 +3,16 @@ from torch import nn
 from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
 
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
 class ViT(nn.Module):
     def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, pool = 'cls', channels = 3):
         super().__init__()
-        assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
+        image_size_h, image_size_w = pair(image_size)
+        assert image_size_h % patch_size == 0 and image_size_w % patch_size == 0, 'image dimensions must be divisible by the patch size'
         assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
-        num_patches = (image_size // patch_size) ** 2
+        num_patches = (image_size_h // patch_size) * (image_size_w // patch_size)
         patch_dim = channels * patch_size ** 2
 
         self.to_patch_embedding = nn.Sequential(

vit_pytorch/extractor.py

@@ -5,7 +5,14 @@ def exists(val):
     return val is not None
 
 class Extractor(nn.Module):
-    def __init__(self, vit, device = None):
+    def __init__(
+        self,
+        vit,
+        device = None,
+        layer_name = 'transformer',
+        layer_save_input = False,
+        return_embeddings_only = False
+    ):
         super().__init__()
         self.vit = vit
 
@@ -16,11 +23,18 @@ class Extractor(nn.Module):
         self.ejected = False
         self.device = device
 
-    def _hook(self, _, input, output):
-        self.latents = output.clone().detach()
+        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()
 
     def _register_hook(self):
-        handle = self.vit.transformer.register_forward_hook(self._hook)
+        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)
+        handle = layer.register_forward_hook(self._hook)
         self.hooks.append(handle)
         self.hook_registered = True
 
@@ -35,7 +49,11 @@ class Extractor(nn.Module):
         del self.latents
         self.latents = None
 
-    def forward(self, img):
+    def forward(
+        self,
+        img,
+        return_embeddings_only = False
+    ):
         assert not self.ejected, 'extractor has been ejected, cannot be used anymore'
         self.clear()
         if not self.hook_registered:
@@ -45,4 +63,8 @@ class Extractor(nn.Module):
 
         target_device = self.device if exists(self.device) else img.device
         latents = self.latents.to(target_device)
+
+        if return_embeddings_only or self.return_embeddings_only:
+            return latents
+
         return pred, latents

vit_pytorch/mae.py

@@ -71,6 +71,10 @@ class MAE(nn.Module):
 
         decoder_tokens = self.enc_to_dec(encoded_tokens)
 
+        # reapply decoder position embedding to unmasked tokens
+
+        decoder_tokens = decoder_tokens + self.decoder_pos_emb(unmasked_indices)
+
         # repeat mask tokens for number of masked, and add the positions using the masked indices derived above
 
         mask_tokens = repeat(self.mask_token, 'd -> b n d', b = batch, n = num_masked)

vit_pytorch/mobile_vit.py

@@ -1,40 +1,27 @@
-"""
-An implementation of MobileViT Model as defined in:
-MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
-Arxiv: https://arxiv.org/abs/2110.02178
-Origin Code: https://github.com/murufeng/awesome_lightweight_networks
-"""
-
 import torch
 import torch.nn as nn
 
 from einops import rearrange
 from einops.layers.torch import Reduce
 
-def _make_divisible(v, divisor, min_value=None):
-    if min_value is None:
-        min_value = divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    if new_v < 0.9 * v:
-        new_v += divisor
-    return new_v
-
-
-def conv_bn_relu(inp, oup, kernel, stride=1):
-    return nn.Sequential(
-        nn.Conv2d(inp, oup, kernel_size=kernel, stride=stride, padding=1, bias=False),
-        nn.BatchNorm2d(oup),
-        nn.ReLU6(inplace=True)
-    )
-
+# helpers
 
 def conv_1x1_bn(inp, oup):
     return nn.Sequential(
         nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
         nn.BatchNorm2d(oup),
-        nn.ReLU6(inplace=True)
+        nn.SiLU()
     )
 
+def conv_nxn_bn(inp, oup, kernal_size=3, stride=1):
+    return nn.Sequential(
+        nn.Conv2d(inp, oup, kernal_size, stride, 1, bias=False),
+        nn.BatchNorm2d(oup),
+        nn.SiLU()
+    )
+
+# classes
+
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):
         super().__init__()
@@ -44,10 +31,11 @@ class PreNorm(nn.Module):
     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.ffn = nn.Sequential(
+        self.net = nn.Sequential(
             nn.Linear(dim, hidden_dim),
             nn.SiLU(),
             nn.Dropout(dropout),
@@ -56,8 +44,7 @@ class FeedForward(nn.Module):
         )
 
     def forward(self, x):
-        return self.ffn(x)
-
+        return self.net(x)
 
 class Attention(nn.Module):
     def __init__(self, dim, heads=8, dim_head=64, dropout=0.):
@@ -76,7 +63,8 @@ class Attention(nn.Module):
 
     def forward(self, x):
         qkv = self.to_qkv(x).chunk(3, dim=-1)
-        q, k, v = map(lambda t: rearrange(t, 'b p n (h d) -> b p h n d', h=self.heads), qkv)
+        q, k, v = map(lambda t: rearrange(
+            t, 'b p n (h d) -> b p h n d', h=self.heads), qkv)
 
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
         attn = self.attend(dots)
@@ -84,15 +72,19 @@ class Attention(nn.Module):
         out = rearrange(out, 'b p h n d -> b p n (h d)')
         return self.to_out(out)
 
-
 class Transformer(nn.Module):
-    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
+    """Transformer block described in ViT.
+    Paper: https://arxiv.org/abs/2010.11929
+    Based on: https://github.com/lucidrains/vit-pytorch
+    """
+
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout=0.):
         super().__init__()
         self.layers = nn.ModuleList([])
         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))
+                PreNorm(dim, Attention(dim, heads, dim_head, dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout))
             ]))
 
     def forward(self, x):
@@ -102,17 +94,24 @@ class Transformer(nn.Module):
         return x
 
 class MV2Block(nn.Module):
-    def __init__(self, inp, oup, stride=1, expand_ratio=4):
-        super(MV2Block, self).__init__()
+    """MV2 block described in MobileNetV2.
+    Paper: https://arxiv.org/pdf/1801.04381
+    Based on: https://github.com/tonylins/pytorch-mobilenet-v2
+    """
+
+    def __init__(self, inp, oup, stride=1, expansion=4):
+        super().__init__()
+        self.stride = stride
         assert stride in [1, 2]
 
-        hidden_dim = round(inp * expand_ratio)
-        self.identity = stride == 1 and inp == oup
+        hidden_dim = int(inp * expansion)
+        self.use_res_connect = self.stride == 1 and inp == oup
 
-        if expand_ratio == 1:
+        if expansion == 1:
             self.conv = nn.Sequential(
                 # dw
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
+                          1, groups=hidden_dim, bias=False),
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # pw-linear
@@ -126,7 +125,8 @@ class MV2Block(nn.Module):
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # dw
-                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
+                nn.Conv2d(hidden_dim, hidden_dim, 3, stride,
+                          1, groups=hidden_dim, bias=False),
                 nn.BatchNorm2d(hidden_dim),
                 nn.SiLU(),
                 # pw-linear
@@ -136,8 +136,7 @@ class MV2Block(nn.Module):
 
     def forward(self, x):
         out = self.conv(x)
-
-        if self.identity:
+        if self.use_res_connect:
             out = out + x
         return out
 
@@ -146,13 +145,13 @@ class MobileViTBlock(nn.Module):
         super().__init__()
         self.ph, self.pw = patch_size
 
-        self.conv1 = conv_bn_relu(channel, channel, kernel_size)
+        self.conv1 = conv_nxn_bn(channel, channel, kernel_size)
         self.conv2 = conv_1x1_bn(channel, dim)
 
-        self.transformer = Transformer(dim, depth, 1, 32, mlp_dim, dropout)
+        self.transformer = Transformer(dim, depth, 4, 8, mlp_dim, dropout)
 
         self.conv3 = conv_1x1_bn(dim, channel)
-        self.conv4 = conv_bn_relu(2 * channel, channel, kernel_size)
+        self.conv4 = conv_nxn_bn(2 * channel, channel, kernel_size)
 
     def forward(self, x):
         y = x.clone()
@@ -163,9 +162,11 @@ class MobileViTBlock(nn.Module):
 
         # Global representations
         _, _, h, w = x.shape
-        x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d', ph=self.ph, pw=self.pw)
+        x = rearrange(x, 'b d (h ph) (w pw) -> b (ph pw) (h w) d',
+                      ph=self.ph, pw=self.pw)
         x = self.transformer(x)
-        x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)', h=h // self.ph, w=w // self.pw, ph=self.ph, pw=self.pw)
+        x = rearrange(x, 'b (ph pw) (h w) d -> b d (h ph) (w pw)',
+                      h=h//self.ph, w=w//self.pw, ph=self.ph, pw=self.pw)
 
         # Fusion
         x = self.conv3(x)
@@ -173,18 +174,22 @@ class MobileViTBlock(nn.Module):
         x = self.conv4(x)
         return x
 
-
 class MobileViT(nn.Module):
+    """MobileViT.
+    Paper: https://arxiv.org/abs/2110.02178
+    Based on: https://github.com/chinhsuanwu/mobilevit-pytorch
+    """
+
     def __init__(
         self,
         image_size,
         dims,
         channels,
         num_classes,
-        expansion = 4,
-        kernel_size = 3,
-        patch_size = (2, 2),
-        depths = (2, 4, 3)
+        expansion=4,
+        kernel_size=3,
+        patch_size=(2, 2),
+        depths=(2, 4, 3)
     ):
         super().__init__()
         assert len(dims) == 3, 'dims must be a tuple of 3'
@@ -196,28 +201,31 @@ class MobileViT(nn.Module):
 
         init_dim, *_, last_dim = channels
 
-        self.conv1 = conv_bn_relu(3, init_dim, kernel=3, stride=2)
+        self.conv1 = conv_nxn_bn(3, init_dim, stride=2)
 
         self.stem = nn.ModuleList([])
         self.stem.append(MV2Block(channels[0], channels[1], 1, expansion))
         self.stem.append(MV2Block(channels[1], channels[2], 2, expansion))
         self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))
         self.stem.append(MV2Block(channels[2], channels[3], 1, expansion))
-    
+
         self.trunk = nn.ModuleList([])
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[3], channels[4], 2, expansion),
-            MobileViTBlock(dims[0], depths[0], channels[5], kernel_size, patch_size, int(dims[0] * 2))
+            MobileViTBlock(dims[0], depths[0], channels[5],
+                           kernel_size, patch_size, int(dims[0] * 2))
         ]))
 
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[5], channels[6], 2, expansion),
-            MobileViTBlock(dims[1], depths[1], channels[7], kernel_size, patch_size, int(dims[1] * 4))
+            MobileViTBlock(dims[1], depths[1], channels[7],
+                           kernel_size, patch_size, int(dims[1] * 4))
         ]))
 
         self.trunk.append(nn.ModuleList([
             MV2Block(channels[7], channels[8], 2, expansion),
-            MobileViTBlock(dims[2], depths[2], channels[9], kernel_size, patch_size, int(dims[2] * 4))
+            MobileViTBlock(dims[2], depths[2], channels[9],
+                           kernel_size, patch_size, int(dims[2] * 4))
         ]))
 
         self.to_logits = nn.Sequential(

vit_pytorch/nest.py

@@ -20,9 +20,9 @@ class LayerNorm(nn.Module):
         self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
 
     def forward(self, x):
-        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (std + self.eps) * self.g + self.b
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
 
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):
@@ -131,10 +131,11 @@ class NesT(nn.Module):
 
         seq_len = (fmap_size // blocks) ** 2   # sequence length is held constant across heirarchy
         hierarchies = list(reversed(range(num_hierarchies)))
-        mults = [2 ** i for i in hierarchies]
+        mults = [2 ** i for i in reversed(hierarchies)]
 
         layer_heads = list(map(lambda t: t * heads, mults))
         layer_dims = list(map(lambda t: t * dim, mults))
+        last_dim = layer_dims[-1]
 
         layer_dims = [*layer_dims, layer_dims[-1]]
         dim_pairs = zip(layer_dims[:-1], layer_dims[1:])
@@ -157,10 +158,11 @@ class NesT(nn.Module):
                 Aggregate(dim_in, dim_out) if not is_last else nn.Identity()
             ]))
 
+
         self.mlp_head = nn.Sequential(
-            LayerNorm(dim),
+            LayerNorm(last_dim),
             Reduce('b c h w -> b c', 'mean'),
-            nn.Linear(dim, num_classes)
+            nn.Linear(last_dim, num_classes)
         )
 
     def forward(self, img):

vit_pytorch/recorder.py

@@ -55,5 +55,5 @@ class Recorder(nn.Module):
         target_device = self.device if self.device is not None else img.device
         recordings = tuple(map(lambda t: t.to(target_device), self.recordings))
 
-        attns = torch.stack(recordings, dim = 1)
+        attns = torch.stack(recordings, dim = 1) if len(recordings) > 0 else None
         return pred, attns

vit_pytorch/scalable_vit.py

@@ -0,0 +1,302 @@
+from functools import partial
+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)
+
+def cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
+# helper classes
+
+class ChanLayerNorm(nn.Module):
+    def __init__(self, dim, eps = 1e-5):
+        super().__init__()
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1, dim, 1, 1))
+        self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = ChanLayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x):
+        return self.fn(self.norm(x))
+
+class Downsample(nn.Module):
+    def __init__(self, dim_in, dim_out):
+        super().__init__()
+        self.conv = nn.Conv2d(dim_in, dim_out, 3, stride = 2, padding = 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+class PEG(nn.Module):
+    def __init__(self, dim, kernel_size = 3):
+        super().__init__()
+        self.proj = nn.Conv2d(dim, dim, kernel_size = kernel_size, padding = kernel_size // 2, groups = dim, stride = 1)
+
+    def forward(self, x):
+        return self.proj(x) + x
+
+# feedforward
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, expansion_factor = 4, dropout = 0.):
+        super().__init__()
+        inner_dim = dim * expansion_factor
+        self.net = nn.Sequential(
+            nn.Conv2d(dim, inner_dim, 1),
+            nn.GELU(),
+            nn.Dropout(dropout),
+            nn.Conv2d(inner_dim, dim, 1),
+            nn.Dropout(dropout)
+        )
+    def forward(self, x):
+        return self.net(x)
+
+# attention
+
+class ScalableSelfAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads = 8,
+        dim_key = 32,
+        dim_value = 32,
+        dropout = 0.,
+        reduction_factor = 1
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_key ** -0.5
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_q = nn.Conv2d(dim, dim_key * heads, 1, bias = False)
+        self.to_k = nn.Conv2d(dim, dim_key * heads, reduction_factor, stride = reduction_factor, bias = False)
+        self.to_v = nn.Conv2d(dim, dim_value * heads, reduction_factor, stride = reduction_factor, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Conv2d(dim_value * heads, dim, 1),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        height, width, heads = *x.shape[-2:], self.heads
+
+        q, k, v = self.to_q(x), self.to_k(x), self.to_v(x)
+
+        # split out heads
+
+        q, k, v = map(lambda t: rearrange(t, 'b (h d) ... -> b h (...) d', h = heads), (q, k, v))
+
+        # similarity
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        # attention
+
+        attn = self.attend(dots)
+
+        # aggregate values
+
+        out = torch.matmul(attn, v)
+
+        # merge back heads
+
+        out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = height, y = width)
+        return self.to_out(out)
+
+class InteractiveWindowedSelfAttention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        window_size,
+        heads = 8,
+        dim_key = 32,
+        dim_value = 32,
+        dropout = 0.
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_key ** -0.5
+        self.window_size = window_size
+        self.attend = nn.Softmax(dim = -1)
+
+        self.local_interactive_module = nn.Conv2d(dim_value * heads, dim_value * heads, 3, padding = 1)
+
+        self.to_q = nn.Conv2d(dim, dim_key * heads, 1, bias = False)
+        self.to_k = nn.Conv2d(dim, dim_key * heads, 1, bias = False)
+        self.to_v = nn.Conv2d(dim, dim_value * heads, 1, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Conv2d(dim_value * heads, dim, 1),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        height, width, heads, wsz = *x.shape[-2:], self.heads, self.window_size
+
+        wsz = default(wsz, height) # take height as window size if not given
+        assert (height % wsz) == 0 and (width % wsz) == 0, f'height ({height}) or width ({width}) of feature map is not divisible by the window size ({wsz})'
+
+        q, k, v = self.to_q(x), self.to_k(x), self.to_v(x)
+
+        # get output of LIM
+
+        local_out = self.local_interactive_module(v)
+
+        # divide into window (and split out heads) for efficient self attention
+
+        q, k, v = map(lambda t: rearrange(t, 'b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d', h = heads, w1 = wsz, w2 = wsz), (q, k, v))
+
+        # similarity
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        # attention
+
+        attn = self.attend(dots)
+
+        # aggregate values
+
+        out = torch.matmul(attn, v)
+
+        # reshape the windows back to full feature map (and merge heads)
+
+        out = rearrange(out, '(b x y) h (w1 w2) d -> b (h d) (x w1) (y w2)', x = height // wsz, y = width // wsz, w1 = wsz, w2 = wsz)
+
+        # add LIM output 
+
+        out = out + local_out
+
+        return self.to_out(out)
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        heads = 8,
+        ff_expansion_factor = 4,
+        dropout = 0.,
+        ssa_dim_key = 32,
+        ssa_dim_value = 32,
+        ssa_reduction_factor = 1,
+        iwsa_dim_key = 32,
+        iwsa_dim_value = 32,
+        iwsa_window_size = None,
+        norm_output = True
+    ):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for ind in range(depth):
+            is_first = ind == 0
+
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, ScalableSelfAttention(dim, heads = heads, dim_key = ssa_dim_key, dim_value = ssa_dim_value, reduction_factor = ssa_reduction_factor, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, expansion_factor = ff_expansion_factor, dropout = dropout)),
+                PEG(dim) if is_first else None,
+                PreNorm(dim, FeedForward(dim, expansion_factor = ff_expansion_factor, dropout = dropout)),
+                PreNorm(dim, InteractiveWindowedSelfAttention(dim, heads = heads, dim_key = iwsa_dim_key, dim_value = iwsa_dim_value, window_size = iwsa_window_size, dropout = dropout))
+            ]))
+
+        self.norm = ChanLayerNorm(dim) if norm_output else nn.Identity()
+
+    def forward(self, x):
+        for ssa, ff1, peg, iwsa, ff2 in self.layers:
+            x = ssa(x) + x
+            x = ff1(x) + x
+
+            if exists(peg):
+                x = peg(x)
+
+            x = iwsa(x) + x
+            x = ff2(x) + x
+
+        return self.norm(x)
+
+class ScalableViT(nn.Module):
+    def __init__(
+        self,
+        *,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        reduction_factor,
+        window_size = None,
+        iwsa_dim_key = 32,
+        iwsa_dim_value = 32,
+        ssa_dim_key = 32,
+        ssa_dim_value = 32,
+        ff_expansion_factor = 4,
+        channels = 3,
+        dropout = 0.
+    ):
+        super().__init__()
+        self.to_patches = nn.Conv2d(channels, dim, 7, stride = 4, padding = 3)
+
+        assert isinstance(depth, tuple), 'depth needs to be tuple if integers indicating number of transformer blocks at that stage'
+
+        num_stages = len(depth)
+        dims = tuple(map(lambda i: (2 ** i) * dim, range(num_stages)))
+
+        hyperparams_per_stage = [
+            heads,
+            ssa_dim_key,
+            ssa_dim_value,
+            reduction_factor,
+            iwsa_dim_key,
+            iwsa_dim_value,
+            window_size,
+        ]
+
+        hyperparams_per_stage = list(map(partial(cast_tuple, length = num_stages), hyperparams_per_stage))
+        assert all(tuple(map(lambda arr: len(arr) == num_stages, hyperparams_per_stage)))
+
+        self.layers = nn.ModuleList([])
+
+        for ind, (layer_dim, layer_depth, layer_heads, layer_ssa_dim_key, layer_ssa_dim_value, layer_ssa_reduction_factor, layer_iwsa_dim_key, layer_iwsa_dim_value, layer_window_size) in enumerate(zip(dims, depth, *hyperparams_per_stage)):
+            is_last = ind == (num_stages - 1)
+
+            self.layers.append(nn.ModuleList([
+                Transformer(dim = layer_dim, depth = layer_depth, heads = layer_heads, ff_expansion_factor = ff_expansion_factor, dropout = dropout, ssa_dim_key = layer_ssa_dim_key, ssa_dim_value = layer_ssa_dim_value, ssa_reduction_factor = layer_ssa_reduction_factor, iwsa_dim_key = layer_iwsa_dim_key, iwsa_dim_value = layer_iwsa_dim_value, iwsa_window_size = layer_window_size),
+                Downsample(layer_dim, layer_dim * 2) if not is_last else None
+            ]))
+
+        self.mlp_head = nn.Sequential(
+            Reduce('b d h w -> b d', 'mean'),
+            nn.LayerNorm(dims[-1]),
+            nn.Linear(dims[-1], num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patches(img)
+
+        for transformer, downsample in self.layers:
+            x = transformer(x)
+
+            if exists(downsample):
+                x = downsample(x)
+
+        return self.mlp_head(x)

vit_pytorch/twins_svt.py

@@ -38,9 +38,9 @@ class LayerNorm(nn.Module):
         self.b = nn.Parameter(torch.zeros(1, dim, 1, 1))
 
     def forward(self, x):
-        std = torch.var(x, dim = 1, unbiased = False, keepdim = True).sqrt()
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
         mean = torch.mean(x, dim = 1, keepdim = True)
-        return (x - mean) / (std + self.eps) * self.g + self.b
+        return (x - mean) / (var + self.eps).sqrt() * self.g + self.b
 
 class PreNorm(nn.Module):
     def __init__(self, dim, fn):

vit_pytorch/vit_for_small_dataset.py

@@ -0,0 +1,142 @@
+from math import sqrt
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# 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 LSA(nn.Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.temperature = nn.Parameter(torch.log(torch.tensor(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)
+        )
+
+    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.temperature.exp()
+
+        mask = torch.eye(dots.shape[-1], device = dots.device, dtype = torch.bool)
+        mask_value = -torch.finfo(dots.dtype).max
+        dots = dots.masked_fill(mask, mask_value)
+
+        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.):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, LSA(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))
+            ]))
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+        return x
+
+class SPT(nn.Module):
+    def __init__(self, *, dim, patch_size, channels = 3):
+        super().__init__()
+        patch_dim = patch_size * patch_size * 5 * channels
+
+        self.to_patch_tokens = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim)
+        )
+
+    def forward(self, x):
+        shifts = ((1, -1, 0, 0), (-1, 1, 0, 0), (0, 0, 1, -1), (0, 0, -1, 1))
+        shifted_x = list(map(lambda shift: F.pad(x, shift), shifts))
+        x_with_shifts = torch.cat((x, *shifted_x), dim = 1)
+        return self.to_patch_tokens(x_with_shifts)
+
+class ViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', 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
+        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
+
+        self.to_patch_embedding = SPT(dim = dim, patch_size = patch_size, channels = channels)
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
+
+        self.pool = pool
+        self.to_latent = nn.Identity()
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patch_embedding(img)
+        b, n, _ = x.shape
+
+        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x += self.pos_embedding[:, :(n + 1)]
+        x = self.dropout(x)
+
+        x = self.transformer(x)
+
+        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
+
+        x = self.to_latent(x)
+        return self.mlp_head(x)

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)