maxvit intent to build (#211)

complete hybrid mbconv + block / grid efficient self attention MaxViT

.github/FUNDING.yml

@@ -0,0 +1,3 @@
+# These are supported funding model platforms
+
+github: [lucidrains]

README.md

@@ -19,6 +19,8 @@
 - [CrossFormer](#crossformer)
 - [RegionViT](#regionvit)
 - [ScalableViT](#scalablevit)
+- [SepViT](#sepvit)
+- [MaxViT](#maxvit)
 - [NesT](#nest)
 - [MobileViT](#mobilevit)
 - [Masked Autoencoder](#masked-autoencoder)
@@ -28,6 +30,7 @@
 - [Patch Merger](#patch-merger)
 - [Vision Transformer for Small Datasets](#vision-transformer-for-small-datasets)
 - [Parallel ViT](#parallel-vit)
+- [Learnable Memory ViT](#learnable-memory-vit)
 - [Dino](#dino)
 - [Accessing Attention](#accessing-attention)
 - [Research Ideas](#research-ideas)
@@ -558,13 +561,73 @@ model = ScalableViT(
     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()
+img = torch.randn(1, 3, 256, 256)
 
 preds = model(img) # (1, 1000)
 ```
 
+## SepViT
+
+<img src="./images/sep-vit.png" width="400px"></img>
+
+Another <a href="https://arxiv.org/abs/2203.15380">Bytedance AI paper</a>, it proposes a depthwise-pointwise self-attention layer that seems largely inspired by mobilenet's depthwise-separable convolution. The most interesting aspect is the reuse of the feature map from the depthwise self-attention stage as the values for the pointwise self-attention, as shown in the diagram above.
+
+I have decided to include only the version of `SepViT` with this specific self-attention layer, as the grouped attention layers are not remarkable nor novel, and the authors were not clear on how they treated the window tokens for the group self-attention layer. Besides, it seems like with `DSSA` layer alone, they were able to beat Swin.
+
+ex. SepViT-Lite
+
+```python
+import torch
+from vit_pytorch.sep_vit import SepViT
+
+v = SepViT(
+    num_classes = 1000,
+    dim = 32,               # dimensions of first stage, which doubles every stage (32, 64, 128, 256) for SepViT-Lite
+    dim_head = 32,          # attention head dimension
+    heads = (1, 2, 4, 8),   # number of heads per stage
+    depth = (1, 2, 6, 2),   # number of transformer blocks per stage
+    window_size = 7,        # window size of DSS Attention block
+    dropout = 0.1           # dropout
+)
+
+img = torch.randn(1, 3, 224, 224)
+
+preds = v(img) # (1, 1000)
+```
+
+## MaxViT
+
+<img src="./images/max-vit.png" width="400px"></img>
+
+This paper proposes a hybrid convolutional / attention network, using MBConv from the convolution side, and then block / grid axial sparse attention.
+
+They also claim this specific vision transformer is good for generative models (GANs).
+
+ex. MaxViT-S
+
+```python
+import torch
+from vit_pytorch.max_vit import MaxViT
+
+v = MaxViT(
+    num_classes = 1000,
+    dim_conv_stem = 64,               # dimension of the convolutional stem, would default to dimension of first layer if not specified
+    dim = 96,                         # dimension of first layer, doubles every layer
+    dim_head = 32,                    # dimension of attention heads, kept at 32 in paper
+    depth = (2, 2, 5, 2),             # number of MaxViT blocks per stage, which consists of MBConv, block-like attention, grid-like attention
+    window_size = 7,                  # window size for block and grids
+    mbconv_expansion_rate = 4,        # expansion rate of MBConv
+    mbconv_shrinkage_rate = 0.25,     # shrinkage rate of squeeze-excitation in MBConv
+    dropout = 0.1                     # dropout
+)
+
+img = torch.randn(2, 3, 224, 224)
+
+preds = v(img) # (2, 1000)
+```
+
 ## NesT
 
 <img src="./images/nest.png" width="400px"></img>
@@ -903,6 +966,61 @@ img = torch.randn(4, 3, 256, 256)
 preds = v(img) # (4, 1000)
 ```
 
+## Learnable Memory ViT
+
+<img src="./images/learnable-memory-vit.png" width="350px"></img>
+
+This <a href="https://arxiv.org/abs/2203.15243">paper</a> shows that adding learnable memory tokens at each layer of a vision transformer can greatly enhance fine-tuning results (in addition to learnable task specific CLS token and adapter head).
+
+You can use this with a specially modified `ViT` as follows
+
+```python
+import torch
+from vit_pytorch.learnable_memory_vit import ViT, Adapter
+
+# normal base ViT
+
+v = ViT(
+    image_size = 256,
+    patch_size = 16,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 6,
+    heads = 8,
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1
+)
+
+img = torch.randn(4, 3, 256, 256)
+logits = v(img) # (4, 1000)
+
+# do your usual training with ViT
+# ...
+
+
+# then, to finetune, just pass the ViT into the Adapter class
+# you can do this for multiple Adapters, as shown below
+
+adapter1 = Adapter(
+    vit = v,
+    num_classes = 2,               # number of output classes for this specific task
+    num_memories_per_layer = 5     # number of learnable memories per layer, 10 was sufficient in paper
+)
+
+logits1 = adapter1(img) # (4, 2) - predict 2 classes off frozen ViT backbone with learnable memories and task specific head
+
+# yet another task to finetune on, this time with 4 classes
+
+adapter2 = Adapter(
+    vit = v,
+    num_classes = 4,
+    num_memories_per_layer = 10
+)
+
+logits2 = adapter2(img) # (4, 4) - predict 4 classes off frozen ViT backbone with learnable memories and task specific head
+
+```
 
 ## Dino
 
@@ -1442,6 +1560,30 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@inproceedings{Sandler2022FinetuningIT,
+    title   = {Fine-tuning Image Transformers using Learnable Memory},
+    author  = {Mark Sandler and Andrey Zhmoginov and Max Vladymyrov and Andrew Jackson},
+    year    = {2022}
+}
+```
+
+```bibtex
+@inproceedings{Li2022SepViTSV,
+    title   = {SepViT: Separable Vision Transformer},
+    author  = {Wei Li and Xing Wang and Xin Xia and Jie Wu and Xuefeng Xiao and Minghang Zheng and Shiping Wen},
+    year    = {2022}
+}
+```
+
+```bibtex
+@inproceedings{Tu2022MaxViTMV,
+    title   = {MaxViT: Multi-Axis Vision Transformer},
+    author  = {Zhe-Wei Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
+    year    = {2022}
+}
+```
+
 ```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 = '0.29.1',
+  version = '0.33.0',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   author = 'Phil Wang',
@@ -16,7 +16,7 @@ setup(
   ],
   install_requires=[
     'einops>=0.4.1',
-    'torch>=1.6',
+    'torch>=1.10',
     'torchvision'
   ],
   setup_requires=[

vit_pytorch/ats_vit.py

@@ -139,6 +139,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.output_num_tokens = output_num_tokens
@@ -163,6 +165,7 @@ class Attention(nn.Module):
             dots = dots.masked_fill(~dots_mask, mask_value)
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         sampled_token_ids = None
 

vit_pytorch/cait.py

@@ -76,6 +76,7 @@ class Attention(nn.Module):
         self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         self.mix_heads_pre_attn = nn.Parameter(torch.randn(heads, heads))
         self.mix_heads_post_attn = nn.Parameter(torch.randn(heads, heads))
@@ -96,7 +97,10 @@ class Attention(nn.Module):
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
 
         dots = einsum('b h i j, h g -> b g i j', dots, self.mix_heads_pre_attn)    # talking heads, pre-softmax
+
         attn = self.attend(dots)
+        attn = self.dropout(attn)
+
         attn = einsum('b h i j, h g -> b g i j', attn, self.mix_heads_post_attn)   # talking heads, post-softmax
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)

vit_pytorch/cross_vit.py

@@ -48,6 +48,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_q = nn.Linear(dim, inner_dim, bias = False)
         self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
 
@@ -69,6 +71,7 @@ class Attention(nn.Module):
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')

vit_pytorch/crossformer.py

@@ -95,6 +95,9 @@ class Attention(nn.Module):
         self.window_size = window_size
 
         self.norm = LayerNorm(dim)
+
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias = False)
         self.to_out = nn.Conv2d(inner_dim, dim, 1)
 
@@ -105,7 +108,7 @@ class Attention(nn.Module):
         # calculate and store indices for retrieving bias
 
         pos = torch.arange(window_size)
-        grid = torch.stack(torch.meshgrid(pos, pos))
+        grid = torch.stack(torch.meshgrid(pos, pos, indexing = 'ij'))
         grid = rearrange(grid, 'c i j -> (i j) c')
         rel_pos = grid[:, None] - grid[None, :]
         rel_pos += window_size - 1
@@ -141,7 +144,7 @@ class Attention(nn.Module):
         # add dynamic positional bias
 
         pos = torch.arange(-wsz, wsz + 1, device = device)
-        rel_pos = torch.stack(torch.meshgrid(pos, pos))
+        rel_pos = torch.stack(torch.meshgrid(pos, pos, indexing = 'ij'))
         rel_pos = rearrange(rel_pos, 'c i j -> (i j) c')
         biases = self.dpb(rel_pos.float())
         rel_pos_bias = biases[self.rel_pos_indices]
@@ -151,6 +154,7 @@ class Attention(nn.Module):
         # attend
 
         attn = sim.softmax(dim = -1)
+        attn = self.dropout(attn)
 
         # merge heads
 

vit_pytorch/cvt.py

@@ -76,6 +76,7 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         self.to_q = DepthWiseConv2d(dim, inner_dim, proj_kernel, padding = padding, stride = 1, bias = False)
         self.to_kv = DepthWiseConv2d(dim, inner_dim * 2, proj_kernel, padding = padding, stride = kv_proj_stride, bias = False)
@@ -94,6 +95,7 @@ class Attention(nn.Module):
         dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b i j, b j d -> b i d', attn, v)
         out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h = h, y = y)

vit_pytorch/deepvit.py

@@ -42,6 +42,8 @@ class Attention(nn.Module):
 
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
+        self.dropout = nn.Dropout(dropout)
+
         self.reattn_weights = nn.Parameter(torch.randn(heads, heads))
 
         self.reattn_norm = nn.Sequential(
@@ -64,6 +66,7 @@ class Attention(nn.Module):
 
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
         attn = dots.softmax(dim=-1)
+        attn = self.dropout(attn)
 
         # re-attention
 

vit_pytorch/learnable_memory_vit.py

@@ -0,0 +1,216 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+# controlling freezing of layers
+
+def set_module_requires_grad_(module, requires_grad):
+    for param in module.parameters():
+        param.requires_grad = requires_grad
+
+def freeze_all_layers_(module):
+    set_module_requires_grad_(module, False)
+
+def unfreeze_all_layers_(module):
+    set_module_requires_grad_(module, True)
+
+# classes
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, hidden_dim, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            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
+
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias = False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, attn_mask = None, memories = None):
+        x = self.norm(x)
+
+        x_kv = x # input for key / values projection
+
+        if exists(memories):
+            # add memories to key / values if it is passed in
+            memories = repeat(memories, 'n d -> b n d', b = x.shape[0]) if memories.ndim == 2 else memories
+            x_kv = torch.cat((x_kv, memories), dim = 1)
+
+        qkv = (self.to_q(x), *self.to_kv(x_kv).chunk(2, 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
+
+        if exists(attn_mask):
+            dots = dots.masked_fill(~attn_mask, -torch.finfo(dots.dtype).max)
+
+        attn = self.attend(dots)
+        attn = self.dropout(attn)
+
+        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([
+                Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
+                FeedForward(dim, mlp_dim, dropout = dropout)
+            ]))
+
+    def forward(self, x, attn_mask = None, memories = None):
+        for ind, (attn, ff) in enumerate(self.layers):
+            layer_memories = memories[ind] if exists(memories) else None
+
+            x = attn(x, attn_mask = attn_mask, memories = layer_memories) + x
+            x = ff(x) + x
+        return x
+
+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 = 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.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.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def img_to_tokens(self, img):
+        x = self.to_patch_embedding(img)
+
+        cls_tokens = repeat(self.cls_token, '1 n d -> b n d', b = x.shape[0])
+        x = torch.cat((cls_tokens, x), dim = 1)
+
+        x += self.pos_embedding
+        x = self.dropout(x)
+        return x
+
+    def forward(self, img):
+        x = self.img_to_tokens(img)        
+
+        x = self.transformer(x)
+
+        cls_tokens = x[:, 0]
+        return self.mlp_head(cls_tokens)
+
+# adapter with learnable memories per layer, memory CLS token, and learnable adapter head
+
+class Adapter(nn.Module):
+    def __init__(
+        self,
+        *,
+        vit,
+        num_memories_per_layer = 10,
+        num_classes = 2,   
+    ):
+        super().__init__()
+        assert isinstance(vit, ViT)
+
+        # extract some model variables needed
+
+        dim = vit.cls_token.shape[-1]
+        layers = len(vit.transformer.layers)
+        num_patches = vit.pos_embedding.shape[-2]
+
+        self.vit = vit
+
+        # freeze ViT backbone - only memories will be finetuned
+
+        freeze_all_layers_(vit)
+
+        # learnable parameters
+
+        self.memory_cls_token = nn.Parameter(torch.randn(dim))
+        self.memories_per_layer = nn.Parameter(torch.randn(layers, num_memories_per_layer, dim))
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+        # specialized attention mask to preserve the output of the original ViT
+        # it allows the memory CLS token to attend to all other tokens (and the learnable memory layer tokens), but not vice versa        
+
+        attn_mask = torch.ones((num_patches, num_patches), dtype = torch.bool)
+        attn_mask = F.pad(attn_mask, (1, num_memories_per_layer), value = False)  # main tokens cannot attend to learnable memories per layer
+        attn_mask = F.pad(attn_mask, (0, 0, 1, 0), value = True)                  # memory CLS token can attend to everything
+        self.register_buffer('attn_mask', attn_mask)
+
+    def forward(self, img):
+        b = img.shape[0]
+
+        tokens = self.vit.img_to_tokens(img)
+
+        # add task specific memory tokens
+
+        memory_cls_tokens = repeat(self.memory_cls_token, 'd -> b 1 d', b = b)
+        tokens = torch.cat((memory_cls_tokens, tokens), dim = 1)        
+
+        # pass memories along with image tokens through transformer for attending
+
+        out = self.vit.transformer(tokens, memories = self.memories_per_layer, attn_mask = self.attn_mask)
+
+        # extract memory CLS tokens
+
+        memory_cls_tokens = out[:, 0]
+
+        # pass through task specific adapter head
+
+        return self.mlp_head(memory_cls_tokens)

vit_pytorch/levit.py

@@ -52,6 +52,7 @@ class Attention(nn.Module):
         self.to_v = nn.Sequential(nn.Conv2d(dim, inner_dim_value, 1, bias = False), nn.BatchNorm2d(inner_dim_value))
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         out_batch_norm = nn.BatchNorm2d(dim_out)
         nn.init.zeros_(out_batch_norm.weight)
@@ -70,8 +71,8 @@ class Attention(nn.Module):
         q_range = torch.arange(0, fmap_size, step = (2 if downsample else 1))
         k_range = torch.arange(fmap_size)
 
-        q_pos = torch.stack(torch.meshgrid(q_range, q_range), dim = -1)
-        k_pos = torch.stack(torch.meshgrid(k_range, k_range), dim = -1)
+        q_pos = torch.stack(torch.meshgrid(q_range, q_range, indexing = 'ij'), dim = -1)
+        k_pos = torch.stack(torch.meshgrid(k_range, k_range, indexing = 'ij'), dim = -1)
 
         q_pos, k_pos = map(lambda t: rearrange(t, 'i j c -> (i j) c'), (q_pos, k_pos))
         rel_pos = (q_pos[:, None, ...] - k_pos[None, :, ...]).abs()
@@ -100,6 +101,7 @@ class Attention(nn.Module):
         dots = self.apply_pos_bias(dots)
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h (x y) d -> b (h d) x y', h = h, y = y)

vit_pytorch/local_vit.py

@@ -78,6 +78,7 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -93,6 +94,7 @@ class Attention(nn.Module):
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')

vit_pytorch/max_vit.py

@@ -0,0 +1,270 @@
+from functools import partial
+
+import torch
+from torch import nn, einsum
+
+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 cast_tuple(val, length = 1):
+    return val if isinstance(val, tuple) else ((val,) * length)
+
+# helper classes
+
+class PreNormResidual(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x):
+        return self.fn(self.norm(x)) + x
+
+# MBConv
+
+class SqueezeExcitation(nn.Module):
+    def __init__(self, dim, shrinkage_rate = 0.25):
+        super().__init__()
+        hidden_dim = int(dim * shrinkage_rate)
+
+        self.gate = nn.Sequential(
+            Reduce('b c h w -> b c', 'mean'),
+            nn.Linear(dim, hidden_dim, bias = False),
+            nn.SiLU(),
+            nn.Linear(hidden_dim, dim, bias = False),
+            nn.Sigmoid(),
+            Rearrange('b c -> b c 1 1')
+        )
+
+    def forward(self, x):
+        return x * self.gate(x)
+
+
+class MBConvResidual(nn.Module):
+    def __init__(self, fn, dropout = 0.):
+        super().__init__()
+        self.fn = fn
+        self.dropsample = Dropsample(dropout)
+
+    def forward(self, x):
+        out = self.fn(x)
+        out = self.dropsample(out)
+        return out
+
+class Dropsample(nn.Module):
+    def __init__(self, prob = 0):
+        super().__init__()
+        self.prob = prob
+  
+    def forward(self, x):
+        device = x.device
+
+        if self.prob == 0. or (not self.training):
+            return x
+
+        keep_mask = torch.FloatTensor((x.shape[0], 1, 1, 1), device = device).uniform_() > self.prob
+        return x * keep_mask / (1 - self.prob)
+
+def MBConv(
+    dim_in,
+    dim_out,
+    *,
+    downsample,
+    expansion_rate = 4,
+    shrinkage_rate = 0.25,
+    dropout = 0.
+):
+    hidden_dim = int(expansion_rate * dim_out)
+    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.BatchNorm2d(dim_out)
+    )
+
+    if dim_in == dim_out and not downsample:
+        net = MBConvResidual(net, dropout = dropout)
+
+    return net
+
+# attention related classes
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_head = 32,
+        dropout = 0.,
+        window_size = 7
+    ):
+        super().__init__()
+        assert (dim % dim_head) == 0, 'dimension should be divisible by dimension per head'
+
+        self.heads = dim // dim_head
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
+
+        self.attend = nn.Sequential(
+            nn.Softmax(dim = -1),
+            nn.Dropout(dropout)
+        )
+
+        self.to_out = nn.Sequential(
+            nn.Linear(dim, dim, bias = False),
+            nn.Dropout(dropout)
+        )
+
+        # relative positional bias
+
+        self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)
+
+        pos = torch.arange(window_size)
+        grid = torch.stack(torch.meshgrid(pos, pos, indexing = 'ij'))
+        grid = rearrange(grid, 'c i j -> (i j) c')
+        rel_pos = rearrange(grid, 'i ... -> i 1 ...') - rearrange(grid, 'j ... -> 1 j ...')
+        rel_pos += window_size - 1
+        rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(dim = -1)
+
+        self.register_buffer('rel_pos_indices', rel_pos_indices, persistent = False)
+
+    def forward(self, x):
+        batch, height, width, window_height, window_width, _, device, h = *x.shape, x.device, self.heads
+
+        # flatten
+
+        x = rearrange(x, 'b x y w1 w2 d -> (b x y) (w1 w2) d')
+
+        # project for queries, keys, values
+
+        q, k, v = self.to_qkv(x).chunk(3, dim = -1)
+
+        # split heads
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d ) -> b h n d', h = h), (q, k, v))
+
+        # scale
+
+        q = q * self.scale
+
+        # sim
+
+        sim = einsum('b h i d, b h j d -> b h i j', q, k)
+
+        # add positional bias
+
+        bias = self.rel_pos_bias(self.rel_pos_indices)
+        sim = sim + rearrange(bias, 'i j h -> h i j')
+
+        # attention
+
+        attn = self.attend(sim)
+
+        # aggregate
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+
+        # merge heads
+
+        out = rearrange(out, 'b h (w1 w2) d -> b w1 w2 (h d)', w1 = window_height, w2 = window_width)
+
+        # combine heads out
+
+        out = self.to_out(out)
+        return rearrange(out, '(b x y) ... -> b x y ...', x = height, y = width)
+
+class MaxViT(nn.Module):
+    def __init__(
+        self,
+        *,
+        num_classes,
+        dim,
+        depth,
+        dim_head = 32,
+        dim_conv_stem = None,
+        window_size = 7,
+        mbconv_expansion_rate = 4,
+        mbconv_shrinkage_rate = 0.25,
+        dropout = 0.1,
+        channels = 3
+    ):
+        super().__init__()
+        assert isinstance(depth, tuple), 'depth needs to be tuple if integers indicating number of transformer blocks at that stage'
+
+        # convolutional stem
+
+        dim_conv_stem = default(dim_conv_stem, dim)
+
+        self.conv_stem = nn.Sequential(
+            nn.Conv2d(channels, dim_conv_stem, 3, stride = 2, padding = 1),
+            nn.Conv2d(dim_conv_stem, dim_conv_stem, 3, padding = 1)
+        )
+
+        # variables
+
+        num_stages = len(depth)
+
+        dims = tuple(map(lambda i: (2 ** i) * dim, range(num_stages)))
+        dims = (dim_conv_stem, *dims)
+        dim_pairs = tuple(zip(dims[:-1], dims[1:]))
+
+        self.layers = nn.ModuleList([])
+
+        # shorthand for window size for efficient block - grid like attention
+
+        w = window_size
+
+        # iterate through stages
+
+        for ind, ((layer_dim_in, layer_dim), layer_depth) in enumerate(zip(dim_pairs, depth)):
+            for stage_ind in range(layer_depth):
+                is_first = stage_ind == 0
+                stage_dim_in = layer_dim_in if is_first else layer_dim
+
+                block = nn.Sequential(
+                    MBConv(
+                        stage_dim_in,
+                        layer_dim,
+                        downsample = is_first,
+                        expansion_rate = mbconv_expansion_rate,
+                        shrinkage_rate = mbconv_shrinkage_rate
+                    ),
+                    Rearrange('b d (x w1) (y w2) -> b x y w1 w2 d', w1 = w, w2 = w),  # block-like attention
+                    PreNormResidual(layer_dim, Attention(dim = layer_dim, dim_head = dim_head, dropout = dropout, window_size = w)),
+                    Rearrange('b x y w1 w2 d -> b d (x w1) (y w2)'),
+
+                    Rearrange('b d (w1 x) (w2 y) -> b x y w1 w2 d', w1 = w, w2 = w),  # grid-like attention
+                    PreNormResidual(layer_dim, Attention(dim = layer_dim, dim_head = dim_head, dropout = dropout, window_size = w)),
+                    Rearrange('b x y w1 w2 d -> b d (w1 x) (w2 y)'),
+                )
+
+                self.layers.append(block)
+
+        # mlp head out
+
+        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, x):
+        x = self.conv_stem(x)
+
+        for stage in self.layers:
+            x = stage(x)
+
+        return self.mlp_head(x)

vit_pytorch/mobile_vit.py

@@ -54,6 +54,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim=-1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False)
 
         self.to_out = nn.Sequential(
@@ -67,7 +69,10 @@ class Attention(nn.Module):
             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)
+        attn = self.dropout(attn)
+
         out = torch.matmul(attn, v)
         out = rearrange(out, 'b p h n d -> b p n (h d)')
         return self.to_out(out)

vit_pytorch/nest.py

@@ -55,6 +55,7 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
         self.to_qkv = nn.Conv2d(dim, inner_dim * 3, 1, bias = False)
 
         self.to_out = nn.Sequential(
@@ -71,6 +72,7 @@ class Attention(nn.Module):
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h (x y) d -> b (h d) x y', x = h, y = w)

vit_pytorch/parallel_vit.py

@@ -50,6 +50,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -64,6 +66,7 @@ class Attention(nn.Module):
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = torch.matmul(attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')

vit_pytorch/pit.py

@@ -48,6 +48,7 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -63,6 +64,7 @@ class Attention(nn.Module):
         dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')

vit_pytorch/regionvit.py

@@ -61,8 +61,13 @@ class Attention(nn.Module):
         inner_dim = dim_head * heads
 
         self.norm = nn.LayerNorm(dim)
+        self.dropout = nn.Dropout(dropout)
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
-        self.to_out = nn.Linear(inner_dim, dim)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
 
     def forward(self, x, rel_pos_bias = None):
         h = self.heads
@@ -86,6 +91,7 @@ class Attention(nn.Module):
             sim = sim + rel_pos_bias
 
         attn = sim.softmax(dim = -1)
+        attn = self.dropout(attn)
 
         # merge heads
 
@@ -132,7 +138,7 @@ class R2LTransformer(nn.Module):
         h_range = torch.arange(window_size_h, device = device)
         w_range = torch.arange(window_size_w, device = device)
 
-        grid_x, grid_y = torch.meshgrid(h_range, w_range)
+        grid_x, grid_y = torch.meshgrid(h_range, w_range, indexing = 'ij')
         grid = torch.stack((grid_x, grid_y))
         grid = rearrange(grid, 'c h w -> c (h w)')
         grid = (grid[:, :, None] - grid[:, None, :]) + (self.window_size - 1)

vit_pytorch/rvt.py

@@ -104,6 +104,7 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         self.use_ds_conv = use_ds_conv
 
@@ -148,6 +149,7 @@ class Attention(nn.Module):
         dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = einsum('b i j, b j d -> b i d', attn, v)
         out = rearrange(out, '(b h) n d -> b n (h d)', h = h)

vit_pytorch/scalable_vit.py

@@ -90,6 +90,7 @@ class ScalableSelfAttention(nn.Module):
         self.heads = heads
         self.scale = dim_key ** -0.5
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         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)
@@ -116,6 +117,7 @@ class ScalableSelfAttention(nn.Module):
         # attention
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         # aggregate values
 
@@ -141,6 +143,7 @@ class InteractiveWindowedSelfAttention(nn.Module):
         self.scale = dim_key ** -0.5
         self.window_size = window_size
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
 
         self.local_interactive_module = nn.Conv2d(dim_value * heads, dim_value * heads, 3, padding = 1)
 
@@ -176,6 +179,7 @@ class InteractiveWindowedSelfAttention(nn.Module):
         # attention
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         # aggregate values
 
@@ -280,7 +284,7 @@ class ScalableViT(nn.Module):
             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),
+                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, norm_output = not is_last),
                 Downsample(layer_dim, layer_dim * 2) if not is_last else None
             ]))
 

vit_pytorch/sep_vit.py

@@ -0,0 +1,294 @@
+from functools import partial
+
+import torch
+from torch import nn, einsum
+
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange, Reduce
+
+# helpers
+
+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 OverlappingPatchEmbed(nn.Module):
+    def __init__(self, dim_in, dim_out, stride = 2):
+        super().__init__()
+        kernel_size = stride * 2 - 1
+        padding = kernel_size // 2
+        self.conv = nn.Conv2d(dim_in, dim_out, kernel_size, stride = stride, padding = padding)
+
+    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, mult = 4, dropout = 0.):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        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 DSSA(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads = 8,
+        dim_head = 32,
+        dropout = 0.,
+        window_size = 7
+    ):
+        super().__init__()
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.window_size = window_size
+        inner_dim = dim_head * heads
+
+        self.attend = nn.Sequential(
+            nn.Softmax(dim = -1),
+            nn.Dropout(dropout)
+        )
+
+        self.to_qkv = nn.Conv1d(dim, inner_dim * 3, 1, bias = False)
+
+        # window tokens
+
+        self.window_tokens = nn.Parameter(torch.randn(dim))
+
+        # prenorm and non-linearity for window tokens
+        # then projection to queries and keys for window tokens
+
+        self.window_tokens_to_qk = nn.Sequential(
+            nn.LayerNorm(dim_head),
+            nn.GELU(),
+            Rearrange('b h n c -> b (h c) n'),
+            nn.Conv1d(inner_dim, inner_dim * 2, 1),
+            Rearrange('b (h c) n -> b h n c', h = heads),
+        )
+
+        # window attention
+
+        self.window_attend = nn.Sequential(
+            nn.Softmax(dim = -1),
+            nn.Dropout(dropout)
+        )
+
+        self.to_out = nn.Sequential(
+            nn.Conv2d(inner_dim, dim, 1),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        """
+        einstein notation
+
+        b - batch
+        c - channels
+        w1 - window size (height)
+        w2 - also window size (width)
+        i - sequence dimension (source)
+        j - sequence dimension (target dimension to be reduced)
+        h - heads
+        x - height of feature map divided by window size
+        y - width of feature map divided by window size
+        """
+
+        batch, height, width, heads, wsz = x.shape[0], *x.shape[-2:], self.heads, self.window_size
+        assert (height % wsz) == 0 and (width % wsz) == 0, f'height {height} and width {width} must be divisible by window size {wsz}'
+        num_windows = (height // wsz) * (width // wsz)
+
+        # fold in windows for "depthwise" attention - not sure why it is named depthwise when it is just "windowed" attention
+
+        x = rearrange(x, 'b c (h w1) (w w2) -> (b h w) c (w1 w2)', w1 = wsz, w2 = wsz)
+
+        # add windowing tokens
+
+        w = repeat(self.window_tokens, 'c -> b c 1', b = x.shape[0])
+        x = torch.cat((w, x), dim = -1)
+
+        # project for queries, keys, value
+
+        q, k, v = self.to_qkv(x).chunk(3, dim = 1)
+
+        # split out heads
+
+        q, k, v = map(lambda t: rearrange(t, 'b (h d) ... -> b h (...) d', h = heads), (q, k, v))
+
+        # scale
+
+        q = q * self.scale
+
+        # similarity
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k)
+
+        # attention
+
+        attn = self.attend(dots)
+
+        # aggregate values
+
+        out = torch.matmul(attn, v)
+
+        # split out windowed tokens
+
+        window_tokens, windowed_fmaps = out[:, :, 0], out[:, :, 1:]
+
+        # early return if there is only 1 window
+
+        if num_windows == 1:
+            fmap = rearrange(windowed_fmaps, '(b x y) h (w1 w2) d -> b (h d) (x w1) (y w2)', x = height // wsz, y = width // wsz, w1 = wsz, w2 = wsz)
+            return self.to_out(fmap)
+
+        # carry out the pointwise attention, the main novelty in the paper
+
+        window_tokens = rearrange(window_tokens, '(b x y) h d -> b h (x y) d', x = height // wsz, y = width // wsz)
+        windowed_fmaps = rearrange(windowed_fmaps, '(b x y) h n d -> b h (x y) n d', x = height // wsz, y = width // wsz)
+
+        # windowed queries and keys (preceded by prenorm activation)
+
+        w_q, w_k = self.window_tokens_to_qk(window_tokens).chunk(2, dim = -1)
+
+        # scale
+
+        w_q = w_q * self.scale
+
+        # similarities
+
+        w_dots = einsum('b h i d, b h j d -> b h i j', w_q, w_k)
+
+        w_attn = self.window_attend(w_dots)
+
+        # aggregate the feature maps from the "depthwise" attention step (the most interesting part of the paper, one i haven't seen before)
+
+        aggregated_windowed_fmap = einsum('b h i j, b h j w d -> b h i w d', w_attn, windowed_fmaps)
+
+        # fold back the windows and then combine heads for aggregation
+
+        fmap = rearrange(aggregated_windowed_fmap, 'b h (x y) (w1 w2) d -> b (h d) (x w1) (y w2)', x = height // wsz, y = width // wsz, w1 = wsz, w2 = wsz)
+        return self.to_out(fmap)
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        dim_head = 32,
+        heads = 8,
+        ff_mult = 4,
+        dropout = 0.,
+        norm_output = True
+    ):
+        super().__init__()
+        self.layers = nn.ModuleList([])
+
+        for ind in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, DSSA(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
+                PreNorm(dim, FeedForward(dim, mult = ff_mult, dropout = dropout)),
+            ]))
+
+        self.norm = ChanLayerNorm(dim) if norm_output else nn.Identity()
+
+    def forward(self, x):
+        for attn, ff in self.layers:
+            x = attn(x) + x
+            x = ff(x) + x
+
+        return self.norm(x)
+
+class SepViT(nn.Module):
+    def __init__(
+        self,
+        *,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        window_size = 7,
+        dim_head = 32,
+        ff_mult = 4,
+        channels = 3,
+        dropout = 0.
+    ):
+        super().__init__()
+        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)))
+        dims = (channels, *dims)
+        dim_pairs = tuple(zip(dims[:-1], dims[1:]))
+
+        strides = (4, *((2,) * (num_stages - 1)))
+
+        hyperparams_per_stage = [heads, 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_in, layer_dim), layer_depth, layer_stride, layer_heads, layer_window_size) in enumerate(zip(dim_pairs, depth, strides, *hyperparams_per_stage)):
+            is_last = ind == (num_stages - 1)
+
+            self.layers.append(nn.ModuleList([
+                OverlappingPatchEmbed(layer_dim_in, layer_dim, stride = layer_stride),
+                PEG(layer_dim),
+                Transformer(dim = layer_dim, depth = layer_depth, heads = layer_heads, ff_mult = ff_mult, dropout = dropout, norm_output = not is_last),
+            ]))
+
+        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, x):
+
+        for ope, peg, transformer in self.layers:
+            x = ope(x)
+            x = peg(x)
+            x = transformer(x)
+
+        return self.mlp_head(x)

vit_pytorch/twins_svt.py

@@ -130,6 +130,8 @@ class GlobalAttention(nn.Module):
         self.to_q = nn.Conv2d(dim, inner_dim, 1, bias = False)
         self.to_kv = nn.Conv2d(dim, inner_dim * 2, k, stride = k, bias = False)
 
+        self.dropout = nn.Dropout(dropout)
+
         self.to_out = nn.Sequential(
             nn.Conv2d(inner_dim, dim, 1),
             nn.Dropout(dropout)
@@ -145,6 +147,7 @@ class GlobalAttention(nn.Module):
         dots = einsum('b i d, b j d -> b i j', q, k) * self.scale
 
         attn = dots.softmax(dim = -1)
+        attn = self.dropout(attn)
 
         out = einsum('b i j, b j d -> b i d', attn, v)
         out = rearrange(out, '(b h) (x y) d -> b (h d) x y', h = h, y = y)

vit_pytorch/vit.py

@@ -42,6 +42,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -56,6 +58,7 @@ class Attention(nn.Module):
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = torch.matmul(attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')
@@ -111,7 +114,7 @@ class ViT(nn.Module):
         x = self.to_patch_embedding(img)
         b, n, _ = x.shape
 
-        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
+        cls_tokens = repeat(self.cls_token, '1 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)

vit_pytorch/vit_for_small_dataset.py

@@ -42,6 +42,8 @@ class LSA(nn.Module):
         self.temperature = nn.Parameter(torch.log(torch.tensor(dim_head ** -0.5)))
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -60,6 +62,7 @@ class LSA(nn.Module):
         dots = dots.masked_fill(mask, mask_value)
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = torch.matmul(attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')

vit_pytorch/vit_with_patch_merger.py

@@ -63,6 +63,8 @@ class Attention(nn.Module):
         self.scale = dim_head ** -0.5
 
         self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
         self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
 
         self.to_out = nn.Sequential(
@@ -77,6 +79,7 @@ class Attention(nn.Module):
         dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
 
         attn = self.attend(dots)
+        attn = self.dropout(attn)
 
         out = torch.matmul(attn, v)
         out = rearrange(out, 'b h n d -> b n (h d)')