address https://github.com/lucidrains/vit-pytorch/issues/300

Co-authored-by: Artem Lukin <artyom.lukin98@gmail.com>

README.md

@@ -25,6 +25,7 @@
 - [MaxViT](#maxvit)
 - [NesT](#nest)
 - [MobileViT](#mobilevit)
+- [XCiT](#xcit)
 - [Masked Autoencoder](#masked-autoencoder)
 - [Simple Masked Image Modeling](#simple-masked-image-modeling)
 - [Masked Patch Prediction](#masked-patch-prediction)
@@ -92,7 +93,7 @@ preds = v(img) # (1, 1000)
 - `image_size`: int.  
 Image size. If you have rectangular images, make sure your image size is the maximum of the width and height
 - `patch_size`: int.  
-Number of patches. `image_size` must be divisible by `patch_size`.  
+Size of patches. `image_size` must be divisible by `patch_size`.  
 The number of patches is: ` n = (image_size // patch_size) ** 2` and `n` **must be greater than 16**.
 - `num_classes`: int.  
 Number of classes to classify.
@@ -772,6 +773,38 @@ img = torch.randn(1, 3, 256, 256)
 pred = mbvit_xs(img) # (1, 1000)
 ```
 
+## XCiT
+
+<img src="./images/xcit.png" width="400px"></img>
+
+This <a href="https://arxiv.org/abs/2106.09681">paper</a> introduces the cross covariance attention (abbreviated XCA). One can think of it as doing attention across the features dimension rather than the spatial one (another perspective would be a dynamic 1x1 convolution, the kernel being attention map defined by spatial correlations).
+
+Technically, this amounts to simply transposing the query, key, values before executing cosine similarity attention with learned temperature.
+
+```python
+import torch
+from vit_pytorch.xcit import XCiT
+
+v = XCiT(
+    image_size = 256,
+    patch_size = 32,
+    num_classes = 1000,
+    dim = 1024,
+    depth = 12,                     # depth of xcit transformer
+    cls_depth = 2,                  # depth of cross attention of CLS tokens to patch, attention pool at end
+    heads = 16,
+    mlp_dim = 2048,
+    dropout = 0.1,
+    emb_dropout = 0.1,
+    layer_dropout = 0.05,           # randomly dropout 5% of the layers
+    local_patch_kernel_size = 3     # kernel size of the local patch interaction module (depthwise convs)
+)
+
+img = torch.randn(1, 3, 256, 256)
+
+preds = v(img) # (1, 1000)
+```
+
 ## Simple Masked Image Modeling
 
 <img src="./images/simmim.png" width="400px"/>
@@ -2029,4 +2062,14 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@inproceedings{ElNouby2021XCiTCI,
+    title   = {XCiT: Cross-Covariance Image Transformers},
+    author  = {Alaaeldin El-Nouby and Hugo Touvron and Mathilde Caron and Piotr Bojanowski and Matthijs Douze and Armand Joulin and Ivan Laptev and Natalia Neverova and Gabriel Synnaeve and Jakob Verbeek and Herv{\'e} J{\'e}gou},
+    booktitle = {Neural Information Processing Systems},
+    year    = {2021},
+    url     = {https://api.semanticscholar.org/CorpusID:235458262}
+}
+```
+
 *I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.* — Claude Shannon

setup.py

@@ -1,11 +1,15 @@
 from setuptools import setup, find_packages
 
+with open('README.md') as f:
+    long_description = f.read()
+
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.5.3',
+  version = '1.6.8',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
+  long_description=long_description,
   long_description_content_type = 'text/markdown',
   author = 'Phil Wang',
   author_email = 'lucidrains@gmail.com',

vit_pytorch/__init__.py

@@ -1,10 +1,3 @@
-import torch
-from packaging import version
-
-if version.parse(torch.__version__) >= version.parse('2.0.0'):
-    from einops._torch_specific import allow_ops_in_compiled_graph
-    allow_ops_in_compiled_graph()
-
 from vit_pytorch.vit import ViT
 from vit_pytorch.simple_vit import SimpleViT
 

vit_pytorch/cross_vit.py

@@ -170,12 +170,13 @@ class ImageEmbedder(nn.Module):
         dim,
         image_size,
         patch_size,
-        dropout = 0.
+        dropout = 0.,
+        channels = 3
     ):
         super().__init__()
         assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
         num_patches = (image_size // patch_size) ** 2
-        patch_dim = 3 * patch_size ** 2
+        patch_dim = channels * patch_size ** 2
 
         self.to_patch_embedding = nn.Sequential(
             Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
@@ -223,11 +224,12 @@ class CrossViT(nn.Module):
         cross_attn_dim_head = 64,
         depth = 3,
         dropout = 0.1,
-        emb_dropout = 0.1
+        emb_dropout = 0.1,
+        channels = 3
     ):
         super().__init__()
-        self.sm_image_embedder = ImageEmbedder(dim = sm_dim, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
-        self.lg_image_embedder = ImageEmbedder(dim = lg_dim, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
+        self.sm_image_embedder = ImageEmbedder(dim = sm_dim, channels= channels, image_size = image_size, patch_size = sm_patch_size, dropout = emb_dropout)
+        self.lg_image_embedder = ImageEmbedder(dim = lg_dim, channels = channels, image_size = image_size, patch_size = lg_patch_size, dropout = emb_dropout)
 
         self.multi_scale_encoder = MultiScaleEncoder(
             depth = depth,

vit_pytorch/cvt.py

@@ -140,12 +140,13 @@ class CvT(nn.Module):
         s3_heads = 6,
         s3_depth = 10,
         s3_mlp_mult = 4,
-        dropout = 0.
+        dropout = 0.,
+        channels = 3
     ):
         super().__init__()
         kwargs = dict(locals())
 
-        dim = 3
+        dim = channels
         layers = []
 
         for prefix in ('s1', 's2', 's3'):

vit_pytorch/na_vit.py

@@ -198,7 +198,7 @@ class NaViT(nn.Module):
             self.calc_token_dropout = token_dropout_prob
 
         elif isinstance(token_dropout_prob, (float, int)):
-            assert 0. < token_dropout_prob < 1.
+            assert 0. <= token_dropout_prob < 1.
             token_dropout_prob = float(token_dropout_prob)
             self.calc_token_dropout = lambda height, width: token_dropout_prob
 
@@ -249,7 +249,7 @@ class NaViT(nn.Module):
         group_images = False,
         group_max_seq_len = 2048
     ):
-        p, c, device, has_token_dropout = self.patch_size, self.channels, self.device, exists(self.calc_token_dropout)
+        p, c, device, has_token_dropout = self.patch_size, self.channels, self.device, exists(self.calc_token_dropout) and self.training
 
         arange = partial(torch.arange, device = device)
         pad_sequence = partial(orig_pad_sequence, batch_first = True)
@@ -260,7 +260,7 @@ class NaViT(nn.Module):
             batched_images = group_images_by_max_seq_len(
                 batched_images,
                 patch_size = self.patch_size,
-                calc_token_dropout = self.calc_token_dropout,
+                calc_token_dropout = self.calc_token_dropout if self.training else None,
                 max_seq_len = group_max_seq_len
             )
 
@@ -314,8 +314,8 @@ class NaViT(nn.Module):
         # derive key padding mask
 
         lengths = torch.tensor([seq.shape[-2] for seq in batched_sequences], device = device, dtype = torch.long)
-        max_length = arange(lengths.amax().item())
-        key_pad_mask = rearrange(lengths, 'b -> b 1') <= rearrange(max_length, 'n -> 1 n')
+        seq_arange = arange(lengths.amax().item())
+        key_pad_mask = rearrange(seq_arange, 'n -> 1 n') < rearrange(lengths, 'b -> b 1')
 
         # derive attention mask, and combine with key padding mask from above
 

vit_pytorch/simple_flash_attn_vit_3d.py

@@ -0,0 +1,171 @@
+from packaging import version
+from collections import namedtuple
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn import Module, ModuleList
+
+from einops import rearrange
+from einops.layers.torch import Rearrange
+
+# constants
+
+Config = namedtuple('FlashAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient'])
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def posemb_sincos_3d(patches, temperature = 10000, dtype = torch.float32):
+    _, f, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
+
+    z, y, x = torch.meshgrid(
+        torch.arange(f, device = device),
+        torch.arange(h, device = device),
+        torch.arange(w, device = device),
+    indexing = 'ij')
+
+    fourier_dim = dim // 6
+
+    omega = torch.arange(fourier_dim, device = device) / (fourier_dim - 1)
+    omega = 1. / (temperature ** omega)
+
+    z = z.flatten()[:, None] * omega[None, :]
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :] 
+
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos(), z.sin(), z.cos()), dim = 1)
+
+    pe = F.pad(pe, (0, dim - (fourier_dim * 6))) # pad if feature dimension not cleanly divisible by 6
+    return pe.type(dtype)
+
+# main class
+
+class Attend(Module):
+    def __init__(self, use_flash = False, config: Config = Config(True, True, True)):
+        super().__init__()
+        self.config = config
+        self.use_flash = use_flash
+        assert not (use_flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above'
+
+    def flash_attn(self, q, k, v):
+        # flash attention - https://arxiv.org/abs/2205.14135
+        
+        with torch.backends.cuda.sdp_kernel(**self.config._asdict()):
+            out = F.scaled_dot_product_attention(q, k, v)
+
+        return out
+
+    def forward(self, q, k, v):
+        n, device, scale = q.shape[-2], q.device, q.shape[-1] ** -0.5
+
+        if self.use_flash:
+            return self.flash_attn(q, k, v)
+
+        # similarity
+
+        sim = einsum("b h i d, b j d -> b h i j", q, k) * scale
+
+        # attention
+
+        attn = sim.softmax(dim=-1)
+
+        # aggregate values
+
+        out = einsum("b h i j, b j d -> b h i d", attn, v)
+
+        return out
+
+# classes
+
+class FeedForward(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(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, use_flash = True):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = Attend(use_flash = use_flash)
+
+        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)
+
+        out = self.attend(q, k, v)
+
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, use_flash):
+        super().__init__()
+        self.layers = ModuleList([])
+        for _ in range(depth):
+            self.layers.append(ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head, use_flash = use_flash),
+                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(Module):
+    def __init__(self, *, image_size, image_patch_size, frames, frame_patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, use_flash_attn = True):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(image_patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
+        assert frames % frame_patch_size == 0, 'Frames must be divisible by the frame patch size'
+
+        num_patches = (image_height // patch_height) * (image_width // patch_width) * (frames // frame_patch_size)
+        patch_dim = channels * patch_height * patch_width * frame_patch_size
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange('b c (f pf) (h p1) (w p2) -> b f h w (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, use_flash_attn)
+
+        self.to_latent = nn.Identity()
+        self.linear_head = nn.Linear(dim, num_classes)
+
+    def forward(self, video):
+        *_, h, w, dtype = *video.shape, video.dtype
+
+        x = self.to_patch_embedding(video)
+        pe = posemb_sincos_3d(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)

vit_pytorch/simple_vit_with_fft.py

@@ -0,0 +1,162 @@
+import torch
+from torch.fft import fft2
+from torch import nn
+
+from einops import rearrange, reduce, pack, unpack
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
+    y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
+    assert (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = 1.0 / (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.norm = nn.LayerNorm(dim)
+        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 self.norm(x)
+
+class SimpleViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, freq_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)
+        freq_patch_height, freq_patch_width = pair(freq_patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
+        assert image_height % freq_patch_height == 0 and image_width % freq_patch_width == 0, 'Image dimensions must be divisible by the freq patch size.'
+
+        patch_dim = channels * patch_height * patch_width
+        freq_patch_dim = channels * 2 * freq_patch_height * freq_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.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.to_freq_embedding = nn.Sequential(
+            Rearrange("b c (h p1) (w p2) ri -> b (h w) (p1 p2 ri c)", p1 = freq_patch_height, p2 = freq_patch_width),
+            nn.LayerNorm(freq_patch_dim),
+            nn.Linear(freq_patch_dim, dim),
+            nn.LayerNorm(dim)
+        )
+
+        self.pos_embedding = posemb_sincos_2d(
+            h = image_height // patch_height,
+            w = image_width // patch_width,
+            dim = dim,
+        )
+
+        self.freq_pos_embedding = posemb_sincos_2d(
+            h = image_height // freq_patch_height,
+            w = image_width // freq_patch_width,
+            dim = dim
+        )
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
+
+        self.pool = "mean"
+        self.to_latent = nn.Identity()
+
+        self.linear_head = nn.Linear(dim, num_classes)
+
+    def forward(self, img):
+        device, dtype = img.device, img.dtype
+
+        x = self.to_patch_embedding(img)
+        freqs = torch.view_as_real(fft2(img))
+
+        f = self.to_freq_embedding(freqs)
+
+        x += self.pos_embedding.to(device, dtype = dtype)
+        f += self.freq_pos_embedding.to(device, dtype = dtype)
+
+        x, ps = pack((f, x), 'b * d')
+
+        x = self.transformer(x)
+
+        _, x = unpack(x, ps, 'b * d')
+        x = reduce(x, 'b n d -> b d', 'mean')
+
+        x = self.to_latent(x)
+        return self.linear_head(x)
+
+if __name__ == '__main__':
+    vit = SimpleViT(
+        num_classes = 1000,
+        image_size = 256,
+        patch_size = 8,
+        freq_patch_size = 8,
+        dim = 1024,
+        depth = 1,
+        heads = 8,
+        mlp_dim = 2048,
+    )
+
+    images = torch.randn(8, 3, 256, 256)
+
+    logits = vit(images)

vit_pytorch/vit_1d.py

@@ -10,7 +10,7 @@ class FeedForward(nn.Module):
     def __init__(self, dim, hidden_dim, dropout = 0.):
         super().__init__()
         self.net = nn.Sequential(
-            nn.Layernorm(dim),
+            nn.LayerNorm(dim),
             nn.Linear(dim, hidden_dim),
             nn.GELU(),
             nn.Dropout(dropout),

vit_pytorch/xcit.py

@@ -0,0 +1,283 @@
+from random import randrange
+
+import torch
+from torch import nn, einsum
+from torch.nn import Module, ModuleList
+import torch.nn.functional as F
+
+from einops import rearrange, repeat, pack, unpack
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def pack_one(t, pattern):
+    return pack([t], pattern)
+
+def unpack_one(t, ps, pattern):
+    return unpack(t, ps, pattern)[0]
+
+def l2norm(t):
+    return F.normalize(t, dim = -1, p = 2)
+
+def dropout_layers(layers, dropout):
+    if dropout == 0:
+        return layers
+
+    num_layers = len(layers)
+    to_drop = torch.zeros(num_layers).uniform_(0., 1.) < dropout
+
+    # make sure at least one layer makes it
+    if all(to_drop):
+        rand_index = randrange(num_layers)
+        to_drop[rand_index] = False
+
+    layers = [layer for (layer, drop) in zip(layers, to_drop) if not drop]
+    return layers
+
+# classes
+
+class LayerScale(Module):
+    def __init__(self, dim, fn, depth):
+        super().__init__()
+        if depth <= 18:
+            init_eps = 0.1
+        elif 18 > depth <= 24:
+            init_eps = 1e-5
+        else:
+            init_eps = 1e-6
+
+        self.fn = fn
+        self.scale = nn.Parameter(torch.full((dim,), init_eps))
+
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) * self.scale
+
+class FeedForward(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(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.to_q = nn.Linear(dim, inner_dim, bias = False)
+        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)
+
+        self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, context = None):
+        h = self.heads
+
+        x = self.norm(x)
+        context = x if not exists(context) else torch.cat((x, context), dim = 1)
+
+        qkv = (self.to_q(x), *self.to_kv(context).chunk(2, dim = -1))
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+
+        sim = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        attn = self.attend(sim)
+        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)')
+        return self.to_out(out)
+
+class XCAttention(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.norm = nn.LayerNorm(dim)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+
+        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
+
+        self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x):
+        h = self.heads
+        x, ps = pack_one(x, 'b * d')
+
+        x = self.norm(x)
+        q, k, v = self.to_qkv(x).chunk(3, dim = -1)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h d n', h = h), (q, k, v))
+
+        q, k = map(l2norm, (q, k))
+
+        sim = einsum('b h i n, b h j n -> b h i j', q, k) * self.temperature.exp()
+
+        attn = self.attend(sim)
+        attn = self.dropout(attn)
+
+        out = einsum('b h i j, b h j n -> b h i n', attn, v)
+        out = rearrange(out, 'b h d n -> b n (h d)')
+
+        out = unpack_one(out, ps, 'b * d')
+        return self.to_out(out)
+
+class LocalPatchInteraction(Module):
+    def __init__(self, dim, kernel_size = 3):
+        super().__init__()
+        assert (kernel_size % 2) == 1
+        padding = kernel_size // 2
+
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            Rearrange('b h w c -> b c h w'),
+            nn.Conv2d(dim, dim, kernel_size, padding = padding, groups = dim),
+            nn.BatchNorm2d(dim),
+            nn.GELU(),
+            nn.Conv2d(dim, dim, kernel_size, padding = padding, groups = dim),
+            Rearrange('b c h w -> b h w c'),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0., layer_dropout = 0.):
+        super().__init__()
+        self.layers = ModuleList([])
+        self.layer_dropout = layer_dropout
+
+        for ind in range(depth):
+            layer = ind + 1
+            self.layers.append(ModuleList([
+                LayerScale(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout), depth = layer),
+                LayerScale(dim, FeedForward(dim, mlp_dim, dropout = dropout), depth = layer)
+            ]))
+
+    def forward(self, x, context = None):
+        layers = dropout_layers(self.layers, dropout = self.layer_dropout)
+
+        for attn, ff in layers:
+            x = attn(x, context = context) + x
+            x = ff(x) + x
+
+        return x
+
+class XCATransformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, local_patch_kernel_size = 3, dropout = 0., layer_dropout = 0.):
+        super().__init__()
+        self.layers = ModuleList([])
+        self.layer_dropout = layer_dropout
+
+        for ind in range(depth):
+            layer = ind + 1
+            self.layers.append(ModuleList([
+                LayerScale(dim, XCAttention(dim, heads = heads, dim_head = dim_head, dropout = dropout), depth = layer),
+                LayerScale(dim, LocalPatchInteraction(dim, local_patch_kernel_size), depth = layer),
+                LayerScale(dim, FeedForward(dim, mlp_dim, dropout = dropout), depth = layer)
+            ]))
+
+    def forward(self, x):
+        layers = dropout_layers(self.layers, dropout = self.layer_dropout)
+
+        for cross_covariance_attn, local_patch_interaction, ff in layers:
+            x = cross_covariance_attn(x) + x
+            x = local_patch_interaction(x) + x
+            x = ff(x) + x
+
+        return x
+
+class XCiT(Module):
+    def __init__(
+        self,
+        *,
+        image_size,
+        patch_size,
+        num_classes,
+        dim,
+        depth,
+        cls_depth,
+        heads,
+        mlp_dim,
+        dim_head = 64,
+        dropout = 0.,
+        emb_dropout = 0.,
+        local_patch_kernel_size = 3,
+        layer_dropout = 0.
+    ):
+        super().__init__()
+        assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.'
+
+        num_patches = (image_size // patch_size) ** 2
+        patch_dim = 3 * patch_size ** 2
+
+        self.to_patch_embedding = 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),
+            nn.LayerNorm(dim)
+        )
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches, dim))
+        self.cls_token = nn.Parameter(torch.randn(dim))
+
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.xcit_transformer = XCATransformer(dim, depth, heads, dim_head, mlp_dim, local_patch_kernel_size, dropout, layer_dropout)
+
+        self.final_norm = nn.LayerNorm(dim)
+
+        self.cls_transformer = Transformer(dim, cls_depth, heads, dim_head, mlp_dim, dropout, layer_dropout)
+
+        self.mlp_head = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, num_classes)
+        )
+
+    def forward(self, img):
+        x = self.to_patch_embedding(img)
+
+        x, ps = pack_one(x, 'b * d')
+
+        b, n, _ = x.shape
+        x += self.pos_embedding[:, :n]
+
+        x = unpack_one(x, ps, 'b * d')
+
+        x = self.dropout(x)
+
+        x = self.xcit_transformer(x)
+
+        x = self.final_norm(x)
+
+        cls_tokens = repeat(self.cls_token, 'd -> b 1 d', b = b)
+
+        x = rearrange(x, 'b ... d -> b (...) d')
+        cls_tokens = self.cls_transformer(cls_tokens, context = x)
+
+        return self.mlp_head(cls_tokens[:, 0])