1.7.5

README.md

@@ -2072,4 +2072,31 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@inproceedings{Koner2024LookupViTCV,
+    title   = {LookupViT: Compressing visual information to a limited number of tokens},
+    author  = {Rajat Koner and Gagan Jain and Prateek Jain and Volker Tresp and Sujoy Paul},
+    year    = {2024},
+    url     = {https://api.semanticscholar.org/CorpusID:271244592}
+}
+```
+
+```bibtex
+@article{Bao2022AllAW,
+    title   = {All are Worth Words: A ViT Backbone for Diffusion Models},
+    author  = {Fan Bao and Shen Nie and Kaiwen Xue and Yue Cao and Chongxuan Li and Hang Su and Jun Zhu},
+    journal = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year    = {2022},
+    pages   = {22669-22679},
+    url     = {https://api.semanticscholar.org/CorpusID:253581703}
+}
+```
+
+```bibtex
+@misc{Rubin2024,
+    author  = {Ohad Rubin},
+    url     = {https://medium.com/@ohadrubin/exploring-weight-decay-in-layer-normalization-challenges-and-a-reparameterization-solution-ad4d12c24950}
+}
+```
+
 *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

@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.6.8',
+  version = '1.7.5',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description=long_description,

vit_pytorch/distill.py

@@ -1,6 +1,8 @@
 import torch
-import torch.nn.functional as F
 from torch import nn
+from torch.nn import Module
+import torch.nn.functional as F
+
 from vit_pytorch.vit import ViT
 from vit_pytorch.t2t import T2TViT
 from vit_pytorch.efficient import ViT as EfficientViT
@@ -12,6 +14,9 @@ from einops import rearrange, repeat
 def exists(val):
     return val is not None
 
+def default(val, d):
+    return val if exists(val) else d
+
 # classes
 
 class DistillMixin:
@@ -20,12 +25,12 @@ class DistillMixin:
         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)]
 
         if distilling:
-            distill_tokens = repeat(distill_token, '() n d -> b n d', b = b)
+            distill_tokens = repeat(distill_token, '1 n d -> b n d', b = b)
             x = torch.cat((x, distill_tokens), dim = 1)
 
         x = self._attend(x)
@@ -97,7 +102,7 @@ class DistillableEfficientViT(DistillMixin, EfficientViT):
 
 # knowledge distillation wrapper
 
-class DistillWrapper(nn.Module):
+class DistillWrapper(Module):
     def __init__(
         self,
         *,
@@ -105,7 +110,8 @@ class DistillWrapper(nn.Module):
         student,
         temperature = 1.,
         alpha = 0.5,
-        hard = False
+        hard = False,
+        mlp_layernorm = False
     ):
         super().__init__()
         assert (isinstance(student, (DistillableViT, DistillableT2TViT, DistillableEfficientViT))) , 'student must be a vision transformer'
@@ -122,14 +128,14 @@ class DistillWrapper(nn.Module):
         self.distillation_token = nn.Parameter(torch.randn(1, 1, dim))
 
         self.distill_mlp = nn.Sequential(
-            nn.LayerNorm(dim),
+            nn.LayerNorm(dim) if mlp_layernorm else nn.Identity(),
             nn.Linear(dim, num_classes)
         )
 
     def forward(self, img, labels, temperature = None, alpha = None, **kwargs):
-        b, *_ = img.shape
-        alpha = alpha if exists(alpha) else self.alpha
-        T = temperature if exists(temperature) else self.temperature
+
+        alpha = default(alpha, self.alpha)
+        T = default(temperature, self.temperature)
 
         with torch.no_grad():
             teacher_logits = self.teacher(img)

vit_pytorch/look_vit.py

@@ -0,0 +1,278 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn import Module, ModuleList
+
+from einops import einsum, rearrange, repeat, reduce
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+def divisible_by(num, den):
+    return (num % den) == 0
+
+# simple vit sinusoidal pos emb
+
+def posemb_sincos_2d(t, temperature = 10000):
+    h, w, d, device = *t.shape[1:], t.device
+    y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij')
+    assert (d % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(d // 4, device = device) / (d // 4 - 1)
+    omega = temperature ** -omega
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pos = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim = 1)
+
+    return pos.float()
+
+# bias-less layernorm with unit offset trick (discovered by Ohad Rubin)
+
+class LayerNorm(Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.ln = nn.LayerNorm(dim, elementwise_affine = False)
+        self.gamma = nn.Parameter(torch.zeros(dim))
+
+    def forward(self, x):
+        normed = self.ln(x)
+        return normed * (self.gamma + 1)
+
+# mlp
+
+def MLP(dim, factor = 4, dropout = 0.):
+    hidden_dim = int(dim * factor)
+    return nn.Sequential(
+        LayerNorm(dim),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Dropout(dropout),
+        nn.Linear(hidden_dim, dim),
+        nn.Dropout(dropout)
+    )
+
+# attention
+
+class Attention(Module):
+    def __init__(
+        self,
+        dim,
+        heads = 8,
+        dim_head = 64,
+        dropout = 0.,
+        cross_attend = False,
+        reuse_attention = False
+    ):
+        super().__init__()
+        inner_dim = dim_head *  heads
+
+        self.scale = dim_head ** -0.5
+        self.heads = heads
+        self.reuse_attention = reuse_attention
+        self.cross_attend = cross_attend
+
+        self.split_heads = Rearrange('b n (h d) -> b h n d', h = heads)
+
+        self.norm = LayerNorm(dim) if not reuse_attention else nn.Identity()
+        self.norm_context = LayerNorm(dim) if cross_attend else nn.Identity()
+
+        self.attend = nn.Softmax(dim = -1)
+        self.dropout = nn.Dropout(dropout)
+
+        self.to_q = nn.Linear(dim, inner_dim, bias = False) if not reuse_attention else None
+        self.to_k = nn.Linear(dim, inner_dim, bias = False) if not reuse_attention else None
+        self.to_v = nn.Linear(dim, inner_dim, bias = False)
+
+        self.to_out = nn.Sequential(
+            Rearrange('b h n d -> b n (h d)'),
+            nn.Linear(inner_dim, dim, bias = False),
+            nn.Dropout(dropout)
+        )
+
+    def forward(
+        self,
+        x,
+        context = None,
+        return_qk_sim = False,
+        qk_sim = None
+    ):
+        x = self.norm(x)
+
+        assert not (exists(context) ^ self.cross_attend)
+
+        if self.cross_attend:
+            context = self.norm_context(context)
+        else:
+            context = x
+
+        v = self.to_v(context)
+        v = self.split_heads(v)
+
+        if not self.reuse_attention:
+            qk = (self.to_q(x), self.to_k(context))
+            q, k = tuple(self.split_heads(t) for t in qk)
+
+            q = q * self.scale
+            qk_sim = einsum(q, k, 'b h i d, b h j d -> b h i j')
+
+        else:
+            assert exists(qk_sim), 'qk sim matrix must be passed in for reusing previous attention'
+
+        attn = self.attend(qk_sim)
+        attn = self.dropout(attn)
+
+        out = einsum(attn, v, 'b h i j, b h j d -> b h i d')
+        out = self.to_out(out)
+
+        if not return_qk_sim:
+            return out
+
+        return out, qk_sim
+
+# LookViT
+
+class LookViT(Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        image_size,
+        num_classes,
+        depth = 3,
+        patch_size = 16,
+        heads = 8,
+        mlp_factor = 4,
+        dim_head = 64,
+        highres_patch_size = 12,
+        highres_mlp_factor = 4,
+        cross_attn_heads = 8,
+        cross_attn_dim_head = 64,
+        patch_conv_kernel_size = 7,
+        dropout = 0.1,
+        channels = 3
+    ):
+        super().__init__()
+        assert divisible_by(image_size, highres_patch_size)
+        assert divisible_by(image_size, patch_size)
+        assert patch_size > highres_patch_size, 'patch size of the main vision transformer should be smaller than the highres patch sizes (that does the `lookup`)'
+        assert not divisible_by(patch_conv_kernel_size, 2)
+
+        self.dim = dim
+        self.image_size = image_size
+        self.patch_size = patch_size
+
+        kernel_size = patch_conv_kernel_size
+        patch_dim = (highres_patch_size * highres_patch_size) * channels
+
+        self.to_patches = nn.Sequential(
+            Rearrange('b c (h p1) (w p2) -> b (p1 p2 c) h w', p1 = highres_patch_size, p2 = highres_patch_size),
+            nn.Conv2d(patch_dim, dim, kernel_size, padding = kernel_size // 2),
+            Rearrange('b c h w -> b h w c'),
+            LayerNorm(dim),
+        )
+
+        # absolute positions
+
+        num_patches = (image_size // highres_patch_size) ** 2
+        self.pos_embedding = nn.Parameter(torch.randn(num_patches, dim))
+
+        # lookvit blocks
+
+        layers = ModuleList([])
+
+        for _ in range(depth):
+            layers.append(ModuleList([
+                Attention(dim = dim, dim_head = dim_head, heads = heads, dropout = dropout),
+                MLP(dim = dim, factor = mlp_factor, dropout = dropout),
+                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout, cross_attend = True),
+                Attention(dim = dim, dim_head = cross_attn_dim_head, heads = cross_attn_heads, dropout = dropout, cross_attend = True, reuse_attention = True),
+                LayerNorm(dim),
+                MLP(dim = dim, factor = highres_mlp_factor, dropout = dropout)
+            ]))
+
+        self.layers = layers
+
+        self.norm = LayerNorm(dim)
+        self.highres_norm = LayerNorm(dim)
+
+        self.to_logits = nn.Linear(dim, num_classes, bias = False)
+
+    def forward(self, img):
+        assert img.shape[-2:] == (self.image_size, self.image_size)
+
+        # to patch tokens and positions
+
+        highres_tokens = self.to_patches(img)
+        size = highres_tokens.shape[-2]
+
+        pos_emb = posemb_sincos_2d(highres_tokens)
+        highres_tokens = highres_tokens + rearrange(pos_emb, '(h w) d -> h w d', h = size)
+
+        tokens = F.interpolate(
+            rearrange(highres_tokens, 'b h w d -> b d h w'),
+            img.shape[-1] // self.patch_size,
+            mode = 'bilinear'
+        )
+
+        tokens = rearrange(tokens, 'b c h w -> b (h w) c')
+        highres_tokens = rearrange(highres_tokens, 'b h w c -> b (h w) c')
+
+        # attention and feedforwards
+
+        for attn, mlp, lookup_cross_attn, highres_attn, highres_norm, highres_mlp in self.layers:
+
+            # main tokens cross attends (lookup) on the high res tokens
+
+            lookup_out, qk_sim = lookup_cross_attn(tokens, highres_tokens, return_qk_sim = True)  # return attention as they reuse the attention matrix
+            tokens = lookup_out + tokens
+
+            tokens = attn(tokens) + tokens
+            tokens = mlp(tokens) + tokens
+
+            # attention-reuse
+
+            qk_sim = rearrange(qk_sim, 'b h i j -> b h j i') # transpose for reverse cross attention
+
+            highres_tokens = highres_attn(highres_tokens, tokens, qk_sim = qk_sim) + highres_tokens
+            highres_tokens = highres_norm(highres_tokens)
+
+            highres_tokens = highres_mlp(highres_tokens) + highres_tokens
+
+        # to logits
+
+        tokens = self.norm(tokens)
+        highres_tokens = self.highres_norm(highres_tokens)
+
+        tokens = reduce(tokens, 'b n d -> b d', 'mean')
+        highres_tokens = reduce(highres_tokens, 'b n d -> b d', 'mean')
+
+        return self.to_logits(tokens + highres_tokens)
+
+# main
+
+if __name__ == '__main__':
+    v = LookViT(
+        image_size = 256,
+        num_classes = 1000,
+        dim = 512,
+        depth = 2,
+        heads = 8,
+        dim_head = 64,
+        patch_size = 32,
+        highres_patch_size = 8,
+        highres_mlp_factor = 2,
+        cross_attn_heads = 8,
+        cross_attn_dim_head = 64,
+        dropout = 0.1
+    ).cuda()
+
+    img = torch.randn(2, 3, 256, 256).cuda()
+    pred = v(img)
+
+    assert pred.shape == (2, 1000)

vit_pytorch/rvt.py

@@ -3,12 +3,14 @@ from math import sqrt, pi, log
 import torch
 from torch import nn, einsum
 import torch.nn.functional as F
+from torch.cuda.amp import autocast
 
 from einops import rearrange, repeat
 from einops.layers.torch import Rearrange
 
 # rotary embeddings
 
+@autocast(enabled = False)
 def rotate_every_two(x):
     x = rearrange(x, '... (d j) -> ... d j', j = 2)
     x1, x2 = x.unbind(dim = -1)
@@ -22,6 +24,7 @@ class AxialRotaryEmbedding(nn.Module):
         scales = torch.linspace(1., max_freq / 2, self.dim // 4)
         self.register_buffer('scales', scales)
 
+    @autocast(enabled = False)
     def forward(self, x):
         device, dtype, n = x.device, x.dtype, int(sqrt(x.shape[-2]))
 

vit_pytorch/simple_uvit.py

@@ -0,0 +1,176 @@
+import torch
+from torch import nn
+from torch.nn import Module, ModuleList
+
+from einops import rearrange, repeat, pack, unpack
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def exists(v):
+    return v is not None
+
+def divisible_by(num, den):
+    return (num % den) == 0
+
+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 divisible_by(dim, 4), "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = 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
+
+def FeedForward(dim, hidden_dim):
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Linear(hidden_dim, dim),
+    )    
+
+class Attention(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(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim):
+        super().__init__()
+        self.depth = depth
+        self.norm = nn.LayerNorm(dim)
+        self.layers = ModuleList([])
+
+        for layer in range(1, depth + 1):
+            latter_half = layer >= (depth / 2 + 1)
+
+            self.layers.append(nn.ModuleList([
+                nn.Linear(dim * 2, dim) if latter_half else None,
+                Attention(dim, heads = heads, dim_head = dim_head),
+                FeedForward(dim, mlp_dim)
+            ]))
+
+    def forward(self, x):
+
+        skips = []
+
+        for ind, (combine_skip, attn, ff) in enumerate(self.layers):
+            layer = ind + 1
+            first_half = layer <= (self.depth / 2)
+
+            if first_half:
+                skips.append(x)
+
+            if exists(combine_skip):
+                skip = skips.pop()
+                skip_and_x = torch.cat((skip, x), dim = -1)
+                x = combine_skip(skip_and_x)
+
+            x = attn(x) + x
+            x = ff(x) + x
+
+        assert len(skips) == 0
+
+        return self.norm(x)
+
+class SimpleUViT(Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, num_register_tokens = 4, channels = 3, dim_head = 64):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert divisible_by(image_height, patch_height) and divisible_by(image_width, patch_width), 'Image dimensions must be divisible by the patch size.'
+
+        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.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        pos_embedding = posemb_sincos_2d(
+            h = image_height // patch_height,
+            w = image_width // patch_width,
+            dim = dim
+        )
+
+        self.register_buffer('pos_embedding', pos_embedding, persistent = False)
+
+        self.register_tokens = nn.Parameter(torch.randn(num_register_tokens, 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):
+        batch, device = img.shape[0], img.device
+
+        x = self.to_patch_embedding(img)
+        x = x + self.pos_embedding.type(x.dtype)
+
+        r = repeat(self.register_tokens, 'n d -> b n d', b = batch)
+
+        x, ps = pack([x, r], 'b * d')
+
+        x = self.transformer(x)
+
+        x, _ = unpack(x, ps, 'b * d')
+
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)
+
+# quick test on odd number of layers
+
+if __name__ == '__main__':
+
+    v = SimpleUViT(
+        image_size = 256,
+        patch_size = 32,
+        num_classes = 1000,
+        dim = 1024,
+        depth = 7,
+        heads = 16,
+        mlp_dim = 2048
+    ).cuda()
+
+    img = torch.randn(2, 3, 256, 256).cuda()
+
+    preds = v(img)
+    assert preds.shape == (2, 1000)

vit_pytorch/t2t.py

@@ -61,10 +61,7 @@ class T2TViT(nn.Module):
         self.pool = pool
         self.to_latent = nn.Identity()
 
-        self.mlp_head = nn.Sequential(
-            nn.LayerNorm(dim),
-            nn.Linear(dim, num_classes)
-        )
+        self.mlp_head = nn.Linear(dim, num_classes)
 
     def forward(self, img):
         x = self.to_patch_embedding(img)