From dfc8df6713cf134ca106d065fd1388af20228ed9 Mon Sep 17 00:00:00 2001 From: Phil Wang Date: Wed, 7 Aug 2024 08:45:50 -0700 Subject: [PATCH] add the u-vit implementation with simple vit + register tokens --- README.md | 11 +++ vit_pytorch/simple_uvit.py | 176 +++++++++++++++++++++++++++++++++++++ 2 files changed, 187 insertions(+) create mode 100644 vit_pytorch/simple_uvit.py diff --git a/README.md b/README.md index 4bcb89d..1afedda 100644 --- a/README.md +++ b/README.md @@ -2081,6 +2081,17 @@ Coming from computer vision and new to transformers? Here are some resources tha } ``` +```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}, diff --git a/vit_pytorch/simple_uvit.py b/vit_pytorch/simple_uvit.py new file mode 100644 index 0000000..94a06df --- /dev/null +++ b/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)