mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
import torch
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from einops import rearrange
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import torch.nn.functional as F
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from torch import nn
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class Residual(nn.Module):
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def __init__(self, fn):
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super().__init__()
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self.fn = fn
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def forward(self, x):
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return self.fn(x) + x
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class PreNorm(nn.Module):
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def __init__(self, dim, fn):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.fn = fn
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def forward(self, x):
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return self.fn(self.norm(x))
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class FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(dim, hidden_dim),
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nn.GELU(),
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nn.Linear(hidden_dim, dim)
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)
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def forward(self, x):
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return self.net(x)
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class Attention(nn.Module):
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def __init__(self, dim, heads = 8):
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super().__init__()
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self.heads = heads
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self.scale = dim ** -0.5
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self.to_qkv = nn.Linear(dim, dim * 3, bias = False)
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self.to_out = nn.Linear(dim, dim)
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def forward(self, x):
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b, n, _, h = *x.shape, self.heads
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qkv = self.to_qkv(x)
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q, k, v = rearrange(qkv, 'b n (qkv h d) -> qkv b h n d', qkv = 3, h = h)
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dots = torch.einsum('bhid,bhjd->bhij', q, k) * self.scale
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attn = dots.softmax(dim=-1)
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out = torch.einsum('bhij,bhjd->bhid', attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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out = self.to_out(out)
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return out
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class Transformer(nn.Module):
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def __init__(self, dim, depth, heads, mlp_dim):
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super().__init__()
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layers = []
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for _ in range(depth):
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layers.extend([
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Residual(PreNorm(dim, Attention(dim, heads = heads))),
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Residual(PreNorm(dim, FeedForward(dim, mlp_dim)))
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])
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self.net = nn.Sequential(*layers)
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def forward(self, x):
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return self.net(x)
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class ViT(nn.Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3):
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super().__init__()
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assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
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num_patches = (image_size // patch_size) ** 2
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patch_dim = channels * patch_size ** 2
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self.patch_size = patch_size
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.patch_to_embedding = nn.Linear(patch_dim, dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
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self.transformer = Transformer(dim, depth, heads, mlp_dim)
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self.to_cls_token = nn.Identity()
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self.mlp_head = nn.Sequential(
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nn.Linear(dim, mlp_dim),
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nn.GELU(),
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nn.Linear(mlp_dim, num_classes)
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)
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def forward(self, img):
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p = self.patch_size
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x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
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x = self.patch_to_embedding(x)
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cls_tokens = self.cls_token.expand(img.shape[0], -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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x += self.pos_embedding
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x = self.transformer(x)
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x = self.to_cls_token(x[:, 0])
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return self.mlp_head(x)
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