mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
129 lines
4.2 KiB
Python
129 lines
4.2 KiB
Python
import torch
|
|
import torch.nn.functional as F
|
|
from torch import nn
|
|
|
|
from einops import rearrange
|
|
from einops.layers.torch import Rearrange
|
|
|
|
# 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)
|
|
|
|
# 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.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 x
|
|
|
|
class SimpleViT(nn.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):
|
|
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.Linear(patch_dim, dim),
|
|
)
|
|
|
|
self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
|
|
|
|
self.to_latent = nn.Identity()
|
|
self.linear_head = nn.Sequential(
|
|
nn.LayerNorm(dim),
|
|
nn.Linear(dim, num_classes)
|
|
)
|
|
|
|
def forward(self, img):
|
|
*_, h, w, dtype = *img.shape, img.dtype
|
|
|
|
x = self.to_patch_embedding(img)
|
|
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)
|