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
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import torch.nn.functional as F
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from torch import nn
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from einops import rearrange
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from einops.layers.torch import Rearrange
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# helpers
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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def posemb_sincos_3d(patches, temperature = 10000, dtype = torch.float32):
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_, f, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype
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z, y, x = torch.meshgrid(
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torch.arange(f, device = device),
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torch.arange(h, device = device),
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torch.arange(w, device = device),
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indexing = 'ij')
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fourier_dim = dim // 6
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omega = torch.arange(fourier_dim, device = device) / (fourier_dim - 1)
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omega = 1. / (temperature ** omega)
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z = z.flatten()[:, None] * omega[None, :]
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y = y.flatten()[:, None] * omega[None, :]
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x = x.flatten()[:, None] * omega[None, :]
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pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos(), z.sin(), z.cos()), dim = 1)
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pe = F.pad(pe, (0, dim - (fourier_dim * 6))) # pad if feature dimension not cleanly divisible by 6
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return pe.type(dtype)
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# classes
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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.LayerNorm(dim),
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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, dim_head = 64):
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super().__init__()
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inner_dim = dim_head * heads
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self.heads = heads
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self.scale = dim_head ** -0.5
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self.norm = nn.LayerNorm(dim)
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self.attend = nn.Softmax(dim = -1)
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self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
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self.to_out = nn.Linear(inner_dim, dim, bias = False)
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def forward(self, x):
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x = self.norm(x)
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qkv = self.to_qkv(x).chunk(3, dim = -1)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
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dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
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attn = self.attend(dots)
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out = torch.matmul(attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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return self.to_out(out)
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class Transformer(nn.Module):
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def __init__(self, dim, depth, heads, dim_head, mlp_dim):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.layers = nn.ModuleList([])
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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Attention(dim, heads = heads, dim_head = dim_head),
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FeedForward(dim, mlp_dim)
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]))
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def forward(self, x):
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for attn, ff in self.layers:
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x = attn(x) + x
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x = ff(x) + x
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return self.norm(x)
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class SimpleViT(nn.Module):
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def __init__(self, *, image_size, image_patch_size, frames, frame_patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64):
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super().__init__()
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image_height, image_width = pair(image_size)
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patch_height, patch_width = pair(image_patch_size)
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assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
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assert frames % frame_patch_size == 0, 'Frames must be divisible by the frame patch size'
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num_patches = (image_height // patch_height) * (image_width // patch_width) * (frames // frame_patch_size)
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patch_dim = channels * patch_height * patch_width * frame_patch_size
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self.to_patch_embedding = nn.Sequential(
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Rearrange('b c (f pf) (h p1) (w p2) -> b f h w (pf p1 p2 c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size),
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nn.LayerNorm(patch_dim),
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nn.Linear(patch_dim, dim),
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nn.LayerNorm(dim),
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)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim)
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self.to_latent = nn.Identity()
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self.linear_head = nn.Linear(dim, num_classes)
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def forward(self, video):
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*_, h, w, dtype = *video.shape, video.dtype
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x = self.to_patch_embedding(video)
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pe = posemb_sincos_3d(x)
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x = rearrange(x, 'b ... d -> b (...) d') + pe
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x = self.transformer(x)
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x = x.mean(dim = 1)
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x = self.to_latent(x)
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return self.linear_head(x)
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