mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
153 lines
4.8 KiB
Python
153 lines
4.8 KiB
Python
from math import sqrt
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import torch
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from torch import nn, einsum
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from einops.layers.torch import Rearrange
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# classes
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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, **kwargs):
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return self.fn(x, **kwargs) + x
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class ExcludeCLS(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, **kwargs):
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cls_token, x = x[:, :1], x[:, 1:]
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x = self.fn(x, **kwargs)
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return torch.cat((cls_token, x), dim = 1)
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# prenorm
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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, **kwargs):
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return self.fn(self.norm(x), **kwargs)
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# feed forward related classes
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class DepthWiseConv2d(nn.Module):
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def __init__(self, dim_in, dim_out, kernel_size, padding, stride = 1, bias = True):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(dim_in, dim_in, kernel_size = kernel_size, padding = padding, groups = dim_in, stride = stride, bias = bias),
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nn.Conv2d(dim_in, dim_out, kernel_size = 1, bias = bias)
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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 FeedForward(nn.Module):
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def __init__(self, dim, hidden_dim, dropout = 0.):
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super().__init__()
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self.net = nn.Sequential(
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nn.Conv2d(dim, hidden_dim, 1),
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nn.Hardswish(),
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DepthWiseConv2d(hidden_dim, hidden_dim, 3, padding = 1),
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nn.Hardswish(),
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nn.Dropout(dropout),
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nn.Conv2d(hidden_dim, dim, 1),
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nn.Dropout(dropout)
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)
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def forward(self, x):
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h = w = int(sqrt(x.shape[-2]))
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x = rearrange(x, 'b (h w) c -> b c h w', h = h, w = w)
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x = self.net(x)
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x = rearrange(x, 'b c h w -> b (h w) c')
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return x
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# attention
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class Attention(nn.Module):
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def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
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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.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.Sequential(
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nn.Linear(inner_dim, dim),
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nn.Dropout(dropout)
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)
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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).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 = h), qkv)
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dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
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attn = self.attend(dots)
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out = einsum('b h i j, b h j d -> b h i d', 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, dropout = 0.):
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super().__init__()
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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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Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))),
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ExcludeCLS(Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))))
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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)
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x = ff(x)
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return x
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# main class
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class LocalViT(nn.Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
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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.to_patch_embedding = nn.Sequential(
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Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size),
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nn.Linear(patch_dim, dim),
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)
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
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self.dropout = nn.Dropout(emb_dropout)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)
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self.mlp_head = nn.Sequential(
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nn.LayerNorm(dim),
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nn.Linear(dim, num_classes)
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)
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def forward(self, img):
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x = self.to_patch_embedding(img)
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b, n, _ = x.shape
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)
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x = torch.cat((cls_tokens, x), dim=1)
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x += self.pos_embedding[:, :(n + 1)]
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x = self.dropout(x)
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x = self.transformer(x)
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return self.mlp_head(x)
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