mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
145 lines
4.7 KiB
Python
145 lines
4.7 KiB
Python
import torch
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from torch import nn
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from einops import rearrange, repeat
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from einops.layers.torch import Rearrange, Reduce
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# helpers
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def exists(val):
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return val is not None
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def default(val ,d):
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return val if exists(val) else d
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def pair(t):
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return t if isinstance(t, tuple) else (t, t)
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# patch merger class
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class PatchMerger(nn.Module):
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def __init__(self, dim, num_tokens_out):
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super().__init__()
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self.scale = dim ** -0.5
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self.norm = nn.LayerNorm(dim)
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self.queries = nn.Parameter(torch.randn(num_tokens_out, dim))
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def forward(self, x):
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x = self.norm(x)
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sim = torch.matmul(self.queries, x.transpose(-1, -2)) * self.scale
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attn = sim.softmax(dim = -1)
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return torch.matmul(attn, x)
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# classes
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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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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.Linear(dim, hidden_dim),
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nn.GELU(),
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nn.Dropout(dropout),
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nn.Linear(hidden_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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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, dropout = 0.):
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super().__init__()
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inner_dim = dim_head * heads
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project_out = not (heads == 1 and dim_head == dim)
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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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) if project_out else nn.Identity()
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def forward(self, 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, dropout = 0., patch_merge_layer = None, patch_merge_num_tokens = 8):
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super().__init__()
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self.layers = nn.ModuleList([])
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self.patch_merge_layer_index = default(patch_merge_layer, depth // 2) - 1 # default to mid-way through transformer, as shown in paper
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self.patch_merger = PatchMerger(dim = dim, num_tokens_out = patch_merge_num_tokens)
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for _ in range(depth):
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self.layers.append(nn.ModuleList([
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PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),
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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 index, (attn, ff) in enumerate(self.layers):
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x = attn(x) + x
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x = ff(x) + x
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if index == self.patch_merge_layer_index:
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x = self.patch_merger(x)
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return 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, patch_merge_layer = None, patch_merge_num_tokens = 8, channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.):
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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(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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num_patches = (image_height // patch_height) * (image_width // patch_width)
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patch_dim = channels * patch_height * patch_width
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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_height, p2 = patch_width),
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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.dropout = nn.Dropout(emb_dropout)
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout, patch_merge_layer, patch_merge_num_tokens)
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self.mlp_head = nn.Sequential(
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Reduce('b n d -> b d', 'mean'),
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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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x += self.pos_embedding[:, :n]
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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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