mirror of
https://github.com/lucidrains/vit-pytorch.git
synced 2025-12-30 08:02:29 +00:00
136 lines
3.9 KiB
Python
136 lines
3.9 KiB
Python
import torch
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from torch import nn
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from einops import rearrange, repeat, pack, unpack
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from einops.layers.torch import Rearrange
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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.dropout = nn.Dropout(dropout)
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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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attn = self.dropout(attn)
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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.):
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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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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 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 x
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class ViT(nn.Module):
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def __init__(self, *, seq_len, 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 (seq_len % patch_size) == 0
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num_patches = seq_len // patch_size
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patch_dim = channels * patch_size
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self.to_patch_embedding = nn.Sequential(
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Rearrange('b c (n p) -> b n (p c)', p = 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.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.cls_token = nn.Parameter(torch.randn(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, series):
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x = self.to_patch_embedding(series)
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b, n, _ = x.shape
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cls_tokens = repeat(self.cls_token, 'd -> b d', b = b)
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x, ps = pack([cls_tokens, x], 'b * d')
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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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cls_tokens, _ = unpack(x, ps, 'b * d')
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return self.mlp_head(cls_tokens)
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if __name__ == '__main__':
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v = ViT(
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seq_len = 256,
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patch_size = 16,
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num_classes = 1000,
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dim = 1024,
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depth = 6,
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heads = 8,
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mlp_dim = 2048,
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dropout = 0.1,
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emb_dropout = 0.1
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)
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time_series = torch.randn(4, 3, 256)
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logits = v(time_series) # (4, 1000)
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