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https://github.com/lucidrains/vit-pytorch.git
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44 lines
1.5 KiB
Python
44 lines
1.5 KiB
Python
import torch
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from einops import rearrange, repeat
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from torch import nn
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class ViT(nn.Module):
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def __init__(self, *, image_size, patch_size, num_classes, dim, transformer, pool = 'cls', channels = 3):
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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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assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'
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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.patch_size = patch_size
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
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self.patch_to_embedding = nn.Linear(patch_dim, dim)
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
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self.transformer = transformer
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self.pool = pool
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self.to_latent = nn.Identity()
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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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p = self.patch_size
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x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = p, p2 = p)
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x = self.patch_to_embedding(x)
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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.transformer(x)
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x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]
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x = self.to_latent(x)
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return self.mlp_head(x)
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