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class ViT(nn.Module): | |
def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dropout = 0., emb_dropout = 0.): | |
super().__init__() | |
assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size' | |
num_patches = (image_size // patch_size) ** 3 | |
patch_dim = channels * patch_size ** 3 | |
self.patch_size = patch_size | |
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) | |
self.patch_to_embedding = nn.Linear(patch_dim, dim) | |
self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) | |
self.dropout = nn.Dropout(emb_dropout) | |
self.transformer = Transformer(dim, depth, heads, mlp_dim, dropout) | |
self.to_cls_token = nn.Identity() | |
self.mlp_head = nn.Sequential( | |
nn.LayerNorm(dim), | |
nn.Linear(dim, mlp_dim), | |
nn.GELU(), | |
nn.Dropout(dropout), | |
nn.Linear(mlp_dim, num_classes), | |
nn.Dropout(dropout) | |
) | |
def forward(self, img, mask = None): | |
p = self.patch_size | |
x = rearrange(img, 'b c (h p1) (w p2) (d p3) -> b (h w d) (p1 p2 p3 c)', p1 = p, p2 = p, p3 = p) | |
x = self.patch_to_embedding(x) | |
cls_tokens = self.cls_token.expand(img.shape[0], -1, -1) | |
x = torch.cat((cls_tokens, x), dim=1) | |
x += self.pos_embedding | |
x = self.dropout(x) | |
x = self.transformer(x, mask) | |
x = self.to_cls_token(x[:, 0]) | |
return self.mlp_head(x) |
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