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import functools |
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import logging |
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import typing |
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import beartype |
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import torch |
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from jaxtyping import Float, jaxtyped |
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from torch import Tensor |
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from torchvision.transforms import v2 |
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logger = logging.getLogger("modeling.py") |
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@jaxtyped(typechecker=beartype.beartype) |
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class SplitDinov2(torch.nn.Module): |
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def __init__(self, *, split_at: int): |
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super().__init__() |
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self.vit = torch.hub.load("facebookresearch/dinov2", "dinov2_vitb14_reg").eval() |
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self.split_at = split_at |
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def forward_start( |
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self, x: Float[Tensor, "batch channels width height"] |
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) -> Float[Tensor, "batch patches dim"]: |
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x_BPD = self.vit.prepare_tokens_with_masks(x) |
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for blk in self.vit.blocks[: self.split_at]: |
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x_BPD = blk(x_BPD) |
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return x_BPD |
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def forward_end( |
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self, x_BPD: Float[Tensor, "batch n_patches dim"] |
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) -> Float[Tensor, "batch patches dim"]: |
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for blk in self.vit.blocks[-self.split_at :]: |
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x_BPD = blk(x_BPD) |
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x_BPD = self.vit.norm(x_BPD) |
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return x_BPD[:, self.vit.num_register_tokens + 1 :] |
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@functools.cache |
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def load_vit(device: str) -> tuple[SplitDinov2, typing.Callable]: |
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vit = SplitDinov2(split_at=11).to(device) |
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vit_transform = v2.Compose([ |
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v2.Resize(size=(256, 256)), |
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v2.CenterCrop(size=(224, 224)), |
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v2.ToImage(), |
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v2.ToDtype(torch.float32, scale=True), |
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v2.Normalize(mean=[0.4850, 0.4560, 0.4060], std=[0.2290, 0.2240, 0.2250]), |
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]) |
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logger.info("Loaded ViT.") |
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return vit, vit_transform |
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