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import itertools
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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_DINOV2_BASE_URL = "https://dl.fbaipublicfiles.com/dinov2"
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def _make_dinov2_model_name(arch_name: str, patch_size: int, num_register_tokens: int = 0) -> str:
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compact_arch_name = arch_name.replace("_", "")[:4]
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registers_suffix = f"_reg{num_register_tokens}" if num_register_tokens else ""
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return f"dinov2_{compact_arch_name}{patch_size}{registers_suffix}"
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class CenterPadding(nn.Module):
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def __init__(self, multiple):
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super().__init__()
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self.multiple = multiple
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def _get_pad(self, size):
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new_size = math.ceil(size / self.multiple) * self.multiple
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pad_size = new_size - size
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pad_size_left = pad_size // 2
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pad_size_right = pad_size - pad_size_left
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return pad_size_left, pad_size_right
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@torch.inference_mode()
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def forward(self, x):
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pads = list(itertools.chain.from_iterable(self._get_pad(m) for m in x.shape[:1:-1]))
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output = F.pad(x, pads)
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return output
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