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37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
from typing import Optional, cast
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import torch.nn.functional as F
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from torch import nn
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from transformers import AutoModel, Dinov2Model
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class DinoCompressor(nn.Module):
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"""DINOv2 feature extractor with optional hash compression.
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When compressor is None: returns normalized DINO embeddings.
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When compressor is provided: returns binary hash bits for CAM storage.
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"""
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def __init__(self, compressor: Optional[nn.Module] = None):
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super().__init__()
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self.dino = cast(
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Dinov2Model,
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AutoModel.from_pretrained("facebook/dinov2-large"),
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)
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self.compressor = compressor
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def forward(self, inputs):
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teacher_tokens = self.dino(**inputs).last_hidden_state # [B,N,1024]
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teacher_embed = teacher_tokens.mean(dim=1)
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teacher_embed = F.normalize(teacher_embed, dim=-1) # [B,1024]
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if self.compressor is None:
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return teacher_embed
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# HashCompressor returns (logits, hash_codes, bits)
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_, _, bits = self.compressor(teacher_tokens)
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return bits # [B, 512] binary bits for CAM
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