| from typing import Literal |
| import numpy as np |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from PIL import Image |
|
|
| from transformers import AutoModel |
|
|
|
|
| class DINOv2ImageEncoder(nn.Module): |
| def __init__(self, model_name: Literal[ |
| "facebook/dinov2-with-registers-large", |
| "facebook/dinov2-large" |
| ]): |
| super().__init__() |
| self.model = AutoModel.from_pretrained(model_name, torch_dtype=torch.bfloat16) |
| self.model.requires_grad_(False) |
| self.model.eval() |
|
|
| DINOv2_INPUT_MEAN = torch.as_tensor([0.485, 0.456, 0.406], dtype=torch.float32)[ |
| None, :, None, None |
| ] |
| DINOv2_INPUT_STD = torch.as_tensor([0.229, 0.224, 0.225], dtype=torch.float32)[ |
| None, :, None, None |
| ] |
| self.register_buffer("DINOv2_INPUT_MEAN", DINOv2_INPUT_MEAN, persistent=False) |
| self.register_buffer("DINOv2_INPUT_STD", DINOv2_INPUT_STD, persistent=False) |
| self.max_size = 518 |
| self.hidden_size = self.model.config.hidden_size |
|
|
| def preprocess(self, image: torch.Tensor): |
| B, C, H, W = image.shape |
| assert C == 3 and H <= self.max_size and W <= self.max_size |
| image = (image - self.DINOv2_INPUT_MEAN.to(image)) / self.DINOv2_INPUT_STD.to(image) |
| return image |
| |
| def forward(self, image: torch.Tensor): |
| image = self.preprocess(image) |
| features = self.model(image).last_hidden_state |
| return features |
