models/feature_extractor.py

import torch
from transformers import AutoModelForDepthEstimation

class FeatureExtractor(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.fe = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14_reg')

        self.patch_size = self.fe.patch_size
        self.embed_dim = self.fe.embed_dim

    def forward(self, x):
        return self.fe.forward_features(x)['x_norm_patchtokens']
    
class FeatureExtractorDepth(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.fe = AutoModelForDepthEstimation.from_pretrained("depth-anything/Depth-Anything-V2-Base-hf")

        self.patch_size = 14
        self.embed_dim = 768

    def forward(self, x):
        x = self.fe(x, output_hidden_states=True).hidden_states
        return x[-1][:,1:,:]