wehub-resource-sync
119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
from typing import *
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import torch
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import torch.nn.functional as F
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from torchvision import transforms
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from transformers import DINOv3ViTModel
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import numpy as np
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from PIL import Image
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class DinoV2FeatureExtractor:
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"""
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Feature extractor for DINOv2 models.
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"""
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def __init__(self, model_name: str):
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self.model_name = model_name
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self.model = torch.hub.load('facebookresearch/dinov2', model_name, pretrained=True)
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self.model.eval()
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self.transform = transforms.Compose([
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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def to(self, device):
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self.model.to(device)
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def cuda(self):
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self.model.cuda()
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def cpu(self):
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self.model.cpu()
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@torch.no_grad()
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def __call__(self, image: Union[torch.Tensor, List[Image.Image]]) -> torch.Tensor:
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"""
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Extract features from the image.
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Args:
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image: A batch of images as a tensor of shape (B, C, H, W) or a list of PIL images.
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Returns:
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A tensor of shape (B, N, D) where N is the number of patches and D is the feature dimension.
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"""
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if isinstance(image, torch.Tensor):
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assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)"
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elif isinstance(image, list):
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assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images"
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image = [i.resize((518, 518), Image.LANCZOS) for i in image]
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image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image]
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image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image]
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image = torch.stack(image).cuda()
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else:
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raise ValueError(f"Unsupported type of image: {type(image)}")
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image = self.transform(image).cuda()
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features = self.model(image, is_training=True)['x_prenorm']
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patchtokens = F.layer_norm(features, features.shape[-1:])
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return patchtokens
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class DinoV3FeatureExtractor:
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"""
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Feature extractor for DINOv3 models.
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"""
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def __init__(self, model_name: str, image_size=512):
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self.model_name = model_name
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self.model = DINOv3ViTModel.from_pretrained(model_name)
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self.model.eval()
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self.image_size = image_size
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self.transform = transforms.Compose([
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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def to(self, device):
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self.model.to(device)
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def cuda(self):
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self.model.cuda()
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def cpu(self):
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self.model.cpu()
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def extract_features(self, image: torch.Tensor) -> torch.Tensor:
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image = image.to(self.model.embeddings.patch_embeddings.weight.dtype)
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hidden_states = self.model.embeddings(image, bool_masked_pos=None)
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position_embeddings = self.model.rope_embeddings(image)
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for i, layer_module in enumerate(self.model.layer):
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hidden_states = layer_module(
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hidden_states,
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position_embeddings=position_embeddings,
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)
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return F.layer_norm(hidden_states, hidden_states.shape[-1:])
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@torch.no_grad()
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def __call__(self, image: Union[torch.Tensor, List[Image.Image]]) -> torch.Tensor:
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"""
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Extract features from the image.
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Args:
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image: A batch of images as a tensor of shape (B, C, H, W) or a list of PIL images.
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Returns:
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A tensor of shape (B, N, D) where N is the number of patches and D is the feature dimension.
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"""
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if isinstance(image, torch.Tensor):
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assert image.ndim == 4, "Image tensor should be batched (B, C, H, W)"
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elif isinstance(image, list):
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assert all(isinstance(i, Image.Image) for i in image), "Image list should be list of PIL images"
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image = [i.resize((self.image_size, self.image_size), Image.LANCZOS) for i in image]
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image = [np.array(i.convert('RGB')).astype(np.float32) / 255 for i in image]
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image = [torch.from_numpy(i).permute(2, 0, 1).float() for i in image]
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image = torch.stack(image).cuda()
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else:
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raise ValueError(f"Unsupported type of image: {type(image)}")
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image = self.transform(image).cuda()
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features = self.extract_features(image)
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return features
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