| import torch |
| import torch.nn as nn |
| from transformers import AutoConfig, AutoImageProcessor, AutoModel, Dinov2Model |
|
|
|
|
| class DinoV2VisionTower(nn.Module): |
| def __init__(self, vision_tower, args, delay_load=False): |
| super().__init__() |
|
|
| self.is_loaded = False |
|
|
| self.vision_tower_name = vision_tower |
| self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') |
|
|
| if not delay_load: |
| self.load_model() |
| elif getattr(args, 'unfreeze_mm_vision_tower', False): |
| self.load_model() |
| else: |
| self.cfg_only = AutoConfig.from_pretrained(self.vision_tower_name) |
|
|
| def load_model(self, device_map=None): |
| if self.is_loaded: |
| print('{} is already loaded, `load_model` called again, skipping.'.format(self.vision_tower_name)) |
| return |
|
|
| self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name) |
| self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, device_map=device_map) |
| self.vision_tower.requires_grad_(False) |
|
|
| self.is_loaded = True |
|
|
| def feature_select(self, image_forward_outs): |
| image_features = image_forward_outs.last_hidden_state |
| if self.select_feature == 'patch': |
| image_features = image_features[:, 1:] |
| elif self.select_feature == 'cls_patch': |
| image_features = image_features |
| else: |
| raise ValueError(f'Unexpected select feature: {self.select_feature}') |
| return image_features |
|
|
| @torch.no_grad() |
| def forward(self, images): |
| if type(images) is list: |
| image_features = [] |
| for image in images: |
| image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0)) |
| image_feature = self.feature_select(image_forward_out).to(image.dtype) |
| image_features.append(image_feature) |
| else: |
| image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype)) |
| image_features = self.feature_select(image_forward_outs).to(images.dtype) |
|
|
| return image_features |
|
|
| @property |
| def dummy_feature(self): |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) |
|
|
| @property |
| def dtype(self): |
| return self.vision_tower.dtype |
|
|
| @property |
| def device(self): |
| return self.vision_tower.device |
|
|
| @property |
| def config(self): |
| if self.is_loaded: |
| return self.vision_tower.config |
| else: |
| return self.cfg_only |
|
|
| @property |
| def hidden_size(self): |
| return self.config.hidden_size |
|
|
| @property |
| def num_patches_per_side(self): |
| return self.config.image_size // self.config.patch_size |
|
|
| @property |
| def num_patches(self): |
| return (self.config.image_size // self.config.patch_size) ** 2 |
|
|
|
|
