| | import torch |
| | import torch.nn as nn |
| |
|
| | from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig |
| |
|
| |
|
| | class CLIPVisionTower(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_layer = args.mm_vision_select_layer |
| | 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 = CLIPVisionConfig.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 = CLIPImageProcessor.from_pretrained(self.vision_tower_name) |
| | self.vision_tower = CLIPVisionModel.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.hidden_states[self.select_layer] |
| | 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), output_hidden_states=True) |
| | 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), output_hidden_states=True) |
| | 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 |
| |
|
