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| from typing import Optional | |
| import torch | |
| import torch.nn as nn | |
| from transformers import ( | |
| CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig,\ | |
| SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig | |
| ) | |
| from llava.s2wrapper import forward as multiscale_forward | |
| class CLIPVisionTower(nn.Module): | |
| def __init__( | |
| self, | |
| vision_tower_name: str="openai/clip-vit-large-patch14-336", | |
| mm_vision_select_layer: int=-2, # v1.5 is -2 | |
| mm_vision_select_feature: str="patch", | |
| delay_load: bool=False, | |
| requires_grad: bool=False, | |
| scales: Optional[float] = None | |
| ): | |
| super().__init__() | |
| self.is_loaded = False | |
| self.requires_grad = requires_grad | |
| self.scales = scales | |
| self.vision_tower_name = vision_tower_name | |
| self.select_layer = mm_vision_select_layer | |
| self.select_feature = mm_vision_select_feature | |
| self.image_processor = None | |
| self.vision_tower = None | |
| if not delay_load: | |
| self.load_model() | |
| else: | |
| if "clip" in self.vision_tower_name: | |
| self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) | |
| elif "siglip" in self.vision_tower_name: | |
| self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name) | |
| else: | |
| raise ValueError(f'Unsupported vision_tower_name: {self.vision_tower_name}') | |
| def load_model(self): | |
| if "clip" in self.vision_tower_name: | |
| self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) | |
| self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) | |
| elif "siglip" in self.vision_tower_name: | |
| self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name) | |
| self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name) | |
| else: | |
| raise ValueError(f'Unsupported vision_tower_name: {self.vision_tower_name}') | |
| self.vision_tower.requires_grad_(self.requires_grad) | |
| 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 | |
| def forward(self, images): | |
| if type(images) is list: | |
| image_features = [] | |
| for image in images: | |
| if self.scales is None: | |
| image_feature = self._forward_feature(images.unsqueeze(0)) | |
| else: | |
| image_feature = multiscale_forward( | |
| self._forward_feature, | |
| images.unsqueeze(0), | |
| scales=self.scales, | |
| num_prefix_token=0, | |
| max_split_size=self.image_processor.size["height"] | |
| ) | |
| #image_feature = self.feature_select(image_forward_out).to(image.dtype) | |
| image_features.append(image_feature) | |
| else: | |
| if self.scales is None: | |
| image_features = self._forward_feature(images) | |
| else: | |
| image_features = multiscale_forward( | |
| self._forward_feature, | |
| images, | |
| scales=self.scales, | |
| num_prefix_token=0, | |
| max_split_size=self.image_processor.size["height"] | |
| ) | |
| #image_features = self.feature_select(image_forward_outs).to(images.dtype) | |
| return image_features | |
| def _forward_feature(self, inputs): | |
| return self.feature_select(self.vision_tower(inputs.to(device=self.device, dtype=self.dtype), output_hidden_states=True)) | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_tower.dtype | |
| def device(self): | |
| return self.vision_tower.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_tower.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| if self.scales is None: | |
| return self.config.hidden_size | |
| return self.config.hidden_size*len(self.scales) | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 | |