| | import torch |
| | import torch.nn.functional as F |
| |
|
| | from transformers import SiglipImageProcessor, SiglipVisionConfig, SiglipVisionModel |
| |
|
| | from .base_encoder import BaseVisionTower, ProcessorWrapper |
| |
|
| |
|
| | class SiglipVisionTower(BaseVisionTower): |
| | def __init__(self, vision_tower_name, args, delay_load=False): |
| | super(SiglipVisionTower, self).__init__(vision_tower_name, args, delay_load) |
| | |
| | model_path = "google/siglip-so400m-patch14-384" |
| | base_model_name, res, interp = model_path, 384, 576 |
| | self.vision_tower_name = base_model_name |
| | self._image_size = res if res is not None else 512 |
| | self._interp_size = interp |
| | if not self.delay_load: |
| | self.load_model() |
| | elif self.unfreeze_mm_vision_tower: |
| | self.load_model() |
| | else: |
| | self._hidden_size = 1152 |
| |
|
| | def load_model(self, device_map=None): |
| | self.vision_model = "siglip" |
| | |
| | self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name) |
| |
|
| | |
| | self.vision_tower.output_tokens = True |
| |
|
| | self._hidden_size = self.vision_tower.config.hidden_size |
| | self._image_size = self.vision_tower.config.image_size |
| | self._patch_size = self.vision_tower.config.patch_size |
| | self.image_processor = SiglipImageProcessor.from_pretrained( |
| | self.vision_tower_name |
| | ) |
| |
|
| | self.vision_tower.requires_grad_(self.unfreeze_mm_vision_tower) |
| | self.is_loaded = True |
| |
|
| | def interpolate(self, image_features): |
| | if self._interp_size is None: |
| | return image_features |
| |
|
| | b, num_tokens, dim = image_features.shape |
| |
|
| | if num_tokens != self.num_patches: |
| | target_h = target_w = int(self._interp_size**0.5) |
| | h = w = int(num_tokens**0.5) |
| |
|
| | image_features = image_features.view(b, h, w, dim) |
| | image_features = image_features.permute(0, 3, 1, 2).contiguous() |
| |
|
| | image_features = F.interpolate( |
| | image_features.to(torch.float32), |
| | size=(target_h, target_w), |
| | mode="bilinear", |
| | align_corners=False, |
| | ).to(image_features.dtype) |
| |
|
| | |
| | image_features = image_features.permute(0, 2, 3, 1).contiguous() |
| |
|
| | |
| | image_features = image_features.flatten(1, 2) |
| |
|
| | return image_features |
| |
|
| | def _forward(self, images, interpolate_token=576): |
| | with torch.set_grad_enabled(self.unfreeze_mm_vision_tower): |
| | image_features = self.vision_tower.forward( |
| | images.to(device=self.device, dtype=self.dtype), |
| | output_hidden_states=True, |
| | ).hidden_states[-1] |
| | interp_features = self.interpolate(image_features) |
| | return interp_features |
| |
|
