|
import os |
|
|
|
import torch |
|
import torch.nn as nn |
|
|
|
from transformers import ( |
|
CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig, |
|
SiglipVisionModel, SiglipImageProcessor, SiglipVisionConfig |
|
) |
|
|
|
|
|
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() |
|
else: |
|
self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name) |
|
|
|
def load_model(self): |
|
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name) |
|
|
|
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) |
|
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(self): |
|
return (self.config.image_size // self.config.patch_size) ** 2 |
|
|
|
@property |
|
def num_patches_per_side(self): |
|
return self.config.image_size // self.config.patch_size |
|
|
|
@property |
|
def image_size(self): |
|
return self.config.image_size |
|
|
|
|
|
class SiglipVisionTower(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() |
|
else: |
|
self.cfg_only = SiglipVisionConfig.from_pretrained(self.vision_tower_name) |
|
|
|
def load_model(self): |
|
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name) |
|
|
|
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name) |
|
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 |
|
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(self): |
|
return (self.config.image_size // self.config.patch_size) ** 2 |
|
|
|
@property |
|
def num_patches_per_side(self): |
|
return self.config.image_size // self.config.patch_size |
|
|
|
@property |
|
def image_size(self): |
|
return self.config.image_size |
|
|
|
|
|
def build_vision_tower(vision_tower_cfg, **kwargs): |
|
vision_tower = getattr(vision_tower_cfg, 'mm_vision_tower', getattr(vision_tower_cfg, 'vision_tower', None)) |
|
|
|
if 'clip' in vision_tower: |
|
vision_tower = CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) |
|
elif 'siglip' in vision_tower: |
|
vision_tower = SiglipVisionTower(vision_tower, args=vision_tower_cfg, **kwargs) |
|
else: |
|
raise ValueError(f'Unknown vision tower: {vision_tower}') |
|
|
|
return vision_tower |
|
|