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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, load_pretrained=False):
super().__init__()
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
config = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
config._attn_implementation = "flash_attention_2"
if not load_pretrained:
self.vision_tower = CLIPVisionModel(config=config)
else:
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name)
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.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, output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
return self.vision_tower.config
@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, load_pretrained=False):
super().__init__()
self.vision_tower_name = vision_tower
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch')
self.image_processor = SiglipImageProcessor.from_pretrained(self.vision_tower_name)
config = SiglipVisionConfig.from_pretrained(self.vision_tower_name)
config._attn_implementation = 'flash_attention_2'
if not load_pretrained:
self.vision_tower = SiglipVisionModel(config=config)
else:
self.vision_tower = SiglipVisionModel.from_pretrained(self.vision_tower_name)
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.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, output_hidden_states=True)
image_features = self.feature_select(image_forward_outs).to(images.dtype)
return image_features
@property
def dtype(self):
return self.vision_tower.dtype
@property
def device(self):
return self.vision_tower.device
@property
def config(self):
return self.vision_tower.config
@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
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