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'''
CopyRight @DeepGlint 2025
'''
import torch
import torch.nn as nn
from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
def build_vision_tower(model_cfg, **kwargs):
vision_tower = getattr(model_cfg, "vision_tower_config", getattr(model_cfg, "vision_tower", None))
return CLIPVisionTower(vision_tower, args=model_cfg, **kwargs)
class CLIPVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_cfg = vision_tower
self.vision_tower_processor = args.vision_tower_processor
self.select_layer = args.mm_vision_select_layer
self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
if not delay_load:
self.init_model()
elif getattr(args, "unfreeze_mm_vision_tower", False):
# TODO: better detector is needed.
self.init_model()
elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
self.init_model()
else:
raise RuntimeError("Not support now, please check config.json or contact us")
def init_model(self, device_map=None):
if self.is_loaded:
return
vision_tower_config = CLIPVisionConfig().from_dict(self.vision_tower_cfg)
self.image_processor = CLIPImageProcessor(**self.vision_tower_processor)
self.vision_tower = CLIPVisionModel(config=vision_tower_config)
self.vision_tower.requires_grad_(False)
self.is_loaded = True
def feature_select(self, image_forward_outs):
select_feature_type = self.select_feature
if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
select_every_k_layer = len(image_forward_outs.hidden_states) // 4
image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1)
select_feature_type = select_feature_type.replace("slicefour_", "")
elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
select_layers = [-2, -5, -8, -11, 6]
image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
else:
image_features = image_forward_outs.hidden_states[self.select_layer]
if select_feature_type == "patch":
image_features = image_features[:, 1:]
elif select_feature_type == "cls_patch":
image_features = image_features
else:
raise ValueError(f"Unexpected select feature: {select_feature_type}")
return image_features
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):
_hidden_size = self.config.hidden_size
if "slicefour" in self.select_feature:
_hidden_size *= 4
if "slice_m25811_f6" in self.select_feature:
_hidden_size *= 5
return _hidden_size
@property
def num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def num_patches(self):
_num_patches = (self.config.image_size // self.config.patch_size) ** 2
if "cls_patch" in self.select_feature:
_num_patches += 1
return _num_patches
@property
def image_size(self):
return self.config.image_size |