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import torch
import torch.nn as nn
from transformers import AutoModel, AutoImageProcessor, AutoConfig, CLIPImageProcessor
from llava.utils import rank0_print
class HFVisionTower(nn.Module):
def __init__(self, vision_tower, args, delay_load=False):
super().__init__()
self.is_loaded = False
self.vision_tower_name = vision_tower.replace("hf:", "", 1)
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 = AutoConfig.from_pretrained(self.vision_tower_name)
def load_model(self):
try:
self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name)
except Exception as e:
if "448" in self.vision_tower_name:
image_size = 448
# use image processor with conig
self.image_processor = CLIPImageProcessor(size={"shortest_edge": image_size}, do_center_crop=True, crop_size=image_size)
else:
self.image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
rank0_print(f"Loaded image processor: {self.image_processor}")
self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to("cuda")
self.device = self.vision_tower.device
self.dtype = self.vision_tower.dtype
self.config = self.vision_tower.config
if hasattr(self.vision_tower, "vision_model"):
self.vision_tower = self.vision_tower.vision_model
self.vision_tower.requires_grad_(False)
# self.vision_tower.eval()
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_", "")
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 hidden_size(self):
try:
_hidden_size = self.config.hidden_size
except:
_hidden_size = self.config.vision_config.hidden_size
if "slicefour" in self.select_feature:
_hidden_size *= 4
return _hidden_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 num_patches_per_side(self):
return self.config.image_size // self.config.patch_size
@property
def image_size(self):
return self.config.image_size
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