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import torch
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import torch.nn as nn
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from transformers import AutoModel, AutoImageProcessor, AutoConfig, CLIPImageProcessor
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from llava.utils import rank0_print
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class HFVisionTower(nn.Module):
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def __init__(self, vision_tower, args, delay_load=False):
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super().__init__()
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self.is_loaded = False
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self.vision_tower_name = vision_tower.replace("hf:", "", 1)
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self.select_layer = args.mm_vision_select_layer
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self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
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if not delay_load:
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self.load_model()
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else:
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self.cfg_only = AutoConfig.from_pretrained(self.vision_tower_name)
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def load_model(self):
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try:
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self.image_processor = AutoImageProcessor.from_pretrained(self.vision_tower_name)
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except Exception as e:
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if "448" in self.vision_tower_name:
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image_size = 448
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self.image_processor = CLIPImageProcessor(size={"shortest_edge": image_size}, do_center_crop=True, crop_size=image_size)
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else:
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self.image_processor = CLIPImageProcessor.from_pretrained("openai/clip-vit-large-patch14")
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rank0_print(f"Loaded image processor: {self.image_processor}")
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self.vision_tower = AutoModel.from_pretrained(self.vision_tower_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to("cuda")
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self.device = self.vision_tower.device
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self.dtype = self.vision_tower.dtype
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self.config = self.vision_tower.config
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if hasattr(self.vision_tower, "vision_model"):
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self.vision_tower = self.vision_tower.vision_model
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self.vision_tower.requires_grad_(False)
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self.is_loaded = True
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def feature_select(self, image_forward_outs):
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select_feature_type = self.select_feature
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if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
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select_every_k_layer = len(image_forward_outs.hidden_states) // 4
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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)
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select_feature_type = select_feature_type.replace("slicefour_", "")
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else:
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image_features = image_forward_outs.hidden_states[self.select_layer]
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if select_feature_type == "patch":
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image_features = image_features[:, 1:]
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elif select_feature_type == "cls_patch":
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image_features = image_features
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else:
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raise ValueError(f"Unexpected select feature: {select_feature_type}")
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return image_features
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def forward(self, images):
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if type(images) is list:
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image_features = []
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for image in images:
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image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
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image_feature = self.feature_select(image_forward_out).to(image.dtype)
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image_features.append(image_feature)
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else:
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image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
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image_features = self.feature_select(image_forward_outs).to(images.dtype)
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return image_features
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@property
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def dummy_feature(self):
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return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)
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@property
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def hidden_size(self):
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try:
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_hidden_size = self.config.hidden_size
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except:
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_hidden_size = self.config.vision_config.hidden_size
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if "slicefour" in self.select_feature:
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_hidden_size *= 4
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return _hidden_size
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@property
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def num_patches(self):
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_num_patches = (self.config.image_size // self.config.patch_size) ** 2
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if "cls_patch" in self.select_feature:
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_num_patches += 1
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return _num_patches
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@property
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def num_patches_per_side(self):
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return self.config.image_size // self.config.patch_size
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@property
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def image_size(self):
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return self.config.image_size
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