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import torch
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import torch.nn as nn
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from llava.utils import rank0_print
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from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
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try:
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from s2wrapper import forward as multiscale_forward
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except:
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pass
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class CLIPVisionTower(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
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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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rank0_print(f"Loading vision tower: {vision_tower}")
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self.load_model()
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elif getattr(args, "unfreeze_mm_vision_tower", False):
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rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
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self.load_model()
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elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
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rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
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self.load_model()
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else:
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self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
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def load_model(self, device_map=None):
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if self.is_loaded:
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rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
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return
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self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
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self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
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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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elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
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select_layers = [-2, -5, -8, -11, 6]
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image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
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select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
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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 dtype(self):
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return self.vision_tower.dtype
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@property
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def device(self):
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return self.vision_tower.device
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@property
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def config(self):
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if self.is_loaded:
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return self.vision_tower.config
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else:
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return self.cfg_only
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@property
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def hidden_size(self):
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_hidden_size = self.config.hidden_size
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if "slicefour" in self.select_feature:
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_hidden_size *= 4
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if "slice_m25811_f6" in self.select_feature:
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_hidden_size *= 5
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return _hidden_size
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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 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 image_size(self):
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return self.config.image_size
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class CLIPVisionTowerS2(CLIPVisionTower):
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def __init__(self, vision_tower, args, delay_load=False):
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self.s2_scales = getattr(args, "s2_scales", "336,672,1008")
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self.s2_scales = list(map(int, self.s2_scales.split(",")))
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self.s2_scales.sort()
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self.s2_split_size = self.s2_scales[0]
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self.s2_image_size = self.s2_scales[-1]
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super().__init__(vision_tower, args, delay_load)
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if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False):
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self.image_processor.size["shortest_edge"] = self.s2_image_size
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self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
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def load_model(self, device_map=None):
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if self.is_loaded:
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rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
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return
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self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
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self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
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self.vision_tower.requires_grad_(False)
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self.image_processor.size["shortest_edge"] = self.s2_image_size
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self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size
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self.is_loaded = True
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def forward_feature(self, images):
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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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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_feature = multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
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image_features.append(image_feature)
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else:
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image_features = multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
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return image_features
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@property
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def hidden_size(self):
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return self.config.hidden_size * len(self.s2_scales)
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