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import math
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from transformers import CLIPImageProcessor
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import drop_path, to_2tuple, trunc_normal_
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from .eva_clip import create_model_and_transforms, get_model_config
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import torch
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import torchvision
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import time
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from llava.utils import rank0_print
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class EvaViTWrapper(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.pretrained = args.vision_tower_pretrained
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self.args = args
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self.select_layer = args.mm_vision_select_layer
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if self.select_layer < -1:
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self.select_layer += 1
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self.select_feature = getattr(args, "mm_vision_select_feature", "patch")
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self.model_config = get_model_config(self.vision_tower_name)
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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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def load_model(self):
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rank0_print(f"Loading: {self.vision_tower_name}")
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rank0_print(f"Pretrained: {self.pretrained}")
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time_start = time.time()
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model, _, image_processor = create_model_and_transforms(self.vision_tower_name, self.pretrained, force_custom_clip=True, precision="fp16")
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time_end = time.time()
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rank0_print(f"Loaded: {self.vision_tower_name} in {time_end - time_start:.2f}s")
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self.device = next(model.parameters()).device
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self.dtype = next(model.parameters()).dtype
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if self.device.type != "meta":
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model = model.to("cuda")
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self.vision_tower = model.visual
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resize_transform = [t for t in image_processor.transforms if isinstance(t, torchvision.transforms.Resize)][0]
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normalize_transform = [t for t in image_processor.transforms if isinstance(t, torchvision.transforms.Normalize)][0]
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self.resize_transform_size = resize_transform.size
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self.image_processor = CLIPImageProcessor.from_pretrained(
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"openai/clip-vit-large-patch14",
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crop_size=resize_transform.size,
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size={"shortest_edge": resize_transform.size},
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image_mean=list(normalize_transform.mean),
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image_std=list(normalize_transform.std),
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)
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rank0_print(f"Loaded image processor: {self.image_processor}")
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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_features):
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select_feature_type = self.select_feature
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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 train(self, mode=True):
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self.training = mode
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if self.is_loaded:
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self.vision_tower.eval()
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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_features = self.vision_tower.forward_features(image.to(self.dtype), return_all_features=True)
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image_features = self.feature_select(image_features).to(self.dtype)
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image_features.append(image_features)
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else:
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image_features = self.vision_tower.forward_features(images.to(self.dtype), return_all_features=True)
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image_features = self.feature_select(image_features).to(self.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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return self.model_config["vision_cfg"]["width"]
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@property
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def num_patches(self):
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return (self.model_config["vision_cfg"]["image_size"] // self.model_config["vision_cfg"]["patch_size"]) ** 2
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@property
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def num_patches_per_side(self):
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return self.model_config["vision_cfg"]["image_size"] // self.model_config["vision_cfg"]["patch_size"]
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@property
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def config(self):
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return self.model_config
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@property
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def image_size(self):
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return self.model_config["vision_cfg"]["image_size"]
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