import torch import torch.nn as nn from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig class CLIPVisionEncoder(nn.Module): def __init__(self, encoder_name="openai/clip-vit-large-patch14", delay_load=False): super().__init__() self.is_loaded = False self.vision_encoder_name = encoder_name # self.select_layer = args.mm_vision_select_layer # self.select_feature = getattr(args, 'mm_vision_select_feature', 'patch') self.select_layer = -1 self.select_feature = "patch" if not delay_load: self.load_model() else: self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_encoder_name) def load_model(self): self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_encoder_name) self.vision_encoder = CLIPVisionModel.from_pretrained(self.vision_encoder_name) self.vision_encoder.requires_grad_(False) self.is_loaded = True def feature_select(self, image_forward_outs): image_features = image_forward_outs.hidden_states[self.select_layer] if self.select_feature == 'patch': image_features = image_features[:, :] elif self.select_feature == 'cls_patch': image_features = image_features else: raise ValueError(f'Unexpected select feature: {self.select_feature}') return image_features @torch.no_grad() def forward(self, images): if type(images) is list: image_features = [] for image in images: image_forward_out = self.vision_encoder(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_encoder(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True) image_features = self.feature_select(image_forward_outs).to(images.dtype) # print("image feature shape", image_features.shape) # print(type(image_forward_outs)) # print(type(image_forward_outs.shape)) # image_features = 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_encoder.dtype @property def device(self): return self.vision_encoder.device @property def config(self): if self.is_loaded: return self.vision_encoder.config else: return self.cfg_only @property def hidden_size(self): return self.config.hidden_size @property def num_patches(self): return (self.config.image_size // self.config.patch_size) ** 2