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| 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 | |
| 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 | |
| def dummy_feature(self): | |
| return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype) | |
| def dtype(self): | |
| return self.vision_encoder.dtype | |
| def device(self): | |
| return self.vision_encoder.device | |
| def config(self): | |
| if self.is_loaded: | |
| return self.vision_encoder.config | |
| else: | |
| return self.cfg_only | |
| def hidden_size(self): | |
| return self.config.hidden_size | |
| def num_patches(self): | |
| return (self.config.image_size // self.config.patch_size) ** 2 |