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| ''' | |
| @File : BLIPScore.py | |
| @Time : 2023/02/19 20:48:00 | |
| @Auther : Jiazheng Xu | |
| @Contact : [email protected] | |
| @Description: BLIPScore. | |
| * Based on BLIP code base | |
| * https://github.com/salesforce/BLIP | |
| ''' | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from PIL import Image | |
| from ImageReward.models.BLIP.blip_pretrain import BLIP_Pretrain | |
| from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize | |
| try: | |
| from torchvision.transforms import InterpolationMode | |
| BICUBIC = InterpolationMode.BICUBIC | |
| except ImportError: | |
| BICUBIC = Image.BICUBIC | |
| def _convert_image_to_rgb(image): | |
| return image.convert("RGB") | |
| def _transform(n_px): | |
| return Compose([ | |
| Resize(n_px, interpolation=BICUBIC), | |
| CenterCrop(n_px), | |
| _convert_image_to_rgb, | |
| ToTensor(), | |
| Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), | |
| ]) | |
| class BLIPScore(nn.Module): | |
| def __init__(self, med_config, device='cpu'): | |
| super().__init__() | |
| self.device = device | |
| self.preprocess = _transform(224) | |
| self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config) | |
| def score(self, prompt, image_path): | |
| if (type(image_path).__name__=='list'): | |
| _, rewards = self.inference_rank(prompt, image_path) | |
| return rewards | |
| # text encode | |
| text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) | |
| text_output = self.blip.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text') | |
| txt_feature = F.normalize(self.blip.text_proj(text_output.last_hidden_state[:,0,:])) | |
| # image encode | |
| pil_image = Image.open(image_path) | |
| image = self.preprocess(pil_image).unsqueeze(0).to(self.device) | |
| image_embeds = self.blip.visual_encoder(image) | |
| image_features = F.normalize(self.blip.vision_proj(image_embeds[:,0,:]), dim=-1) | |
| # score | |
| rewards = torch.sum(torch.mul(txt_feature, image_features), dim=1, keepdim=True) | |
| return rewards.detach().cpu().numpy().item() | |
| def inference_rank(self, prompt, generations_list): | |
| text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device) | |
| text_output = self.blip.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text') | |
| txt_feature = F.normalize(self.blip.text_proj(text_output.last_hidden_state[:,0,:])) | |
| txt_set = [] | |
| img_set = [] | |
| for generations in generations_list: | |
| # image encode | |
| img_path = generations | |
| pil_image = Image.open(img_path) | |
| image = self.preprocess(pil_image).unsqueeze(0).to(self.device) | |
| image_embeds = self.blip.visual_encoder(image) | |
| image_features = F.normalize(self.blip.vision_proj(image_embeds[:,0,:]), dim=-1) | |
| img_set.append(image_features) | |
| txt_set.append(txt_feature) | |
| txt_features = torch.cat(txt_set, 0).float() # [image_num, feature_dim] | |
| img_features = torch.cat(img_set, 0).float() # [image_num, feature_dim] | |
| rewards = torch.sum(torch.mul(txt_features, img_features), dim=1, keepdim=True) | |
| rewards = torch.squeeze(rewards) | |
| _, rank = torch.sort(rewards, dim=0, descending=True) | |
| _, indices = torch.sort(rank, dim=0) | |
| indices = indices + 1 | |
| return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist() |