99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
'''
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@File : BLIPScore.py
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@Time : 2023/02/19 20:48:00
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@Auther : Jiazheng Xu
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@Contact : xjz22@mails.tsinghua.edu.cn
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@Description: BLIPScore.
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* Based on BLIP code base
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* https://github.com/salesforce/BLIP
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'''
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import os
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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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from PIL import Image
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from ImageReward.models.BLIP.blip import load_checkpoint
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from ImageReward.models.BLIP.blip_pretrain import BLIP_Pretrain
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from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
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try:
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from torchvision.transforms import InterpolationMode
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BICUBIC = InterpolationMode.BICUBIC
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except ImportError:
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BICUBIC = Image.BICUBIC
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def _convert_image_to_rgb(image):
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return image.convert("RGB")
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def _transform(n_px):
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return Compose([
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Resize(n_px, interpolation=BICUBIC),
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CenterCrop(n_px),
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_convert_image_to_rgb,
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ToTensor(),
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Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
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])
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class BLIPScore(nn.Module):
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def __init__(self, med_config, device='cpu'):
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super().__init__()
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self.device = device
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self.preprocess = _transform(224)
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self.blip = BLIP_Pretrain(image_size=224, vit='large', med_config=med_config)
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def score(self, prompt, image_path):
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if (type(image_path).__name__=='list'):
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_, rewards = self.inference_rank(prompt, image_path)
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return rewards
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# text encode
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text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
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text_output = self.blip.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
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txt_feature = F.normalize(self.blip.text_proj(text_output.last_hidden_state[:,0,:]))
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# image encode
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pil_image = Image.open(image_path)
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image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
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image_embeds = self.blip.visual_encoder(image)
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image_features = F.normalize(self.blip.vision_proj(image_embeds[:,0,:]), dim=-1)
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# score
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rewards = torch.sum(torch.mul(txt_feature, image_features), dim=1, keepdim=True)
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return rewards.detach().cpu().numpy().item()
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def inference_rank(self, prompt, generations_list):
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text_input = self.blip.tokenizer(prompt, padding='max_length', truncation=True, max_length=35, return_tensors="pt").to(self.device)
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text_output = self.blip.text_encoder(text_input.input_ids, attention_mask = text_input.attention_mask, mode='text')
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txt_feature = F.normalize(self.blip.text_proj(text_output.last_hidden_state[:,0,:]))
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txt_set = []
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img_set = []
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for generations in generations_list:
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# image encode
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img_path = generations
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pil_image = Image.open(img_path)
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image = self.preprocess(pil_image).unsqueeze(0).to(self.device)
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image_embeds = self.blip.visual_encoder(image)
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image_features = F.normalize(self.blip.vision_proj(image_embeds[:,0,:]), dim=-1)
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img_set.append(image_features)
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txt_set.append(txt_feature)
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txt_features = torch.cat(txt_set, 0).float() # [image_num, feature_dim]
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img_features = torch.cat(img_set, 0).float() # [image_num, feature_dim]
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rewards = torch.sum(torch.mul(txt_features, img_features), dim=1, keepdim=True)
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rewards = torch.squeeze(rewards)
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_, rank = torch.sort(rewards, dim=0, descending=True)
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_, indices = torch.sort(rank, dim=0)
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indices = indices + 1
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return indices.detach().cpu().numpy().tolist(), rewards.detach().cpu().numpy().tolist() |