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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() |