benchmark/metrics/hps.py

import os
from typing import Dict

import torch
from PIL import Image
from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
import huggingface_hub
from hpsv2.utils import root_path, hps_version_map


class HPSMetric:
    def __init__(self):
        self.hps_version = "v2.1"
        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
        self.model_dict = {}
        self._initialize_model()
    
    def _initialize_model(self):
        if not self.model_dict:
            model, preprocess_train, preprocess_val = create_model_and_transforms(
                'ViT-H-14',
                'laion2B-s32B-b79K',
                precision='amp',
                device=self.device,
                jit=False,
                force_quick_gelu=False,
                force_custom_text=False,
                force_patch_dropout=False,
                force_image_size=None,
                pretrained_image=False,
                image_mean=None,
                image_std=None,
                light_augmentation=True,
                aug_cfg={},
                output_dict=True,
                with_score_predictor=False,
                with_region_predictor=False
            )
            self.model_dict['model'] = model
            self.model_dict['preprocess_val'] = preprocess_val
            
            # Load checkpoint
            if not os.path.exists(root_path):
                os.makedirs(root_path)
            cp = huggingface_hub.hf_hub_download("xswu/HPSv2", hps_version_map[self.hps_version])
            
            checkpoint = torch.load(cp, map_location=self.device)
            model.load_state_dict(checkpoint['state_dict'])
            self.tokenizer = get_tokenizer('ViT-H-14')
            model = model.to(self.device)
            model.eval()
    
    @property
    def name(self) -> str:
        return "hps"
    
    def compute_score(
        self,
        image: Image.Image,
        prompt: str,
    ) -> Dict[str, float]:
        model = self.model_dict['model']
        preprocess_val = self.model_dict['preprocess_val']
        
        with torch.no_grad():
            # Process the image
            image_tensor = preprocess_val(image).unsqueeze(0).to(device=self.device, non_blocking=True)
            # Process the prompt
            text = self.tokenizer([prompt]).to(device=self.device, non_blocking=True)
            # Calculate the HPS
            with torch.cuda.amp.autocast():
                outputs = model(image_tensor, text)
                image_features, text_features = outputs["image_features"], outputs["text_features"]
                logits_per_image = image_features @ text_features.T
                hps_score = torch.diagonal(logits_per_image).cpu().numpy()
        
        return {"hps": float(hps_score[0])}