benchmark/metrics/clip_iqa.py

from typing import Dict

import numpy as np
import torch
from PIL import Image
from torchmetrics.multimodal import CLIPImageQualityAssessment


class CLIPIQAMetric:
    def __init__(self):
        self.device = torch.device(
            "cuda"
            if torch.cuda.is_available()
            else "mps"
            if torch.backends.mps.is_available()
            else "cpu"
        )
        self.metric = CLIPImageQualityAssessment(
            model_name_or_path="clip_iqa", data_range=255.0, prompts=("quality",)
        )
        self.metric.to(self.device)

    @property
    def name(self) -> str:
        return "clip_iqa"

    def compute_score(self, image: Image.Image, prompt: str) -> Dict[str, float]:
        image_tensor = torch.from_numpy(np.array(image)).permute(2, 0, 1).float()
        image_tensor = image_tensor.unsqueeze(0)
        image_tensor = image_tensor.to(self.device)
        scores = self.metric(image_tensor)
        return {"clip_iqa": scores.item()}