Reproduction of benchmark

[irsLu]

Hi~, HPSv2 is really nice work. But when I reproduce the v2.1 benchmark, I can not get the same results reported in your readme. Could you tell me how to fix it please? These are my codes of jupyter notebook:

import torch
from PIL import Image
import hpsv2
from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
import warnings
import argparse
import os
import requests
from clint.textui import progress
from typing import Union
import huggingface_hub
from hpsv2.utils import root_path, hps_version_map

#warnings.filterwarnings("ignore", category=UserWarning)

def score(model, img_path, prompt) -> list:    
    if isinstance(img_path, list):
        result = []
        for one_img_path in img_path:
            # Load your image and prompt
            with torch.no_grad():
                # Process the image
                if isinstance(one_img_path, str):
                    image = preprocess_val(Image.open(one_img_path)).unsqueeze(0).to(device=device, non_blocking=True)
                elif isinstance(one_img_path, Image.Image):
                    image = preprocess_val(one_img_path).unsqueeze(0).to(device=device, non_blocking=True)
                else:
                    raise TypeError('The type of parameter img_path is illegal.')
                # Process the prompt
                text = tokenizer([prompt]).to(device=device, non_blocking=True)
                # Calculate the HPS
                with torch.cuda.amp.autocast():
                    outputs = model(image, 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()
            result.append(hps_score[0])    
        return result
    elif isinstance(img_path, str):
        # Load your image and prompt
        with torch.no_grad():
            # Process the image
            image = preprocess_val(Image.open(img_path)).unsqueeze(0).to(device=device, non_blocking=True)
            # Process the prompt
            text = tokenizer([prompt]).to(device=device, non_blocking=True)
            # Calculate the HPS
            with torch.cuda.amp.autocast():
                outputs = model(image, 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_score[0]]
    elif isinstance(img_path, Image.Image):
        # Load your image and prompt
        with torch.no_grad():
            # Process the image
            image = preprocess_val(img_path).unsqueeze(0).to(device=device, non_blocking=True)
            # Process the prompt
            text = tokenizer([prompt]).to(device=device, non_blocking=True)
            # Calculate the HPS
            with torch.cuda.amp.autocast():
                outputs = model(image, 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_score[0]]
    else:
        raise TypeError('The type of parameter img_path is illegal.')
        

For easily running with every image once, I split the original codes

model_dict = {}
device = 'cuda' if torch.cuda.is_available() else 'cpu'

    
model, preprocess_train, preprocess_val = create_model_and_transforms(
    'ViT-H-14',
    'laion2B-s32B-b79K',
    precision='amp',
    device=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
)
model_dict['model'] = model
model_dict['preprocess_val'] = preprocess_val



checkpoint = os.path.join(root_path,'HPS_v2_compressed.pt')
cp = None
hps_version = "v2.1"

model = model_dict['model']
preprocess_val = model_dict['preprocess_val']

# check if the checkpoint exists
if not os.path.exists(root_path):
    os.makedirs(root_path)
if cp is None:
    cp = huggingface_hub.hf_hub_download("xswu/HPSv2", hps_version_map[hps_version])

checkpoint = torch.load(cp, map_location=device)
model.load_state_dict(checkpoint['state_dict'])
tokenizer = get_tokenizer('ViT-H-14')
model = model.to(device)
model.eval()

Then I download the test data and reproduce the results of each categories(for example, photo).

from numpy import *
prompts = ["A man taking a drink from a water fountain.", ...]
root = '/my_path/HPDv2/SDXL-refiner-0.9/photo'
imgs = os.listdir(root)
imgs.sort()
ret = []
for i,n in enumerate(imgs):
    print(n, prompts[i])
    s = score(model, os.path.join(root, n), prompts[i])
    ret.append(s)
    
print(mean(ret))

And I get (31.52 v.s. 33.26) for anime, (26.51 v.s. 28.38 ) for photo.

[irsLu]

And this the running result of anime