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from hpsv2.src.open_clip import create_model_and_transforms, get_tokenizer
import torch
from torchvision import transforms
from PIL import Image
import os
from tqdm import tqdm
from torch.nn import functional as F
from open_clip import create_model_from_pretrained, get_tokenizer
def initialize_model():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_dict = {}
processor = get_tokenizer('ViT-H-14')
reward_model, preprocess_dgn5b = create_model_from_pretrained(
'local-dir:ckpt/clip_score')
reward_model.to(device).eval()
model_dict['model'] = reward_model
model_dict['preprocess_val'] = preprocess_dgn5b
return model_dict, device
def load_images_from_folder(folder):
images = []
filenames = []
for filename in os.listdir(folder):
if filename.endswith(".png"):
img_path = os.path.join(folder, filename)
image = Image.open(img_path).convert("RGB")
images.append(image)
filenames.append(filename)
return images, filenames
def main():
model_dict, device = initialize_model()
model = model_dict['model']
preprocess_val = model_dict['preprocess_val']
tokenizer = get_tokenizer('ViT-H-14')
reward_model = model.to(device)
reward_model.eval()
img_folder = "IMAGE_SAVE_FOLDER"
images, filenames = load_images_from_folder(img_folder)
eval_rewards = []
with torch.no_grad():
for image_pil, filename in tqdm(zip(images, filenames), total=400):
image = preprocess_val(image_pil).unsqueeze(0).to(device=device, non_blocking=True)
prompt = os.path.splitext(filename)[0]
text = tokenizer([prompt]).to(device=device, non_blocking=True)
## get score
clip_image_features = reward_model.encode_image(image)
clip_text_features = reward_model.encode_text(text)
clip_image_features = F.normalize(clip_image_features, dim=-1)
clip_text_features = F.normalize(clip_text_features, dim=-1)
clip_score = (clip_image_features @ clip_text_features.T)[0]
clip_score = clip_score.item()
eval_rewards.append(clip_score)
avg_reward = sum(eval_rewards) / len(eval_rewards) if eval_rewards else 0
print(f"Average CLIP score: {avg_reward:.4f}")
if __name__ == "__main__":
main() |