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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
from transformers import AutoProcessor, AutoModel
def initialize_model():
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_dict = {}
process_path = "ckpt/CLIP-ViT-H-14-laion2B-s32B-b79K"
# download from https://huggingface.co/yuvalkirstain/PickScore_v1
model_path = "ckpt/PickScore_v1"
processor = AutoProcessor.from_pretrained(process_path)
reward_model = AutoModel.from_pretrained(model_path)
reward_model.to(device).eval()
model_dict['model'] = reward_model
model_dict['preprocess_val'] = processor
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_inputs = preprocess_val(
images=[image_pil],
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(device)
prompt = os.path.splitext(filename)[0] # 剔除文件扩展名
text_inputs = preprocess_val(
text=prompt,
padding=True,
truncation=True,
max_length=77,
return_tensors="pt",
).to(device)
# Get embeddings
image_embs = reward_model.get_image_features(**image_inputs)
image_embs = image_embs / torch.norm(image_embs, dim=-1, keepdim=True)
text_embs = reward_model.get_text_features(**text_inputs)
text_embs = text_embs / torch.norm(text_embs, dim=-1, keepdim=True)
# Calculate scores
score = reward_model.logit_scale.exp() * (text_embs @ image_embs.T)[0]
eval_rewards.append(score.item())
avg_reward = sum(eval_rewards) / len(eval_rewards) if eval_rewards else 0
print(f"Average pickscore score: {avg_reward:.4f}")
if __name__ == "__main__":
main() |