| | 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) |
| |
|
| | |
| | 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() |
