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