| 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 |
|
|
| def initialize_model(): |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model_dict = {} |
| model, preprocess_train, preprocess_val = create_model_and_transforms( |
| 'ViT-H-14', |
| '/mnt/dolphinfs/ssd_pool/docker/user/hadoop-videogen-hl/hadoop-camera3d/zhangshengjun/checkpoints/G2RPO/ckpt/CLIP-ViT-H-14-laion2B-s32B-b79K/pytorch_model.bin', |
| 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 |
| 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'] |
|
|
| cp = "/mnt/dolphinfs/ssd_pool/docker/user/hadoop-videogen-hl/hadoop-camera3d/zhangshengjun/checkpoints/G2RPO/ckpt/hps/HPS_v2.1_compressed.pt" |
| checkpoint = torch.load(cp, map_location=device) |
| model.load_state_dict(checkpoint['state_dict']) |
| 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) |
| outputs = reward_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).item() |
| eval_rewards.append(hps_score) |
|
|
| avg_reward = sum(eval_rewards) / len(eval_rewards) if eval_rewards else 0 |
| print(f"Average HPS score: {avg_reward:.4f}") |
|
|
| if __name__ == "__main__": |
| main() |
