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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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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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model, preprocess_train, preprocess_val = create_model_and_transforms( |
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'ViT-H-14', |
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'/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', |
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precision='amp', |
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device=device, |
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jit=False, |
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force_quick_gelu=False, |
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force_custom_text=False, |
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force_patch_dropout=False, |
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force_image_size=None, |
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pretrained_image=False, |
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image_mean=None, |
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image_std=None, |
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light_augmentation=True, |
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aug_cfg={}, |
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output_dict=True, |
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with_score_predictor=False, |
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with_region_predictor=False |
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) |
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model_dict['model'] = model |
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model_dict['preprocess_val'] = preprocess_val |
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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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cp = "/mnt/dolphinfs/ssd_pool/docker/user/hadoop-videogen-hl/hadoop-camera3d/zhangshengjun/checkpoints/G2RPO/ckpt/hps/HPS_v2.1_compressed.pt" |
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checkpoint = torch.load(cp, map_location=device) |
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model.load_state_dict(checkpoint['state_dict']) |
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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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outputs = reward_model(image, text) |
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image_features, text_features = outputs["image_features"], outputs["text_features"] |
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logits_per_image = image_features @ text_features.T |
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hps_score = torch.diagonal(logits_per_image).item() |
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eval_rewards.append(hps_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 HPS score: {avg_reward:.4f}") |
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if __name__ == "__main__": |
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main() |
