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import os |
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import json |
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import argparse |
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import numpy as np |
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import pandas as pd |
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from tqdm import tqdm |
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from easydict import EasyDict as edict |
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from concurrent.futures import ThreadPoolExecutor |
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if __name__ == '__main__': |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--output_dir', type=str, required=True, |
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help='Directory to save the metadata') |
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parser.add_argument('--filter_low_aesthetic_score', type=float, default=None, |
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help='Filter objects with aesthetic score lower than this value') |
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parser.add_argument('--model', type=str, default='dinov2_vitl14_reg_slat_enc_swin8_B_64l8_fp16', |
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help='Latent model to use') |
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parser.add_argument('--num_samples', type=int, default=50000, |
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help='Number of samples to use for calculating stats') |
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opt = parser.parse_args() |
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opt = edict(vars(opt)) |
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if os.path.exists(os.path.join(opt.output_dir, 'metadata.csv')): |
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metadata = pd.read_csv(os.path.join(opt.output_dir, 'metadata.csv')) |
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else: |
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raise ValueError('metadata.csv not found') |
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if opt.filter_low_aesthetic_score is not None: |
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metadata = metadata[metadata['aesthetic_score'] >= opt.filter_low_aesthetic_score] |
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metadata = metadata[metadata[f'latent_{opt.model}'] == True] |
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sha256s = metadata['sha256'].values |
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sha256s = np.random.choice(sha256s, min(opt.num_samples, len(sha256s)), replace=False) |
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means = [] |
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mean2s = [] |
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with ThreadPoolExecutor(max_workers=16) as executor, \ |
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tqdm(total=len(sha256s), desc="Extracting features") as pbar: |
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def worker(sha256): |
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try: |
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feats = np.load(os.path.join(opt.output_dir, 'latents', opt.model, f'{sha256}.npz')) |
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feats = feats['feats'] |
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means.append(feats.mean(axis=0)) |
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mean2s.append((feats ** 2).mean(axis=0)) |
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pbar.update() |
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except Exception as e: |
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print(f"Error extracting features for {sha256}: {e}") |
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pbar.update() |
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executor.map(worker, sha256s) |
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executor.shutdown(wait=True) |
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mean = np.array(means).mean(axis=0) |
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mean2 = np.array(mean2s).mean(axis=0) |
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std = np.sqrt(mean2 - mean ** 2) |
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print('mean:', mean) |
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print('std:', std) |
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with open(os.path.join(opt.output_dir, 'latents', opt.model, 'stats.json'), 'w') as f: |
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json.dump({ |
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'mean': mean.tolist(), |
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'std': std.tolist(), |
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}, f, indent=4) |
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