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dataset_toolkits/stat_latent.py

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