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dataset_toolkits/encode_ss_latent.py

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+import os
+import sys
+sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
+import copy
+import json
+import argparse
+import torch
+import numpy as np
+import pandas as pd
+import utils3d
+from tqdm import tqdm
+from easydict import EasyDict as edict
+from concurrent.futures import ThreadPoolExecutor
+from queue import Queue
+
+import trellis.models as models
+
+
+torch.set_grad_enabled(False)
+
+
+def get_voxels(instance):
+    position = utils3d.io.read_ply(os.path.join(opt.output_dir, 'voxels', f'{instance}.ply'))[0]
+    coords = ((torch.tensor(position) + 0.5) * opt.resolution).int().contiguous()
+    ss = torch.zeros(1, opt.resolution, opt.resolution, opt.resolution, dtype=torch.long)
+    ss[:, coords[:, 0], coords[:, 1], coords[:, 2]] = 1
+    return ss
+
+
+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('--enc_pretrained', type=str, default='microsoft/TRELLIS-image-large/ckpts/ss_enc_conv3d_16l8_fp16',
+                        help='Pretrained encoder model')
+    parser.add_argument('--model_root', type=str, default='results',
+                        help='Root directory of models')
+    parser.add_argument('--enc_model', type=str, default=None,
+                        help='Encoder model. if specified, use this model instead of pretrained model')
+    parser.add_argument('--ckpt', type=str, default=None,
+                        help='Checkpoint to load')
+    parser.add_argument('--resolution', type=int, default=64,
+                        help='Resolution')
+    parser.add_argument('--instances', type=str, default=None,
+                        help='Instances to process')
+    parser.add_argument('--rank', type=int, default=0)
+    parser.add_argument('--world_size', type=int, default=1)
+    opt = parser.parse_args()
+    opt = edict(vars(opt))
+
+    if opt.enc_model is None:
+        latent_name = f'{opt.enc_pretrained.split("/")[-1]}'
+        encoder = models.from_pretrained(opt.enc_pretrained).eval().cuda()
+    else:
+        latent_name = f'{opt.enc_model}_{opt.ckpt}'
+        cfg = edict(json.load(open(os.path.join(opt.model_root, opt.enc_model, 'config.json'), 'r')))
+        encoder = getattr(models, cfg.models.encoder.name)(**cfg.models.encoder.args).cuda()
+        ckpt_path = os.path.join(opt.model_root, opt.enc_model, 'ckpts', f'encoder_{opt.ckpt}.pt')
+        encoder.load_state_dict(torch.load(ckpt_path), strict=False)
+        encoder.eval()
+        print(f'Loaded model from {ckpt_path}')
+    
+    os.makedirs(os.path.join(opt.output_dir, 'ss_latents', latent_name), exist_ok=True)
+
+    # 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.instances is not None:
+        with open(opt.instances, 'r') as f:
+            instances = f.read().splitlines()
+        metadata = metadata[metadata['sha256'].isin(instances)]
+    else:
+        if opt.filter_low_aesthetic_score is not None:
+            metadata = metadata[metadata['aesthetic_score'] >= opt.filter_low_aesthetic_score]
+        metadata = metadata[metadata['voxelized'] == True]
+        if f'ss_latent_{latent_name}' in metadata.columns:
+            metadata = metadata[metadata[f'ss_latent_{latent_name}'] == False]
+
+    start = len(metadata) * opt.rank // opt.world_size
+    end = len(metadata) * (opt.rank + 1) // opt.world_size
+    metadata = metadata[start:end]
+    records = []
+    
+    # filter out objects that are already processed
+    sha256s = list(metadata['sha256'].values)
+    for sha256 in copy.copy(sha256s):
+        if os.path.exists(os.path.join(opt.output_dir, 'ss_latents', latent_name, f'{sha256}.npz')):
+            records.append({'sha256': sha256, f'ss_latent_{latent_name}': True})
+            sha256s.remove(sha256)
+
+    # encode latents
+    load_queue = Queue(maxsize=4)
+    try:
+        with ThreadPoolExecutor(max_workers=32) as loader_executor, \
+            ThreadPoolExecutor(max_workers=32) as saver_executor:
+            def loader(sha256):
+                try:
+                    ss = get_voxels(sha256)[None].float()
+                    load_queue.put((sha256, ss))
+                except Exception as e:
+                    print(f"Error loading features for {sha256}: {e}")
+            loader_executor.map(loader, sha256s)
+            
+            def saver(sha256, pack):
+                save_path = os.path.join(opt.output_dir, 'ss_latents', latent_name, f'{sha256}.npz')
+                np.savez_compressed(save_path, **pack)
+                records.append({'sha256': sha256, f'ss_latent_{latent_name}': True})
+                
+            for _ in tqdm(range(len(sha256s)), desc="Extracting latents"):
+                sha256, ss = load_queue.get()
+                ss = ss.cuda().float()
+                latent = encoder(ss, sample_posterior=False)
+                assert torch.isfinite(latent).all(), "Non-finite latent"
+                pack = {
+                    'mean': latent[0].cpu().numpy(),
+                }
+                saver_executor.submit(saver, sha256, pack)
+                
+            saver_executor.shutdown(wait=True)
+    except:
+        print("Error happened during processing.")
+        
+    records = pd.DataFrame.from_records(records)
+    records.to_csv(os.path.join(opt.output_dir, f'ss_latent_{latent_name}_{opt.rank}.csv'), index=False)