mirror of
https://github.com/microsoft/TRELLIS.2.git
synced 2026-04-02 02:27:08 -04:00
182 lines
8.6 KiB
Python
182 lines
8.6 KiB
Python
import os
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import sys
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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import json
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import argparse
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import torch
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import numpy as np
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import pandas as pd
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import o_voxel
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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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from queue import Queue
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import trellis2.models as models
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import trellis2.modules.sparse as sp
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torch.set_grad_enabled(False)
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def is_valid_sparse_tensor(tensor):
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return torch.isfinite(tensor.feats).all() and torch.isfinite(tensor.coords).all()
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def clear_cuda_error():
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torch.cuda.synchronize()
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torch.cuda.empty_cache()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--root', type=str, required=True,
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help='Directory to save the metadata')
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parser.add_argument('--pbr_voxel_root', type=str, default=None,
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help='Directory to save the pbr voxel files')
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parser.add_argument('--pbr_latent_root', type=str, default=None,
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help='Directory to save the pbr latent files')
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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('--resolution', type=int, default=1024,
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help='Sparse voxel resolution')
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parser.add_argument('--enc_pretrained', type=str, default='microsoft/TRELLIS.2-4B/ckpts/tex_enc_next_dc_f16c32_fp16',
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help='Pretrained encoder model')
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parser.add_argument('--model_root', type=str,
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help='Root directory of models')
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parser.add_argument('--enc_model', type=str,
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help='Encoder model. if specified, use this model instead of pretrained model')
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parser.add_argument('--ckpt', type=str,
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help='Checkpoint to load')
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parser.add_argument('--instances', type=str, default=None,
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help='Instances to process')
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parser.add_argument('--rank', type=int, default=0)
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parser.add_argument('--world_size', type=int, default=1)
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opt = parser.parse_args()
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opt = edict(vars(opt))
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opt.pbr_voxel_root = opt.pbr_voxel_root or opt.root
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opt.pbr_latent_root = opt.pbr_latent_root or opt.root
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if opt.enc_model is None:
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latent_name = f'{opt.enc_pretrained.split("/")[-1]}_{opt.resolution}'
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encoder = models.from_pretrained(opt.enc_pretrained).eval().cuda()
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else:
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latent_name = f'{opt.enc_model.split("/")[-1]}_{opt.ckpt}_{opt.resolution}'
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cfg = edict(json.load(open(os.path.join(opt.model_root, opt.enc_model, 'config.json'), 'r')))
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encoder = getattr(models, cfg.models.encoder.name)(**cfg.models.encoder.args).cuda()
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ckpt_path = os.path.join(opt.model_root, opt.enc_model, 'ckpts', f'encoder_{opt.ckpt}.pt')
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encoder.load_state_dict(torch.load(ckpt_path), strict=False)
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encoder.eval()
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print(f'Loaded model from {ckpt_path}')
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os.makedirs(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, 'new_records'), exist_ok=True)
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# get file list
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if not os.path.exists(os.path.join(opt.root, 'metadata.csv')):
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raise ValueError('metadata.csv not found')
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metadata = pd.read_csv(os.path.join(opt.root, 'metadata.csv')).set_index('sha256')
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if os.path.exists(os.path.join(opt.root, 'aesthetic_scores', 'metadata.csv')):
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metadata = metadata.combine_first(pd.read_csv(os.path.join(opt.root, 'aesthetic_scores','metadata.csv')).set_index('sha256'))
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if os.path.exists(os.path.join(opt.pbr_voxel_root, f'pbr_voxels_{opt.resolution}', 'metadata.csv')):
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metadata = metadata.combine_first(pd.read_csv(os.path.join(opt.pbr_voxel_root, f'pbr_voxels_{opt.resolution}','metadata.csv')).set_index('sha256'))
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if os.path.exists(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, 'metadata.csv')):
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metadata = metadata.combine_first(pd.read_csv(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name,'metadata.csv')).set_index('sha256'))
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metadata = metadata.reset_index()
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if opt.instances is None:
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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['pbr_voxelized'] == True]
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if 'pbr_latent_encoded' in metadata.columns:
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metadata = metadata[metadata['pbr_latent_encoded'] != True]
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else:
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if os.path.exists(opt.instances):
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with open(opt.instances, 'r') as f:
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instances = f.read().splitlines()
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else:
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instances = opt.instances.split(',')
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metadata = metadata[metadata['sha256'].isin(instances)]
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start = len(metadata) * opt.rank // opt.world_size
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end = len(metadata) * (opt.rank + 1) // opt.world_size
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metadata = metadata[start:end]
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records = []
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# filter out objects that are already processed
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with ThreadPoolExecutor(max_workers=os.cpu_count()) as executor, \
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tqdm(total=len(metadata), desc="Filtering existing objects") as pbar:
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def check_sha256(sha256):
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if os.path.exists(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, f'{sha256}.npz')):
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coords = np.load(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, f'{sha256}.npz'))['coords']
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records.append({'sha256': sha256, 'pbr_latent_encoded': True, 'pbr_latent_tokens': coords.shape[0]})
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pbar.update()
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executor.map(check_sha256, metadata['sha256'].values)
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executor.shutdown(wait=True)
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existing_sha256 = set(r['sha256'] for r in records)
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print(f'Found {len(existing_sha256)} processed objects')
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metadata = metadata[~metadata['sha256'].isin(existing_sha256)]
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print(f'Processing {len(metadata)} objects...')
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sha256s = list(metadata['sha256'].values)
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load_queue = Queue(maxsize=32)
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with ThreadPoolExecutor(max_workers=32) as loader_executor, \
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ThreadPoolExecutor(max_workers=32) as saver_executor:
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def loader(sha256):
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try:
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attrs = ['base_color', 'metallic', 'roughness', 'alpha']
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coords, attr = o_voxel.io.read_vxz(
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os.path.join(opt.pbr_voxel_root, f'pbr_voxels_{opt.resolution}', f'{sha256}.vxz'),
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num_threads=4
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)
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feats = torch.concat([attr[k] for k in attrs], dim=-1) / 255.0 * 2 - 1
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x = sp.SparseTensor(
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feats.float(),
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torch.cat([torch.zeros_like(coords[:, 0:1]), coords], dim=-1),
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)
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load_queue.put((sha256, x))
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except Exception as e:
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print(f"[Loader Error] {sha256}: {e}")
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load_queue.put((sha256, None))
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loader_executor.map(loader, sha256s)
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def saver(sha256, pack):
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save_path = os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, f'{sha256}.npz')
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np.savez_compressed(save_path, **pack)
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records.append({'sha256': sha256, 'pbr_latent_encoded': True, 'pbr_latent_tokens': pack['coords'].shape[0]})
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for _ in tqdm(range(len(sha256s)), desc="Extracting latents"):
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try:
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sha256, voxels = load_queue.get()
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if voxels is None:
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print(f"[Skip] {sha256}: Failed to load input")
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continue
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num_voxels = voxels.feats.shape[0]
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# NaN/Inf
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if not (is_valid_sparse_tensor(voxels)):
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print(f"[Skip] {sha256}: NaN/Inf in input")
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continue
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z = encoder(voxels.cuda())
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torch.cuda.synchronize()
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if not torch.isfinite(z.feats).all():
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print(f"[Skip] {sha256}: Non-finite latent in z.feats")
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clear_cuda_error()
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continue
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pack = {
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'feats': z.feats.cpu().numpy().astype(np.float32),
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'coords': z.coords[:, 1:].cpu().numpy().astype(np.uint8),
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}
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saver_executor.submit(saver, sha256, pack)
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except Exception as e:
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print(f"[Error] {sha256} ({num_voxels} voxels): {e}")
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clear_cuda_error()
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continue
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saver_executor.shutdown(wait=True)
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records = pd.DataFrame.from_records(records)
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records.to_csv(os.path.join(opt.pbr_latent_root, 'pbr_latents', latent_name, 'new_records', f'part_{opt.rank}.csv'), index=False)
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