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"""Batch ReconViaGen driver: load the pipeline ONCE, process an object list.
Usage:
python batch_reconviagen.py --selection SEL.json --inputs DIR --out OUTDIR --views 1|2 [--seed 42]
SEL.json: {"selections": [{"object": NAME, ...}, ...]}
Images: DIR/<object>_front.png (+ DIR/<object>_side.png when --views 2) RGBA crops.
Output: OUTDIR/<object>.glb -- byte-for-byte the same pipeline/settings as
models/ReconViaGen/run_infer.sh -> ReconViaGen/infer.py (seed 42,
multidiffusion, ss_steps=25, slat_steps=12, simplify=0.95, tex=1024).
Idempotent: existing OUTDIR/<object>.glb is skipped. Per-object try/except.
The script re-execs itself inside the reconviagen conda env (same env as
run_infer.sh) if it is not already running there, so it can be launched with
any python.
"""
import os
import sys
REPO = "/lp-dev/jonghoon/mv-mesh/ReconViaGen"
ENV = "/lp-dev/jonghoon/mv-mesh/envs/reconviagen"
ENV_PY = os.path.join(ENV, "bin", "python")
HF = "/lp-dev/jonghoon/mv-mesh/hf_cache"
# ---- env identical to run_infer.sh -----------------------------------------
_ENV_VARS = {
"PYTHONUNBUFFERED": "1",
"OMP_NUM_THREADS": "4",
"MKL_NUM_THREADS": "4",
"SPCONV_ALGO": "native",
"ATTN_BACKEND": "flash-attn",
# a python parent that already imported numpy/MKL leaks
# MKL_THREADING_LAYER=INTEL which clashes with libgomp -> force GNU
"MKL_THREADING_LAYER": "GNU",
"MKL_SERVICE_FORCE_INTEL": "0",
"HF_HOME": HF,
"HUGGINGFACE_HUB_CACHE": HF,
"HF_HUB_CACHE": HF,
"TORCH_HOME": os.path.join(HF, "torch"),
}
def _reexec_in_env():
"""Re-exec this script with the reconviagen env's python + env vars."""
env = dict(os.environ)
for k, v in _ENV_VARS.items():
env[k] = v # these are fixed by run_infer.sh, not user-overridable
# OMP/MKL thread caps: honour an explicit non-empty user setting
for k in ("OMP_NUM_THREADS", "MKL_NUM_THREADS"):
if os.environ.get(k):
env[k] = os.environ[k]
# conda activate equivalent (prefix env)
env["CONDA_PREFIX"] = ENV
env["PATH"] = os.path.join(ENV, "bin") + os.pathsep + env.get("PATH", "")
# CUDA_VISIBLE_DEVICES: respect if set, else pick the GPU with most free mem
if not env.get("CUDA_VISIBLE_DEVICES"):
try:
import subprocess
out = subprocess.check_output(
["nvidia-smi", "--query-gpu=index,memory.free",
"--format=csv,noheader,nounits"], text=True)
best = max((l.split(",") for l in out.strip().splitlines()),
key=lambda r: int(r[1]))
env["CUDA_VISIBLE_DEVICES"] = best[0].strip()
except Exception as e:
print(f"[batch] (non-fatal) GPU autopick failed: {e}", flush=True)
env["_RVG_BATCH_INENV"] = "1"
print(f"[batch] re-exec in {ENV_PY} "
f"(CUDA_VISIBLE_DEVICES={env.get('CUDA_VISIBLE_DEVICES')})", flush=True)
os.execve(ENV_PY, [ENV_PY, os.path.abspath(__file__)] + sys.argv[1:], env)
if not os.environ.get("_RVG_BATCH_INENV"):
if not os.path.isfile(ENV_PY):
sys.exit(f"ERROR: env python not found: {ENV_PY}")
_reexec_in_env()
# ---------------------------------------------------------------------------
for _k, _v in _ENV_VARS.items():
os.environ.setdefault(_k, _v)
import argparse
import json
import time
import traceback
os.chdir(REPO) # trellis does sys.path.append("wheels/vggt") relative to CWD
sys.path.insert(0, REPO)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--selection", required=True)
ap.add_argument("--inputs", required=True)
ap.add_argument("--out", required=True)
ap.add_argument("--views", type=int, required=True, choices=[1, 2, 4, 8])
ap.add_argument("--seed", type=int, default=42)
ap.add_argument("--limit", type=int, default=None,
help="only the first N objects (debug)")
# inference settings -- defaults match infer.py exactly; do not change
ap.add_argument("--mode", default="multidiffusion",
choices=["multidiffusion", "stochastic"])
ap.add_argument("--ss-steps", type=int, default=25)
ap.add_argument("--slat-steps", type=int, default=12)
ap.add_argument("--simplify", type=float, default=0.95)
ap.add_argument("--texture-size", type=int, default=1024)
ap.add_argument("--shard", type=int, default=0)
ap.add_argument("--nshards", type=int, default=1)
ap.add_argument("--reverse", action="store_true",
help="process objects in reverse order (for a convergent "
"co-located helper against a forward-order run; both "
"skip-existing + atomic-write, so they safely meet in "
"the middle)")
args = ap.parse_args()
with open(args.selection) as f:
sel = json.load(f)["selections"]
objects = [s["object"] for s in sel]
if args.limit:
objects = objects[: args.limit]
if args.nshards > 1:
objects = objects[args.shard::args.nshards]
if args.reverse:
objects = objects[::-1]
outdir = os.path.abspath(args.out)
os.makedirs(outdir, exist_ok=True)
# ---- resolve jobs up front -------------------------------------------
jobs = []
for name in objects:
out = os.path.join(outdir, f"{name}.glb")
_vtags = {1: ["front"], 2: ["front", "side"],
4: ["front", "side", "back", "oside"],
8: ["front", "side", "back", "oside", "top", "bottom", "top2", "bottom2"]}[args.views]
imgs = [os.path.join(args.inputs, f"{name}_{t}.png") for t in _vtags]
jobs.append((name, imgs, out))
todo = [j for j in jobs if not os.path.isfile(j[2])]
print(f"[batch] {len(jobs)} object(s), {len(jobs) - len(todo)} already done, "
f"{len(todo)} to run | views={args.views} seed={args.seed}", flush=True)
n_ok = n_fail = n_skip = 0
pipeline = None
t_load = None
import torch
from PIL import Image
from trellis.utils import postprocessing_utils
for i, (name, imgs, out) in enumerate(jobs, 1):
t0 = time.time()
try:
if os.path.isfile(out):
n_skip += 1
print(f"[{i}/{len(jobs)}] {name} SKIP (exists) {out}", flush=True)
continue
for p in imgs:
if not os.path.isfile(p):
raise FileNotFoundError(p)
if pipeline is None:
from infer import load_pipeline # ReconViaGen/infer.py
tl = time.time()
pipeline = load_pipeline()
t_load = time.time() - tl
print(f"[batch] pipeline ready in {t_load:.1f}s", flush=True)
t0 = time.time() # don't bill the load to this object
inputs_dir = os.path.join(outdir, "inputs")
os.makedirs(inputs_dir, exist_ok=True)
images = []
for p in imgs:
fed = pipeline.preprocess_image(Image.open(p))
images.append(fed)
stem = os.path.splitext(os.path.basename(p))[0]
fed.save(os.path.join(inputs_dir, f"FED_{stem}.png"))
outputs, _, _ = pipeline.run(
image=images,
seed=args.seed,
formats=["gaussian", "mesh"],
preprocess_image=False,
sparse_structure_sampler_params={
"steps": args.ss_steps, "cfg_strength": 7.5,
"cfg_interval": [0.6, 1.0], "guidance_rescale": 0.7,
"rescale_t": 5.0,
},
slat_sampler_params={
"steps": args.slat_steps, "cfg_strength": 7.5,
"cfg_interval": [0.6, 1.0], "guidance_rescale": 0.5,
"rescale_t": 3.0,
},
mode=args.mode,
)
torch.cuda.empty_cache()
glb = postprocessing_utils.to_glb(
outputs["gaussian"][0], outputs["mesh"][0],
simplify=args.simplify, texture_size=args.texture_size,
verbose=False,
)
tmp = out + ".tmp.glb"
glb.export(tmp)
if not os.path.isfile(tmp):
raise RuntimeError("export produced no file")
os.replace(tmp, out)
del outputs, glb, images
n_ok += 1
print(f"[{i}/{len(jobs)}] {name} OK {time.time() - t0:.1f}s -> {out}",
flush=True)
except Exception:
n_fail += 1
traceback.print_exc()
print(f"[{i}/{len(jobs)}] {name} FAIL {time.time() - t0:.1f}s", flush=True)
finally:
try:
torch.cuda.empty_cache()
except Exception:
pass
print(f"BATCH DONE ok={n_ok} fail={n_fail} skip={n_skip} total={len(jobs)} "
f"load={'%.1fs' % t_load if t_load else 'n/a'}", flush=True)
return 1 if n_fail else 0
if __name__ == "__main__":
sys.exit(main())
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