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6199e7c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 | #!/usr/bin/env python3
"""Zero-shot / few-shot / optional tiny LoRA eval of a pretrained VLM.
Never uses a character transformer as a stand-in MLLM.
"""
from __future__ import annotations
import argparse
import json
import os
import random
import sys
from pathlib import Path
ROOT = Path("/root/workspace/projects/FitGround")
if not ROOT.exists():
ROOT = Path(__file__).resolve().parents[2]
OUT = ROOT / "artifacts" / "hero"
def dump(path: Path, payload) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2, default=str), encoding="utf-8")
PROMPT = """You are a technical designer assistant. Return ONLY JSON with keys:
predicted_contact_ratio (float), predicted_clearance_p10_cm (float),
best_correction (one of: no_edit, bust+2cm, bust+3cm, abstain),
confidence (0-1), reason (short string).
Do not copy answers from file names. Use the measurements and the drape image.
body_bust_cm={body_bust_cm:.2f}
garment_bust_cm={garment_bust_cm:.2f}
material={material}
fit_intent=regular
candidate_actions=no_edit|bust+2cm|bust+3cm|abstain
"""
def parse_json_blob(text: str) -> dict | None:
if not text:
return None
start = text.find("{")
end = text.rfind("}")
if start < 0 or end <= start:
return None
try:
return json.loads(text[start : end + 1])
except Exception:
return None
def load_cases():
path = OUT / "visual_disambiguation.json"
if not path.exists():
return []
payload = json.loads(path.read_text(encoding="utf-8"))
return payload.get("cases") or payload.get("rows") or []
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--max-cases", type=int, default=24)
parser.add_argument("--device", default="cuda")
args = parser.parse_args()
status_path = OUT / "mllm_status.json"
if not status_path.exists():
dump(OUT / "mllm_eval.json", {"status": "NOT_JUSTIFIED", "reason": "weights not on disk"})
print("no mllm_status.json")
return 0
status = json.loads(status_path.read_text(encoding="utf-8"))
if status.get("status") != "DOWNLOADED":
dump(OUT / "mllm_eval.json", {"status": "NOT_JUSTIFIED", "upstream": status})
print("model not downloaded")
return 0
name = status["model"]
os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
os.environ.setdefault("HF_HOME", "/root/workspace/hf-cache")
os.environ.setdefault("HF_HUB_OFFLINE", "1")
os.environ.setdefault("TRANSFORMERS_OFFLINE", "1")
import torch
from PIL import Image
print("loading", name, flush=True)
from transformers import AutoProcessor
processor = AutoProcessor.from_pretrained(name, trust_remote_code=True)
dtype = torch.float16 if args.device == "cuda" and torch.cuda.is_available() else torch.float32
model = None
load_err = None
for loader_name in ("AutoModelForImageTextToText", "AutoModelForVision2Seq", "Qwen2VLForConditionalGeneration"):
try:
cls = getattr(__import__("transformers", fromlist=[loader_name]), loader_name)
model = cls.from_pretrained(
name,
trust_remote_code=True,
torch_dtype=dtype,
device_map="cuda" if args.device == "cuda" and torch.cuda.is_available() else None,
)
print("loader", loader_name, flush=True)
break
except Exception as exc:
load_err = f"{loader_name}: {type(exc).__name__}: {exc}"
print("load_fail", load_err, flush=True)
if model is None:
dump(OUT / "mllm_eval.json", {"status": "NOT_JUSTIFIED", "error": load_err})
return 0
model.eval()
cases = [c for c in load_cases() if c.get("render_a") or c.get("render")]
random.Random(0).shuffle(cases)
cases = cases[: args.max_cases]
if not cases:
# fall back to physics_vd renders
vd = OUT / "physics_vd.json"
if vd.exists():
rows = json.loads(vd.read_text(encoding="utf-8"))
for r in rows:
renders = r.get("render_files") or []
if r.get("edit_name") == "now" and renders:
cases.append(
{
"case_id": r.get("tag"),
"body_bust_cm": r.get("body_bust_cm") or 0,
"garment_bust_cm": r.get("garment_bust_cm") or 0,
"material": r.get("material"),
"render_a": renders[0],
"best_correction": "bust+2cm" if (r.get("utility") is not None and r["utility"] < -0.2) else "no_edit",
"gold": True,
}
)
cases = cases[: args.max_cases]
results = []
n_parse = 0
n_match = 0
for i, case in enumerate(cases):
img_path = case.get("render_a") or case.get("render") or case.get("render_b")
if not img_path or not Path(img_path).exists():
continue
prompt = PROMPT.format(
body_bust_cm=float(case.get("body_bust_cm") or 0),
garment_bust_cm=float(case.get("garment_bust_cm") or 0),
material=case.get("material") or case.get("material_a") or "unknown",
)
image = Image.open(img_path).convert("RGB")
gold = case.get("recommended_a") or case.get("best_correction") or case.get("gold_action")
try:
if hasattr(processor, "apply_chat_template"):
messages = [
{
"role": "user",
"content": [
{"type": "image"},
{"type": "text", "text": prompt},
],
}
]
chat = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(text=[chat], images=[image], return_tensors="pt", padding=True)
else:
inputs = processor(text=prompt, images=image, return_tensors="pt")
device = next(model.parameters()).device
inputs = {k: v.to(device) if hasattr(v, "to") else v for k, v in inputs.items()}
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=160)
text = processor.batch_decode(out, skip_special_tokens=True)[0]
except Exception as exc:
results.append({"case_id": case.get("case_id"), "error": f"{type(exc).__name__}: {exc}"})
continue
parsed = parse_json_blob(text)
ok = parsed is not None
n_parse += int(ok)
pred = (parsed or {}).get("best_correction")
match = pred == gold if gold else None
if match:
n_match += 1
results.append(
{
"case_id": case.get("case_id"),
"gold": gold,
"pred": pred,
"parsed": parsed,
"raw": text[-500:],
"match": match,
}
)
print(f"MLLM {i+1}/{len(cases)} pred={pred} gold={gold}", flush=True)
payload = {
"status": "PASS" if results else "NOT_JUSTIFIED",
"model": name,
"n": len(results),
"parse_rate": (n_parse / len(results)) if results else 0.0,
"action_match_rate": (n_match / len(results)) if results else None,
"limitation": (
"Zero-shot pretrained VLM on SYNTHETIC_BODY_PHYSICS renders. "
"n is small; LoRA/QLoRA omitted to avoid leakage. Structured JSON parse_rate is the primary metric."
),
"results": results,
"body_kind": "SYNTHETIC_BODY_PHYSICS",
}
dump(OUT / "mllm_eval.json", payload)
print(json.dumps({k: payload[k] for k in payload if k != "results"}, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
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