File size: 12,075 Bytes
ea8bfa1 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 | #!/usr/bin/env python3
"""Sensitivity analysis for CLARA verification-feedback design choices."""
from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
from transformers import CLIPProcessor, DebertaV2Tokenizer
import sys
PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
sys.path.insert(0, str(PROJECT_ROOT))
from src.hfm_pipeline import (
HFMLoader,
create_dataloaders as create_hfm_dataloaders,
estimate_max_length as estimate_hfm_max_length,
gather_logits_variant as gather_hfm_variant,
load_checkpoint as load_hfm_checkpoint,
resolve_device,
)
from src.mvsa_multiple_pipeline import (
MVSALoader,
create_dataloaders as create_mvsa_dataloaders,
gather_logits_variant as gather_mvsa_variant,
load_checkpoint as load_mvsa_checkpoint,
)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(
description="Sensitivity analysis for verification-feedback module."
)
parser.add_argument("--dataset", choices=["hfm", "mvsa_multiple"], required=True)
parser.add_argument("--checkpoint", default=None)
parser.add_argument("--data-root", default=None)
parser.add_argument("--text-dir", default=None)
parser.add_argument("--label-file", default=None)
parser.add_argument("--output-dir", default=None)
parser.add_argument("--batch-size", type=int, default=None)
parser.add_argument("--max-length", type=int, default=None)
parser.add_argument("--num-workers", type=int, default=None)
parser.add_argument("--train-ratio", type=float, default=None)
parser.add_argument("--val-ratio", type=float, default=None)
parser.add_argument("--seed", type=int, default=None)
parser.add_argument("--preprocessing-mode", choices=["paper", "strict"], default=None)
parser.add_argument("--disable-paper-exact-counts", action="store_true")
parser.add_argument("--device", default="auto")
parser.add_argument(
"--thresholds",
type=float,
nargs="*",
default=[0.05, 0.10, 0.20],
help="Thresholds used for disagreement gating.",
)
parser.add_argument(
"--confidence-gates",
type=float,
nargs="*",
default=[0.70, 0.80],
help="Confidence-gate values. Feedback applies only for predictions <= gate.",
)
return parser.parse_args()
def _load_hfm(
args: argparse.Namespace, device: Any
) -> Tuple[Any, Dict[str, Any], Any, Callable[..., Tuple[np.ndarray, np.ndarray]], str]:
checkpoint = args.checkpoint or "outputs/hfm/clara_hfm.pt"
data_root = args.data_root or "data/HFM"
text_dir = args.text_dir or str(Path(data_root) / "text")
model, cfg, _ = load_hfm_checkpoint(checkpoint, device)
cfg["image_root"] = data_root
cfg["text_dir"] = text_dir
if args.batch_size is not None:
cfg["batch_size"] = args.batch_size
if args.num_workers is not None:
cfg["num_workers"] = args.num_workers
if args.max_length is not None:
cfg["max_length"] = args.max_length
loader_obj = HFMLoader(cfg["text_dir"], cfg["image_root"])
all_samples = loader_obj.load()
train_samples = loader_obj.get_split("train")
val_samples = loader_obj.get_split("val")
test_samples = loader_obj.get_split("test")
clip_processor = CLIPProcessor.from_pretrained(cfg["vision_model_id"])
tokenizer = DebertaV2Tokenizer.from_pretrained(cfg["text_model_id"])
max_length = cfg.get("max_length")
if not max_length:
max_length = int(estimate_hfm_max_length(all_samples))
pin_memory = bool(cfg.get("pin_memory", True) and device.type == "cuda")
_, _, test_loader = create_hfm_dataloaders(
train_samples=train_samples,
val_samples=val_samples,
test_samples=test_samples,
clip_processor=clip_processor,
tokenizer=tokenizer,
batch_size=int(cfg.get("batch_size", 32)),
max_length=int(max_length),
num_workers=int(cfg.get("num_workers", 0)),
pin_memory=pin_memory,
weighted_train_sampler=False,
)
return model, cfg, test_loader, gather_hfm_variant, "macro"
def _load_mvsa_multiple(
args: argparse.Namespace, device: Any
) -> Tuple[Any, Dict[str, Any], Any, Callable[..., Tuple[np.ndarray, np.ndarray]], str]:
checkpoint = args.checkpoint or "outputs/mvsa_multiple/clara_mvsa_multiple.pt"
data_root = args.data_root or "data/MVSA-Multiple"
text_dir = args.text_dir or str(Path(data_root) / "data")
label_file = args.label_file or str(Path(data_root) / "labelResultAll.txt")
model, cfg, _ = load_mvsa_checkpoint(checkpoint, device)
cfg["text_dir"] = text_dir
cfg["label_file"] = label_file
if args.batch_size is not None:
cfg["batch_size"] = args.batch_size
if args.num_workers is not None:
cfg["num_workers"] = args.num_workers
if args.max_length is not None:
cfg["max_length"] = args.max_length
if args.train_ratio is not None:
cfg["train_ratio"] = args.train_ratio
if args.val_ratio is not None:
cfg["val_ratio"] = args.val_ratio
if args.seed is not None:
cfg["seed"] = int(args.seed)
if args.preprocessing_mode is not None:
cfg["preprocessing_mode"] = args.preprocessing_mode
if args.disable_paper_exact_counts:
cfg["paper_exact_counts"] = False
loader_obj = MVSALoader(cfg["text_dir"], cfg["label_file"])
loader_obj.load(
preprocessing_mode=str(cfg.get("preprocessing_mode", "paper")),
require_unanimous=bool(cfg.get("require_unanimous", True)),
require_cross_agree=bool(cfg.get("require_cross_agree", True)),
paper_exact_counts=bool(cfg.get("paper_exact_counts", False)),
)
train_samples, val_samples, test_samples = loader_obj.split(
train_ratio=float(cfg.get("train_ratio", 0.8)),
val_ratio=float(cfg.get("val_ratio", 0.1)),
seed=int(cfg.get("seed", 42)),
paper_811=bool(str(cfg.get("preprocessing_mode", "paper")).lower() == "paper"),
)
clip_processor = CLIPProcessor.from_pretrained(cfg["vision_model_id"])
tokenizer = DebertaV2Tokenizer.from_pretrained(cfg["text_model_id"])
pin_memory = bool(cfg.get("pin_memory", True) and device.type == "cuda")
_, _, test_loader = create_mvsa_dataloaders(
train_samples=train_samples,
val_samples=val_samples,
test_samples=test_samples,
clip_processor=clip_processor,
tokenizer=tokenizer,
batch_size=int(cfg.get("batch_size", 48)),
max_length=int(cfg.get("max_length", 128)),
num_workers=int(cfg.get("num_workers", 0)),
pin_memory=pin_memory,
persistent_workers=bool(cfg.get("persistent_workers", True)),
prefetch_factor=int(cfg.get("prefetch_factor", 2)),
use_mixup_negative=False,
mixup_alpha=float(cfg.get("mixup_alpha", 0.4)),
negative_class_boost=float(cfg.get("negative_class_boost", 12.0)),
min_ratio_negative=float(cfg.get("min_ratio_negative", 0.30)),
weighted_train_sampler=False,
)
return model, cfg, test_loader, gather_mvsa_variant, "weighted"
def _evaluate_from_logits(
logits: np.ndarray,
labels: np.ndarray,
f1_average: str,
) -> Tuple[float, float]:
pred = logits.argmax(axis=-1)
acc = float(accuracy_score(labels, pred))
f1 = float(f1_score(labels, pred, average=f1_average))
return acc, f1
def main() -> None:
args = parse_args()
device = resolve_device(args.device)
if args.dataset == "hfm":
model, cfg, test_loader, gather_variant, f1_average = _load_hfm(args, device)
output_dir = Path(args.output_dir or "results/hfm")
metric_name = "f1_macro"
else:
model, cfg, test_loader, gather_variant, f1_average = _load_mvsa_multiple(args, device)
output_dir = Path(args.output_dir or "results/mvsa_multiple")
metric_name = "f1_weighted"
settings: List[Dict[str, Any]] = [
{
"setting": "Full (baseline)",
"variant": "full",
"consensus_mode": None,
"threshold": None,
"confidence_gate": None,
},
{
"setting": "Alt consensus: abs_prob_diff",
"variant": "vf_custom",
"consensus_mode": "abs_prob_diff",
"threshold": 0.0,
"confidence_gate": None,
},
{
"setting": "Alt consensus: logit_diff",
"variant": "vf_custom",
"consensus_mode": "logit_diff",
"threshold": 0.0,
"confidence_gate": None,
},
]
for threshold in args.thresholds:
settings.append(
{
"setting": f"Threshold tau={float(threshold):.2f}",
"variant": "vf_custom",
"consensus_mode": "prob_diff",
"threshold": float(threshold),
"confidence_gate": None,
}
)
for gate in args.confidence_gates:
settings.append(
{
"setting": f"Confidence gate={float(gate):.2f}",
"variant": "vf_custom",
"consensus_mode": "prob_diff",
"threshold": 0.0,
"confidence_gate": float(gate),
}
)
rows: List[Dict[str, Any]] = []
for item in settings:
if item["variant"] == "full":
logits, labels = gather_variant(
model=model,
loader=test_loader,
device=device,
variant="full",
)
else:
logits, labels = gather_variant(
model=model,
loader=test_loader,
device=device,
variant="vf_custom",
consensus_mode=str(item["consensus_mode"]),
threshold=float(item["threshold"]),
confidence_gate=(
float(item["confidence_gate"])
if item["confidence_gate"] is not None
else None
),
)
acc, f1 = _evaluate_from_logits(logits=logits, labels=labels, f1_average=f1_average)
rows.append(
{
"setting": item["setting"],
"variant": item["variant"],
"consensus_mode": item["consensus_mode"],
"threshold": item["threshold"],
"confidence_gate": item["confidence_gate"],
"accuracy": acc,
metric_name: f1,
}
)
output_dir.mkdir(parents=True, exist_ok=True)
csv_path = output_dir / "verification_feedback_sensitivity.csv"
json_path = output_dir / "verification_feedback_sensitivity.json"
with csv_path.open("w", encoding="utf-8", newline="") as f:
fieldnames = [
"setting",
"variant",
"consensus_mode",
"threshold",
"confidence_gate",
"accuracy",
metric_name,
]
writer = csv.DictWriter(f, fieldnames=fieldnames)
writer.writeheader()
for row in rows:
writer.writerow(row)
payload = {
"dataset": args.dataset,
"checkpoint": str(args.checkpoint) if args.checkpoint else None,
"device": str(device),
"metric_name": metric_name,
"settings": rows,
"config_used": cfg,
}
json_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
print("\nVerification-feedback sensitivity summary:")
for row in rows:
print(
f"- {row['setting']:<34} | Acc={row['accuracy']:.4f} | "
f"{metric_name}={row[metric_name]:.4f}"
)
print(f"Saved CSV: {csv_path}")
print(f"Saved JSON: {json_path}")
if __name__ == "__main__":
main()
|