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f234112 | 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 | """Phase 5B post-training analysis: errors, qualitative results, test eval.
Tools for the selected Phase 5B checkpoint:
- false-negative / false-positive visual + tabular analysis (validation)
- qualitative prediction panels (GT + predictions + confidence)
- the single locked TEST evaluation
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
from typing import Any
import numpy as np
import torch
if str(Path(__file__).resolve().parents[2] / "src") not in sys.path:
sys.path.insert(0, str(Path(__file__).resolve().parents[2] / "src"))
from accident import CLASS_NAMES
from accident.dataset import AccidentParquetDataset
from accident.metrics import _compute_iou, threshold_analysis
from accident.model import build_detector
from accident.phase5b import THRESHOLD_SWEEP, evaluate_metrics
from accident.utils import FIGURE_DIR, MODEL_DIR, REPORT_DIR, get_device, seed_everything
from accident.visualization import save_prediction_example
def match_boxes(
gt_boxes: np.ndarray,
gt_labels: np.ndarray,
pred_boxes: np.ndarray,
pred_labels: np.ndarray,
pred_scores: np.ndarray,
iou_threshold: float = 0.5,
) -> tuple[list[tuple[int, int, float]], list[int], list[int]]:
"""Greedy IoU matching per class.
Returns (matches, unmatched_gt_indices, unmatched_pred_indices) where
matches are (gt_idx, pred_idx, iou) triples.
"""
matches: list[tuple[int, int, float]] = []
used_gt: set[int] = set()
used_pred: set[int] = set()
order = np.argsort(-pred_scores) if len(pred_scores) else []
for p in order:
best_iou, best_g = 0.0, -1
for g in range(len(gt_boxes)):
if g in used_gt or int(gt_labels[g]) != int(pred_labels[p]):
continue
iou = _compute_iou(pred_boxes[p], gt_boxes[g])
if iou > best_iou:
best_iou, best_g = iou, g
if best_g >= 0 and best_iou >= iou_threshold:
used_gt.add(best_g)
used_pred.add(int(p))
matches.append((best_g, int(p), float(best_iou)))
unmatched_gt = [g for g in range(len(gt_boxes)) if g not in used_gt]
unmatched_pred = [int(p) for p in range(len(pred_boxes)) if p not in used_pred]
return matches, unmatched_gt, unmatched_pred
def box_size_bucket(box: np.ndarray) -> str:
w = float(box[2] - box[0])
h = float(box[3] - box[1])
area = w * h
if area < 32 * 32:
return "small"
if area < 96 * 96:
return "medium"
return "large"
def collect_error_examples(
model: torch.nn.Module,
split: str = "validation",
image_size: tuple[int, int] = (320, 320),
score_threshold: float = 0.05,
dataset_dir: Path | None = None,
) -> dict[str, Any]:
"""Gather FN/FP statistics and example indices for one split."""
from accident.utils import DATASET_DIR
if dataset_dir is None:
dataset_dir = DATASET_DIR
device = get_device()
ds = AccidentParquetDataset(split=split, target_size=image_size, dataset_dir=dataset_dir)
loader = torch.utils.data.DataLoader(
ds, batch_size=8, shuffle=False, collate_fn=collate_fn_ref()
)
model.eval()
fn_rows: list[dict[str, Any]] = []
fp_rows: list[str] = []
fn_examples: list[int] = []
fp_examples: list[int] = []
for item_iter, (images, targets) in enumerate(loader):
outputs = model([img.to(device) for img in images])
for t, o in zip(targets, outputs):
image_id = int(t["image_id"].item())
gt_boxes = t["boxes"].numpy()
gt_labels = t["labels"].numpy()
keep = o["scores"] >= score_threshold
pred_boxes = o["boxes"][keep].cpu().numpy()
# Model label space -> dataset space.
pred_labels = (o["labels"][keep].cpu().numpy() - 1)
pred_scores = o["scores"][keep].cpu().numpy()
matches, unmatched_gt, unmatched_pred = match_boxes(
gt_boxes, gt_labels, pred_boxes, pred_labels, pred_scores
)
for g in unmatched_gt:
if int(gt_labels[g]) != 0:
continue # FN analysis focuses on accident objects
best_iou = 0.0
best_score = 0.0
for p in range(len(pred_boxes)):
if int(pred_labels[p]) != 0:
continue
iou = _compute_iou(pred_boxes[p], gt_boxes[g])
if iou > best_iou:
best_iou = iou
best_score = float(pred_scores[p])
fn_rows.append(
{
"image_id": image_id,
"size_bucket": box_size_bucket(gt_boxes[g]),
"width": float(gt_boxes[g][2] - gt_boxes[g][0]),
"height": float(gt_boxes[g][3] - gt_boxes[g][1]),
"center_x": float((gt_boxes[g][0] + gt_boxes[g][2]) / 2),
"center_y": float((gt_boxes[g][1] + gt_boxes[g][3]) / 2),
"best_pred_iou": round(best_iou, 4),
"best_accident_score": round(best_score, 4),
}
)
if len(fn_examples) < 12:
fn_examples.append(image_id)
accident_fps = [
p for p in unmatched_pred if int(pred_labels[p]) == 0
]
for p in accident_fps:
fp_rows.append(
{
"image_id": image_id,
"score": round(float(pred_scores[p]), 4),
"size_bucket": box_size_bucket(pred_boxes[p]),
"center_x": float((pred_boxes[p][0] + pred_boxes[p][2]) / 2),
"center_y": float((pred_boxes[p][1] + pred_boxes[p][3]) / 2),
}
)
if len(fp_examples) < 12:
fp_examples.append(image_id)
return {
"fn_rows": fn_rows,
"fp_rows": fp_rows,
"fn_example_ids": sorted(set(fn_examples)),
"fp_example_ids": sorted(set(fp_examples)),
}
def collate_fn_ref():
from accident.dataset import collate_detection
return collate_detection
def save_error_visuals(
model: torch.nn.Module,
split: str,
image_ids: list[int],
out_dir: Path,
image_size: tuple[int, int] = (320, 320),
score_threshold: float = 0.05,
prefix: str = "fn",
dataset_dir: Path | None = None,
) -> None:
"""Save GT+prediction panels for the given example image ids."""
from accident.utils import DATASET_DIR
if dataset_dir is None:
dataset_dir = DATASET_DIR
device = get_device()
ds = AccidentParquetDataset(split=split, target_size=image_size, dataset_dir=dataset_dir)
out_dir.mkdir(parents=True, exist_ok=True)
model.eval()
with torch.no_grad():
for image_id in image_ids:
item = ds[image_id]
output = model([item["image"].to(device)])[0]
keep = output["scores"] >= score_threshold
pred_boxes = output["boxes"][keep].cpu().numpy()
pred_labels = output["labels"][keep].cpu().numpy() - 1
pred_scores = output["scores"][keep].cpu().numpy()
save_prediction_example(
image_tensor=item["image"],
gt_boxes=item["boxes"].numpy(),
gt_labels=item["labels"].numpy(),
pred_boxes=pred_boxes,
pred_labels=pred_labels,
pred_scores=pred_scores,
save_path=out_dir / f"{prefix}_{image_id:04d}.png",
title=f"{split} image {image_id} (thr={score_threshold})",
)
def run_test_evaluation_phase5b(
checkpoint_path: Path,
config: dict[str, Any],
report_dir: Path = REPORT_DIR,
dataset_dir: Path | None = None,
) -> dict[str, Any]:
"""The single locked TEST evaluation for the selected Phase 5B model."""
from accident.utils import DATASET_DIR
if dataset_dir is None:
dataset_dir = DATASET_DIR
seed_everything(config["seed"])
device = get_device()
image_size = tuple(config["image_size"])
model = build_detector(num_classes=config["num_classes"], pretrained=False)
model.load_state_dict(torch.load(checkpoint_path, map_location=device, weights_only=True))
model.to(device)
model.eval()
ds = AccidentParquetDataset(split="test", target_size=image_size, dataset_dir=dataset_dir)
loader = torch.utils.data.DataLoader(
ds, batch_size=config["batch_size"], shuffle=False, collate_fn=collate_fn_ref()
)
metrics = evaluate_metrics(model, loader, device, image_size, config.get("score_threshold", 0.05))
thresholds = threshold_analysis(
metrics["gt_records"], metrics["pred_records"],
thresholds=THRESHOLD_SWEEP, image_size=image_size,
)
result = {
"checkpoint": str(checkpoint_path),
"config": {k: v for k, v in config.items()},
"num_images": len(ds),
"num_ground_truth_objects": int(sum(len(r["boxes"]) for r in metrics["gt_records"])),
"metrics": {
"mAP_50": metrics["map50"],
"mAP_50_95": metrics["map50_95"],
"accident": {
"AP_50": metrics["accident_ap50"],
"AP_50_95": metrics["accident_ap50_95"],
"precision": metrics["accident_precision"],
"recall": metrics["accident_recall"],
},
"non_accident": {"AP_50": metrics["non_accident_ap50"]},
},
"threshold_analysis": thresholds,
}
report_dir.mkdir(parents=True, exist_ok=True)
with open(report_dir / "phase5b_test_results.json", "w", encoding="utf-8") as f:
json.dump(result, f, indent=2)
return result
def main() -> int:
"""Run the full analysis suite for the selected Phase 5B checkpoint."""
checkpoint = MODEL_DIR / "phase5b_fasterrcnn_best.pth"
with open(REPORT_DIR / "phase5b_selected_config.json", encoding="utf-8") as f:
config = json.load(f)
image_size = tuple(config["image_size"])
score_threshold = config.get("score_threshold", 0.05)
seed_everything(config["seed"])
device = get_device()
model = build_detector(num_classes=config["num_classes"], pretrained=False)
model.load_state_dict(torch.load(checkpoint, map_location=device, weights_only=True))
model.to(device)
val_dir = FIGURE_DIR / "phase5b_val_qualitative"
errors = collect_error_examples(model, "validation", image_size, score_threshold)
save_error_visuals(
model, "validation", errors["fn_example_ids"],
FIGURE_DIR / "phase5b_false_negatives", image_size, score_threshold,
prefix="fn",
)
save_error_visuals(
model, "validation", errors["fp_example_ids"],
FIGURE_DIR / "phase5b_false_positives", image_size, score_threshold,
prefix="fp",
)
# Qualitative panels: a mix of correct hits and errors.
ds = AccidentParquetDataset(split="validation", target_size=image_size)
val_dir.mkdir(parents=True, exist_ok=True)
picked = sorted(set(errors["fn_example_ids"][:3] + errors["fp_example_ids"][:3] + [0, 5, 13, 21]))
save_error_visuals(
model, "validation", picked, val_dir, image_size, score_threshold, prefix="val",
)
with open(REPORT_DIR / "phase5b_error_analysis.json", "w", encoding="utf-8") as f:
json.dump(
{
"false_negatives": errors["fn_rows"],
"false_positives": errors["fp_rows"],
"fn_size_buckets": {
k: sum(1 for r in errors["fn_rows"] if r["size_bucket"] == k)
for k in ("small", "medium", "large")
},
"fp_size_buckets": {
k: sum(1 for r in errors["fp_rows"] if r["size_bucket"] == k)
for k in ("small", "medium", "large")
},
},
f, indent=2,
)
print(f"FN rows: {len(errors['fn_rows'])}, FP rows: {len(errors['fp_rows'])}")
print("Analysis artifacts written.")
return 0
if __name__ == "__main__":
raise SystemExit(main()) |