a gold outline the reviewer kept from the model's proposal is not judged
CI / ledger-and-tools (push) Successful in 19s
CI / ledger-and-tools (push) Successful in 19s
454 of the 1 029 gold outlines are a proposal kept unchanged (loop 159 of 317, pin 140 of
224, face 140 of 295, crystal 14 of 149): the pre-labelling model's own masks. The
evaluation now ignores them object by object for every model, like a crowd region: they
leave the gold, and an unmatched prediction on one of them is not a false alarm.
tools/gold_kept.py finds them against the studio's batch proposals (mask IoU >= 0.98) and
writes data/gold/kept_machine.json; evaluate_image, evaluate_dirs and gate_stats.per_frame
read it. With it, the frame-level exclusion of 845bd87 is no longer applied (it stays as
the fallback without the file), so the gold set can grow with frames the active model
pre-labelled. Every registry model is measured again on this basis and the three v5 runs
are re-gated, without promotion.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013uu3Bxsu5JqFhaWa9Ftu6z
This commit is contained in:
jungfrauandClaude Fable 5.1 committed
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@@ -83,5 +84,5 @@
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@@ -62,20 +62,21 @@
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@@ -83,5 +84,5 @@
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@@ -62,20 +62,21 @@
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@@ -83,5 +84,5 @@
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@@ -62,20 +62,21 @@
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@@ -83,7 +84,7 @@
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@@ -83,7 +84,7 @@
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@@ -83,7 +84,7 @@
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@@ -62,20 +62,21 @@
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|
||||
"mask_score": 0.7867,
|
||||
"mask_score_baseline": 0.7967,
|
||||
"matched_only_iou_delta": -0.0064,
|
||||
"f1_delta": 0.0207,
|
||||
"mask_iou_delta": 0.0104,
|
||||
"mask_score": 0.6831,
|
||||
"mask_score_baseline": 0.6726,
|
||||
"matched_only_iou_delta": -0.002,
|
||||
"class_f1_delta": {
|
||||
"loop_all": -0.0061,
|
||||
"pin": -0.0043
|
||||
"loop_all": -0.0085,
|
||||
"pin": 0.0064
|
||||
},
|
||||
"pass_point_rule": false,
|
||||
"pass_point_rule": true,
|
||||
"rule": "paired bootstrap",
|
||||
"bootstrap": {
|
||||
"frames": 285,
|
||||
"mounts": 250,
|
||||
"frames": 335,
|
||||
"mounts": 300,
|
||||
"draws": 2000,
|
||||
"f1": {
|
||||
"delta": 0.006,
|
||||
"lo": -0.0112,
|
||||
"hi": 0.0239,
|
||||
"win": 0.771
|
||||
"delta": 0.0207,
|
||||
"lo": -0.0028,
|
||||
"hi": 0.0438,
|
||||
"win": 0.961
|
||||
},
|
||||
"mask_score": {
|
||||
"delta": -0.0004,
|
||||
"lo": -0.0133,
|
||||
"hi": 0.0122,
|
||||
"win": 0.494
|
||||
"delta": 0.0104,
|
||||
"lo": -0.0053,
|
||||
"hi": 0.027,
|
||||
"win": 0.9
|
||||
}
|
||||
},
|
||||
"pass": false
|
||||
"pass": true
|
||||
},
|
||||
"training": {
|
||||
"epochs_run": 235,
|
||||
@@ -53,5 +53,5 @@
|
||||
"mAP50-95(M)": 0.64104
|
||||
},
|
||||
"version": "yolo26s-seg-v5-s-noflip-mask1-2026-10-25",
|
||||
"measured_at": "2026-10-25T02:47:58"
|
||||
"measured_at": "2026-10-25T09:47:58"
|
||||
}
|
||||
@@ -2,7 +2,7 @@
|
||||
"id": "2026-10-23_v5-s-noflip-mask1",
|
||||
"dataset": "dataset/2026-10-22_v5",
|
||||
"base_weights": "models/base/yolo26s-seg.pt",
|
||||
"status": "gate_failed",
|
||||
"status": "gate_passed",
|
||||
"created": "2026-10-23T03:53:49",
|
||||
"created_by": "jungfrau@mx-spark-01",
|
||||
"hyperparams": {
|
||||
@@ -24,11 +24,11 @@
|
||||
},
|
||||
"version": "yolo26s-seg-v5-s-noflip-mask1-2026-10-25",
|
||||
"metrics": {
|
||||
"f1": 0.8530534351145038,
|
||||
"mean_mask_iou": 0.9054766149636467,
|
||||
"gate_pass": false,
|
||||
"f1": 0.7606338615512926,
|
||||
"mean_mask_iou": 0.8613183483285363,
|
||||
"gate_pass": true,
|
||||
"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json",
|
||||
"measured_at": "2026-10-25T02:47:58"
|
||||
"measured_at": "2026-10-25T09:47:58"
|
||||
},
|
||||
"history": [
|
||||
{
|
||||
@@ -72,8 +72,26 @@
|
||||
"at": "2026-10-25T02:47:58",
|
||||
"by": "jungfrau@mx-spark-01",
|
||||
"note": "{\"baseline\": \"/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json\", \"f1_delta\": 0.0053, \"mask_iou_delta\": -0.01, \"mask_score\": 0.7867, \"mask_score_baseline\": 0.7967, \"matched_only_iou_delta\": -0.0064, \"class_f1_delta\": {\"loop_all\": -0.0061, \"pin\": -0.0043}, \"pass_point_rule\": false, \"rule\": \"paired bootstrap\", \"bootstrap\": {\"frames\": 285, \"mounts\": 250, \"draws\": 2000, \"f1\": {\"delta\": 0.006, \"lo\": -0.0112, \"hi\": 0.0239, \"win\": 0.771}, \"mask_score\": {\"delta\": -0.0004, \"lo\": -0.0133, \"hi\": 0.0122, \"win\": 0.494}}, \"pass\": false}"
|
||||
},
|
||||
{
|
||||
"status": "training",
|
||||
"at": "2026-10-25T09:47:49",
|
||||
"by": "jungfrau@mx-spark-01",
|
||||
"note": "train_run.py"
|
||||
},
|
||||
{
|
||||
"status": "measured",
|
||||
"at": "2026-10-25T09:47:58",
|
||||
"by": "jungfrau@mx-spark-01",
|
||||
"note": ""
|
||||
},
|
||||
{
|
||||
"status": "gate_passed",
|
||||
"at": "2026-10-25T09:47:58",
|
||||
"by": "jungfrau@mx-spark-01",
|
||||
"note": "{\"baseline\": \"/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json\", \"f1_delta\": 0.0207, \"mask_iou_delta\": 0.0104, \"mask_score\": 0.6831, \"mask_score_baseline\": 0.6726, \"matched_only_iou_delta\": -0.002, \"class_f1_delta\": {\"loop_all\": -0.0085, \"pin\": 0.0064}, \"pass_point_rule\": true, \"rule\": \"paired bootstrap\", \"bootstrap\": {\"frames\": 335, \"mounts\": 300, \"draws\": 2000, \"f1\": {\"delta\": 0.0207, \"lo\": -0.0028, \"hi\": 0.0438, \"win\": 0.961}, \"mask_score\": {\"delta\": 0.0104, \"lo\": -0.0053, \"hi\": 0.027, \"win\": 0.9}}, \"pass\": true}"
|
||||
}
|
||||
],
|
||||
"host": "mx-spark-01",
|
||||
"started": "2026-10-25T02:47:48"
|
||||
"started": "2026-10-25T09:47:49"
|
||||
}
|
||||
+12
-3
@@ -20,10 +20,10 @@ Generated by `python tools/ledger.py render`; do not edit by hand.
|
||||
| 2026-09-30_v3-s-640px | gate_passed | dataset/2026-09-29_v3 | models/base/yolo26s-seg.pt | yolo26s-seg-v3-s-640px-2026-10-01 | 0.861 | 0.886 | yes | 2026-10-02T13:29 |
|
||||
| 2026-10-01_v4-noflip-mask1 | promoted | dataset/2026-10-01_v4 | models/base/yolo26n-seg.pt | yolo26n-seg-v4-noflip-mask1-2026-10-02 | 0.852 | 0.891 | yes | 2026-10-02T13:25 |
|
||||
| 2026-10-01_v4-recipeA | gate_failed | dataset/2026-10-01_v4 | models/base/yolo26n-seg.pt | yolo26n-seg-v4-recipeA-2026-10-02 | 0.832 | 0.884 | no | 2026-10-02T04:12 |
|
||||
| 2026-10-22_v5-noflip-mask1 | gate_failed | dataset/2026-10-22_v5 | models/base/yolo26n-seg.pt | yolo26n-seg-v5-noflip-mask1-2026-10-25 | 0.853 | 0.906 | no | 2026-10-25T02:47 |
|
||||
| 2026-10-22_v5-noflip-mask1 | gate_failed | dataset/2026-10-22_v5 | models/base/yolo26n-seg.pt | yolo26n-seg-v5-noflip-mask1-2026-10-25 | 0.755 | 0.859 | no | 2026-10-25T09:47 |
|
||||
| 2026-10-23_v5-m-noflip-mask1 | aborted | dataset/2026-10-22_v5 | models/base/yolo26m-seg.pt | - | - | - | - | 2026-10-23T18:53 |
|
||||
| 2026-10-23_v5-n-1024px | gate_failed | dataset/2026-10-22_v5 | models/base/yolo26n-seg.pt | yolo26n-seg-v5-n-1024px-2026-10-25 | 0.850 | 0.905 | no | 2026-10-25T02:48 |
|
||||
| 2026-10-23_v5-s-noflip-mask1 | gate_failed | dataset/2026-10-22_v5 | models/base/yolo26s-seg.pt | yolo26s-seg-v5-s-noflip-mask1-2026-10-25 | 0.853 | 0.905 | no | 2026-10-25T02:47 |
|
||||
| 2026-10-23_v5-n-1024px | gate_failed | dataset/2026-10-22_v5 | models/base/yolo26n-seg.pt | yolo26n-seg-v5-n-1024px-2026-10-25 | 0.751 | 0.864 | no | 2026-10-25T09:48 |
|
||||
| 2026-10-23_v5-s-noflip-mask1 | gate_passed | dataset/2026-10-22_v5 | models/base/yolo26s-seg.pt | yolo26s-seg-v5-s-noflip-mask1-2026-10-25 | 0.761 | 0.861 | yes | 2026-10-25T09:47 |
|
||||
|
||||
States: exported → queued → training → measured → gate_passed → gate_failed → promoted → deployed (or aborted).
|
||||
|
||||
@@ -157,6 +157,9 @@ States: exported → queued → training → measured → gate_passed → gate_f
|
||||
- 2026-10-25T02:47:39 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T02:47:47 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T02:47:47 · gate_failed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0049, "mask_iou_delta": -0.0125, "mask_score": 0.7842, "mask_score_baseline": 0.7967, "matched_only_iou_delta": -0.0063, "class_f1_delta": {"loop_all": -0.0133, "pin": 0.0121}, "pass_point_rule": false, "rule": "paired bootstrap", "bootstrap": {"frames": 285, "mounts": 250, "draws": 2000, "f1": {"delta": 0.0017, "lo": -0.0137, "hi": 0.0179, "win": 0.592}, "mask_score": {"delta": -0.0056, "lo": -0.0174, "hi": 0.0056, "win": 0.174}}, "pass": false}
|
||||
- 2026-10-25T09:47:41 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T09:47:49 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T09:47:49 · gate_failed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0147, "mask_iou_delta": -0.0016, "mask_score": 0.6711, "mask_score_baseline": 0.6726, "matched_only_iou_delta": -0.0039, "class_f1_delta": {"loop_all": -0.025, "pin": 0.0352}, "pass_point_rule": false, "rule": "paired bootstrap", "bootstrap": {"frames": 335, "mounts": 300, "draws": 2000, "f1": {"delta": 0.0147, "lo": -0.0078, "hi": 0.0402, "win": 0.906}, "mask_score": {"delta": -0.0016, "lo": -0.0182, "hi": 0.0149, "win": 0.446}}, "pass": false}
|
||||
|
||||
## 2026-10-23_v5-m-noflip-mask1
|
||||
|
||||
@@ -173,6 +176,9 @@ States: exported → queued → training → measured → gate_passed → gate_f
|
||||
- 2026-10-25T02:47:59 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T02:48:09 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T02:48:09 · gate_failed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0022, "mask_iou_delta": -0.0099, "mask_score": 0.7867, "mask_score_baseline": 0.7967, "matched_only_iou_delta": -0.0073, "class_f1_delta": {"loop_all": -0.0115, "pin": 0.0066}, "pass_point_rule": false, "rule": "paired bootstrap", "bootstrap": {"frames": 285, "mounts": 250, "draws": 2000, "f1": {"delta": 0.0024, "lo": -0.0143, "hi": 0.0204, "win": 0.595}, "mask_score": {"delta": 0.0009, "lo": -0.0127, "hi": 0.0145, "win": 0.553}}, "pass": false}
|
||||
- 2026-10-25T09:47:59 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T09:48:08 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T09:48:08 · gate_failed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0114, "mask_iou_delta": 0.0037, "mask_score": 0.6764, "mask_score_baseline": 0.6726, "matched_only_iou_delta": 0.0009, "class_f1_delta": {"loop_all": -0.0131, "pin": 0.0285}, "pass_point_rule": true, "rule": "paired bootstrap", "bootstrap": {"frames": 335, "mounts": 300, "draws": 2000, "f1": {"delta": 0.0114, "lo": -0.0119, "hi": 0.0351, "win": 0.816}, "mask_score": {"delta": 0.0037, "lo": -0.0143, "hi": 0.0224, "win": 0.662}}, "pass": false}
|
||||
|
||||
## 2026-10-23_v5-s-noflip-mask1
|
||||
|
||||
@@ -183,3 +189,6 @@ States: exported → queued → training → measured → gate_passed → gate_f
|
||||
- 2026-10-25T02:47:48 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T02:47:58 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T02:47:58 · gate_failed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0053, "mask_iou_delta": -0.01, "mask_score": 0.7867, "mask_score_baseline": 0.7967, "matched_only_iou_delta": -0.0064, "class_f1_delta": {"loop_all": -0.0061, "pin": -0.0043}, "pass_point_rule": false, "rule": "paired bootstrap", "bootstrap": {"frames": 285, "mounts": 250, "draws": 2000, "f1": {"delta": 0.006, "lo": -0.0112, "hi": 0.0239, "win": 0.771}, "mask_score": {"delta": -0.0004, "lo": -0.0133, "hi": 0.0122, "win": 0.494}}, "pass": false}
|
||||
- 2026-10-25T09:47:49 · training · jungfrau@mx-spark-01 — train_run.py
|
||||
- 2026-10-25T09:47:58 · measured · jungfrau@mx-spark-01
|
||||
- 2026-10-25T09:47:58 · gate_passed · jungfrau@mx-spark-01 — {"baseline": "/home/jungfrau/AareLC_train/models/registry/yolo26n-seg-v4-noflip-mask1-2026-10-02/gold_report.json", "f1_delta": 0.0207, "mask_iou_delta": 0.0104, "mask_score": 0.6831, "mask_score_baseline": 0.6726, "matched_only_iou_delta": -0.002, "class_f1_delta": {"loop_all": -0.0085, "pin": 0.0064}, "pass_point_rule": true, "rule": "paired bootstrap", "bootstrap": {"frames": 335, "mounts": 300, "draws": 2000, "f1": {"delta": 0.0207, "lo": -0.0028, "hi": 0.0438, "win": 0.961}, "mask_score": {"delta": 0.0104, "lo": -0.0053, "hi": 0.027, "win": 0.9}}, "pass": true}
|
||||
+2579
-2111
File diff suppressed because it is too large.
Load diff
@@ -14,7 +14,7 @@ from pathlib import Path
|
||||
from typing import Dict, List
|
||||
|
||||
from src.eval.metrics import aggregate, evaluate_image, full_report
|
||||
from src.eval.yolo_labels import load_class_names, read_label_file
|
||||
from src.eval.yolo_labels import load_class_names, read_kept_outlines, read_label_file
|
||||
|
||||
|
||||
def _stems(label_dir: Path) -> Dict[str, Path]:
|
||||
@@ -26,17 +26,22 @@ def evaluate_dirs(pred_dir, gold_dir, iou_thr: float = 0.5,
|
||||
pred_dir, gold_dir = Path(pred_dir), Path(gold_dir)
|
||||
gold_files = _stems(gold_dir)
|
||||
pred_files = _stems(pred_dir)
|
||||
# outlines a model proposed and the reviewer kept are not judged (see evaluate_image)
|
||||
kept = read_kept_outlines(gold_dir.parent)
|
||||
|
||||
# Gold defines the evaluation set; a missing prediction counts as all-FN.
|
||||
per_image = []
|
||||
missing_pred = 0
|
||||
ignored = 0
|
||||
for stem, gpath in sorted(gold_files.items()):
|
||||
gold = read_label_file(gpath)
|
||||
ppath = pred_files.get(stem)
|
||||
pred = read_label_file(ppath) if ppath else []
|
||||
if ppath is None:
|
||||
missing_pred += 1
|
||||
per_image.append(evaluate_image(gold, pred, iou_thr))
|
||||
ign = kept.get(stem, ())
|
||||
ignored += len(ign)
|
||||
per_image.append(evaluate_image(gold, pred, iou_thr, ignore=ign))
|
||||
|
||||
report = full_report(aggregate(per_image), names)
|
||||
report["dataset"] = {
|
||||
@@ -44,6 +49,7 @@ def evaluate_dirs(pred_dir, gold_dir, iou_thr: float = 0.5,
|
||||
"pred_images": len(pred_files),
|
||||
"missing_predictions": missing_pred,
|
||||
"iou_threshold": iou_thr,
|
||||
"ignored_objects": ignored,
|
||||
}
|
||||
return report
|
||||
|
||||
|
||||
+19
-7
@@ -64,8 +64,9 @@ class ClassCount:
|
||||
|
||||
|
||||
def _match_class(gold: List[PolygonInstance], pred: List[PolygonInstance],
|
||||
iou_thr: float) -> ClassCount:
|
||||
"""Greedy bbox-IoU matching within a single class."""
|
||||
iou_thr: float, ignored: Sequence[PolygonInstance] = ()) -> ClassCount:
|
||||
"""Greedy bbox-IoU matching within a single class. A prediction left unmatched that overlaps
|
||||
an ``ignored`` object (bbox IoU >= iou_thr) is neither a hit nor a false alarm."""
|
||||
cc = ClassCount()
|
||||
gold_boxes = [g.bbox() for g in gold]
|
||||
pred_boxes = [p.bbox() for p in pred]
|
||||
@@ -89,18 +90,29 @@ def _match_class(gold: List[PolygonInstance], pred: List[PolygonInstance],
|
||||
|
||||
cc.fn = len(gold) - len(used_g)
|
||||
cc.fp = len(pred) - len(used_p)
|
||||
for pi, pb in enumerate(pred_boxes):
|
||||
if pi not in used_p and any(bbox_iou(pb, g.bbox()) >= iou_thr for g in ignored):
|
||||
cc.fp -= 1
|
||||
return cc
|
||||
|
||||
|
||||
def evaluate_image(gold: List[PolygonInstance], pred: List[PolygonInstance],
|
||||
iou_thr: float = 0.5) -> Dict[int, ClassCount]:
|
||||
"""Per-class counts for one image."""
|
||||
iou_thr: float = 0.5, ignore: Sequence[int] = ()) -> Dict[int, ClassCount]:
|
||||
"""Per-class counts for one image.
|
||||
|
||||
``ignore`` names gold objects by index that are not judged: outlines a model proposed and
|
||||
the reviewer kept unchanged (tools/gold_kept.py), which are that model's own masks and
|
||||
would score a perfect overlap against it. They leave the gold, and an unmatched prediction
|
||||
of their class that overlaps one of them leaves the count too."""
|
||||
skip = {int(i) for i in ignore}
|
||||
judged = [g for i, g in enumerate(gold) if i not in skip]
|
||||
ignored = [g for i, g in enumerate(gold) if i in skip]
|
||||
by_class: Dict[int, ClassCount] = {}
|
||||
classes = {i.class_id for i in gold} | {i.class_id for i in pred}
|
||||
classes = {i.class_id for i in judged} | {i.class_id for i in pred}
|
||||
for c in classes:
|
||||
g = [i for i in gold if i.class_id == c]
|
||||
g = [i for i in judged if i.class_id == c]
|
||||
p = [i for i in pred if i.class_id == c]
|
||||
by_class[c] = _match_class(g, p, iou_thr)
|
||||
by_class[c] = _match_class(g, p, iou_thr, [i for i in ignored if i.class_id == c])
|
||||
return by_class
|
||||
|
||||
|
||||
|
||||
+15
-1
@@ -10,9 +10,10 @@ offers helpers to convert to/from absolute pixels.
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import json
|
||||
from dataclasses import dataclass
|
||||
from pathlib import Path
|
||||
from typing import List, Optional, Tuple
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
Point = Tuple[float, float]
|
||||
|
||||
@@ -121,3 +122,16 @@ def load_class_names(dataset_yaml) -> List[str]:
|
||||
except ValueError:
|
||||
continue
|
||||
return [names_map[k] for k in sorted(names_map)] if names_map else []
|
||||
|
||||
|
||||
def read_kept_outlines(gold_root) -> Dict[str, List[int]]:
|
||||
"""``<gold root>/kept_machine.json`` (tools/gold_kept.py): frame stem -> indices of the gold
|
||||
objects that are a model's proposal kept unchanged by the reviewer. Such an outline is the
|
||||
model's own mask and is not judged (an ignore region in the evaluation). Empty when the file
|
||||
is missing."""
|
||||
p = Path(gold_root) / "kept_machine.json"
|
||||
if not p.exists():
|
||||
return {}
|
||||
data = json.loads(p.read_text(encoding="utf-8"))
|
||||
return {str(k): [int(i) for i in v] for k, v in (data.get("frames") or {}).items()}
|
||||
|
||||
@@ -0,0 +1,32 @@
|
||||
"""A gold outline the reviewer kept from the model's proposal is not judged: neither a hit nor a miss,
|
||||
and a prediction on it is not a false alarm."""
|
||||
from src.eval.metrics import evaluate_image
|
||||
from src.eval.yolo_labels import PolygonInstance
|
||||
|
||||
|
||||
def sq(x, y, s=0.1, c=0):
|
||||
return PolygonInstance(c, [(x, y), (x + s, y), (x + s, y + s), (x, y + s)])
|
||||
|
||||
|
||||
def test_ignored_gold_object_is_neither_hit_nor_miss():
|
||||
gold = [sq(0.1, 0.1), sq(0.5, 0.5)]
|
||||
pred = [sq(0.1, 0.1), sq(0.5, 0.5)]
|
||||
plain = evaluate_image(gold, pred)[0]
|
||||
assert (plain.tp, plain.fp, plain.fn) == (2, 0, 0)
|
||||
ign = evaluate_image(gold, pred, ignore=[1])[0]
|
||||
assert (ign.tp, ign.fp, ign.fn) == (1, 0, 0), "the prediction on the ignored outline is not a false alarm"
|
||||
assert len(ign.mask_ious) == 1, "its overlap does not enter the mask score"
|
||||
|
||||
|
||||
def test_missing_prediction_on_an_ignored_object_is_not_a_miss():
|
||||
gold = [sq(0.1, 0.1), sq(0.5, 0.5)]
|
||||
cc = evaluate_image(gold, [sq(0.1, 0.1)], ignore=[1])[0]
|
||||
assert (cc.tp, cc.fp, cc.fn) == (1, 0, 0)
|
||||
|
||||
|
||||
def test_a_real_false_alarm_still_counts_and_other_classes_are_untouched():
|
||||
gold = [sq(0.1, 0.1), sq(0.5, 0.5, c=1)]
|
||||
pred = [sq(0.1, 0.1), sq(0.8, 0.8), sq(0.5, 0.5, c=1)]
|
||||
out = evaluate_image(gold, pred, ignore=[0])
|
||||
assert (out[0].tp, out[0].fp, out[0].fn) == (0, 1, 0), "the far prediction is a false alarm, the one on the ignored outline is not"
|
||||
assert (out[1].tp, out[1].fp, out[1].fn) == (1, 0, 0)
|
||||
+12
-5
@@ -10,9 +10,10 @@ Two sets of frames:
|
||||
stays comparable;
|
||||
* **all unseen** frames: the core plus every gold frame added since that no dataset has used.
|
||||
|
||||
A frame a model pre-labelled (``data/gold/prelabel.json``) is unseen by every other model but not
|
||||
by that one: where the reviewer kept a proposed outline, the gold holds the model's own mask. It is
|
||||
left out of that model's measure, and a pair is compared on the frames neither of them touched.
|
||||
Where the reviewer kept a proposed outline, the gold holds the pre-labelling model's own mask,
|
||||
which overlaps itself perfectly. With ``data/gold/kept_machine.json`` (tools/gold_kept.py) such
|
||||
outlines are ignored object by object for every model; without it, a frame a model pre-labelled
|
||||
(``data/gold/prelabel.json``) leaves that model's measure altogether.
|
||||
It grows with the gold set and is the one to judge a candidate on.
|
||||
|
||||
A model is listed when its predictions on the current gold set exist under
|
||||
@@ -111,7 +112,10 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
|
||||
frames: Dict[str, Dict[str, gate_stats.FrameCounts]] = {}
|
||||
label_of: Dict[str, str] = {}
|
||||
prelabel = gate_stats.load_prelabel(gold.parent)
|
||||
# with kept_machine.json the evaluation ignores a model's own outlines object by object;
|
||||
# without it, a frame the model pre-labelled leaves its measure altogether (the older rule)
|
||||
object_level = gate_stats.kept_outlines_known(gold.parent)
|
||||
prelabel = {} if object_level else gate_stats.load_prelabel(gold.parent)
|
||||
own: Dict[str, int] = {}
|
||||
for d in sorted((root / "runs").glob("_measure_*/gold_predictions")):
|
||||
ver = d.parent.name[len("_measure_"):]
|
||||
@@ -131,7 +135,10 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
usable_reps = {k: report({s: f for s, f in frames[k].items() if s in core and s not in bad_light}) for k in order}
|
||||
|
||||
print(f"clean gold frames: {len(unseen)}" + (f" (core {len(core_now)})" if len(unseen) != len(core_now) else ""))
|
||||
if any(own.values()):
|
||||
if object_level:
|
||||
kept = sum(len(v) for v in __import__("src.eval.yolo_labels", fromlist=["x"]).read_kept_outlines(gold.parent).values())
|
||||
print(f"{kept} gold outlines are a model's proposal kept unchanged by the reviewer: not judged for any model (data/gold/kept_machine.json)")
|
||||
elif any(own.values()):
|
||||
print("frames a model pre-labelled itself, left out of its own measure: "
|
||||
+ ", ".join(f"{k} {n}" for k, n in own.items() if n))
|
||||
elif not prelabel:
|
||||
|
||||
+9
-2
@@ -33,6 +33,12 @@ def load_groups(gold_root) -> Dict[str, Dict[str, str]]:
|
||||
return {"mount": dict(g.get("mount") or {}), "light": dict(g.get("light") or {}), "excluded": dict(g.get("excluded") or {})}
|
||||
|
||||
|
||||
def kept_outlines_known(gold_root) -> bool:
|
||||
"""True when ``kept_machine.json`` exists: the evaluation then ignores a model's own outlines
|
||||
object by object, and the frame-level exclusion of ``without_own_proposals`` is not needed."""
|
||||
return (Path(gold_root) / "kept_machine.json").exists()
|
||||
|
||||
|
||||
def load_prelabel(gold_root) -> Dict[str, str]:
|
||||
"""``data/gold/prelabel.json`` (tools/gold_prelabel.py): frame -> version of the model that
|
||||
proposed its labels before a reviewer saw them. Empty when the file is missing."""
|
||||
@@ -59,10 +65,11 @@ def per_frame(pred_dir, gold_dir, iou_thr: float = 0.5, stems: Optional[Iterable
|
||||
``stems`` restricts the frames; a gold frame without a prediction file is left out
|
||||
(the caller pairs on the frames both models have)."""
|
||||
from src.eval.metrics import evaluate_image
|
||||
from src.eval.yolo_labels import read_label_file
|
||||
from src.eval.yolo_labels import read_kept_outlines, read_label_file
|
||||
|
||||
pred_dir, gold_dir = Path(pred_dir), Path(gold_dir)
|
||||
wanted = set(stems) if stems is not None else None
|
||||
kept = read_kept_outlines(gold_dir.parent) # a model's own outlines are not judged
|
||||
out: Dict[str, FrameCounts] = {}
|
||||
for g in sorted(gold_dir.glob("*.txt")):
|
||||
if wanted is not None and g.stem not in wanted:
|
||||
@@ -71,7 +78,7 @@ def per_frame(pred_dir, gold_dir, iou_thr: float = 0.5, stems: Optional[Iterable
|
||||
if not p.exists():
|
||||
continue
|
||||
frame: FrameCounts = {}
|
||||
for c, cc in evaluate_image(read_label_file(g), read_label_file(p), iou_thr).items():
|
||||
for c, cc in evaluate_image(read_label_file(g), read_label_file(p), iou_thr, ignore=kept.get(g.stem, ())).items():
|
||||
frame[int(c)] = (float(cc.tp), float(cc.fp), float(cc.fn), float(sum(cc.mask_ious)))
|
||||
out[g.stem] = frame
|
||||
return out
|
||||
|
||||
@@ -0,0 +1,101 @@
|
||||
"""Which gold outlines are a model's proposal that the reviewer kept unchanged.
|
||||
|
||||
Written to ``data/gold/kept_machine.json`` as ``{"frames": {stem: [gold object indices]}}``: a
|
||||
gold object is kept-machine when a proposed object of the same class in the studio's batch
|
||||
annotation overlaps it with mask IoU >= 0.98. Such an outline is the pre-labelling model's own
|
||||
mask; the evaluation ignores it for every model (src/eval/metrics.evaluate_image), so a model
|
||||
is never scored against itself and the gold set can grow with any reviewed frame.
|
||||
|
||||
python tools/gold_kept.py [--studio ~/AareLC_studio/data/pipeline_prod] [--iou 0.98]
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import glob
|
||||
import json
|
||||
import re
|
||||
from collections import Counter
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
|
||||
HERE = Path(__file__).resolve().parent
|
||||
NAMES = ["loop_all", "pin", "crystal", "loop_face", "ice", "needle"]
|
||||
GRID = 512
|
||||
|
||||
|
||||
def gold_objects(path: Path):
|
||||
out = []
|
||||
for line in path.read_text(encoding="utf-8").split("\n"):
|
||||
p = line.split()
|
||||
if len(p) < 7:
|
||||
continue
|
||||
out.append((int(p[0]), np.array([float(v) for v in p[1:]], dtype=np.float64).reshape(-1, 2)))
|
||||
return out
|
||||
|
||||
|
||||
def raster(pts_norm) -> np.ndarray:
|
||||
m = np.zeros((GRID, GRID), np.uint8)
|
||||
cv2.fillPoly(m, [np.round(pts_norm * GRID).astype(np.int32).reshape(-1, 1, 2)], 1)
|
||||
return m.astype(bool)
|
||||
|
||||
|
||||
def iou(a, b) -> float:
|
||||
u = int((a | b).sum())
|
||||
return int((a & b).sum()) / u if u else 0.0
|
||||
|
||||
|
||||
def main() -> int:
|
||||
ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
||||
ap.add_argument("--root", default=str(HERE.parent))
|
||||
ap.add_argument("--studio", default=str(Path.home() / "AareLC_studio" / "data" / "pipeline_prod"))
|
||||
ap.add_argument("--iou", type=float, default=0.98, help="mask IoU from which a gold outline counts as the proposal kept")
|
||||
args = ap.parse_args()
|
||||
root, studio = Path(args.root).expanduser(), Path(args.studio).expanduser()
|
||||
labels = root / "data" / "gold" / "labels"
|
||||
proposals = {}
|
||||
for f in glob.glob(str(studio / "20*/annotations/tpi_*.json")):
|
||||
proposals.setdefault(Path(f).stem, f)
|
||||
frames, kept, total = {}, Counter(), Counter()
|
||||
with_proposal = 0
|
||||
for g in sorted(labels.glob("*.txt")):
|
||||
m = re.search(r"tpi_(\d+)", g.stem)
|
||||
objs = gold_objects(g)
|
||||
for c, _ in objs:
|
||||
total[NAMES[c]] += 1
|
||||
f = proposals.get("tpi_" + m.group(1)) if m else None
|
||||
if not f:
|
||||
continue
|
||||
with_proposal += 1
|
||||
a = json.loads(Path(f).read_text(encoding="utf-8"))
|
||||
W, H = float(a.get("width") or 2040), float(a.get("height") or 2040)
|
||||
props = []
|
||||
for o in a.get("objects") or []:
|
||||
poly = (o.get("polygons") or [None])[0]
|
||||
if poly and len(poly) >= 3:
|
||||
props.append((o.get("class_id"), raster(np.array(poly, dtype=np.float64) / [W, H])))
|
||||
idx = []
|
||||
for i, (c, pts) in enumerate(objs):
|
||||
rm = raster(pts)
|
||||
if any(pc == c and iou(rm, pm) >= args.iou for pc, pm in props):
|
||||
kept[NAMES[c]] += 1
|
||||
idx.append(i)
|
||||
if idx:
|
||||
frames[g.stem] = idx
|
||||
out = {"built": datetime.now().isoformat(timespec="seconds"), "source": str(studio), "iou": args.iou,
|
||||
"gold_frames": sum(1 for _ in labels.glob("*.txt")), "with_a_proposal": with_proposal,
|
||||
"kept_by_class": dict(kept), "gold_by_class": dict(total), "frames": frames}
|
||||
dest = root / "data" / "gold" / "kept_machine.json"
|
||||
dest.write_text(json.dumps(out, indent=1, sort_keys=True) + "\n", encoding="utf-8")
|
||||
n = sum(len(v) for v in frames.values())
|
||||
print(f"{n} of {sum(total.values())} gold outlines are a proposal kept unchanged, on {len(frames)} frames -> {dest}")
|
||||
for c in NAMES:
|
||||
if total[c]:
|
||||
print(f" {c:10} {kept[c]:4} of {total[c]:4}")
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
raise SystemExit(main())
|
||||
+3
-2
@@ -237,8 +237,9 @@ def paired_with_baseline(run_dir: Path, baseline: Optional[Path]) -> Optional[Di
|
||||
gold = ROOT / "data" / "gold" / "labels"
|
||||
cand = gate_stats.per_frame(run_dir / "gold_predictions", gold)
|
||||
base = gate_stats.per_frame(base_pred, gold)
|
||||
# neither model is judged on frames it pre-labelled: the gold there holds its own outlines
|
||||
prelabel = gate_stats.load_prelabel(gold.parent)
|
||||
# neither model is judged on its own outlines: object by object with kept_machine.json
|
||||
# (per_frame does it), else by leaving out whole frames a model pre-labelled
|
||||
prelabel = {} if gate_stats.kept_outlines_known(gold.parent) else gate_stats.load_prelabel(gold.parent)
|
||||
cand_version = (json.loads((run_dir / "run.json").read_text(encoding="utf-8")).get("version") if (run_dir / "run.json").exists() else None)
|
||||
base_version = Path(baseline).parent.name
|
||||
if base_version.startswith("_measure_"):
|
||||
|
||||
Reference in new issue
Block a user