File size: 4,406 Bytes
c50dde6 | 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 | import json
import logging
import os
from typing import Dict, List, Tuple
import torch
def _label_to_name(label: int, label2cat_id: Dict[int, int], cats: Dict[int, dict]) -> str:
"""Return human readable category name for a dataset label."""
cat_id = label2cat_id.get(label)
if cat_id is None:
return f"label_{label}"
cat_info = cats.get(cat_id, {})
return cat_info.get("name", f"cat_{cat_id}")
def _top_mismatch_pairs(
preds: torch.Tensor,
labels: torch.Tensor,
num_classes: int,
label2cat_id: Dict[int, int],
cats: Dict[int, dict],
top_k: int = 30,
) -> Tuple[float, List[dict]]:
"""Compute mismatch rate and the most frequent gt->pred pairs."""
if labels.numel() == 0:
return 0.0, []
mismatch_mask = preds != labels
total_mismatch = mismatch_mask.sum().item()
mismatch_rate = float(total_mismatch) / float(labels.numel())
if total_mismatch == 0:
return mismatch_rate, []
pair_labels = torch.stack([labels[mismatch_mask], preds[mismatch_mask]], dim=1)
# flatten pair (gt, pred) to single index for counting
flat = pair_labels[:, 0] * num_classes + pair_labels[:, 1]
counts = torch.bincount(flat, minlength=num_classes * num_classes)
# guard in case bincount returns empty
if counts.numel() == 0:
return mismatch_rate, []
values, indices = torch.topk(counts, k=min(top_k, counts.numel()))
results = []
for idx, cnt in zip(indices.tolist(), values.tolist()):
if cnt == 0:
continue
gt_label = idx // num_classes
pred_label = idx % num_classes
results.append(
{
"gt_label": int(gt_label),
"pred_label": int(pred_label),
"gt_name": _label_to_name(gt_label, label2cat_id, cats),
"pred_name": _label_to_name(pred_label, label2cat_id, cats),
"count": int(cnt),
"ratio_within_mismatch": float(cnt) / float(total_mismatch),
}
)
return mismatch_rate, results
def save_mismatch_reports(
preds_dict: Dict[str, torch.Tensor],
labels: torch.Tensor,
dataset,
save_dir: str,
epoch: int,
top_k: int = 30,
) -> None:
"""
Save mismatch statistics to disk.
Args:
preds_dict: mapping from head name to top1 predictions.
labels: ground-truth labels (long tensor).
dataset: dataset object providing label2cat_id and coco.cats metadata.
save_dir: root directory to write reports.
epoch: current epoch number (used in filenames).
top_k: number of most frequent mismatch pairs to keep.
"""
if not hasattr(dataset, "label2cat_id") or not hasattr(dataset, "coco") or not hasattr(dataset.coco, "cats"):
logging.warning("Dataset missing category metadata, skip mismatch report.")
return
os.makedirs(save_dir, exist_ok=True)
label2cat_id = dataset.label2cat_id
cats = dataset.coco.cats
num_classes = len(label2cat_id)
summary = {}
for head, preds in preds_dict.items():
if preds.numel() != labels.numel():
logging.warning("Preds and labels length mismatch for head %s, skip.", head)
continue
mismatch_rate, top_pairs = _top_mismatch_pairs(
preds=preds,
labels=labels,
num_classes=num_classes,
label2cat_id=label2cat_id,
cats=cats,
top_k=top_k,
)
report = {
"epoch": int(epoch),
"total_samples": int(labels.numel()),
"total_mismatch": int((preds != labels).sum().item()),
"mismatch_rate": mismatch_rate,
"top_pairs": top_pairs,
}
summary[head] = report
filename = os.path.join(save_dir, f"{head}_mismatch_epoch{epoch}.json")
try:
with open(filename, "w") as f:
json.dump(report, f, indent=2)
except OSError as e:
logging.error("Failed to write mismatch report for %s: %s", head, e)
# save a combined summary for quick inspection
combined_path = os.path.join(save_dir, f"mismatch_summary_epoch{epoch}.json")
try:
with open(combined_path, "w") as f:
json.dump(summary, f, indent=2)
except OSError as e:
logging.error("Failed to write combined mismatch summary: %s", e)
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