From 43569d53da5cf74c2bc0c0f5fcaf49ef0bf3e5cc Mon Sep 17 00:00:00 2001 From: Anton Lebedev Date: Fri, 20 May 2022 12:33:10 +0300 Subject: [PATCH] Bug fix mAP0.5-0.95 (#6787) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Improve mAP0.5-0.95 Two changes provided 1. Added limit on the maximum number of detections for each image likewise pycocotools 2. Rework process_batch function Changes #2 solved issue #4251 I also independently encountered the problem described in issue #4251 that the values for the same thresholds do not match when changing the limits in the torch.linspace function. These changes solve this problem. Currently during validation yolov5x.pt model the following results were obtained: from yolov5 validation Class Images Labels P R mAP@.5 mAP@.5:.95: 100%|██████████| 157/157 [01:07<00:00, 2.33it/s] all 5000 36335 0.743 0.626 0.682 0.506 from pycocotools Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.505 Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.685 These results are very close, although not completely pass the competition issue #2258. I think it's problem with false positive bboxes matched ignored criteria, but this is not actual for custom datasets and does not require an additional solution. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove line to retain pycocotools results * Update val.py * Update val.py * Remove to device op * Higher precision int conversion * Update val.py Co-authored-by: Glenn Jocher Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- utils/metrics.py | 4 ++-- val.py | 23 ++++++++++++----------- 2 files changed, 14 insertions(+), 13 deletions(-) diff --git a/utils/metrics.py b/utils/metrics.py index 43b723e..e17747b 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -90,7 +90,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names p, r, f1 = p[:, i], r[:, i], f1[:, i] tp = (r * nt).round() # true positives fp = (tp / (p + eps) - tp).round() # false positives - return tp, fp, p, r, f1, ap, unique_classes.astype('int32') + return tp, fp, p, r, f1, ap, unique_classes.astype(int) def compute_ap(recall, precision): @@ -156,7 +156,7 @@ class ConfusionMatrix: matches = np.zeros((0, 3)) n = matches.shape[0] > 0 - m0, m1, _ = matches.transpose().astype(np.int16) + m0, m1, _ = matches.transpose().astype(int) for i, gc in enumerate(gt_classes): j = m0 == i if n and sum(j) == 1: diff --git a/val.py b/val.py index d886cf3..42b0e18 100644 --- a/val.py +++ b/val.py @@ -79,16 +79,17 @@ def process_batch(detections, labels, iouv): """ correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device) iou = box_iou(labels[:, 1:], detections[:, :4]) - x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5])) # IoU above threshold and classes match - if x[0].shape[0]: - matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detection, iou] - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - # matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - matches = torch.from_numpy(matches).to(iouv.device) - correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv + correct_class = labels[:, 0:1] == detections[:, 5] + for i in range(len(iouv)): + x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou] + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + # matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + correct[matches[:, 1].astype(int), i] = True return correct @@ -265,7 +266,7 @@ def run( tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() - nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class + nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class else: nt = torch.zeros(1)