From f7904b7b301308390e8afa36dff70b85517860c3 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Sat, 20 Nov 2021 01:00:16 +0100 Subject: [PATCH] Explicitly compute TP, FP in val.py --- utils/metrics.py | 21 +++++++++++++++------ val.py | 2 +- 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/utils/metrics.py b/utils/metrics.py index 2e0e0c65e63d..3f1dc559c75a 100644 --- a/utils/metrics.py +++ b/utils/metrics.py @@ -18,7 +18,7 @@ def fitness(x): return (x[:, :4] * w).sum(1) -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16): """ Compute the average precision, given the recall and precision curves. Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. # Arguments @@ -37,7 +37,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] # Find unique classes - unique_classes = np.unique(target_cls) + unique_classes, nt = np.unique(target_cls, return_counts=True) nc = unique_classes.shape[0] # number of classes, number of detections # Create Precision-Recall curve and compute AP for each class @@ -45,7 +45,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) for ci, c in enumerate(unique_classes): i = pred_cls == c - n_l = (target_cls == c).sum() # number of labels + n_l = nt[ci] # number of labels n_p = i.sum() # number of predictions if n_p == 0 or n_l == 0: @@ -56,7 +56,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names tpc = tp[i].cumsum(0) # Recall - recall = tpc / (n_l + 1e-16) # recall curve + recall = tpc / (n_l + eps) # recall curve r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases # Precision @@ -70,7 +70,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + 1e-16) + f1 = 2 * p * r / (p + r + eps) names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data names = {i: v for i, v in enumerate(names)} # to dict if plot: @@ -80,7 +80,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') i = f1.mean(0).argmax() # max F1 index - return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') + 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') def compute_ap(recall, precision): @@ -162,6 +165,12 @@ def process_batch(self, detections, labels): def matrix(self): return self.matrix + def tp_fp(self): + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return tp[:-1], fp[:-1] # remove background class + def plot(self, normalize=True, save_dir='', names=()): try: import seaborn as sn diff --git a/val.py b/val.py index dfabb65b979c..cc6ff027b070 100644 --- a/val.py +++ b/val.py @@ -237,7 +237,7 @@ def run(data, # Compute metrics stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy if len(stats) and stats[0].any(): - p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + 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