Explicitly compute TP, FP in val.py (#5727)

utils/metrics.py

     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
     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
     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:
             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
                     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:
         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):
     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

val.py

     # 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