Metric-Confidence plots feature addition (#2057)

* Metric-Confidence plots feature addition

* cleanup

* Metric-Confidence plots feature addition

* cleanup

* Update run-once lines

* cleanup

* save all 4 curves to wandb

test.py

@@ -215,7 +215,7 @@ def test(data,
     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)
-        p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # [P, R, AP@0.5, AP@0.5:0.95]
+        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
     else:

train.py

@@ -403,7 +403,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         if plots:
             plot_results(save_dir=save_dir)  # save as results.png
             if wandb:
-                files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png']
+                files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
                 wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files
                                        if (save_dir / f).exists()]})
                 if opt.log_artifacts:

utils/metrics.py

@@ -15,7 +15,7 @@ def fitness(x):
     return (x[:, :4] * w).sum(1)
 
 
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]):
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
     """ Compute the average precision, given the recall and precision curves.
     Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
     # Arguments
@@ -35,12 +35,11 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
     # Find unique classes
     unique_classes = np.unique(target_cls)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
 
     # Create Precision-Recall curve and compute AP for each class
     px, py = np.linspace(0, 1, 1000), []  # for plotting
-    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
-    s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
-    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
+    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
@@ -55,25 +54,28 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
             # Recall
             recall = tpc / (n_l + 1e-16)  # recall curve
-            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
+            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
 
             # Precision
             precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
+            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
 
             # AP from recall-precision curve
             for j in range(tp.shape[1]):
                 ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
-                if plot and (j == 0):
+                if plot and j == 0:
                     py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
 
-    # Compute F1 score (harmonic mean of precision and recall)
+    # Compute F1 (harmonic mean of precision and recall)
     f1 = 2 * p * r / (p + r + 1e-16)
-
     if plot:
-        plot_pr_curve(px, py, ap, save_dir, names)
+        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
+        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
 
-    return p, r, ap, f1, unique_classes.astype('int32')
+    i = f1.mean(0).argmax()  # max F1 index
+    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
 
 
 def compute_ap(recall, precision):
@@ -181,13 +183,14 @@ class ConfusionMatrix:
 
 # Plots ----------------------------------------------------------------------------------------------------------------
 
-def plot_pr_curve(px, py, ap, save_dir='.', names=()):
+def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
+    # Precision-recall curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
     py = np.stack(py, axis=1)
 
-    if 0 < len(names) < 21:  # show mAP in legend if < 10 classes
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
         for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0])  # plot(recall, precision)
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
     else:
         ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
 
@@ -197,4 +200,24 @@ def plot_pr_curve(px, py, ap, save_dir='.', names=()):
     ax.set_xlim(0, 1)
     ax.set_ylim(0, 1)
     plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250)
+    fig.savefig(Path(save_dir), dpi=250)
+
+
+def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
+    # Metric-confidence curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
+    else:
+        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
+
+    y = py.mean(0)
+    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    fig.savefig(Path(save_dir), dpi=250)