Precision-Recall Curve feature update (#1206)

* Precision-Recall Curve feature update

* sentinel value update

utils/general.py

@@ -293,9 +293,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
             p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
 
             # AP from recall-precision curve
-            py.append(np.interp(px, recall[:, 0], precision[:, 0]))  # precision at [email protected]
             for j in range(tp.shape[1]):
-                ap[ci, j] = compute_ap(recall[:, j], precision[:, j])
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if j == 0:
+                    py.append(np.interp(px, mrec, mpre))  # precision at [email protected]
 
     # Compute F1 score (harmonic mean of precision and recall)
     f1 = 2 * p * r / (p + r + 1e-16)
@@ -304,7 +305,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
         py = np.stack(py, axis=1)
         fig, ax = plt.subplots(1, 1, figsize=(5, 5))
         ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
-        ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes')
+        ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f [email protected]' % ap[:, 0].mean())
         ax.set_xlabel('Recall')
         ax.set_ylabel('Precision')
         ax.set_xlim(0, 1)
@@ -327,8 +328,8 @@ def compute_ap(recall, precision):
     """
 
     # Append sentinel values to beginning and end
-    mrec = np.concatenate(([0.], recall, [min(recall[-1] + 1E-3, 1.)]))
-    mpre = np.concatenate(([0.], precision, [0.]))
+    mrec = recall  # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
+    mpre = precision  # np.concatenate(([0.], precision, [0.]))
 
     # Compute the precision envelope
     mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
@@ -336,13 +337,13 @@ def compute_ap(recall, precision):
     # Integrate area under curve
     method = 'interp'  # methods: 'continuous', 'interp'
     if method == 'interp':
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        x = np.linspace(0, 1, 1001)  # 101-point interp (COCO)
         ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
     else:  # 'continuous'
         i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
         ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
 
-    return ap
+    return ap, mpre, mrec
 
 
 def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9):
@@ -1259,7 +1260,7 @@ def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_
 
 
 def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
-    # from utils.general import *; plot_results()
+    # from utils.general import *; plot_results(save_dir='runs/exp0')
     # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
     fig, ax = plt.subplots(2, 5, figsize=(12, 6))
     ax = ax.ravel()
@@ -1273,6 +1274,7 @@ def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
         os.system(c)
     else:
         files = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
+    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
     for fi, f in enumerate(files):
         try:
             results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T