Refactor collections and fstrings (#7821)

* Update torch_utils.py

* Additional code refactoring

* tuples to sets

* Cleanup

utils/metrics.py

@@ -55,32 +55,31 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
         i = pred_cls == c
         n_l = nt[ci]  # number of labels
         n_p = i.sum()  # number of predictions
-
         if n_p == 0 or n_l == 0:
             continue
-        else:
-            # Accumulate FPs and TPs
-            fpc = (1 - tp[i]).cumsum(0)
-            tpc = tp[i].cumsum(0)
 
-            # Recall
-            recall = tpc / (n_l + eps)  # recall curve
-            r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+        # Accumulate FPs and TPs
+        fpc = (1 - tp[i]).cumsum(0)
+        tpc = tp[i].cumsum(0)
 
-            # Precision
-            precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+        # Recall
+        recall = tpc / (n_l + eps)  # recall curve
+        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
 
-            # 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:
-                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+        # Precision
+        precision = tpc / (tpc + fpc)  # precision curve
+        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:
+                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 + 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
+    names = dict(enumerate(names))  # to dict
     if plot:
         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')
@@ -314,7 +313,7 @@ def wh_iou(wh1, wh2):
 # Plots ----------------------------------------------------------------------------------------------------------------
 
 
-def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
+def plot_pr_curve(px, py, ap, save_dir=Path('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)
@@ -331,11 +330,11 @@ def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', 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), dpi=250)
+    fig.savefig(save_dir, dpi=250)
     plt.close()
 
 
-def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
+def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
     # Metric-confidence curve
     fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
 
@@ -352,5 +351,5 @@ def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence'
     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)
+    fig.savefig(save_dir, dpi=250)
     plt.close()