Fix hyp_evolve.yaml indexing bug (#6604)

* Fix `hyp_evolve.yaml` indexing bug

Bug caused hyp_evolve.yaml to display latest generation result rather than best generation result.

* Update plots.py

* Update general.py

* Update general.py

* Update general.py

utils/general.py

@@ -783,7 +783,7 @@ def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_op
     LOGGER.info(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
 
 
-def print_mutation(results, hyp, save_dir, bucket):
+def print_mutation(results, hyp, save_dir, bucket, prefix=colorstr('evolve: ')):
     evolve_csv = save_dir / 'evolve.csv'
     evolve_yaml = save_dir / 'hyp_evolve.yaml'
     keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
@@ -803,21 +803,23 @@ def print_mutation(results, hyp, save_dir, bucket):
     with open(evolve_csv, 'a') as f:
         f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n')
 
-    # Print to screen
-    LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys))
-    LOGGER.info(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
-
     # Save yaml
     with open(evolve_yaml, 'w') as f:
         data = pd.read_csv(evolve_csv)
         data = data.rename(columns=lambda x: x.strip())  # strip keys
-        i = np.argmax(fitness(data.values[:, :7]))  #
+        i = np.argmax(fitness(data.values[:, :4]))  #
+        generations = len(data)
         f.write('# YOLOv5 Hyperparameter Evolution Results\n' +
                 f'# Best generation: {i}\n' +
-                f'# Last generation: {len(data) - 1}\n' +
+                f'# Last generation: {generations - 1}\n' +
                 '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' +
                 '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n')
-        yaml.safe_dump(hyp, f, sort_keys=False)
+        yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False)
+
+    # Print to screen
+    LOGGER.info(prefix + f'{generations} generations finished, current result:\n' +
+                prefix + ', '.join(f'{x.strip():>20s}' for x in keys) + '\n' +
+                prefix + ', '.join(f'{x:20.5g}' for x in vals) + '\n\n')
 
     if bucket:
         os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}')  # upload

utils/plots.py

@@ -381,6 +381,7 @@ def plot_evolve(evolve_csv='path/to/evolve.csv'):  # from utils.plots import *;
     j = np.argmax(f)  # max fitness index
     plt.figure(figsize=(10, 12), tight_layout=True)
     matplotlib.rc('font', **{'size': 8})
+    print(f'Best results from row {j} of {evolve_csv}:')
     for i, k in enumerate(keys[7:]):
         v = x[:, 7 + i]
         mu = v[j]  # best single result