hyperparameter evolution bug fix (#566)

utils/utils.py

@@ -818,11 +818,11 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
     return print_results(k)
 
 
-def print_mutation(hyp, results, bucket=''):
+def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
     # Print mutation results to evolve.txt (for use with train.py --evolve)
     a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
     b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
-    c = '%10.4g' * len(results) % results  # results (P, R, mAP, F1, test_loss)
+    c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
     print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
 
     if bucket:
@@ -831,11 +831,19 @@ def print_mutation(hyp, results, bucket=''):
     with open('evolve.txt', 'a') as f:  # append result
         f.write(c + b + '\n')
     x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0)  # load unique rows
-    np.savetxt('evolve.txt', x[np.argsort(-fitness(x))], '%10.3g')  # save sort by fitness
+    x = x[np.argsort(-fitness(x))]  # sort
+    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
 
     if bucket:
         os.system('gsutil cp evolve.txt gs://%s' % bucket)  # upload evolve.txt
 
+    # Save yaml
+    for i, k in enumerate(hyp.keys()):
+        hyp[k] = float(x[0, i + 7])
+    with open(yaml_file, 'w') as f:
+        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
+        yaml.dump(hyp, f, sort_keys=False)
+
 
 def apply_classifier(x, model, img, im0):
     # applies a second stage classifier to yolo outputs
@@ -1146,23 +1154,26 @@ def plot_labels(labels, save_dir=''):
     plt.close()
 
 
-def plot_evolution_results(hyp):  # from utils.utils import *; plot_evolution_results(hyp)
+def plot_evolution_results(yaml_file='hyp_evolved.yaml'):  # from utils.utils import *; plot_evolution_results()
     # Plot hyperparameter evolution results in evolve.txt
+    with open(yaml_file) as f:
+        hyp = yaml.load(f, Loader=yaml.FullLoader)
     x = np.loadtxt('evolve.txt', ndmin=2)
     f = fitness(x)
     # weights = (f - f.min()) ** 2  # for weighted results
-    plt.figure(figsize=(12, 10), tight_layout=True)
+    plt.figure(figsize=(14, 10), tight_layout=True)
     matplotlib.rc('font', **{'size': 8})
     for i, (k, v) in enumerate(hyp.items()):
         y = x[:, i + 7]
         # mu = (y * weights).sum() / weights.sum()  # best weighted result
         mu = y[f.argmax()]  # best single result
-        plt.subplot(4, 5, i + 1)
+        plt.subplot(4, 6, i + 1)
         plt.plot(mu, f.max(), 'o', markersize=10)
         plt.plot(y, f, '.')
         plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
         print('%15s: %.3g' % (k, mu))
     plt.savefig('evolve.png', dpi=200)
+    print('\nPlot saved as evolve.png')
 
 
 def plot_results_overlay(start=0, stop=0):  # from utils.utils import *; plot_results_overlay()