hyperparameter evolution bug fix (#566)

train.py

@@ -465,7 +465,7 @@ if __name__ == '__main__':
     # Evolve hyperparameters (optional)
     else:
         # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
-        meta = {'lr0': (1, 1e-5, 1e-2),  # initial learning rate (SGD=1E-2, Adam=1E-3)
+        meta = {'lr0': (1, 1e-5, 1e-1),  # initial learning rate (SGD=1E-2, Adam=1E-3)
                 'momentum': (0.1, 0.6, 0.98),  # SGD momentum/Adam beta1
                 'weight_decay': (1, 0.0, 0.001),  # optimizer weight decay
                 'giou': (1, 0.02, 0.2),  # GIoU loss gain
@@ -534,6 +534,6 @@ if __name__ == '__main__':
             print_mutation(hyp.copy(), results, yaml_file, opt.bucket)
 
         # Plot results
-        plot_evolution_results(yaml_file)
+        plot_evolution(yaml_file)
         print('Hyperparameter evolution complete. Best results saved as: %s\nCommand to train a new model with these '
               'hyperparameters: $ python train.py --hyp %s' % (yaml_file, yaml_file))

utils/utils.py

@@ -919,6 +919,15 @@ def increment_dir(dir, comment=''):
 
 
 # Plotting functions ---------------------------------------------------------------------------------------------------
+def hist2d(x, y, n=100):
+    # 2d histogram used in labels.png and evolve.png
+    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+    return np.log(hist[xidx, yidx])
+
+
 def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
     # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
     def butter_lowpass(cutoff, fs, order):
@@ -1130,13 +1139,6 @@ def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_st
 
 def plot_labels(labels, save_dir=''):
     # plot dataset labels
-    def hist2d(x, y, n=100):
-        xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
-        hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
-        xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
-        yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
-        return np.log(hist[xidx, yidx])
-
     c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
     nc = int(c.max() + 1)  # number of classes
 
@@ -1154,23 +1156,25 @@ def plot_labels(labels, save_dir=''):
     plt.close()
 
 
-def plot_evolution_results(yaml_file='hyp_evolved.yaml'):  # from utils.utils import *; plot_evolution_results()
+def plot_evolution(yaml_file='runs/evolve/hyp_evolved.yaml'):  # from utils.utils import *; plot_evolution()
     # 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=(14, 10), tight_layout=True)
+    plt.figure(figsize=(10, 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, 6, i + 1)
-        plt.plot(mu, f.max(), 'o', markersize=10)
-        plt.plot(y, f, '.')
+        plt.subplot(5, 5, i + 1)
+        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
+        plt.plot(mu, f.max(), 'k+', markersize=15)
         plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
+        if i % 5 != 0:
+            plt.yticks([])
         print('%15s: %.3g' % (k, mu))
     plt.savefig('evolve.png', dpi=200)
     print('\nPlot saved as evolve.png')