Write results to file

kitti_evaluate.py

@@ -49,18 +49,20 @@
     X_test = np.transpose(X_test, (1, 2, 0))
     X_hat = np.transpose(X_hat, (1, 2, 0))
 
-# Compare MSE of PredNet predictions vs. using last frame.
+# Compare MSE of PredNet predictions vs. using last frame.  Write results to prediction_scores.txt
 mse_model = np.mean( (X_test[:, 1:] - X_hat[:, 1:])**2 )  # look at all timesteps except the first
 mse_prev = np.mean( (X_test[:, :-1] - X_test[:, 1:])**2 )
-print "Model MSE: %f" % mse_model
-print "Previous Frame MSE: %f" % mse_prev
+if not os.path.exists(results_save_dir): os.mkdir(results_save_dir)
+f = open(results_save_dir + 'prediction_scores.txt', 'w')
+f.write("Model MSE: %f\n" % mse_model)
+f.write("Previous Frame MSE: %f" % mse_prev)
+f.close()
 
 # Plot some predictions
 aspect_ratio = float(X_hat.shape[3]) / X_hat.shape[4]
 plt.figure(figsize = (nt, 2*aspect_ratio))
 gs = gridspec.GridSpec(2, nt)
 gs.update(wspace=0.025, hspace=0.05)
-if not os.path.exists(results_save_dir): os.mkdir(results_save_dir)
 plot_save_dir = os.path.join(results_save_dir, 'prediction_plots/')
 if not os.path.exists(plot_save_dir): os.mkdir(plot_save_dir)
 for i in range(n_plot):