test

+'''
+Evaluate trained PredNet on KITTI sequences.
+Calculates mean-squared error and plots predictions.
+'''
+
+import os
+import numpy as np
+from six.moves import cPickle
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import matplotlib.gridspec as gridspec
+
+from keras import backend as K
+from keras.models import Model, model_from_json
+from keras.layers import Input, Dense, Flatten
+
+from prednet import PredNet
+from data_utils import SequenceGenerator
+from setting import *
+
+
+n_plot = 18
+batch_size = 6
+nt = 11
+
+weights_file = os.path.join(WEIGHTS_DIR, 'prednet_weights.hdf5') #★★★★★★★★★★★★★★★
+json_file = os.path.join(WEIGHTS_DIR, 'prednet_model.json')    #★★★★★★★★★★★★★★★
+test_file = os.path.join(DATA_DIR, 'X_test.hkl')
+test_sources = os.path.join(DATA_DIR, 'sources_test.hkl')
+
+# Load trained model
+f = open(json_file, 'r')
+json_string = f.read()
+f.close()
+train_model = model_from_json(json_string, custom_objects = {'PredNet': PredNet})
+train_model.load_weights(weights_file)
+
+# Create testing model (to output predictions)
+layer_config = train_model.layers[1].get_config()
+layer_config['output_mode'] = 'prediction'
+data_format = layer_config['data_format'] if 'data_format' in layer_config else layer_config['dim_ordering']
+test_prednet = PredNet(weights=train_model.layers[1].get_weights(), **layer_config)
+input_shape = list(train_model.layers[0].batch_input_shape[1:])
+input_shape[0] = nt
+inputs = Input(shape=tuple(input_shape))
+predictions = test_prednet(inputs)
+test_model = Model(inputs=inputs, outputs=predictions)
+
+test_generator = SequenceGenerator(test_file, test_sources, nt, sequence_start_mode='unique', data_format=data_format)
+X_test = test_generator.create_all()
+X_hat = test_model.predict(X_test, batch_size)
+if data_format == 'channels_first':
+    X_test = np.transpose(X_test, (0, 1, 3, 4, 2))
+    X_hat = np.transpose(X_hat, (0, 1, 3, 4, 2))
+
+# 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 )
+if not os.path.exists(RESULTS_SAVE_DIR): os.mkdir(RESULTS_SAVE_DIR)
+f = open(RESULTS_SAVE_DIR + 'prediction_scores_222.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[2]) / X_hat.shape[3]
+plt.figure(figsize = (nt, 2*aspect_ratio))
+gs = gridspec.GridSpec(2, nt)
+gs.update(wspace=0., hspace=0.)
+plot_save_dir = os.path.join(RESULTS_SAVE_DIR, 'prediction_plots222/')
+if not os.path.exists(plot_save_dir): os.mkdir(plot_save_dir)
+plot_idx = np.random.permutation(X_test.shape[0])[:n_plot]
+for i in plot_idx:
+    for t in range(nt):
+        plt.subplot(gs[t])
+        X_test[i,t] = X_test[i,t]*255
+        plt.imshow(X_test[i,t], interpolation='none')
+        plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')
+        if t==0: plt.ylabel('Actual', fontsize=10)
+
+        plt.subplot(gs[t + nt])
+        X_hat[i,t] = X_hat[i,t]*1000
+        plt.imshow(X_hat[i,t], interpolation='none')
+        plt.tick_params(axis='both', which='both', bottom='off', top='off', left='off', right='off', labelbottom='off', labelleft='off')
+        if t==0: plt.ylabel('Predicted', fontsize=10)
+
+    plt.savefig(plot_save_dir +  'plot_' + str(i) + '.png')
+    plt.clf()