Changed default shuffle behavior

data_utils.py

@@ -36,7 +36,8 @@ def __init__(self, data_file, source_file, nt,
                     curr_location += 1
             self.possible_starts = possible_starts
 
-        self.possible_starts = np.random.permutation(self.possible_starts)
+        if shuffle:
+            self.possible_starts = np.random.permutation(self.possible_starts)
         if N_seq is not None and len(self.possible_starts) > N_seq:  # select a subset of sequences if want to
             self.possible_starts = self.possible_starts[:N_seq]
         self.N_sequences = len(self.possible_starts)

kitti_evaluate.py

@@ -41,7 +41,9 @@
 layer_config['output_mode'] = 'prediction'
 dim_ordering = layer_config['dim_ordering']
 test_prednet = PredNet(weights=train_model.layers[1].get_weights(), **layer_config)
-inputs = Input(shape=train_model.layers[0].batch_input_shape[1:])
+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(input=inputs, output=predictions)
 

kitti_train.py

@@ -62,7 +62,7 @@
 model = Model(input=inputs, output=final_errors)
 model.compile(loss='mean_absolute_error', optimizer='adam')
 
-train_generator = SequenceGenerator(train_file, train_sources, nt, batch_size=batch_size)
+train_generator = SequenceGenerator(train_file, train_sources, nt, batch_size=batch_size, shuffle=True)
 val_generator = SequenceGenerator(val_file, val_sources, nt, batch_size=batch_size, N_seq=N_seq_val)
 
 lr_schedule = lambda epoch: 0.001 if epoch < 75 else 0.0001    # start with lr of 0.001 and then drop to 0.0001 after 75 epochs