data utils code

+import hickle as hkl
+import numpy as np
+from keras import backend as K
+from keras.preprocessing.image import Iterator
+
+# Data generator that creates sequences for input into PredNet.
+class SequenceGenerator(Iterator):
+    def __init__(self, data_file, source_file, nt,
+                 batch_size=8, shuffle=False, seed=None,
+                 output_mode='error', sequence_start_mode='all', N_seq=None,
+                 data_format=K.image_data_format()):
+        self.X = hkl.load(data_file)  # X will be like (n_images, nb_cols, nb_rows, nb_channels)
+        self.sources = hkl.load(source_file) # source for each image so when creating sequences can assure that consecutive frames are from same video
+        self.nt = nt
+        self.batch_size = batch_size
+        self.data_format = data_format
+        assert sequence_start_mode in {'all', 'unique'}, 'sequence_start_mode must be in {all, unique}'
+        self.sequence_start_mode = sequence_start_mode
+        assert output_mode in {'error', 'prediction'}, 'output_mode must be in {error, prediction}'
+        self.output_mode = output_mode
+
+        if self.data_format == 'channels_first':
+            self.X = np.transpose(self.X, (0, 3, 1, 2))
+        self.im_shape = self.X[0].shape
+
+        if self.sequence_start_mode == 'all':  # allow for any possible sequence, starting from any frame
+            self.possible_starts = np.array([i for i in range(self.X.shape[0] - self.nt) if self.sources[i] == self.sources[i + self.nt - 1]])
+        elif self.sequence_start_mode == 'unique':  #create sequences where each unique frame is in at most one sequence
+            curr_location = 0
+            possible_starts = []
+            while curr_location < self.X.shape[0] - self.nt + 1:
+                if self.sources[curr_location] == self.sources[curr_location + self.nt - 1]:
+                    possible_starts.append(curr_location)
+                    curr_location += self.nt
+                else:
+                    curr_location += 1
+            self.possible_starts = 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)
+        super(SequenceGenerator, self).__init__(len(self.possible_starts), batch_size, shuffle, seed)
+
+    def __getitem__(self, null):
+        return self.next()
+
+    def next(self):
+        with self.lock:
+            current_index = (self.batch_index * self.batch_size) % self.n
+            index_array, current_batch_size = next(self.index_generator), self.batch_size
+        batch_x = np.zeros((current_batch_size, self.nt) + self.im_shape, np.float32)
+        for i, idx in enumerate(index_array):
+            idx = self.possible_starts[idx]
+            batch_x[i] = self.preprocess(self.X[idx:idx+self.nt])
+        if self.output_mode == 'error':  # model outputs errors, so y should be zeros
+            batch_y = np.zeros(current_batch_size, np.float32)
+        elif self.output_mode == 'prediction':  # output actual pixels
+            batch_y = batch_x
+        return batch_x, batch_y
+
+    def preprocess(self, X):
+        return X.astype(np.float32) / 255
+
+    def create_all(self):
+        X_all = np.zeros((self.N_sequences, self.nt) + self.im_shape, np.float32)
+        for i, idx in enumerate(self.possible_starts):
+            X_all[i] = self.preprocess(self.X[idx:idx+self.nt])
+        return X_all
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