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])