def __getitem__(self, index):
    '\n        Keras sequence method for generating batches.\n        '
    group = self.groups[self.current_index]
    if self.multi_scale:
        if ((self.current_index % 10) == 0):
            random_size_index = np.random.randint(0, len(self.multi_image_sizes))
            self.image_size = self.multi_image_sizes[random_size_index]
    (inputs, targets) = self.compute_inputs_targets(group)
    while (inputs is None):
        current_index = (self.current_index + 1)
        if (current_index >= len(self.groups)):
            current_index = (current_index % len(self.groups))
        self.current_index = current_index
        group = self.groups[self.current_index]
        (inputs, targets) = self.compute_inputs_targets(group)
    current_index = (self.current_index + 1)
    if (current_index >= len(self.groups)):
        current_index = (current_index % len(self.groups))
    self.current_index = current_index
    return (inputs, targets)
Keras sequence method for generating batches.
def __getitem__(self, index):
    '\n        \n        '
    group = self.groups[self.current_index]
    if self.multi_scale:
        if ((self.current_index % 10) == 0):
            random_size_index = np.random.randint(0, len(self.multi_image_sizes))
            self.image_size = self.multi_image_sizes[random_size_index]
    (inputs, targets) = self.compute_inputs_targets(group)
    while (inputs is None):
        current_index = (self.current_index + 1)
        if (current_index >= len(self.groups)):
            current_index = (current_index % len(self.groups))
        self.current_index = current_index
        group = self.groups[self.current_index]
        (inputs, targets) = self.compute_inputs_targets(group)
    current_index = (self.current_index + 1)
    if (current_index >= len(self.groups)):
        current_index = (current_index % len(self.groups))
    self.current_index = current_index
    return (inputs, targets)