Setup data layer according to parameters:

        - voc_dir: path to PASCAL VOC year dir
        - split: train / val / test
        - mean: tuple of mean values to subtract
        - randomize: load in random order (default: True)
        - seed: seed for randomization (default: None / current time)

        for PASCAL VOC semantic segmentation.

        example

        params = dict(voc_dir="/path/to/PASCAL/VOC2011",
            mean=(104.00698793, 116.66876762, 122.67891434),
            split="val")

def setup(self, bottom, top):
        """
        Setup data layer according to parameters:

        - voc_dir: path to PASCAL VOC year dir
        - split: train / val / test
        - mean: tuple of mean values to subtract
        - randomize: load in random order (default: True)
        - seed: seed for randomization (default: None / current time)

        for PASCAL VOC semantic segmentation.

        example

        params = dict(voc_dir="/path/to/PASCAL/VOC2011",
            mean=(104.00698793, 116.66876762, 122.67891434),
            split="val")
        """
        # config
        params = eval(self.param_str)
        self.voc_dir = params['voc_dir']
        self.split = params['split']
        self.mean = np.array(params['mean'])
        self.random = params.get('randomize', True)
        self.seed = params.get('seed', None)

        # two tops: data and label
        if len(top) != 2:
            raise Exception("Need to define two tops: data and label.")
        # data layers have no bottoms
        if len(bottom) != 0:
            raise Exception("Do not define a bottom.")

        # load indices for images and labels
        split_f  = '{}/ImageSets/Segmentation/{}.txt'.format(self.voc_dir,
                self.split)
        self.indices = open(split_f, 'r').read().splitlines()
        self.idx = 0

        # make eval deterministic
        if 'train' not in self.split:
            self.random = False

        # randomization: seed and pick
        if self.random:
            random.seed(self.seed)
            self.idx = random.randint(0, len(self.indices)-1)