Adding 2-stage nn

        num_batch_tiles = np.empty((data_ds.shape[0],self.hist_to_batch.max()+1),dtype = int) 

        num_batch_tiles = np.empty((data_ds.shape[0],self.hist_to_batch.max()+1),dtype = int) 

        bb = self.getBB(data_ds)

        bb = self.getBB(data_ds)

        use_neibs = not ((disp_var is None) or (disp_neibs is None) or (min_var is None) or (max_var is None) or (min_neibs is None))

        use_neibs = not ((disp_var is None) or (disp_neibs is None) or (min_var is None) or (max_var is None) or (min_neibs is None))

        '''

        bb = np.empty((data_ds.shape[0],data_ds.shape[1],data_ds.shape[2]),int)

        for findx in range(data_ds.shape[0]):

            ds = data_ds[findx]

            gt = ds[...,1] > 0.0 # all true - check

            db = (((ds[...,0] - self.disparity_min_clip)/disp_step).astype(int))*gt

            sb = (((ds[...,1] - self.strength_min_clip)/ str_step).astype(int))*gt

            np.clip(db, 0, self.disparity_bins-1, out = db)

            np.clip(sb, 0, self.strength_bins-1, out = sb)

            bb[findx] = (self.hist_to_batch[sb.reshape(self.num_tiles),db.reshape(self.num_tiles)])   .reshape(db.shape[0],db.shape[1]) + (gt -1)

            pass

        '''

        list_of_file_lists=[]

        list_of_file_lists=[]

        for findx in range(data_ds.shape[0]):

        for findx in range(data_ds.shape[0]):

            foffs = findx * self.num_tiles 

            foffs = findx * self.num_tiles 

#$        files_list = [self.files_train, self.files_test][test_set]

#$        files_list = [self.files_train, self.files_test][test_set]

        seed_list = np.arange(len(files_list))

        seed_list = np.arange(len(files_list))

        np.random.shuffle(seed_list)

        np.random.shuffle(seed_list)

        cluster_size = (2 * radius + 1) * (2 * radius + 1)

        for nscene, seed_index in enumerate(seed_list):

        for nscene, seed_index in enumerate(seed_list):

            corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(ml_list, seed_index, min_choices=None, max_files = None, ml_num = None, set_ds = set_ds, radius = radius)

            corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(ml_list, seed_index, min_choices=None, max_files = None, ml_num = None, set_ds = set_ds, radius = radius)

            #shuffles tiles in a batch

            #shuffles tiles in a batch

            tiles_in_batch = len(target_disparity_batch)

#            tiles_in_batch = len(target_disparity_batch)

            permut = np.random.permutation(tiles_in_batch)

            tiles_in_batch =    corr2d_batch.shape[0]

            corr2d_batch_shuffled =           corr2d_batch[permut].reshape((corr2d_batch.shape[0], corr2d_batch.shape[1]*corr2d_batch.shape[2]))

            clusters_in_batch = tiles_in_batch // cluster_size

            target_disparity_batch_shuffled = target_disparity_batch[permut].reshape((tiles_in_batch,1))

#            permut = np.random.permutation(tiles_in_batch)

            gt_ds_batch_shuffled =            gt_ds_batch[permut]

            permut = np.random.permutation(clusters_in_batch)

            corr2d_clusters =           corr2d_batch.          reshape((clusters_in_batch,-1)) 

            target_disparity_clusters = target_disparity_batch.reshape((clusters_in_batch,-1)) 

            gt_ds_clusters =            gt_ds_batch.           reshape((clusters_in_batch,-1)) 

#            corr2d_batch_shuffled =           corr2d_batch[permut].reshape((corr2d_batch.shape[0], corr2d_batch.shape[1]*corr2d_batch.shape[2]))

#            target_disparity_batch_shuffled = target_disparity_batch[permut].reshape((tiles_in_batch,1))

#            gt_ds_batch_shuffled =            gt_ds_batch[permut]

            corr2d_batch_shuffled =           corr2d_clusters[permut].          reshape((tiles_in_batch, -1))

            target_disparity_batch_shuffled = target_disparity_clusters[permut].reshape((tiles_in_batch, -1))

            gt_ds_batch_shuffled =            gt_ds_clusters[permut].           reshape((tiles_in_batch, -1))

            if nscene == 0:

            if nscene == 0:

                dtype_feature_corr2d =   _dtype_feature(corr2d_batch_shuffled)

                dtype_feature_corr2d =   _dtype_feature(corr2d_batch_shuffled)

                dtype_target_disparity = _dtype_feature(target_disparity_batch_shuffled)

                dtype_target_disparity = _dtype_feature(target_disparity_batch_shuffled)

                dtype_feature_gt_ds =    _dtype_feature(gt_ds_batch_shuffled)

                dtype_feature_gt_ds =    _dtype_feature(gt_ds_batch_shuffled)

            for i in range(tiles_in_batch):

            for i in range(tiles_in_batch):

                x = corr2d_batch_shuffled[i].astype(np.float32)

                x = corr2d_batch_shuffled[i].astype(np.float32)

                y = target_disparity_batch_shuffled[i].astype(np.float32)

                y = target_disparity_batch_shuffled[i].astype(np.float32)

                z = gt_ds_batch_shuffled[i].astype(np.float32)

                z = gt_ds_batch_shuffled[i].astype(np.float32)

                example = tf.train.Example(features=tf.train.Features(feature=d_feature))

                example = tf.train.Example(features=tf.train.Features(feature=d_feature))

                writer.write(example.SerializeToString())

                writer.write(example.SerializeToString())

            if (self.debug_level > 0):

            if (self.debug_level > 0):

                print("Scene %d of %d"%(nscene, len(seed_list)))        

                print("Scene %d of %d -> %s"%(nscene, len(seed_list), tfr_filename))        

        writer.close()

        writer.close()

        sys.stdout.flush()        

        sys.stdout.flush()