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import os
import sys
import numpy as np
import json
import math
from sklearn.preprocessing import LabelBinarizer, LabelEncoder
import torch
from transformers import RobertaTokenizer, BertTokenizer
from torch.utils.data import Dataset
sys.path.append('/home/zekun/spatial_bert/spatial_bert/datasets')
from dataset_loader import SpatialDataset
import pdb
class USGS_MapDataset(SpatialDataset):
def __init__(self, data_file_path, tokenizer=None, max_token_len = 512, distance_norm_factor = 1, spatial_dist_fill=100, sep_between_neighbors = False):
if tokenizer is None:
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
else:
self.tokenizer = tokenizer
self.max_token_len = max_token_len
self.spatial_dist_fill = spatial_dist_fill # should be normalized distance fill, larger than all normalized neighbor distance
self.sep_between_neighbors = sep_between_neighbors
self.read_file(data_file_path)
super(USGS_MapDataset, self).__init__(self.tokenizer , max_token_len , distance_norm_factor, sep_between_neighbors )
def read_file(self, data_file_path):
with open(data_file_path, 'r') as f:
data = f.readlines()
len_data = len(data)
self.len_data = len_data
self.data = data
def load_data(self, index):
spatial_dist_fill = self.spatial_dist_fill
line = self.data[index] # take one line from the input data according to the index
line_data_dict = json.loads(line)
# process pivot
pivot_name = line_data_dict['info']['name']
pivot_pos = line_data_dict['info']['geometry']
neighbor_info = line_data_dict['neighbor_info']
neighbor_name_list = neighbor_info['name_list']
neighbor_geometry_list = neighbor_info['geometry_list']
parsed_data = self.parse_spatial_context(pivot_name, pivot_pos, neighbor_name_list, neighbor_geometry_list, spatial_dist_fill )
return parsed_data
def __len__(self):
return self.len_data
def __getitem__(self, index):
return self.load_data(index)
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