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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) | |