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#!/usr/bin/env python3
# coding=utf-8
import torch
from data.field.mini_torchtext.field import RawField
from data.field.mini_torchtext.vocab import Vocab
from collections import Counter
import types
class EdgeLabelField(RawField):
def process(self, edges, device=None):
edges, masks = self.numericalize(edges)
edges, masks = self.pad(edges, masks, device)
return edges, masks
def pad(self, edges, masks, device):
n_labels = len(self.vocab)
tensor = torch.zeros(edges[0], edges[1], n_labels, dtype=torch.long, device=device)
mask_tensor = torch.zeros(edges[0], edges[1], dtype=torch.bool, device=device)
for edge in edges[-1]:
tensor[edge[0], edge[1], edge[2]] = 1
for mask in masks[-1]:
mask_tensor[mask[0], mask[1]] = mask[2]
return tensor, mask_tensor
def numericalize(self, arr):
def multi_map(array, function):
if isinstance(array, tuple):
return (array[0], array[1], function(array[2]))
elif isinstance(array, list):
return [multi_map(array[i], function) for i in range(len(array))]
else:
return array
mask = multi_map(arr, lambda x: x is None)
arr = multi_map(arr, lambda x: self.vocab.stoi[x] if x in self.vocab.stoi else 0)
return arr, mask
def build_vocab(self, *args):
def generate(l):
if isinstance(l, tuple):
yield l[2]
elif isinstance(l, list) or isinstance(l, types.GeneratorType):
for i in l:
yield from generate(i)
else:
return
counter = Counter()
sources = []
for arg in args:
if isinstance(arg, torch.utils.data.Dataset):
sources += [arg.get_examples(name) for name, field in arg.fields.items() if field is self]
else:
sources.append(arg)
for x in generate(sources):
if x is not None:
counter.update([x])
self.vocab = Vocab(counter, specials=[])
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