mirror of
https://github.com/wassname/Castor.git
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Victor Yang
10e4e56817
* add prediction/qrel files dump option * fix comment * add twitter-url dataset, minor refactor * fix minor error
72 lines
4.3 KiB
Python
72 lines
4.3 KiB
Python
import os
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import torch
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import torch.nn as nn
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from datasets.sick import SICK
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from datasets.msrvid import MSRVID
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from datasets.trecqa import TRECQA
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from datasets.wikiqa import WikiQA
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from datasets.pit2015 import PIT2015
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class UnknownWordVecCache(object):
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"""
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Caches the first randomly generated word vector for a certain size to make it is reused.
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"""
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cache = {}
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@classmethod
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def unk(cls, tensor):
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size_tup = tuple(tensor.size())
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if size_tup not in cls.cache:
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cls.cache[size_tup] = torch.Tensor(tensor.size())
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cls.cache[size_tup].normal_(0, 0.01)
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return cls.cache[size_tup]
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class DatasetFactory(object):
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"""
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Get the corresponding Dataset class for a particular dataset.
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"""
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@staticmethod
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def get_dataset(dataset_name, word_vectors_dir, word_vectors_file, batch_size, device, castor_dir="./", utils_trecqa="utils/trec_eval-9.0.5/trec_eval"):
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if dataset_name == 'sick':
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'sick/')
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train_loader, dev_loader, test_loader = SICK.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(SICK.TEXT_FIELD.vocab.vectors)
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return SICK, embedding, train_loader, test_loader, dev_loader
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elif dataset_name == 'msrvid':
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'msrvid/')
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dev_loader = None
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train_loader, test_loader = MSRVID.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(MSRVID.TEXT_FIELD.vocab.vectors)
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return MSRVID, embedding, train_loader, test_loader, dev_loader
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elif dataset_name == 'trecqa':
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if not os.path.exists(os.path.join(castor_dir, utils_trecqa)):
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raise FileNotFoundError('TrecQA requires the trec_eval tool to run. Please run get_trec_eval.sh inside Castor/utils (as working directory) before continuing.')
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'TrecQA/')
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train_loader, dev_loader, test_loader = TRECQA.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(TRECQA.TEXT_FIELD.vocab.vectors)
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return TRECQA, embedding, train_loader, test_loader, dev_loader
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elif dataset_name == 'wikiqa':
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if not os.path.exists(os.path.join(castor_dir, utils_trecqa)):
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raise FileNotFoundError('WikiQA requires the trec_eval tool to run. Please run get_trec_eval.sh inside Castor/utils (as working directory) before continuing.')
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'WikiQA/')
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train_loader, dev_loader, test_loader = WikiQA.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(WikiQA.TEXT_FIELD.vocab.vectors)
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return WikiQA, embedding, train_loader, test_loader, dev_loader
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elif dataset_name == 'pit2015':
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'SemEval-PIT2015/')
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train_loader, dev_loader, test_loader = PIT2015.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(PIT2015.TEXT_FIELD.vocab.vectors)
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return PIT2015, embedding, train_loader, test_loader, dev_loader
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elif dataset_name == 'twitterurl':
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dataset_root = os.path.join(castor_dir, os.pardir, 'Castor-data', 'datasets', 'Twitter-URL/')
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train_loader, dev_loader, test_loader = PIT2015.iters(dataset_root, word_vectors_file, word_vectors_dir, batch_size, device=device, unk_init=UnknownWordVecCache.unk)
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embedding = nn.Embedding.from_pretrained(PIT2015.TEXT_FIELD.vocab.vectors)
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return PIT2015, embedding, train_loader, test_loader, dev_loader
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else:
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raise ValueError('{} is not a valid dataset.'.format(dataset_name))
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