mirror of
https://github.com/wassname/Castor.git
synced 2026-09-28 13:42:00 +08:00
Victor Yang
51d8e29525
* update nce-sm * refactor code, update torchtext * use shared evaluation * refactor code, use shared data loader * refactor code * refactor code * refactor code according to Michael's great suggestions * update readme and requirement * update datasets and readme * update data loader * add space between + * update refactor code * add nce-mp * remove duplicate files * update readme, refactor code according to mp_cnn and delete duplicate code, follow PEP8 standard * refactor code, add/delete comments * import exit from sys
69 lines
3.8 KiB
Python
69 lines
3.8 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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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 MPCNNDatasetFactory(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(os.pardir, castor_dir, 'data', '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_dim = SICK.TEXT_FIELD.vocab.vectors.size()
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embedding = nn.Embedding(embedding_dim[0], embedding_dim[1])
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embedding.weight = nn.Parameter(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(os.pardir, castor_dir, 'data', '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_dim = MSRVID.TEXT_FIELD.vocab.vectors.size()
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embedding = nn.Embedding(embedding_dim[0], embedding_dim[1])
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embedding.weight = nn.Parameter(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(os.pardir, castor_dir, 'data', '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_dim = TRECQA.TEXT_FIELD.vocab.vectors.size()
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embedding = nn.Embedding(embedding_dim[0], embedding_dim[1])
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embedding.weight = nn.Parameter(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('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(os.pardir, castor_dir, 'data', '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_dim = WikiQA.TEXT_FIELD.vocab.vectors.size()
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embedding = nn.Embedding(embedding_dim[0], embedding_dim[1])
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embedding.weight = nn.Parameter(WikiQA.TEXT_FIELD.vocab.vectors)
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return WikiQA, 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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