mirror of
https://github.com/wassname/Castor.git
synced 2026-08-22 11:40:35 +08:00
Michael Tu
d7a631b0a9
* Refactor datasets * Update evaluators * Update trainers * Update main and MP-CNN model * Add serialization util * Fix bugs * Refactoring for NCE to use new parent class
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import math
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import numpy as np
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import torch
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from torchtext.data.field import Field, RawField
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from torchtext.data.iterator import BucketIterator
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from torchtext.data.pipeline import Pipeline
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from torchtext.vocab import Vectors
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from datasets.castor_dataset import CastorPairDataset
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def get_class_probs(sim, *args):
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"""
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Convert a single label into class probabilities.
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"""
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class_probs = np.zeros(SICK.NUM_CLASSES)
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ceil, floor = math.ceil(sim), math.floor(sim)
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if ceil == floor:
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class_probs[floor - 1] = 1
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else:
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class_probs[floor - 1] = ceil - sim
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class_probs[ceil - 1] = sim - floor
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return class_probs
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class SICK(CastorPairDataset):
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NAME = 'sick'
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NUM_CLASSES = 5
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ID_FIELD = Field(sequential=False, use_vocab=False, batch_first=True)
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TEXT_FIELD = Field(batch_first=True, tokenize=lambda x: x) # tokenizer is identity since we already tokenized it to compute external features
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EXT_FEATS_FIELD = Field(tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, tokenize=lambda x: x)
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LABEL_FIELD = Field(sequential=False, tensor_type=torch.FloatTensor, use_vocab=False, batch_first=True, postprocessing=Pipeline(get_class_probs))
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RAW_TEXT_FIELD = RawField()
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@staticmethod
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def sort_key(ex):
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return len(ex.sentence_1)
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def __init__(self, path):
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"""
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Create a SICK dataset instance
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"""
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super(SICK, self).__init__(path)
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@classmethod
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def splits(cls, path, train='train', validation='dev', test='test', **kwargs):
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return super(SICK, cls).splits(path, train=train, validation=validation, test=test, **kwargs)
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@classmethod
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def iters(cls, path, vectors_name, vectors_cache, batch_size=64, shuffle=True, device=0, vectors=None, unk_init=torch.Tensor.zero_):
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"""
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:param path: directory containing train, test, dev files
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:param vectors_name: name of word vectors file
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:param vectors_cache: path to word vectors file
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:param batch_size: batch size
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:param device: GPU device
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:param vectors: custom vectors - either predefined torchtext vectors or your own custom Vector classes
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:param unk_init: function used to generate vector for OOV words
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:return:
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"""
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if vectors is None:
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vectors = Vectors(name=vectors_name, cache=vectors_cache, unk_init=unk_init)
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train, val, test = cls.splits(path)
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cls.TEXT_FIELD.build_vocab(train, val, test, vectors=vectors)
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return BucketIterator.splits((train, val, test), batch_size=batch_size, repeat=False, shuffle=shuffle,
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sort_within_batch=True, device=device)
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