# Adopted from https://github.com/allenai/allennlp under Apache Licence 2.0.
# Changed the packaging and created a subclass CharCNNEmbedding
from typing import Union, Tuple, Optional, Callable
import torch
from torch import nn
from hanlp.layers.cnn_encoder import CnnEncoder
from hanlp.layers.time_distributed import TimeDistributed
from hanlp_common.configurable import AutoConfigurable
from hanlp.common.transform import VocabDict, ToChar
from hanlp.common.vocab import Vocab
from hanlp.layers.embeddings.embedding import EmbeddingDim, Embedding
[docs]class CharCNN(nn.Module):
def __init__(self,
field: str,
embed: Union[int, Embedding], num_filters: int,
ngram_filter_sizes: Tuple[int, ...] = (2, 3, 4, 5),
conv_layer_activation: str = 'ReLU',
output_dim: Optional[int] = None,
vocab_size=None) -> None:
"""A `CnnEncoder` is a combination of multiple convolution layers and max pooling layers.
The input to this module is of shape `(batch_size, num_tokens,
input_dim)`, and the output is of shape `(batch_size, output_dim)`.
The CNN has one convolution layer for each ngram filter size. Each convolution operation gives
out a vector of size num_filters. The number of times a convolution layer will be used
is `num_tokens - ngram_size + 1`. The corresponding maxpooling layer aggregates all these
outputs from the convolution layer and outputs the max.
This operation is repeated for every ngram size passed, and consequently the dimensionality of
the output after maxpooling is `len(ngram_filter_sizes) * num_filters`. This then gets
(optionally) projected down to a lower dimensional output, specified by `output_dim`.
We then use a fully connected layer to project in back to the desired output_dim. For more
details, refer to "A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural
Networks for Sentence Classification", Zhang and Wallace 2016, particularly Figure 1.
See allennlp.modules.seq2vec_encoders.cnn_encoder.CnnEncoder, Apache 2.0
Args:
field: The field in samples this encoder will work on.
embed: An ``Embedding`` object or the feature size to create an ``Embedding`` object.
num_filters: This is the output dim for each convolutional layer, which is the number of "filters"
learned by that layer.
ngram_filter_sizes: This specifies both the number of convolutional layers we will create and their sizes. The
default of `(2, 3, 4, 5)` will have four convolutional layers, corresponding to encoding
ngrams of size 2 to 5 with some number of filters.
conv_layer_activation: `Activation`, optional (default=`torch.nn.ReLU`)
Activation to use after the convolution layers.
output_dim: After doing convolutions and pooling, we'll project the collected features into a vector of
this size. If this value is `None`, we will just return the result of the max pooling,
giving an output of shape `len(ngram_filter_sizes) * num_filters`.
vocab_size: The size of character vocab.
Returns:
A tensor of shape `(batch_size, output_dim)`.
"""
super().__init__()
EmbeddingDim.__init__(self)
# the embedding layer
if isinstance(embed, int):
embed = nn.Embedding(num_embeddings=vocab_size,
embedding_dim=embed)
else:
raise ValueError(f'Unrecognized type for {embed}')
self.field = field
self.embed = TimeDistributed(embed)
self.encoder = TimeDistributed(
CnnEncoder(embed.embedding_dim, num_filters, ngram_filter_sizes, conv_layer_activation, output_dim))
self.embedding_dim = output_dim or num_filters * len(ngram_filter_sizes)
[docs] def forward(self, batch: dict, **kwargs):
tokens: torch.Tensor = batch[f'{self.field}_char_id']
mask = tokens.ge(0)
x = self.embed(tokens)
return self.encoder(x, mask)
def get_output_dim(self) -> int:
return self.embedding_dim
[docs]class CharCNNEmbedding(Embedding, AutoConfigurable):
def __init__(self,
field,
embed: Union[int, Embedding],
num_filters: int,
ngram_filter_sizes: Tuple[int, ...] = (2, 3, 4, 5),
conv_layer_activation: str = 'ReLU',
output_dim: Optional[int] = None,
min_word_length=None
) -> None:
"""
Args:
field: The character field in samples this encoder will work on.
embed: An ``Embedding`` object or the feature size to create an ``Embedding`` object.
num_filters: This is the output dim for each convolutional layer, which is the number of "filters"
learned by that layer.
ngram_filter_sizes: This specifies both the number of convolutional layers we will create and their sizes. The
default of `(2, 3, 4, 5)` will have four convolutional layers, corresponding to encoding
ngrams of size 2 to 5 with some number of filters.
conv_layer_activation: `Activation`, optional (default=`torch.nn.ReLU`)
Activation to use after the convolution layers.
output_dim: After doing convolutions and pooling, we'll project the collected features into a vector of
this size. If this value is `None`, we will just return the result of the max pooling,
giving an output of shape `len(ngram_filter_sizes) * num_filters`.
min_word_length: For ngram filter with max size, the input (chars) is required to have at least max size
chars.
"""
super().__init__()
if min_word_length is None:
min_word_length = max(ngram_filter_sizes)
self.min_word_length = min_word_length
self.output_dim = output_dim
self.conv_layer_activation = conv_layer_activation
self.ngram_filter_sizes = ngram_filter_sizes
self.num_filters = num_filters
self.embed = embed
self.field = field
@property
def vocab_name(self):
vocab_name = f'{self.field}_char'
return vocab_name
[docs] def module(self, vocabs: VocabDict, **kwargs) -> Optional[nn.Module]:
embed = self.embed
if isinstance(embed, Embedding):
embed = embed.module(vocabs=vocabs)
return CharCNN(self.field,
embed,
self.num_filters,
self.ngram_filter_sizes,
self.conv_layer_activation,
self.output_dim,
vocab_size=len(vocabs[self.vocab_name]))
