mirror of
https://github.com/wassname/Castor.git
synced 2026-08-20 12:00:37 +08:00
73 lines
3.5 KiB
Python
73 lines
3.5 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class XmlCNN(nn.Module):
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
dataset = config.dataset
|
|
self.output_channel = config.output_channel
|
|
target_class = config.target_class
|
|
words_num = config.words_num
|
|
words_dim = config.words_dim
|
|
self.mode = config.mode
|
|
self.num_bottleneck_hidden = config.num_bottleneck_hidden
|
|
self.dynamic_pool_length = config.dynamic_pool_length
|
|
self.ks = 3 # There are three conv nets here
|
|
|
|
input_channel = 1
|
|
if config.mode == 'rand':
|
|
rand_embed_init = torch.Tensor(words_num, words_dim).uniform_(-0.25, 0.25)
|
|
self.embed = nn.Embedding.from_pretrained(rand_embed_init, freeze=False)
|
|
elif config.mode == 'static':
|
|
self.static_embed = nn.Embedding.from_pretrained(dataset.TEXT_FIELD.vocab.vectors, freeze=True)
|
|
elif config.mode == 'non-static':
|
|
self.non_static_embed = nn.Embedding.from_pretrained(dataset.TEXT_FIELD.vocab.vectors, freeze=False)
|
|
elif config.mode == 'multichannel':
|
|
self.static_embed = nn.Embedding.from_pretrained(dataset.TEXT_FIELD.vocab.vectors, freeze=True)
|
|
self.non_static_embed = nn.Embedding.from_pretrained(dataset.TEXT_FIELD.vocab.vectors, freeze=False)
|
|
input_channel = 2
|
|
else:
|
|
print("Unsupported Mode")
|
|
exit()
|
|
|
|
## Different filter sizes in xml_cnn than kim_cnn
|
|
self.conv1 = nn.Conv2d(input_channel, self.output_channel, (2, words_dim), padding=(1,0))
|
|
self.conv2 = nn.Conv2d(input_channel, self.output_channel, (4, words_dim), padding=(3,0))
|
|
self.conv3 = nn.Conv2d(input_channel, self.output_channel, (8, words_dim), padding=(7,0))
|
|
|
|
self.dropout = nn.Dropout(config.dropout)
|
|
self.bottleneck = nn.Linear(self.ks * self.output_channel * self.dynamic_pool_length, self.num_bottleneck_hidden)
|
|
self.fc1 = nn.Linear(self.num_bottleneck_hidden, target_class)
|
|
|
|
self.pool = nn.AdaptiveMaxPool1d(self.dynamic_pool_length) #Adaptive pooling
|
|
|
|
def forward(self, x, **kwargs):
|
|
if self.mode == 'rand':
|
|
word_input = self.embed(x) # (batch, sent_len, embed_dim)
|
|
x = word_input.unsqueeze(1) # (batch, channel_input, sent_len, embed_dim)
|
|
elif self.mode == 'static':
|
|
static_input = self.static_embed(x)
|
|
x = static_input.unsqueeze(1) # (batch, channel_input, sent_len, embed_dim)
|
|
elif self.mode == 'non-static':
|
|
non_static_input = self.non_static_embed(x)
|
|
x = non_static_input.unsqueeze(1) # (batch, channel_input, sent_len, embed_dim)
|
|
elif self.mode == 'multichannel':
|
|
non_static_input = self.non_static_embed(x)
|
|
static_input = self.static_embed(x)
|
|
x = torch.stack([non_static_input, static_input], dim=1) # (batch, channel_input=2, sent_len, embed_dim)
|
|
else:
|
|
print("Unsupported Mode")
|
|
exit()
|
|
x = [F.relu(self.conv1(x)).squeeze(3), F.relu(self.conv2(x)).squeeze(3), F.relu(self.conv3(x)).squeeze(3)]
|
|
x = [self.pool(i).squeeze(2) for i in x]
|
|
|
|
# (batch, channel_output) * ks
|
|
x = torch.cat(x, 1) # (batch, channel_output * ks)
|
|
x = F.relu(self.bottleneck(x.view(-1, self.ks * self.output_channel * self.dynamic_pool_length)))
|
|
x = self.dropout(x)
|
|
logit = self.fc1(x) # (batch, target_size)
|
|
return logit
|