-
Notifications
You must be signed in to change notification settings - Fork 0
/
Tagger.py
140 lines (106 loc) · 4.83 KB
/
Tagger.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
import sys
import configparser
import argparse
import torch
import Loader
import torch.nn.functional as F
from torch.autograd import Variable
from Runner import build_data
from Helpers import process_batch
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true')
parser.add_argument('--cuda', action='store_true')
parser.add_argument('--config', default='./config.ini')
parser.add_argument('--train', default='./data/en-ud-train.conllu.sem')
parser.add_argument('--dev', default='./data/en-ud-dev.conllu.sem')
parser.add_argument('--test', default='./data/en-ud-test.conllu.sem')
parser.add_argument('--embedd', default='')
parser.add_argument('--train', action='append')
parser.add_argument('--dev', action='append')
parser.add_argument('--test', action='append')
parser.add_argument('--embed', action='append')
args = parser.parse_args()
config = configparser.ConfigParser()
config.read(args.config)
BATCH_SIZE = int(config['tagger']['BATCH_SIZE'])
EMBED_DIM = int(config['tagger']['EMBED_DIM'])
LSTM_DIM = int(config['tagger']['LSTM_DIM'])
LSTM_LAYERS = int(config['tagger']['LSTM_LAYERS'])
MLP_DIM = int(config['tagger']['MLP_DIM'])
LEARNING_RATE = float(config['tagger']['LEARNING_RATE'])
EPOCHS = int(config['tagger']['EPOCHS'])
class Tagger(torch.nn.Module):
def __init__(self, sizes, vocab, args):
super().__init__()
self.embeds = torch.nn.Embedding(sizes['vocab'], EMBED_DIM)
self.embeds.weight.data.copy_(vocab.vectors)
self.lstm = torch.nn.LSTM(EMBED_DIM, LSTM_DIM, LSTM_LAYERS, batch_first=True, bidirectional=True, dropout=0.5)
self.relu = torch.nn.ReLU()
self.mlp = torch.nn.Linear(2 * LSTM_DIM, MLP_DIM)
self.out = torch.nn.Linear(MLP_DIM, sizes['semtags'])
self.criterion = torch.nn.CrossEntropyLoss(ignore_index=-1)
self.optimizer = torch.optim.Adam(self.parameters(), lr=LEARNING_RATE, betas=(0.9, 0.9))
self.dropout = torch.nn.Dropout(p=0.5)
def forward(self, forms, pack):
# embeds + dropout
form_embeds = self.dropout(self.embeds(forms))
# pack/unpack for LSTM
packed = torch.nn.utils.rnn.pack_padded_sequence(form_embeds, pack.tolist(), batch_first=True)
lstm_out, _ = self.lstm(packed)
lstm_out, _ = torch.nn.utils.rnn.pad_packed_sequence(lstm_out, batch_first=True)
# LSTM => dense ReLU
mlp_out = self.dropout(self.relu(self.mlp(lstm_out)))
# reduce to dim no_of_tags
return self.out(mlp_out)
def train_(self, epoch, train_loader):
self.train()
train_loader.init_epoch()
for i, batch in enumerate(train_loader):
(x_forms, pack), x_tags, y_heads, y_deprels = batch.form, batch.sem, batch.head, batch.deprel
mask = torch.zeros(pack.size()[0], max(pack)).type(torch.LongTensor)
for n, size in enumerate(pack):
mask[n, 0:size] = 1
y_pred = self(x_forms, pack)
# reshape for cross-entropy
batch_size, longest_sentence_in_batch = x_forms.size()
# predictions: (B x S x T) => (B * S, T)
# heads: (B x S) => (B * S)
y_pred = y_pred.view(batch_size * longest_sentence_in_batch, -1)
x_tags = x_tags.contiguous().view(batch_size * longest_sentence_in_batch)
train_loss = self.criterion(y_pred, x_tags)
self.zero_grad()
train_loss.backward()
self.optimizer.step()
print("Epoch: {}\t{}/{}\tloss: {}".format(
epoch, (i + 1) * len(x_forms), len(train_loader.dataset), train_loss.data[0]))
def evaluate_(self, test_loader):
correct, total = 0, 0
self.eval()
for i, batch in enumerate(test_loader):
(x_forms, pack), x_tags, y_heads, y_deprels = batch.form, batch.sem, batch.head, batch.deprel
mask = torch.zeros(pack.size()[0], max(pack)).type(torch.LongTensor)
for n, size in enumerate(pack):
mask[n, 0:size] = 1
# get tags
y_pred = self(x_forms, pack).max(2)[1]
mask = Variable(mask.type(torch.ByteTensor))
correct += ((x_tags == y_pred) * mask).nonzero().size(0)
total += mask.nonzero().size(0)
print("Accuracy = {}/{} = {}".format(correct, total, (correct / total)))
def main():
sets = (args.train[0], args.dev[0], args.test[0])
(train_loader, dev_loader, test_loader), sizes, vocab = Loader.get_iterators(sets, args.embed[0], BATCH_SIZE, args.cuda)
print(len(train_loader))
tagger = Tagger(sizes, vocab, args)
if args.cuda:
tagger.cuda()
# training
print("Training")
for epoch in range(EPOCHS):
tagger.train_(epoch, train_loader)
tagger.evaluate_(dev_loader)
# test
print("Eval")
tagger.evaluate_(test_loader)
if __name__ == '__main__':
main()