-
Notifications
You must be signed in to change notification settings - Fork 816
/
Copy pathtrain.py
191 lines (159 loc) · 7.49 KB
/
train.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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
import os
import logging
import argparse
import time
from typing import Text
import torch
import sys
from torchtext.utils import download_from_url
from torchtext.datasets import DATASETS
from model import TextClassificationModel
from torch.utils.data.dataset import random_split
from torch.utils.data import DataLoader
from torchtext.data.functional import to_map_style_dataset
from torchtext.data.utils import(
get_tokenizer,
ngrams_iterator,
)
from torchtext.vocab import build_vocab_from_iterator
from torchtext.experimental.transforms import(
SentencePieceTokenizer,
load_sp_model,
PRETRAINED_SP_MODEL,
)
from torchtext.vocab import build_vocab_from_iterator
r"""
This file shows the training process of the text classification model.
"""
def yield_tokens(data_iter, ngrams):
for _, text in data_iter:
yield ngrams_iterator(tokenizer(text), ngrams)
def collate_batch(batch):
label_list, text_list, offsets = [], [], [0]
for (_label, _text) in batch:
label_list.append(label_pipeline(_label))
processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)
text_list.append(processed_text)
offsets.append(processed_text.size(0))
label_list = torch.tensor(label_list, dtype=torch.int64)
offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)
text_list = torch.cat(text_list)
return label_list.to(device), text_list.to(device), offsets.to(device)
def train(dataloader, model, optimizer, criterion, epoch):
model.train()
total_acc, total_count = 0, 0
log_interval = 500
start_time = time.time()
for idx, (label, text, offsets) in enumerate(dataloader):
optimizer.zero_grad()
predited_label = model(text, offsets)
loss = criterion(predited_label, label)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.1)
optimizer.step()
total_acc += (predited_label.argmax(1) == label).sum().item()
total_count += label.size(0)
if idx % log_interval == 0 and idx > 0:
elapsed = time.time() - start_time
print('| epoch {:3d} | {:5d}/{:5d} batches '
'| accuracy {:8.3f}'.format(epoch, idx, len(dataloader),
total_acc / total_count))
total_acc, total_count = 0, 0
start_time = time.time()
def evaluate(dataloader, model):
model.eval()
total_acc, total_count = 0, 0
with torch.no_grad():
for idx, (label, text, offsets) in enumerate(dataloader):
predited_label = model(text, offsets)
total_acc += (predited_label.argmax(1) == label).sum().item()
total_count += label.size(0)
return total_acc / total_count
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description='Train a text classification model on text classification datasets.')
parser.add_argument('dataset', type=str, default="AG_NEWS")
parser.add_argument('--num-epochs', type=int, default=5,
help='num epochs (default=5)')
parser.add_argument('--embed-dim', type=int, default=32,
help='embed dim. (default=32)')
parser.add_argument('--batch-size', type=int, default=16,
help='batch size (default=16)')
parser.add_argument('--split-ratio', type=float, default=0.95,
help='train/valid split ratio (default=0.95)')
parser.add_argument('--lr', type=float, default=4.0,
help='learning rate (default=4.0)')
parser.add_argument('--lr-gamma', type=float, default=0.8,
help='gamma value for lr (default=0.8)')
parser.add_argument('--ngrams', type=int, default=2,
help='ngrams (default=2)')
parser.add_argument('--num-workers', type=int, default=1,
help='num of workers (default=1)')
parser.add_argument('--device', default='cpu',
help='device (default=cpu)')
parser.add_argument('--data-dir', default='.data',
help='data directory (default=.data)')
parser.add_argument('--use-sp-tokenizer', type=bool, default=False,
help='use sentencepiece tokenizer (default=False)')
parser.add_argument('--dictionary',
help='path to save vocab')
parser.add_argument('--save-model-path',
help='path for saving model')
parser.add_argument('--logging-level', default='WARNING',
help='logging level (default=WARNING)')
args = parser.parse_args()
num_epochs = args.num_epochs
embed_dim = args.embed_dim
batch_size = args.batch_size
lr = args.lr
device = args.device
data_dir = args.data_dir
split_ratio = args.split_ratio
ngrams = args.ngrams
use_sp_tokenizer = args.use_sp_tokenizer
logging.basicConfig(level=getattr(logging, args.logging_level))
if use_sp_tokenizer:
sp_model_path = download_from_url(https://github.com/PRETRAINED_SP_MODEL['text_unigram_15000'],%20root=data_dir)
sp_model = load_sp_model(sp_model_path)
tokenizer = SentencePieceTokenizer(sp_model)
else:
tokenizer = get_tokenizer("basic_english")
train_iter = DATASETS[args.dataset](root=data_dir, split='train')
vocab = build_vocab_from_iterator(yield_tokens(train_iter, ngrams), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
def text_pipeline(x): return vocab(list(ngrams_iterator(tokenizer(x), ngrams)))
def label_pipeline(x): return int(x) - 1
train_iter = DATASETS[args.dataset](root=data_dir, split='train')
num_class = len(set([label for (label, _) in train_iter]))
criterion = torch.nn.CrossEntropyLoss().to(device)
model = TextClassificationModel(len(vocab), embed_dim, num_class).to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
train_iter, test_iter = DATASETS[args.dataset]()
train_dataset = to_map_style_dataset(train_iter)
test_dataset = to_map_style_dataset(test_iter)
num_train = int(len(train_dataset) * 0.95)
split_train_, split_valid_ = random_split(train_dataset, [num_train, len(train_dataset) - num_train])
train_dataloader = DataLoader(split_train_, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)
valid_dataloader = DataLoader(split_valid_, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)
test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_batch)
for epoch in range(1, num_epochs + 1):
epoch_start_time = time.time()
train(train_dataloader, model, optimizer, criterion, epoch)
accu_val = evaluate(valid_dataloader, model)
scheduler.step()
print('-' * 59)
print('| end of epoch {:3d} | time: {:5.2f}s | '
'valid accuracy {:8.3f} '.format(epoch,
time.time() - epoch_start_time,
accu_val))
print('-' * 59)
print('Checking the results of test dataset.')
accu_test = evaluate(test_dataloader, model)
print('test accuracy {:8.3f}'.format(accu_test))
if args.save_model_path:
print("Saving model to {}".format(args.save_model_path))
torch.save(model.to('cpu'), args.save_model_path)
if args.dictionary is not None:
print("Save vocab to {}".format(args.dictionary))
torch.save(vocab, args.dictionary)