-
Notifications
You must be signed in to change notification settings - Fork 0
/
kgcvae_swda.py
193 lines (145 loc) · 7.45 KB
/
kgcvae_swda.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
# Copyright (C) 2017 Tiancheng Zhao, Carnegie Mellon University
import os
import time
import numpy as np
import tensorflow as tf
from beeprint import pp
from config_utils import KgCVAEConfig as Config
from data_apis.corpus import SWDADialogCorpus
from data_apis.data_utils import SWDADataLoader
from models.cvae import KgRnnCVAE
import torch
import glob
# constants
tf.app.flags.DEFINE_string("word2vec_path", None, "The path to word2vec. Can be None.")
tf.app.flags.DEFINE_string("data_dir", "data/full_swda_clean_42da_sentiment_dialog_corpus.p", "Raw data directory.")
tf.app.flags.DEFINE_string("work_dir", "working", "Experiment results directory.")
tf.app.flags.DEFINE_string("equal_batch", True, "Make each batch has similar length.")
tf.app.flags.DEFINE_bool("resume", False, "Resume from previous")
tf.app.flags.DEFINE_bool("forward_only", False, "Only do decoding")
tf.app.flags.DEFINE_bool("save_model", True, "Create checkpoints")
tf.app.flags.DEFINE_string("test_path", "kgCVAE_woFeat", "the dir to load checkpoint for forward only")
FLAGS = tf.app.flags.FLAGS
def get_checkpoint_state(ckp_dir):
files = os.path.join(ckp_dir, "*.pth")
files = glob.glob(files)
files.sort(key=os.path.getmtime)
return len(files) > 0 and files[-1] or None
def main():
# config for training
config = Config()
# config for validation
valid_config = Config()
valid_config.keep_prob = 1.0
valid_config.dec_keep_prob = 1.0
valid_config.batch_size = 60
# configuration for testing
test_config = Config()
test_config.keep_prob = 1.0
test_config.dec_keep_prob = 1.0
test_config.batch_size = 1
pp(config)
# get data set
api = SWDADialogCorpus(config=config, corpus_path=FLAGS.data_dir, word2vec=FLAGS.word2vec_path, word2vec_dim=config.embed_size)
dial_corpus = api.get_dialog_corpus()
meta_corpus = api.get_meta_corpus()
train_meta, valid_meta, test_meta = meta_corpus.get("train"), meta_corpus.get("valid"), meta_corpus.get("test")
train_dial, valid_dial, test_dial = dial_corpus.get("train"), dial_corpus.get("valid"), dial_corpus.get("test")
# convert to numeric input outputs that fits into TF models
train_feed = SWDADataLoader("Train", train_dial, train_meta, config)
valid_feed = SWDADataLoader("Valid", valid_dial, valid_meta, config)
test_feed = SWDADataLoader("Test", test_dial, test_meta, config)
if FLAGS.forward_only or FLAGS.resume:
log_dir = os.path.join(FLAGS.work_dir, FLAGS.test_path)
else:
log_dir = os.path.join(FLAGS.work_dir, "run"+str(int(time.time())))
# begin training
if True:
scope = "model"
model = KgRnnCVAE(config, api, log_dir=None if FLAGS.forward_only else log_dir, scope=scope)
print("Created computation graphs")
# write config to a file for logging
if not FLAGS.forward_only:
with open(os.path.join(log_dir, "run.log"), "wb") as f:
f.write(pp(config, output=False))
# create a folder by force
ckp_dir = os.path.join(log_dir, "checkpoints")
if not os.path.exists(ckp_dir):
os.mkdir(ckp_dir)
ckpt = get_checkpoint_state(ckp_dir)
print("Created models with fresh parameters.")
model.apply(lambda m: [torch.nn.init.uniform_(p.data, -1.0 * config.init_w, config.init_w) for p in m.parameters()])
# Load word2vec weight
if api.word2vec is not None and not FLAGS.forward_only:
print("Loaded word2vec")
model.embedding.weight.data.copy_(torch.from_numpy(np.array(api.word2vec)))
model.embedding.weight.data[0].fill_(0)
if ckpt:
print("Reading dm models parameters from %s" % ckpt)
model.load_state_dict(torch.load(ckpt))
#
# turn to cuda
model.cuda()
if not FLAGS.forward_only:
dm_checkpoint_path = os.path.join(ckp_dir, model.__class__.__name__+ "-%d.pth")
global_t = 1
patience = 10 # wait for at least 10 epoch before stop
dev_loss_threshold = np.inf
best_dev_loss = np.inf
for epoch in range(config.max_epoch):
print(">> Epoch %d with lr %f" % (epoch, model.learning_rate))
# begin training
if train_feed.num_batch is None or train_feed.ptr >= train_feed.num_batch:
train_feed.epoch_init(config.batch_size, config.backward_size,
config.step_size, shuffle=True)
global_t, train_loss = model.train_model(global_t, train_feed, update_limit=config.update_limit)
# begin validation
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
model.eval()
valid_loss = model.valid_model("ELBO_VALID", valid_feed)
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=True, intra_shuffle=False)
model.test_model(test_feed, num_batch=5)
model.train()
done_epoch = epoch + 1
# only save a models if the dev loss is smaller
# Decrease learning rate if no improvement was seen over last 3 times.
if config.op == "sgd" and done_epoch > config.lr_hold:
model.learning_rate_decay()
if valid_loss < best_dev_loss:
if valid_loss <= dev_loss_threshold * config.improve_threshold:
patience = max(patience, done_epoch * config.patient_increase)
dev_loss_threshold = valid_loss
# still save the best train model
if FLAGS.save_model:
print("Save model!! to {}".format(log_dir))
torch.save(model.state_dict(), dm_checkpoint_path %(epoch))
best_dev_loss = valid_loss
if config.early_stop and patience <= done_epoch:
print("!!Early stop due to run out of patience!!")
break
print('DAseq: {}, Feature: {}'.format(str(config.use_da_seq), str(config.use_feat)))
print("Best validation loss %f" % best_dev_loss)
print("Done training")
else:
# begin validation
valid_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
model.eval()
model.valid_model("ELBO_VALID", valid_feed)
test_feed.epoch_init(valid_config.batch_size, valid_config.backward_size,
valid_config.step_size, shuffle=False, intra_shuffle=False)
model.valid_model("ELBO_TEST", test_feed)
dest_f = open(os.path.join(log_dir, "test.txt"), "wb")
test_feed.epoch_init(test_config.batch_size, test_config.backward_size,
test_config.step_size, shuffle=False, intra_shuffle=False)
model.test_model(test_feed, num_batch=None, repeat=10, dest=dest_f)
model.train()
dest_f.close()
if __name__ == "__main__":
if FLAGS.forward_only:
if FLAGS.test_path is None:
print("Set test_path before forward only")
exit(1)
main()