-
Notifications
You must be signed in to change notification settings - Fork 7
/
train_timit.py
179 lines (161 loc) · 6.34 KB
/
train_timit.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
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.autograd import Variable
import torchvision.datasets as dsets
import time
import click
import numpy
import numpy as np
import os
import random
from itertools import chain
import load
import torch.nn.functional as F
from model import ZForcing
def evaluate(dataset, model, split='valid'):
def get_batch():
if split == 'valid':
return dataset.get_valid_batch()
else:
return dataset.get_test_batch()
model.eval()
hidden = model.init_hidden(dataset.batch_size)
loss = []
for x, y, x_mask in get_batch():
x = Variable(torch.from_numpy(x), volatile=True).float().cuda()
y = Variable(torch.from_numpy(y), volatile=True).float().cuda()
x_mask = Variable(torch.from_numpy(x_mask), volatile=True).float().cuda()
# compute all the states for forward and backward
fwd_nll, bwd_nll, aux_nll, kld = \
model(x, y, x_mask, hidden)
loss.append((fwd_nll + kld).data[0])
return np.mean(np.asarray(loss))
@click.command()
@click.option('--expname', default='timit_logs')
@click.option('--nlayers', default=1)
@click.option('--seed', default=1234)
@click.option('--num_epochs', default=100)
@click.option('--rnn_dim', default=1024)
@click.option('--data', default='./')
@click.option('--bsz', default=32)
@click.option('--lr', default=0.001)
@click.option('--z_dim', default=256)
@click.option('--emb_dim', default=512)
@click.option('--mlp_dim', default=512)
@click.option('--aux_end', default=0.0)
@click.option('--aux_sta', default=0.0)
@click.option('--kla_sta', default=0.2)
@click.option('--bwd', default=0.)
@click.option('--cond_ln', is_flag=True)
@click.option('--z_force', is_flag=True)
def train(expname, nlayers, seed, num_epochs, rnn_dim, data, bsz, lr, z_dim,
emb_dim, mlp_dim, aux_end, aux_sta, kla_sta, bwd, cond_ln, z_force):
rng = np.random.RandomState(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
log_interval = 10
model_id = 'timit_seed{}_cln{}_zf{}_auxsta{}_auxend{}_klasta{}_bwd{}'.format(
seed, int(cond_ln), z_force, aux_sta, aux_end, kla_sta, bwd)
if not os.path.exists(expname):
os.makedirs(expname)
log_file_name = os.path.join(expname, model_id + '.txt')
model_file_name = os.path.join(expname, model_id + '.pt')
log_file = open(log_file_name, 'w')
model = ZForcing(200, emb_dim, rnn_dim, z_dim,
mlp_dim, 400, nlayers=nlayers,
cond_ln=cond_ln)
model.z_force = z_force
print('Loading data..')
timit = load.TimitData(data + 'timit_raw_batchsize64_seqlen40.npz', bsz)
print('Done.')
model.cuda()
hidden = model.init_hidden(bsz)
opt = torch.optim.Adam(model.parameters(), lr=lr, eps=1e-5)
kld_step = 0.00005
aux_step = 0.00005
kld_weight = kla_sta
aux_weight = aux_sta
nbatches = timit.u_train.shape[0] // bsz
t = time.time()
for epoch in range(num_epochs):
step = 0
old_valid_loss = np.inf
b_fwd_loss, b_bwd_loss, b_kld_loss, b_aux_loss, b_all_loss = \
(0., 0., 0., 0., 0.)
model.train()
print('Epoch {}: ({})'.format(epoch, model_id.upper()))
for x, y, x_mask in timit.get_train_batch():
step += 1
opt.zero_grad()
x = Variable(torch.from_numpy(x)).float().cuda()
y = Variable(torch.from_numpy(y)).float().cuda()
x_mask = Variable(torch.from_numpy(x_mask)).float().cuda()
# compute all the states for forward and backward
fwd_nll, bwd_nll, aux_nll, kld = model(x, y, x_mask, hidden)
bwd_nll = (aux_weight > 0.) * (bwd * bwd_nll)
aux_nll = aux_weight * aux_nll
all_loss = fwd_nll + bwd_nll + aux_nll + kld_weight * kld
# anneal kld cost
kld_weight += kld_step
kld_weight = min(kld_weight, 1.)
# anneal auxiliary cost
if aux_sta <= aux_end:
aux_weight += aux_step
aux_weight = min(aux_weight, aux_end)
else:
aux_weight -= aux_step
aux_weight = max(aux_weight, aux_end)
if np.isnan(all_loss.data[0]) or np.isinf(all_loss.data[0]):
continue
all_loss.backward()
torch.nn.utils.clip_grad_norm(model.parameters(), 100.)
opt.step()
b_all_loss += all_loss.data[0]
b_fwd_loss += fwd_nll.data[0]
b_bwd_loss += bwd_nll.data[0]
b_kld_loss += kld.data[0]
b_aux_loss += aux_nll.data[0]
if step % log_interval == 0:
s = time.time()
log_line = 'epoch: [%d/%d], step: [%d/%d], loss: %f, fwd loss: %f, aux loss: %f, bwd loss: %f, kld: %f, kld weight: %f, aux weight: %.4f, %.2fit/s' % (
epoch, num_epochs, step, nbatches,
b_all_loss / log_interval,
b_fwd_loss / log_interval,
b_aux_loss / log_interval,
b_bwd_loss / log_interval,
b_kld_loss / log_interval,
kld_weight,
aux_weight,
log_interval / (s - t))
b_all_loss = 0.
b_fwd_loss = 0.
b_bwd_loss = 0.
b_aux_loss = 0.
b_kld_loss = 0.
t = time.time()
print(log_line)
log_file.write(log_line + '\n')
log_file.flush()
# evaluate per epoch
print('--- Epoch finished ----')
val_loss = evaluate(timit, model)
log_line = 'valid -- epoch: %s, nll: %f' % (epoch, val_loss)
print(log_line)
log_file.write(log_line + '\n')
test_loss = evaluate(timit, model, split='test')
log_line = 'test -- epoch: %s, nll: %f' % (epoch, test_loss)
print(log_line)
log_file.write(log_line + '\n')
log_file.flush()
if old_valid_loss > val_loss:
old_valid_loss = val_loss
model.save(model_file_name)
else:
for param_group in opt.param_groups:
lr = param_group['lr']
if lr > 0.0001:
lr *= 0.5
param_group['lr'] = lr
if __name__ == '__main__':
train()