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
|
""" Report manager utility """
import time
from datetime import datetime
import onmt
from onmt.utils.logging import logger
def build_report_manager(opt, gpu_rank):
if opt.tensorboard and gpu_rank <= 0:
from torch.utils.tensorboard import SummaryWriter
if not hasattr(opt, 'tensorboard_log_dir_dated'):
opt.tensorboard_log_dir_dated = (
opt.tensorboard_log_dir +
datetime.now().strftime("/%b-%d_%H-%M-%S")
)
writer = SummaryWriter(opt.tensorboard_log_dir_dated, comment="Unmt")
else:
writer = None
report_mgr = ReportMgr(opt.report_every, start_time=-1,
tensorboard_writer=writer)
return report_mgr
class ReportMgrBase(object):
"""
Report Manager Base class
Inherited classes should override:
* `_report_training`
* `_report_step`
"""
def __init__(self, report_every, start_time=-1.):
"""
Args:
report_every(int): Report status every this many sentences
start_time(float): manually set report start time. Negative values
means that you will need to set it later or use `start()`
"""
self.report_every = report_every
self.start_time = start_time
def start(self):
self.start_time = time.time()
def log(self, *args, **kwargs):
logger.info(*args, **kwargs)
def report_training(self, step, num_steps, learning_rate, patience,
report_stats, multigpu=False):
"""
This is the user-defined batch-level traing progress
report function.
Args:
step(int): current step count.
num_steps(int): total number of batches.
learning_rate(float): current learning rate.
report_stats(Statistics): old Statistics instance.
Returns:
report_stats(Statistics): updated Statistics instance.
"""
if self.start_time < 0:
raise ValueError("""ReportMgr needs to be started
(set 'start_time' or use 'start()'""")
if step % self.report_every == 0:
if multigpu:
report_stats = \
onmt.utils.Statistics.all_gather_stats(report_stats)
self._report_training(
step, num_steps, learning_rate, patience, report_stats)
return onmt.utils.Statistics()
else:
return report_stats
def _report_training(self, *args, **kwargs):
""" To be overridden """
raise NotImplementedError()
def report_step(self, lr, patience, step, train_stats=None,
valid_stats=None):
"""
Report stats of a step
Args:
lr(float): current learning rate
patience(int): current patience
step(int): current step
train_stats(Statistics): training stats
valid_stats(Statistics): validation stats
"""
self._report_step(
lr, patience, step,
train_stats=train_stats,
valid_stats=valid_stats)
def _report_step(self, *args, **kwargs):
raise NotImplementedError()
class ReportMgr(ReportMgrBase):
def __init__(self, report_every, start_time=-1., tensorboard_writer=None):
"""
A report manager that writes statistics on standard output as well as
(optionally) TensorBoard
Args:
report_every(int): Report status every this many sentences
tensorboard_writer(:obj:`tensorboard.SummaryWriter`):
The TensorBoard Summary writer to use or None
"""
super(ReportMgr, self).__init__(report_every, start_time)
self.tensorboard_writer = tensorboard_writer
def maybe_log_tensorboard(self, stats, prefix, learning_rate,
patience, step):
if self.tensorboard_writer is not None:
stats.log_tensorboard(
prefix, self.tensorboard_writer, learning_rate, patience, step)
def _report_training(self, step, num_steps, learning_rate, patience,
report_stats):
"""
See base class method `ReportMgrBase.report_training`.
"""
report_stats.output(step, num_steps,
learning_rate, self.start_time)
self.maybe_log_tensorboard(report_stats,
"progress",
learning_rate,
patience,
step)
report_stats = onmt.utils.Statistics()
return report_stats
def _report_step(self, lr, patience, step,
train_stats=None,
valid_stats=None):
"""
See base class method `ReportMgrBase.report_step`.
"""
if train_stats is not None:
self.log('Train perplexity: %g' % train_stats.ppl())
self.log('Train accuracy: %g' % train_stats.accuracy())
self.maybe_log_tensorboard(train_stats,
"train",
lr,
patience,
step)
if valid_stats is not None:
self.log('Validation perplexity: %g' % valid_stats.ppl())
self.log('Validation accuracy: %g' % valid_stats.accuracy())
self.maybe_log_tensorboard(valid_stats,
"valid",
lr,
patience,
step)
|
