forked from google-research/fixmatch
/
uda.py
238 lines (208 loc) · 10.6 KB
/
uda.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
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
# Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unsupervised data augmentation (UDA)
"""
import functools
import os
import numpy as np
import tensorflow as tf
from absl import app
from absl import flags
from tqdm import trange
from libml import models, utils
from libml.data import PAIR_DATASETS
FLAGS = flags.FLAGS
class UDA(models.MultiModel):
TSA_MODES = 'no exp linear log'.split()
def tsa_threshold(self, tsa, scale=5, tsa_pos=10, **kwargs):
del kwargs
# step ratio will be maxed at (2 ** 14) * (2 ** 10) ~ 16.8M updates
step_ratio = tf.to_float(self.step) / tf.to_float(min(FLAGS.train_kimg, 1 << 14) << tsa_pos)
if tsa == 'linear':
coeff = step_ratio
elif tsa == 'exp': # [exp(-5), exp(0)] = [1e-2, 1]
coeff = tf.exp((step_ratio - 1) * scale)
elif tsa == 'log': # [1 - exp(0), 1 - exp(-5)] = [0, 0.99]
coeff = 1 - tf.exp((-step_ratio) * scale)
elif tsa == 'no':
coeff = tf.to_float(1.0)
elif tsa != 'no':
raise NotImplementedError(tsa)
coeff = tf.math.minimum(coeff, 1.0) # bound the coefficient
p_min = 1. / self.nclass
return coeff * (1 - p_min) + p_min
def tsa_loss_mask(self, tsa, logits, labels, tsa_pos, **kwargs):
thresh = self.tsa_threshold(tsa, tsa_pos=tsa_pos, **kwargs)
p_class = tf.nn.softmax(logits, axis=-1)
p_correct = tf.reduce_sum(labels * p_class, axis=-1)
loss_mask = tf.cast(p_correct <= thresh, tf.float32) # Ignore confident predictions.
return tf.stop_gradient(loss_mask)
@staticmethod
def confidence_based_masking(logits, p_class=None, thresh=0.9):
if logits is not None:
p_class = tf.nn.softmax(logits, axis=-1)
p_class_max = tf.reduce_max(p_class, axis=-1)
loss_mask = tf.cast(p_class_max >= thresh, tf.float32) # Ignore unconfident predictions.
return tf.stop_gradient(loss_mask)
@staticmethod
def softmax_temperature_controlling(logits, T):
# this is essentially the same as sharpening in mixmatch
logits = logits / T
return tf.stop_gradient(logits)
@staticmethod
def kl_divergence_from_logits(p_logits, q_logits):
p = tf.nn.softmax(p_logits)
log_p = tf.nn.log_softmax(p_logits)
log_q = tf.nn.log_softmax(q_logits)
kl = tf.reduce_sum(p * (log_p - log_q), -1)
return kl
@staticmethod
def entropy_from_logits(logits):
log_prob = tf.nn.log_softmax(logits, axis=-1)
prob = tf.exp(log_prob)
ent = tf.reduce_sum(-prob * log_prob, axis=-1)
return ent
def train(self, train_nimg, report_nimg):
if FLAGS.eval_ckpt:
self.eval_checkpoint(FLAGS.eval_ckpt)
return
batch = FLAGS.batch
train_labeled = self.dataset.train_labeled.repeat().shuffle(FLAGS.shuffle).parse().augment()
train_labeled = train_labeled.batch(batch).prefetch(16).make_one_shot_iterator().get_next()
train_unlabeled = self.dataset.train_unlabeled.repeat().shuffle(FLAGS.shuffle).parse().augment()
train_unlabeled = train_unlabeled.batch(batch * self.params['uratio']).prefetch(16)
train_unlabeled = train_unlabeled.make_one_shot_iterator().get_next()
scaffold = tf.train.Scaffold(saver=tf.train.Saver(max_to_keep=FLAGS.keep_ckpt,
pad_step_number=10))
with tf.Session(config=utils.get_config()) as sess:
self.session = sess
self.cache_eval()
with tf.train.MonitoredTrainingSession(
scaffold=scaffold,
checkpoint_dir=self.checkpoint_dir,
config=utils.get_config(),
save_checkpoint_steps=FLAGS.save_kimg << 10,
save_summaries_steps=report_nimg - batch) as train_session:
self.session = train_session._tf_sess()
gen_labeled = self.gen_labeled_fn(train_labeled)
gen_unlabeled = self.gen_unlabeled_fn(train_unlabeled)
self.tmp.step = self.session.run(self.step)
while self.tmp.step < train_nimg:
loop = trange(self.tmp.step % report_nimg, report_nimg, batch,
leave=False, unit='img', unit_scale=batch,
desc='Epoch %d/%d' % (1 + (self.tmp.step // report_nimg), train_nimg // report_nimg))
for _ in loop:
self.train_step(train_session, gen_labeled, gen_unlabeled)
while self.tmp.print_queue:
loop.write(self.tmp.print_queue.pop(0))
while self.tmp.print_queue:
print(self.tmp.print_queue.pop(0))
def model(self, batch, lr, wd, wu, we, confidence, uratio,
temperature=1.0, tsa='no', tsa_pos=10, ema=0.999, **kwargs):
hwc = [self.dataset.height, self.dataset.width, self.dataset.colors]
xt_in = tf.placeholder(tf.float32, [batch] + hwc, 'xt') # For training
x_in = tf.placeholder(tf.float32, [None] + hwc, 'x')
y_in = tf.placeholder(tf.float32, [batch * uratio, 2] + hwc, 'y')
l_in = tf.placeholder(tf.int32, [batch], 'labels')
l = tf.one_hot(l_in, self.nclass)
lrate = tf.clip_by_value(tf.to_float(self.step) / (FLAGS.train_kimg << 10), 0, 1)
lr *= tf.cos(lrate * (7 * np.pi) / (2 * 8))
tf.summary.scalar('monitors/lr', lr)
# Compute logits for xt_in and y_in
classifier = lambda x, **kw: self.classifier(x, **kw, **kwargs).logits
skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
logits = utils.para_cat(lambda x: classifier(x, training=True), tf.concat([xt_in, y_in[:, 0], y_in[:, 1]], 0))
post_ops = [v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if v not in skip_ops]
logits_x = logits[:batch]
logits_weak, logits_strong = tf.split(logits[batch:], 2)
del logits, skip_ops
# softmax temperature control
logits_weak_tgt = self.softmax_temperature_controlling(logits_weak, T=temperature)
# generate confidence mask based on sharpened distribution
pseudo_labels = tf.stop_gradient(tf.nn.softmax(logits_weak))
pseudo_mask = self.confidence_based_masking(logits=None, p_class=pseudo_labels, thresh=confidence)
tf.summary.scalar('monitors/mask', tf.reduce_mean(pseudo_mask))
tf.summary.scalar('monitors/conf_weak', tf.reduce_mean(tf.reduce_max(tf.nn.softmax(logits_weak), axis=1)))
tf.summary.scalar('monitors/conf_strong', tf.reduce_mean(tf.reduce_max(tf.nn.softmax(logits_strong), axis=1)))
kld = self.kl_divergence_from_logits(logits_weak_tgt, logits_strong)
entropy = self.entropy_from_logits(logits_weak)
loss_xeu = tf.reduce_mean(kld * pseudo_mask)
tf.summary.scalar('losses/xeu', loss_xeu)
loss_ent = tf.reduce_mean(entropy)
tf.summary.scalar('losses/entropy', loss_ent)
# supervised loss with TSA
loss_mask = self.tsa_loss_mask(tsa=tsa, logits=logits_x, labels=l, tsa_pos=tsa_pos)
loss_xe = tf.nn.softmax_cross_entropy_with_logits_v2(labels=l, logits=logits_x)
loss_xe = tf.reduce_sum(loss_xe * loss_mask) / tf.math.maximum(tf.reduce_sum(loss_mask), 1.0)
tf.summary.scalar('losses/xe', loss_xe)
tf.summary.scalar('losses/mask_sup', tf.reduce_mean(loss_mask))
# L2 regularization
loss_wd = sum(tf.nn.l2_loss(v) for v in utils.model_vars('classify') if 'kernel' in v.name)
tf.summary.scalar('losses/wd', loss_wd)
ema = tf.train.ExponentialMovingAverage(decay=ema)
ema_op = ema.apply(utils.model_vars())
ema_getter = functools.partial(utils.getter_ema, ema)
post_ops.append(ema_op)
train_op = tf.train.MomentumOptimizer(lr, 0.9, use_nesterov=True).minimize(
loss_xe + loss_xeu * wu + loss_ent * we + loss_wd * wd, colocate_gradients_with_ops=True)
with tf.control_dependencies([train_op]):
train_op = tf.group(*post_ops)
return utils.EasyDict(
xt=xt_in, x=x_in, y=y_in, label=l_in, train_op=train_op,
classify_raw=tf.nn.softmax(classifier(x_in, training=False)), # No EMA, for debugging.
classify_op=tf.nn.softmax(classifier(x_in, getter=ema_getter, training=False)))
def main(argv):
utils.setup_main()
del argv # Unused.
dataset = PAIR_DATASETS()[FLAGS.dataset]()
log_width = utils.ilog2(dataset.width)
model = UDA(
os.path.join(FLAGS.train_dir, dataset.name),
dataset,
lr=FLAGS.lr,
wd=FLAGS.wd,
wu=FLAGS.wu,
we=FLAGS.we,
arch=FLAGS.arch,
batch=FLAGS.batch,
nclass=dataset.nclass,
temperature=FLAGS.temperature,
tsa=FLAGS.tsa,
tsa_pos=FLAGS.tsa_pos,
confidence=FLAGS.confidence,
uratio=FLAGS.uratio,
scales=FLAGS.scales or (log_width - 2),
filters=FLAGS.filters,
repeat=FLAGS.repeat)
model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10) # 1024 epochs
if __name__ == '__main__':
utils.setup_tf()
flags.DEFINE_float('wu', 1, 'Consistency weight.')
flags.DEFINE_float('wd', 0.0005, 'Weight decay.')
flags.DEFINE_float('we', 0, 'Entropy minimization weight.')
flags.DEFINE_float('ema', 0.999, 'Exponential moving average of params.')
flags.DEFINE_float('confidence', 0.95, 'Confidence threshold.')
flags.DEFINE_float('temperature', 1, 'Softmax sharpening temperature.')
flags.DEFINE_integer('scales', 0, 'Number of 2x2 downscalings in the classifier.')
flags.DEFINE_integer('filters', 32, 'Filter size of convolutions.')
flags.DEFINE_integer('repeat', 4, 'Number of residual layers per stage.')
flags.DEFINE_integer('tsa_pos', 8, 'TSA change rate.')
flags.DEFINE_integer('uratio', 7, 'Unlabeled batch size ratio.')
flags.DEFINE_enum('tsa', 'no', UDA.TSA_MODES, 'TSA mode.')
FLAGS.set_default('augment', 'd.d.rac')
FLAGS.set_default('dataset', 'cifar10.3@250-1')
FLAGS.set_default('batch', 64)
FLAGS.set_default('lr', 0.03)
FLAGS.set_default('train_kimg', 1 << 16)
app.run(main)