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mixmatch.py
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# 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.
"""MixMatch training.
- Ensure class consistency by producing a group of `nu` augmentations of the same image and guessing the label for the
group.
- Sharpen the target distribution.
- Use the sharpened distribution directly as a smooth label in MixUp.
"""
import functools
import os
from absl import app
from absl import flags
from easydict import EasyDict
from libml import layers, utils, models
from libml.data_pair import DATASETS
from libml.layers import MixMode
import tensorflow as tf
FLAGS = flags.FLAGS
class MixMatch(models.MultiModel):
def augment(self, x, l, beta, **kwargs):
assert 0, 'Do not call.'
def guess_label(self, y, classifier, T, **kwargs):
del kwargs
logits_y = [classifier(yi, training=True) for yi in y]
logits_y = tf.concat(logits_y, 0)
# Compute predicted probability distribution py.
p_model_y = tf.reshape(tf.nn.softmax(logits_y), [len(y), -1, self.nclass])
p_model_y = tf.reduce_mean(p_model_y, axis=0)
# Compute the target distribution.
p_target = tf.pow(p_model_y, 1. / T)
p_target /= tf.reduce_sum(p_target, axis=1, keep_dims=True)
return EasyDict(p_target=p_target, p_model=p_model_y)
def model(self, batch, lr, wd, ema, beta, w_match, warmup_kimg=1024, nu=2, mixmode='xxy.yxy', **kwargs):
hwc = [self.dataset.height, self.dataset.width, self.dataset.colors]
x_in = tf.placeholder(tf.float32, [None] + hwc, 'x')
y_in = tf.placeholder(tf.float32, [None, nu] + hwc, 'y')
l_in = tf.placeholder(tf.int32, [None], 'labels')
wd *= lr
w_match *= tf.clip_by_value(tf.cast(self.step, tf.float32) / (warmup_kimg << 10), 0, 1)
augment = MixMode(mixmode)
classifier = functools.partial(self.classifier, **kwargs)
y = tf.reshape(tf.transpose(y_in, [1, 0, 2, 3, 4]), [-1] + hwc)
guess = self.guess_label(tf.split(y, nu), classifier, T=0.5, **kwargs)
ly = tf.stop_gradient(guess.p_target)
lx = tf.one_hot(l_in, self.nclass)
xy, labels_xy = augment([x_in] + tf.split(y, nu), [lx] + [ly] * nu, [beta, beta])
x, y = xy[0], xy[1:]
labels_x, labels_y = labels_xy[0], tf.concat(labels_xy[1:], 0)
del xy, labels_xy
batches = layers.interleave([x] + y, batch)
skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
logits = [classifier(batches[0], training=True)]
post_ops = [v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS) if v not in skip_ops]
for batchi in batches[1:]:
logits.append(classifier(batchi, training=True))
logits = layers.interleave(logits, batch)
logits_x = logits[0]
logits_y = tf.concat(logits[1:], 0)
loss_xe = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels_x, logits=logits_x)
loss_xe = tf.reduce_mean(loss_xe)
loss_l2u = tf.square(labels_y - tf.nn.softmax(logits_y))
loss_l2u = tf.reduce_mean(loss_l2u)
tf.summary.scalar('losses/xe', loss_xe)
tf.summary.scalar('losses/l2u', loss_l2u)
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)
post_ops.extend([tf.assign(v, v * (1 - wd)) for v in utils.model_vars('classify') if 'kernel' in v.name])
train_op = tf.train.AdamOptimizer(lr).minimize(loss_xe + w_match * loss_l2u, colocate_gradients_with_ops=True)
with tf.control_dependencies([train_op]):
train_op = tf.group(*post_ops)
# Tuning op: only retrain batch norm.
skip_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
classifier(batches[0], training=True)
train_bn = tf.group(*[v for v in tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if v not in skip_ops])
return EasyDict(
x=x_in, y=y_in, label=l_in, train_op=train_op, tune_op=train_bn,
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):
del argv # Unused.
assert FLAGS.nu == 2
dataset = DATASETS[FLAGS.dataset]()
log_width = utils.ilog2(dataset.width)
model = MixMatch(
os.path.join(FLAGS.train_dir, dataset.name),
dataset,
lr=FLAGS.lr,
wd=FLAGS.wd,
arch=FLAGS.arch,
batch=FLAGS.batch,
nclass=dataset.nclass,
ema=FLAGS.ema,
beta=FLAGS.beta,
w_match=FLAGS.w_match,
scales=FLAGS.scales or (log_width - 2),
filters=FLAGS.filters,
repeat=FLAGS.repeat)
model.train(FLAGS.train_kimg << 10, FLAGS.report_kimg << 10)
if __name__ == '__main__':
utils.setup_tf()
flags.DEFINE_float('wd', 0.02, 'Weight decay.')
flags.DEFINE_float('ema', 0.999, 'Exponential moving average of params.')
flags.DEFINE_float('beta', 0.5, 'Mixup beta distribution.')
flags.DEFINE_float('w_match', 100, 'Weight for distribution matching loss.')
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.set_default('dataset', 'cifar10.3@250-5000')
FLAGS.set_default('batch', 64)
FLAGS.set_default('lr', 0.002)
FLAGS.set_default('train_kimg', 1 << 16)
app.run(main)