def main(argv):
utils.setup_main()
del argv
utils.setup_tf()
nbatch = FLAGS.samples // FLAGS.batch
dataset = data.DATASETS()[FLAGS.dataset]()
groups = [('labeled', dataset.train_labeled),
('unlabeled', dataset.train_unlabeled),
('test', dataset.test.repeat())]
groups = [(name, ds.batch(
FLAGS.batch).prefetch(16).make_one_shot_iterator().get_next())
for name, ds in groups]
with tf.train.MonitoredSession() as sess:
for group, train_data in groups:
stats = np.zeros(dataset.nclass, np.int32)
minmax = [], []
for _ in trange(nbatch,
leave=False,
unit='img',
unit_scale=FLAGS.batch,
desc=group):
v = sess.run(train_data)['label']
for u in v:
stats[u] += 1
minmax[0].append(v.min())
minmax[1].append(v.max())
print(group)
print(' Label range', min(minmax[0]), max(minmax[1]))
print(
' Stats',
' '.join(['%.2f' % (100 * x) for x in (stats / stats.max())]))
def main(argv):
del argv
utils.setup_tf()
dataset = DATASETS[FLAGS.dataset]()
with tf.Session(config=utils.get_config()) as sess:
hashes = (collect_hashes(sess, 'labeled', dataset.eval_labeled),
collect_hashes(sess, 'unlabeled', dataset.eval_unlabeled),
collect_hashes(sess, 'validation', dataset.valid),
collect_hashes(sess, 'test', dataset.test))
print('Overlap matrix (should be an almost perfect diagonal matrix with counts).')
groups = 'labeled unlabeled validation test'.split()
fmt = '%-10s %10s %10s %10s %10s'
print(fmt % tuple([''] + groups))
for p, x in enumerate(hashes):
overlaps = [len(x & y) for y in hashes]
print(fmt % tuple([groups[p]] + overlaps))
wd=FLAGS.wd,
arch=FLAGS.arch,
warmup_pos=FLAGS.warmup_pos,
batch=FLAGS.batch,
nclass=dataset.nclass,
ema=FLAGS.ema,
beta=FLAGS.beta,
consistency_weight=FLAGS.consistency_weight,
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('consistency_weight', 50., 'Consistency weight.')
flags.DEFINE_float(
'warmup_pos', 0.4,
'Relative position at which constraint loss warmup ends.')
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.')
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('augment', 'd.d.d')
FLAGS.set_default('dataset', 'cifar10.3@250-5000')
FLAGS.set_default('batch', 64)
FLAGS.set_default('lr', 0.002)
