def test_hashes_regression(self):
"""Test that the hashes are fixed and repeatable.
None of these hashes should change through any modification you make to the code.
You can avoid this by ensuring you don't change existing hyperparameters and only
add new hyperparameters that have default values.
If they do, you will no longer be able to access your old models.
"""
desc = LotteryDesc(
dataset_hparams=hparams.DatasetHparams('cifar10', 128),
model_hparams=hparams.ModelHparams('cifar_resnet_20', 'kaiming_normal', 'uniform'),
training_hparams=hparams.TrainingHparams('sgd', 0.1, '160ep'),
pruning_hparams=Strategy.get_pruning_hparams()('sparse_global')
)
self.assertEqual(desc.hashname, 'lottery_da8fd50859ba6d59aceca9d50ebcbf7e')
with self.subTest():
desc.training_hparams.momentum = 0.9
self.assertEqual(desc.hashname, 'lottery_028eb999ecd1980cd012589829c945a3')
with self.subTest():
desc.training_hparams.milestone_steps = '80ep,120ep'
desc.training_hparams.gamma = 0.1
self.assertEqual(desc.hashname, 'lottery_e696cbf42d8758b8afdf2a16fad1de15')
with self.subTest():
desc.training_hparams.weight_decay = 1e-4
self.assertEqual(desc.hashname, 'lottery_93bc65d66dfa64ffaf2a0ab105433a2c')
with self.subTest():
desc.training_hparams.warmup_steps = '20ep'
self.assertEqual(desc.hashname, 'lottery_4e7b9ee929e8b1c911c5295233e6828f')
with self.subTest():
desc.training_hparams.data_order_seed = 0
self.assertEqual(desc.hashname, 'lottery_d51482c0d378de4cc71b87b38df2ea84')
with self.subTest():
desc.dataset_hparams.do_not_augment = True
self.assertEqual(desc.hashname, 'lottery_231b1efe748045875f738d860f4cb547')
with self.subTest():
desc.dataset_hparams.transformation_seed = 0
self.assertEqual(desc.hashname, 'lottery_4dfd57a481be9a2d840f7ad5d1e6f5f0')
with self.subTest():
desc.dataset_hparams.subsample_fraction = 0.5
self.assertEqual(desc.hashname, 'lottery_59ea6f2fab91a9515ae4bccd5de70878')
with self.subTest():
desc.dataset_hparams.random_labels_fraction = 0.7
self.assertEqual(desc.hashname, 'lottery_8b59e5a4d5d72575f1fba67b476899fc')
with self.subTest():
desc.dataset_hparams.unsupervised_labels = 'rotation'
self.assertEqual(desc.hashname, 'lottery_81f340e038ec29ffa9f858d9a8762211')
with self.subTest():
desc.dataset_hparams.blur_factor = 4
self.assertEqual(desc.hashname, 'lottery_4e78e2719ef5c16ba3e0444bc10dfb08')
with self.subTest():
desc.model_hparams.batchnorm_frozen = True
self.assertEqual(desc.hashname, 'lottery_8db76b3c3a08c4a1643f066768ff4e56')
with self.subTest():
desc.model_hparams.batchnorm_frozen = False
desc.model_hparams.others_frozen = True
self.assertEqual(desc.hashname, 'lottery_3a0f8b86c0813802537aea2ebe723051')
with self.subTest():
desc.model_hparams.others_frozen = False
desc.pruning_hparams.pruning_layers_to_ignore = 'fc.weight'
self.assertEqual(desc.hashname, 'lottery_d74aca8d02109ec0816739c2f7057433')
def test_get_pruning_hparams(self):
self.assertTrue(
issubclass(Strategy.get_pruning_hparams(), PruningHparams))