class ModelingTest(
six.with_metaclass(parameterized.TestGeneratorMetaclass,
tf.test.TestCase)):
@parameterized.parameters((model_class().name, type(model_class()))
for model_class in modeling.all_models())
def test_get_model_existing_models(self, model_name, expected_class):
self.assertIsInstance(modeling.get_model(model_name), expected_class)
def test_get_model_unknown_model_signals_error(self):
with six.assertRaisesRegex(self, ValueError, 'Unknown model'):
modeling.get_model('unknown_model_1234')
def test_make_deepvariant_slim_model(self):
model = modeling.DeepVariantSlimModel(
name='foo',
n_classes_model_variable=['n_classes'],
excluded_scopes_for_incompatible_classes=['logits'],
excluded_scopes_for_incompatible_channels=['logits'],
pretrained_model_path='path')
self.assertEqual('foo', model.name)
self.assertEqual(['n_classes'], model.n_classes_model_variable)
self.assertEqual(['logits'],
model.excluded_scopes_for_incompatible_classes)
self.assertEqual(['logits'],
model.excluded_scopes_for_incompatible_channels)
self.assertEqual('path', model.pretrained_model_path)
def test_is_encoded_variant_type(self):
types = [
tf_utils.EncodedVariantType.SNP.value,
tf_utils.EncodedVariantType.INDEL.value
]
tensor = tf.constant(types * 4, dtype=tf.int64)
def _run(tensor_to_run):
with self.test_session() as sess:
return list(sess.run(tensor_to_run))
self.assertEqual(
_run(
modeling.is_encoded_variant_type(
tensor, tf_utils.EncodedVariantType.SNP)),
[True, False] * 4)
self.assertEqual(
_run(
modeling.is_encoded_variant_type(
tensor, tf_utils.EncodedVariantType.INDEL)),
[False, True] * 4)
@parameterized.parameters(
dict(labels=[0, 2, 1, 0], target_class=0, expected=[0, 1, 1, 0]),
dict(labels=[0, 2, 1, 0], target_class=1, expected=[1, 1, 0, 1]),
dict(labels=[0, 2, 1, 0], target_class=2, expected=[1, 0, 1, 1]),
)
def test_binarize(self, labels, target_class, expected):
with self.test_session() as sess:
result = sess.run(
modeling.binarize(np.array(labels), np.array(target_class)))
self.assertListEqual(result.tolist(), expected)
@parameterized.parameters([True, False])
def test_eval_metric_fn(self, include_variant_types):
labels = tf.constant([1, 0], dtype=tf.int64)
predictions = tf.constant([[1, 0], [0, 1]], dtype=tf.int64)
if include_variant_types:
variant_types = tf.constant([0, 1], dtype=tf.int64)
else:
variant_types = None
expected = modeling.eval_function_metrics(
has_variant_types=include_variant_types)
actual = modeling.eval_metric_fn(labels, predictions, variant_types)
self.assertEqual(set(expected.keys()), set(actual.keys()))
def test_variables_to_restore_from_model(self):
model = modeling.DeepVariantModel('test', 'path')
# We haven't created a slim model, so the variables_to_restore_from_model
# should be returning an empty list.
self.assertEqual([], model.variables_to_restore_from_model())
# Create two model variable and one regular variables.
with tf.compat.v1.variable_scope('model'):
with tf.compat.v1.variable_scope('l1'):
w1 = slim.model_variable('w1', shape=[10, 3, 3])
with tf.compat.v1.variable_scope('l2'):
w2 = slim.model_variable('w2', shape=[10, 3, 3])
w3 = slim.model_variable('w3', shape=[10, 3, 3])
v1 = slim.variable('my_var', shape=[20, 1])
# The only variables in the system are the three we've created.
six.assertCountEqual(self, [w1, w2, w3, v1], slim.get_variables())
# We get just the three model variables without any excludes.
six.assertCountEqual(self, [w1, w2, w3],
model.variables_to_restore_from_model())
# As well as when exclude_scopes is an empty list.
six.assertCountEqual(
self, [w1, w2, w3],
model.variables_to_restore_from_model(exclude_scopes=[]))
# Excluding model/l1 variables gives us w2 and w3.
six.assertCountEqual(
self, [w2, w3],
model.variables_to_restore_from_model(exclude_scopes=['model/l1']))
# Excluding model/l2 gives us just w1 back.
six.assertCountEqual(
self, [w1],
model.variables_to_restore_from_model(exclude_scopes=['model/l2']))
# Excluding multiple scopes works as expected.
six.assertCountEqual(
self, [],
model.variables_to_restore_from_model(
exclude_scopes=['model/l1', 'model/l2']))
# Excluding the root model scope also produces no variables..
six.assertCountEqual(
self, [],
model.variables_to_restore_from_model(exclude_scopes=['model']))
class ModelingTest(
six.with_metaclass(parameterized.TestGeneratorMetaclass, tf.test.TestCase)):
@parameterized.parameters(
(model.name, type(model)) for model in modeling.all_models())
def test_get_model_existing_models(self, model_name, expected_class):
self.assertIsInstance(modeling.get_model(model_name), expected_class)
def test_get_model_unknown_model_signals_error(self):
with self.assertRaisesRegexp(ValueError, 'Unknown model'):
modeling.get_model('unknown_model_1234')
def test_make_deepvariant_slim_model(self):
model = modeling.DeepVariantSlimModel(
name='foo',
n_classes_model_variable=['n_classes'],
excluded_scopes=['logits'],
pretrained_model_path='path')
self.assertEqual('foo', model.name)
self.assertEqual(['n_classes'], model.n_classes_model_variable)
self.assertEqual(['logits'], model.excluded_scopes)
self.assertEqual('path', model.pretrained_model_path)
def test_variables_to_restore_from_model(self):
model = modeling.DeepVariantModel('test', 'path')
# We haven't created a slim model, so the variables_to_restore_from_model
# should be returning an empty list.
self.assertEqual([], model.variables_to_restore_from_model())
# Create two model variable and one regular variables.
with tf.variable_scope('model'):
with tf.variable_scope('l1'):
w1 = slim.model_variable('w1', shape=[10, 3, 3])
with tf.variable_scope('l2'):
w2 = slim.model_variable('w2', shape=[10, 3, 3])
w3 = slim.model_variable('w3', shape=[10, 3, 3])
v1 = slim.variable('my_var', shape=[20, 1])
# The only variables in the system are the three we've created.
self.assertItemsEqual([w1, w2, w3, v1], slim.get_variables())
# We get just the three model variables without any excludes.
self.assertItemsEqual([w1, w2, w3], model.variables_to_restore_from_model())
# As well as when exclude_scopes is an empty list.
self.assertItemsEqual(
[w1, w2, w3], model.variables_to_restore_from_model(exclude_scopes=[]))
# Excluding model/l1 variables gives us w2 and w3.
self.assertItemsEqual(
[w2, w3],
model.variables_to_restore_from_model(exclude_scopes=['model/l1']))
# Excluding model/l2 gives us just w1 back.
self.assertItemsEqual(
[w1],
model.variables_to_restore_from_model(exclude_scopes=['model/l2']))
# Excluding multiple scopes works as expected.
self.assertItemsEqual(
[],
model.variables_to_restore_from_model(
exclude_scopes=['model/l1', 'model/l2']))
# Excluding the root model scope also produces no variables..
self.assertItemsEqual(
[], model.variables_to_restore_from_model(exclude_scopes=['model']))