def test_get_shape_from_examples_path_invalid_path(self):
with self.assertRaisesRegexp(Exception, '/this/path/does/not'):
data_providers.DeepVariantInput(
mode=tf.estimator.ModeKeys.PREDICT,
name='test_invalid_path',
input_file_spec='/this/path/does/not/exist',
num_examples=1)
def test_max_examples_overrides_num_examples(self, num_examples,
max_examples, expected):
dataset = data_providers.DeepVariantInput(
# Use predict mode so we can have num_examples == None.
mode=tf.estimator.ModeKeys.PREDICT,
input_file_spec=testdata.GOLDEN_TRAINING_EXAMPLES,
num_examples=num_examples,
max_examples=max_examples)
self.assertEqual(expected, dataset.num_examples)
def get_batch_feed(self, batch_size=1, use_tpu=False):
# This is an input_fn reading test_utils.N_GOLDEN_CALLING_EXAMPLES records.
# Use PREDICT mode so we get finite input.
dvi = data_providers.DeepVariantInput(
mode=tf.estimator.ModeKeys.PREDICT,
input_file_spec=testdata.GOLDEN_CALLING_EXAMPLES,
num_examples=testdata.N_GOLDEN_CALLING_EXAMPLES,
tensor_shape=None,
use_tpu=use_tpu)
params = {'batch_size': batch_size}
batch_feed = dvi(params).make_one_shot_iterator().get_next()
return batch_feed
def test_get_shape_from_examples_path(self, file_name_to_write,
tfrecord_path_to_match):
example = example_pb2.Example()
valid_shape = [1, 2, 3]
example.features.feature['image/shape'].int64_list.value.extend(valid_shape)
output_file = test_utils.test_tmpfile(file_name_to_write)
tfrecord.write_tfrecords([example], output_file)
ds = data_providers.DeepVariantInput(
mode=tf.estimator.ModeKeys.PREDICT,
name='test_shape',
input_file_spec=test_utils.test_tmpfile(tfrecord_path_to_match),
num_examples=1)
self.assertEqual(valid_shape, ds.tensor_shape)
def test_dataset_definition(self):
ds = data_providers.DeepVariantInput(
mode=tf.estimator.ModeKeys.PREDICT,
name='name',
input_file_spec='test.tfrecord',
num_examples=10,
num_classes=dv_constants.NUM_CLASSES,
tensor_shape=[11, 13, dv_constants.PILEUP_NUM_CHANNELS])
self.assertEqual('name', ds.name)
self.assertEqual('test.tfrecord', ds.input_file_spec)
self.assertEqual(10, ds.num_examples)
self.assertEqual(dv_constants.NUM_CLASSES, ds.num_classes)
self.assertEqual([11, 13, dv_constants.PILEUP_NUM_CHANNELS],
ds.tensor_shape)
