def test_get_shape_from_examples_path_invalid_path(self):
with self.assertRaisesRegexp(Exception, '/this/path/does/not'):
data_providers.DeepVariantDataSet(
name='test_invalid_path',
source='/this/path/does/not/exist',
num_examples=1,
num_classes=3)
def test_dataset_definition(self):
ds = data_providers.DeepVariantDataSet(name='name',
source='test.tfrecord',
num_examples=10,
num_classes=2,
tensor_shape=[11, 13, 7])
self.assertEqual('name', ds.name)
self.assertEqual('test.tfrecord', ds.source)
self.assertEqual(10, ds.num_examples)
self.assertEqual(2, ds.num_classes)
self.assertEqual([11, 13, 7], ds.tensor_shape)
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'make_golden_dataset.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(test_utils.GOLDEN_TRAINING_EXAMPLES),
source_path)
else:
source_path = test_utils.GOLDEN_TRAINING_EXAMPLES
return data_providers.DeepVariantDataSet(name='labeled_golden',
source=source_path,
num_examples=49)
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)
io_utils.write_tfrecords([example], output_file)
ds = data_providers.DeepVariantDataSet(
name='test_shape',
source=test_utils.test_tmpfile(tfrecord_path_to_match),
num_examples=1)
self.assertEqual(valid_shape, ds.tensor_shape)