def test_create_pileup_image_returns_none_for_bad_region(self):
self.mock_ref_reader.is_valid_interval.return_value = False
self.dv_call.variant.start = 3
self.assertIsNone(self.pic.create_pileup_images(self.dv_call))
test_utils.assert_called_once_workaround(
self.mock_ref_reader.is_valid_interval)
self.mock_ref_reader.bases.assert_not_called()
def test_get_reference_bases_bad_region_returns_none(self):
self.mock_ref_reader.is_valid_interval.return_value = False
self.dv_call.variant.start = 3
self.assertIsNone(self.pic.get_reference_bases(self.variant))
test_utils.assert_called_once_workaround(
self.mock_ref_reader.is_valid_interval)
self.mock_ref_reader.bases.assert_not_called()
def test_on_demand_initialization_called_if_not_initialized(self):
self.assertFalse(self.processor.initialized)
self.processor.in_memory_sam_reader = mock.Mock()
mock_rr = self.add_mock('region_reads', retval=[])
mock_cir = self.add_mock('candidates_in_region', retval=([], []))
self.processor.process(self.region)
test_utils.assert_called_once_workaround(self.mock_init)
mock_rr.assert_called_once_with(self.region)
self.processor.in_memory_sam_reader.replace_reads.assert_called_once_with(
[])
mock_cir.assert_called_once_with(self.region)
def test_get_training_batches(self, compressed_inputs):
golden_dataset = make_golden_dataset(compressed_inputs)
batch_size = 16
with tf.Session() as sess:
mock_model = mock.MagicMock(autospec=modeling.DeepVariantModel)
mock_model.preprocess_image.side_effect = functools.partial(
tf.image.resize_image_with_crop_or_pad,
target_height=107,
target_width=221)
batch = data_providers.make_training_batches(
golden_dataset.get_slim_dataset(), mock_model, batch_size)
# We should have called our preprocess_image exactly once. We don't have
# the actual objects to test for the call, though.
test_utils.assert_called_once_workaround(
mock_model.preprocess_image)
# Get our images, labels, and variants for further testing.
sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
images, labels, variants = sess.run(batch)
# Checks that our labels are the right shape and are one-hot encoded.
self.assertEqual(
(batch_size, 107, 221, pileup_image.DEFAULT_NUM_CHANNEL),
images.shape)
self.assertEqual((batch_size, ), labels.shape)
for label in labels:
self.assertTrue(0 <= label <= 2)
# Check that our variants has the shape we expect and actually contain
# variants by decoding them and checking the reference_name.
self.assertEqual((batch_size, ), variants.shape)
for variant in variantutils.decode_variants(variants):
self.assertEqual(variant.reference_name, 'chr20')
# Shutdown tensorflow
coord.request_stop()
coord.join(threads)