def testGetNoneShapeFromEmptyExamplesPath(self, file_name_to_write,
tfrecord_path_to_match):
output_file = test_utils.test_tmpfile(file_name_to_write)
io_utils.write_tfrecords([], output_file)
self.assertIsNone(
tf_utils.get_shape_from_examples_path(
test_utils.test_tmpfile(tfrecord_path_to_match)))
def test_call_variants_with_empty_input(self):
source_path = test_utils.test_tmpfile('empty.tfrecord')
io_utils.write_tfrecords([], source_path)
# Make sure that prepare_inputs don't crash on empty input.
call_variants.prepare_inputs(source_path,
modeling.get_model('random_guess'),
batch_size=1)
def testGetShapeFromExamplesPath(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)
tf_utils.get_shape_from_examples_path(
test_utils.test_tmpfile(tfrecord_path_to_match))
def test_call_end2end_with_empty_shards(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(
test_utils.GOLDEN_CALLING_EXAMPLES, max_records=10))
# Write to 15 shards, which means there will be multiple empty shards.
source_path = test_utils.test_tmpfile('sharded@{}'.format(15))
io_utils.write_tfrecords(examples, source_path)
self.assertCallVariantsEmitsNRecordsForRandomGuess(source_path,
len(examples))
def test_call_end2end_empty_first_shard(self):
# Get only up to 10 examples.
examples = list(
io_utils.read_tfrecords(
test_utils.GOLDEN_CALLING_EXAMPLES, max_records=10))
empty_first_file = test_utils.test_tmpfile('empty_1st_shard-00000-of-00002')
io_utils.write_tfrecords([], empty_first_file)
second_file = test_utils.test_tmpfile('empty_1st_shard-00001-of-00002')
io_utils.write_tfrecords(examples, second_file)
self.assertCallVariantsEmitsNRecordsForRandomGuess(
test_utils.test_tmpfile('empty_1st_shard@2'), len(examples))
def test_call_variants_with_no_shape(self, model):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(test_utils.GOLDEN_CALLING_EXAMPLES))
# Remove image/shape.
del example.features.feature['image/shape']
source_path = test_utils.test_tmpfile('make_examples_out_noshape.tfrecord')
io_utils.write_tfrecords([example], source_path)
with self.assertRaisesRegexp(
ValueError, 'Invalid image/shape: we expect to find an image/shape '
'field with length 3.'):
call_variants.prepare_inputs(source_path, model, batch_size=1)
def make_golden_dataset(compressed_inputs=False):
if compressed_inputs:
source_path = test_utils.test_tmpfile(
'golden.postprocess_single_site_input.tfrecord.gz')
io_utils.write_tfrecords(
io_utils.read_tfrecords(test_utils.GOLDEN_POSTPROCESS_INPUT,
proto=deepvariant_pb2.CallVariantsOutput),
source_path)
else:
source_path = test_utils.GOLDEN_POSTPROCESS_INPUT
return source_path
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)
def test_reading_sharded_dataset(self, compressed_inputs):
golden_dataset = make_golden_dataset(compressed_inputs)
n_shards = 3
sharded_path = test_utils.test_tmpfile('sharded@{}'.format(n_shards))
io_utils.write_tfrecords(
io_utils.read_tfrecords(golden_dataset.source), sharded_path)
config_file = _test_dataset_config(
'test_sharded.pbtxt',
name='sharded_test',
tfrecord_path=sharded_path,
num_examples=golden_dataset.num_examples)
self.assertDataSetExamplesMatchExpected(
data_providers.get_dataset(config_file).get_slim_dataset(),
golden_dataset)
def test_call_variants_with_invalid_format(self, model, bad_format):
# Read one good record from a valid file.
example = next(io_utils.read_tfrecords(test_utils.GOLDEN_CALLING_EXAMPLES))
# Overwrite the image/format field to be an invalid value
# (anything but 'raw').
example.features.feature['image/format'].bytes_list.value[0] = bad_format
source_path = test_utils.test_tmpfile('make_examples_output.tfrecord')
io_utils.write_tfrecords([example], source_path)
outfile = test_utils.test_tmpfile('call_variants_invalid_format.tfrecord')
with self.assertRaises(ValueError):
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=1,
max_batches=1)
def test_prepare_inputs(self, filename, expand_to_file_pattern):
source_path = test_utils.test_tmpfile(filename)
io_utils.write_tfrecords(self.examples, source_path)
if expand_to_file_pattern:
# Transform foo@3 to foo-?????-of-00003.
source_path = io_utils.NormalizeToShardedFilePattern(source_path)
with self.test_session() as sess:
_, variants, _ = call_variants.prepare_inputs(
source_path, self.model, batch_size=1)
sess.run(tf.local_variables_initializer())
sess.run(tf.global_variables_initializer())
seen_variants = []
try:
while True:
seen_variants.extend(sess.run(variants))
except tf.errors.OutOfRangeError:
pass
self.assertItemsEqual(self.variants,
variantutils.decode_variants(seen_variants))
def test_call_end2end(self, model, shard_inputs, include_debug_info):
FLAGS.include_debug_info = include_debug_info
examples = list(io_utils.read_tfrecords(test_utils.GOLDEN_CALLING_EXAMPLES))
if shard_inputs:
# Create a sharded version of our golden examples.
source_path = test_utils.test_tmpfile('sharded@{}'.format(3))
io_utils.write_tfrecords(examples, source_path)
else:
source_path = test_utils.GOLDEN_CALLING_EXAMPLES
batch_size = 4
if model.name == 'random_guess':
# For the random guess model we can run everything.
max_batches = None
else:
# For all other models we only run a single batch for inference.
max_batches = 1
outfile = test_utils.test_tmpfile('call_variants.tfrecord')
call_variants.call_variants(
examples_filename=source_path,
checkpoint_path=modeling.SKIP_MODEL_INITIALIZATION_IN_TEST,
model=model,
output_file=outfile,
batch_size=batch_size,
max_batches=max_batches)
call_variants_outputs = list(
io_utils.read_tfrecords(outfile, deepvariant_pb2.CallVariantsOutput))
# Check that we have the right number of output protos.
self.assertEqual(
len(call_variants_outputs), batch_size * max_batches
if max_batches else len(examples))
# Check that our CallVariantsOutput (CVO) have the following critical
# properties:
# - we have one CVO for each example we processed.
# - the variant in the CVO is exactly what was in the example.
# - the alt_allele_indices of the CVO match those of its corresponding
# example.
# - there are 3 genotype probabilities and these are between 0.0 and 1.0.
# We can only do this test when processing all of the variants (max_batches
# is None), since we processed all of the examples with that model.
if max_batches is None:
self.assertItemsEqual([cvo.variant for cvo in call_variants_outputs],
[tf_utils.example_variant(ex) for ex in examples])
# Check the CVO debug_info: not filled if include_debug_info is False;
# else, filled by logic based on CVO.
if not include_debug_info:
for cvo in call_variants_outputs:
self.assertEqual(cvo.debug_info,
deepvariant_pb2.CallVariantsOutput.DebugInfo())
else:
for cvo in call_variants_outputs:
self.assertEqual(cvo.debug_info.has_insertion,
variantutils.has_insertion(cvo.variant))
self.assertEqual(cvo.debug_info.has_deletion,
variantutils.has_deletion(cvo.variant))
self.assertEqual(cvo.debug_info.is_snp, variantutils.is_snp(
cvo.variant))
self.assertEqual(cvo.debug_info.predicted_label,
np.argmax(cvo.genotype_probabilities))
def example_matches_call_variants_output(example, call_variants_output):
return (tf_utils.example_variant(example) == call_variants_output.variant
and tf_utils.example_alt_alleles_indices(
example) == call_variants_output.alt_allele_indices.indices)
for call_variants_output in call_variants_outputs:
# Find all matching examples.
matches = [
ex for ex in examples
if example_matches_call_variants_output(ex, call_variants_output)
]
# We should have exactly one match.
self.assertEqual(len(matches), 1)
example = matches[0]
# Check that we've faithfully copied in the alt alleles (though currently
# as implemented we find our example using this information so it cannot
# fail). Included here in case that changes in the future.
self.assertEqual(
list(tf_utils.example_alt_alleles_indices(example)),
list(call_variants_output.alt_allele_indices.indices))
# We should have exactly three genotype probabilities (assuming our
# ploidy == 2).
self.assertEqual(len(call_variants_output.genotype_probabilities), 3)
# These are probabilities so they should be between 0 and 1.
self.assertTrue(
0 <= gp <= 1 for gp in call_variants_output.genotype_probabilities)
def write_test_protos(self, filename):
protos = [core_pb2.ContigInfo(name=str(i)) for i in range(10)]
path = test_utils.test_tmpfile(filename)
io.write_tfrecords(protos, path)
return protos, path
