def setUp(self):
ops.reset_default_graph()
dim = 1
num = 3
with ops.name_scope('some_scope'):
# Basically from 0 to dim*num-1.
flat_data = math_ops.linspace(0.0, dim * num - 1, dim * num)
bias = variables.Variable(array_ops.reshape(flat_data, (num, dim)),
name='bias')
save = saver.Saver([bias])
with self.test_session() as sess:
variables.global_variables_initializer().run()
self.bundle_file = os.path.join(test.get_temp_dir(),
'bias_checkpoint')
save.save(sess, self.bundle_file)
self.new_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword_new.txt')
self.old_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword.txt')
self.init_val = 42
def _init_val_initializer(shape, dtype=None, partition_info=None):
del dtype, partition_info # Unused by this unit-testing initializer.
return array_ops.tile(
constant_op.constant([[self.init_val]], dtype=dtypes.float32),
shape)
self.initializer = _init_val_initializer
def testMaybeSavedModelDir(self):
base_path = test.test_src_dir_path("/python/saved_model")
self.assertFalse(loader.maybe_saved_model_directory(base_path))
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.assertTrue(loader.maybe_saved_model_directory(base_path))
base_path = "complete_garbage"
self.assertFalse(loader.maybe_saved_model_directory(base_path))
def setUp(self):
ops.reset_default_graph()
dim = 1
num = 3
with ops.name_scope('some_scope'):
# Basically from 0 to dim*num-1.
flat_data = math_ops.linspace(0.0, dim * num - 1, dim * num)
bias = variables.Variable(
array_ops.reshape(flat_data, (num, dim)), name='bias')
save = saver.Saver([bias])
with self.test_session() as sess:
variables.global_variables_initializer().run()
self.bundle_file = os.path.join(test.get_temp_dir(), 'bias_checkpoint')
save.save(sess, self.bundle_file)
self.new_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword_new.txt')
self.old_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword.txt')
self.init_val = 42
def _init_val_initializer(shape, dtype=None, partition_info=None):
del dtype, partition_info # Unused by this unit-testing initializer.
return array_ops.tile(
constant_op.constant([[self.init_val]], dtype=dtypes.float32), shape)
self.initializer = _init_val_initializer
def testMaybeSessionBundleDir(self): base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH) self.assertTrue(session_bundle.maybe_session_bundle_dir(base_path)) base_path = test.test_src_dir_path(SAVED_MODEL_PATH) self.assertFalse(session_bundle.maybe_session_bundle_dir(base_path)) base_path = "complete_garbage" self.assertFalse(session_bundle.maybe_session_bundle_dir(base_path))
def testMaybeSavedModelDir(self):
base_path = test.test_src_dir_path("/python/saved_model")
self.assertFalse(loader.maybe_saved_model_directory(base_path))
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.assertTrue(loader.maybe_saved_model_directory(base_path))
base_path = "complete_garbage"
self.assertFalse(loader.maybe_saved_model_directory(base_path))
def testMaybeSessionBundleDir(self): base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH) self.assertTrue(session_bundle.maybe_session_bundle_dir(base_path)) base_path = test.test_src_dir_path(SAVED_MODEL_PATH) self.assertFalse(session_bundle.maybe_session_bundle_dir(base_path)) base_path = "complete_garbage" self.assertFalse(session_bundle.maybe_session_bundle_dir(base_path))
def testRunCommandWithDebuggerEnabled(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(),
'testRunCommandNewOutdir_inputs.npz')
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
np.savez(input_path, x0=x, x1=x_notused)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
output_dir, '--tf_debug'
])
def fake_wrapper_session(sess):
return sess
with test.mock.patch.object(local_cli_wrapper,
'LocalCLIDebugWrapperSession',
side_effect=fake_wrapper_session,
autospec=True) as fake:
saved_model_cli.run(args)
fake.assert_called_with(test.mock.ANY)
y_actual = np.load(os.path.join(output_dir, 'y.npy'))
y_expected = np.array([[2.5], [3.0]])
self.assertAllClose(y_expected, y_actual)
def testEval(self):
if not is_tensorrt_enabled():
return
# TODO(b/162447069): Enable the test for TRT 7.1.3.
if trt_test.IsTensorRTVersionGreaterEqual(7, 1, 3):
return
model_dir = test.test_src_dir_path(
'python/compiler/tensorrt/test/testdata/mnist')
accuracy_tf_native = self._Run(is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(0.9662, accuracy_tf_native, rtol=3e-3, atol=3e-3)
if not trt_test.IsTensorRTVersionGreaterEqual(5):
return
accuracy_tf_trt = self._Run(is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(0.9675, accuracy_tf_trt, rtol=1e-3, atol=1e-3)
def testEval(self):
if not is_tensorrt_enabled():
return
model_dir = test.test_src_dir_path('python/compiler/tensorrt/test/testdata')
accuracy_tf_native = self._Run(
is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(0.9662, accuracy_tf_native, rtol=3e-3, atol=3e-3)
if get_linked_tensorrt_version()[0] < 5:
return
accuracy_tf_trt = self._Run(
is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(0.9675, accuracy_tf_trt, rtol=1e-3, atol=1e-3)
def setUpClass(cls):
gpu_memory_fraction_opt = ("--gpu_memory_fraction=%f" %
cls.PER_PROC_GPU_MEMORY_FRACTION)
worker_port = portpicker.pick_unused_port()
cluster_spec = "worker|localhost:%d" % worker_port
tf_logging.info("cluster_spec: %s", cluster_spec)
server_bin = test.test_src_dir_path(
"python/debug/grpc_tensorflow_server")
cls.server_target = "grpc://localhost:%d" % worker_port
cls.server_procs = {}
cls.server_procs["worker"] = subprocess.Popen([
server_bin,
"--cluster_spec=%s" % cluster_spec,
"--job_name=worker",
"--task_id=0",
gpu_memory_fraction_opt,
],
stdout=sys.stdout,
stderr=sys.stderr)
# Start debug server in-process, on separate thread.
(cls.debug_server_port, cls.debug_server_url, _,
cls.debug_server_thread, cls.debug_server
) = grpc_debug_test_server.start_server_on_separate_thread(
dump_to_filesystem=False)
tf_logging.info("debug server url: %s", cls.debug_server_url)
cls.session_config = config_pb2.ConfigProto(
gpu_options=config_pb2.GPUOptions(
per_process_gpu_memory_fraction=cls.
PER_PROC_GPU_MEMORY_FRACTION))
def testShowCommandErrorNoTagSet(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args(
['show', '--dir', base_path, '--tag_set', 'badtagset'])
with self.assertRaises(RuntimeError):
saved_model_cli.show(args)
def testBasic(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
ops.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path,
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[
constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
self._checkRegressionSignature(signatures, sess)
self._checkNamedSignatures(signatures, sess)
def testBasic(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
ops.reset_default_graph()
sess, meta_graph_def = session_bundle.load_session_bundle_from_path(
base_path,
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2}))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEquals(len(signatures_any), 1)
signatures = manifest_pb2.Signatures()
signatures_any[0].Unpack(signatures)
self._checkRegressionSignature(signatures, sess)
self._checkNamedSignatures(signatures, sess)
def run_all_tests():
"""Runs all sample model with TensorRT FP32/FP16 and reports latency."""
# The model_configs contains (saved_model_dir, batch_size) for each model
model_configs = ((platform_test.test_src_dir_path(
"python/compiler/tensorrt/model_tests/sample_model"), 128),)
model_handler_cls = model_handler.ModelHandlerV1
trt_model_handeler_cls = model_handler.TrtModelHandlerV1
default_trt_convert_params = DEFAUL_TRT_CONVERT_PARAMS._replace(
is_dynamic_op=False)
for saved_model_dir, batch_size in model_configs:
base_model = model_handler_cls(saved_model_dir=saved_model_dir)
random_inputs = base_model.generate_random_inputs(batch_size)
base_model_result = base_model.run(random_inputs)
trt_fp32_model_result = trt_model_handeler_cls(
saved_model_dir=saved_model_dir,
trt_convert_params=default_trt_convert_params._replace(
precision_mode=trt.TrtPrecisionMode.FP32,
max_batch_size=batch_size)).run(random_inputs)
trt_fp16_model_result = trt_model_handeler_cls(
saved_model_dir=saved_model_dir,
trt_convert_params=default_trt_convert_params._replace(
precision_mode=trt.TrtPrecisionMode.FP16,
max_batch_size=batch_size)).run(random_inputs)
logging.info("Base model latency: %f ms",
_get_mean_latency(base_model_result))
logging.info("TensorRT FP32 model latency: %f ms",
_get_mean_latency(trt_fp32_model_result))
logging.info("TensorRT FP16 model latency: %f ms",
_get_mean_latency(trt_fp16_model_result))
def testEval(self):
if not trt_convert.is_tensorrt_enabled():
return
model_dir = test.test_src_dir_path('contrib/tensorrt/test/testdata')
accuracy_tf_native = self._Run(
is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(accuracy_tf_native, 0.9662)
if trt_convert.get_linked_tensorrt_version()[0] < 5:
return
accuracy_tf_trt = self._Run(
is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(accuracy_tf_trt, 0.9677)
def setUpClass(cls):
gpu_memory_fraction_opt = (
"--gpu_memory_fraction=%f" % cls.PER_PROC_GPU_MEMORY_FRACTION)
worker_port = portpicker.pick_unused_port()
cluster_spec = "worker|localhost:%d" % worker_port
tf_logging.info("cluster_spec: %s", cluster_spec)
server_bin = test.test_src_dir_path("python/debug/grpc_tensorflow_server")
cls.server_target = "grpc://localhost:%d" % worker_port
cls.server_procs = {}
cls.server_procs["worker"] = subprocess.Popen(
[
server_bin,
"--cluster_spec=%s" % cluster_spec,
"--job_name=worker",
"--task_id=0",
gpu_memory_fraction_opt,
],
stdout=sys.stdout,
stderr=sys.stderr)
# Start debug server in-process, on separate thread.
(cls.debug_server_port, cls.debug_server_url, _, cls.debug_server_thread,
cls.debug_server
) = grpc_debug_test_server.start_server_on_separate_thread(
dump_to_filesystem=False)
tf_logging.info("debug server url: %s", cls.debug_server_url)
cls.session_config = config_pb2.ConfigProto(
gpu_options=config_pb2.GPUOptions(
per_process_gpu_memory_fraction=cls.PER_PROC_GPU_MEMORY_FRACTION))
def setUp(self):
super(CategoryLookupVocabFileTest, self).setUp()
# Contains strings, character names from 'The Wire': omar, stringer, marlo
self._wire_vocabulary_file_name = test.test_src_dir_path(
'python/keras/layers/preprocessing/testdata/wire_vocabulary.txt')
self._wire_vocabulary_size = 3
def testRunCommandWithDebuggerEnabled(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(),
'testRunCommandNewOutdir_inputs.npz')
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
np.savez(input_path, x0=x, x1=x_notused)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
output_dir, '--tf_debug'
])
def fake_wrapper_session(sess):
return sess
with test.mock.patch.object(
local_cli_wrapper,
'LocalCLIDebugWrapperSession',
side_effect=fake_wrapper_session,
autospec=True) as fake:
saved_model_cli.run(args)
fake.assert_called_with(test.mock.ANY)
y_actual = np.load(os.path.join(output_dir, 'y.npy'))
y_expected = np.array([[2.5], [3.0]])
self.assertAllClose(y_expected, y_actual)
def testShowCommandErrorNoTagSet(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args(
['show', '--dir', base_path, '--tag_set', 'badtagset'])
with self.assertRaises(RuntimeError):
saved_model_cli.show(args)
def testEval(self):
if not is_tensorrt_enabled():
return
model_dir = test.test_src_dir_path(
'python/compiler/tensorrt/test/testdata/mnist')
accuracy_tf_native = self._Run(
is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(0.9662, accuracy_tf_native, rtol=3e-3, atol=3e-3)
if get_linked_tensorrt_version()[0] < 5:
return
accuracy_tf_trt = self._Run(
is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(0.9675, accuracy_tf_trt, rtol=1e-3, atol=1e-3)
def testScanCommand(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args(['scan', '--dir', base_path])
with captured_output() as (out, _):
saved_model_cli.scan(args)
output = out.getvalue().strip()
self.assertTrue('does not contain denylisted ops' in output)
def __init__(self):
self.asset = asset.Asset(
test.test_src_dir_path(
"cc/saved_model/testdata/static_hashtable_asset.txt"))
self.table = lookup_ops.StaticHashTable(
lookup_ops.TextFileInitializer(
self.asset, dtypes.string, lookup_ops.TextFileIndex.WHOLE_LINE,
dtypes.int64, lookup_ops.TextFileIndex.LINE_NUMBER), -1)
def testRunCommandInvalidInputKeyError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x2_to_y3', '--input_exprs', 'x2=np.ones((3,1))'
])
with self.assertRaises(ValueError):
saved_model_cli.run(args)
def testRunCommandInputNotGivenError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default'
])
with self.assertRaises(AttributeError):
saved_model_cli.run(args)
def test_restore_old_saved_model(self):
saved_model_dir = test.test_src_dir_path(
'python/keras/mixed_precision/experimental/testdata/'
'lso_savedmodel_tf2.2')
model = save.load_model(saved_model_dir)
expected_kernel = np.array([[9.229685, 10.901115], [10.370763, 9.757362]])
self.assertAllClose(backend.eval(model.weights[0]), expected_kernel)
self.assertIsInstance(model.optimizer,
loss_scale_optimizer.LossScaleOptimizer)
def testRunCommandInvalidInputKeyError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x2_to_y3', '--input_exprs', 'x2=np.ones((3,1))'
])
with self.assertRaises(ValueError):
saved_model_cli.run(args)
def testBadPath(self):
base_path = test.test_src_dir_path("/no/such/a/dir")
ops.reset_default_graph()
with self.assertRaises(RuntimeError) as cm:
_, _ = session_bundle.load_session_bundle_from_path(
base_path,
target="local",
config=config_pb2.ConfigProto(device_count={"CPU": 2}))
self.assertTrue("Expected meta graph file missing" in str(cm.exception))
def testRunCommandInputNotGivenError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default'
])
with self.assertRaises(AttributeError):
saved_model_cli.run(args)
def testShowCommandTags(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args(['show', '--dir', base_path])
with captured_output() as (out, err):
saved_model_cli.show(args)
output = out.getvalue().strip()
exp_out = 'The given SavedModel contains the following tag-sets:\nserve'
self.assertMultiLineEqual(output, exp_out)
self.assertEqual(err.getvalue().strip(), '')
def testShowCommandTags(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args(['show', '--dir', base_path])
with captured_output() as (out, err):
saved_model_cli.show(args)
output = out.getvalue().strip()
exp_out = 'The given SavedModel contains the following tag-sets:\n\'serve\''
self.assertMultiLineEqual(output, exp_out)
self.assertEqual(err.getvalue().strip(), '')
def testRunCommandInvalidSignature(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'INVALID_SIGNATURE', '--input_exprs', 'x2=np.ones((3,1))'
])
with self.assertRaisesRegex(ValueError,
'Could not find signature "INVALID_SIGNATURE"'):
saved_model_cli.run(args)
def testRunCommandInputExamplesFeatureBadType(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples', 'inputs=[{"x":[[1],[2]]}]',
'--outdir', output_dir
])
with self.assertRaisesRegex(ValueError, 'is not supported'):
saved_model_cli.run(args)
def testRunCommandInputExamplesFeatureBadType(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples', 'inputs=[{"x":[[1],[2]]}]',
'--outdir', output_dir
])
with self.assertRaisesRegexp(ValueError, 'is not supported'):
saved_model_cli.run(args)
def testRunCommandInputExamplesFeatureValueNotListError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples', 'inputs=[{"x":8.0,"x2":5.0}]',
'--outdir', output_dir
])
with self.assertRaisesRegexp(ValueError, 'feature value must be a list'):
saved_model_cli.run(args)
def testRunCommandInputExamplesFeatureValueNotListError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples', 'inputs=[{"x":8.0,"x2":5.0}]',
'--outdir', output_dir
])
with self.assertRaisesRegex(ValueError, 'feature value must be a list'):
saved_model_cli.run(args)
def testConvertSignaturesToSignatureDefs(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
meta_graph_filename = os.path.join(base_path,
constants.META_GRAPH_DEF_FILENAME)
metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(len(default_signature_def.inputs), 1)
self.assertEqual(len(default_signature_def.outputs), 1)
self.assertProtoEquals(
default_signature_def.inputs[signature_constants.REGRESS_INPUTS],
meta_graph_pb2.TensorInfo(name="tf_example:0"))
self.assertProtoEquals(
default_signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
meta_graph_pb2.TensorInfo(name="Identity:0"))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(len(named_signature_def.inputs), 1)
self.assertEqual(len(named_signature_def.outputs), 1)
self.assertProtoEquals(named_signature_def.inputs["x"],
meta_graph_pb2.TensorInfo(name="x:0"))
self.assertProtoEquals(named_signature_def.outputs["y"],
meta_graph_pb2.TensorInfo(name="y:0"))
# Now try default signature only
collection_def = metagraph_def.collection_def
signatures_proto = manifest_pb2.Signatures()
signatures = collection_def[constants.SIGNATURES_KEY].any_list.value[0]
signatures.Unpack(signatures_proto)
named_only_signatures_proto = manifest_pb2.Signatures()
named_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto = manifest_pb2.Signatures()
default_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto.named_signatures.clear()
default_only_signatures_proto.ClearField("named_signatures")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(default_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(named_signature_def, None)
named_only_signatures_proto.ClearField("default_signature")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(named_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(default_signature_def, None)
def setUp(self):
self.bundle_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'bundle_checkpoint')
self.new_feature_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'bundle_checkpoint_vocab.txt')
self.old_feature_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH),
'bundle_checkpoint_vocab_with_oov.txt')
self.new_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword_new.txt')
self.old_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword.txt')
self.init_val = 42
def _init_val_initializer(shape, dtype=None, partition_info=None):
del dtype, partition_info # Unused by this unit-testing initializer.
return array_ops.tile(
constant_op.constant([[self.init_val]], dtype=dtypes.float32), shape)
self.initializer = _init_val_initializer
def testMetricsCollection(self):
def _enqueue_vector(sess, queue, values, shape=None):
if not shape:
shape = (1, len(values))
dtype = queue.dtypes[0]
sess.run(
queue.enqueue(
constant_op.constant(values, dtype=dtype, shape=shape)))
meta_graph_filename = os.path.join(_TestDir("metrics_export"),
"meta_graph.pb")
graph = ops.Graph()
with self.session(graph=graph) as sess:
values_queue = data_flow_ops.FIFOQueue(4,
dtypes.float32,
shapes=(1, 2))
_enqueue_vector(sess, values_queue, [0, 1])
_enqueue_vector(sess, values_queue, [-4.2, 9.1])
_enqueue_vector(sess, values_queue, [6.5, 0])
_enqueue_vector(sess, values_queue, [-3.2, 4.0])
values = values_queue.dequeue()
_, update_op = metrics.mean(values)
initializer = variables.local_variables_initializer()
sess.run(initializer)
sess.run(update_op)
meta_graph.export_scoped_meta_graph(filename=meta_graph_filename,
graph=graph)
# Verifies that importing a meta_graph with LOCAL_VARIABLES collection
# works correctly.
graph = ops.Graph()
with self.session(graph=graph) as sess:
meta_graph.import_scoped_meta_graph(meta_graph_filename)
initializer = variables.local_variables_initializer()
sess.run(initializer)
# Verifies that importing an old meta_graph where "local_variables"
# collection is of node_list type works, but cannot build initializer
# with the collection.
graph = ops.Graph()
with self.session(graph=graph) as sess:
meta_graph.import_scoped_meta_graph(
test.test_src_dir_path(
"python/framework/testdata/metrics_export_meta_graph.pb"))
self.assertEqual(
len(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)), 2)
with self.assertRaisesRegexp(
AttributeError,
"'Tensor' object has no attribute 'initializer'"):
initializer = variables.local_variables_initializer()
def testConvertSignaturesToSignatureDefs(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
meta_graph_filename = os.path.join(base_path,
constants.META_GRAPH_DEF_FILENAME)
metagraph_def = meta_graph.read_meta_graph_file(meta_graph_filename)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(len(default_signature_def.inputs), 1)
self.assertEqual(len(default_signature_def.outputs), 1)
self.assertProtoEquals(
default_signature_def.inputs[signature_constants.REGRESS_INPUTS],
meta_graph_pb2.TensorInfo(name="tf_example:0"))
self.assertProtoEquals(
default_signature_def.outputs[signature_constants.REGRESS_OUTPUTS],
meta_graph_pb2.TensorInfo(name="Identity:0"))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(len(named_signature_def.inputs), 1)
self.assertEqual(len(named_signature_def.outputs), 1)
self.assertProtoEquals(
named_signature_def.inputs["x"], meta_graph_pb2.TensorInfo(name="x:0"))
self.assertProtoEquals(
named_signature_def.outputs["y"], meta_graph_pb2.TensorInfo(name="y:0"))
# Now try default signature only
collection_def = metagraph_def.collection_def
signatures_proto = manifest_pb2.Signatures()
signatures = collection_def[constants.SIGNATURES_KEY].any_list.value[0]
signatures.Unpack(signatures_proto)
named_only_signatures_proto = manifest_pb2.Signatures()
named_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto = manifest_pb2.Signatures()
default_only_signatures_proto.CopyFrom(signatures_proto)
default_only_signatures_proto.named_signatures.clear()
default_only_signatures_proto.ClearField("named_signatures")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(default_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(default_signature_def.method_name,
signature_constants.REGRESS_METHOD_NAME)
self.assertEqual(named_signature_def, None)
named_only_signatures_proto.ClearField("default_signature")
metagraph_def.collection_def[constants.SIGNATURES_KEY].any_list.value[
0].Pack(named_only_signatures_proto)
default_signature_def, named_signature_def = (
bundle_shim._convert_signatures_to_signature_defs(metagraph_def))
self.assertEqual(named_signature_def.method_name,
signature_constants.PREDICT_METHOD_NAME)
self.assertEqual(default_signature_def, None)
def setUp(self):
self.bundle_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'bundle_checkpoint')
self.new_feature_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'bundle_checkpoint_vocab.txt')
self.old_feature_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH),
'bundle_checkpoint_vocab_with_oov.txt')
self.new_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword_new.txt')
self.old_class_vocab_file = os.path.join(
test.test_src_dir_path(_TESTDATA_PATH), 'keyword.txt')
self.init_val = 42
def _init_val_initializer(shape, dtype=None, partition_info=None):
del dtype, partition_info # Unused by this unit-testing initializer.
return array_ops.tile(
constant_op.constant([[self.init_val]], dtype=dtypes.float32), shape)
self.initializer = _init_val_initializer
def testMetricsCollection(self):
def _enqueue_vector(sess, queue, values, shape=None):
if not shape:
shape = (1, len(values))
dtype = queue.dtypes[0]
sess.run(
queue.enqueue(constant_op.constant(
values, dtype=dtype, shape=shape)))
meta_graph_filename = os.path.join(
_TestDir("metrics_export"), "meta_graph.pb")
graph = ops.Graph()
with self.session(graph=graph) as sess:
values_queue = data_flow_ops.FIFOQueue(
4, dtypes.float32, shapes=(1, 2))
_enqueue_vector(sess, values_queue, [0, 1])
_enqueue_vector(sess, values_queue, [-4.2, 9.1])
_enqueue_vector(sess, values_queue, [6.5, 0])
_enqueue_vector(sess, values_queue, [-3.2, 4.0])
values = values_queue.dequeue()
_, update_op = metrics.mean(values)
initializer = variables.local_variables_initializer()
self.evaluate(initializer)
self.evaluate(update_op)
meta_graph.export_scoped_meta_graph(
filename=meta_graph_filename, graph=graph)
# Verifies that importing a meta_graph with LOCAL_VARIABLES collection
# works correctly.
graph = ops.Graph()
with self.session(graph=graph) as sess:
meta_graph.import_scoped_meta_graph(meta_graph_filename)
initializer = variables.local_variables_initializer()
self.evaluate(initializer)
# Verifies that importing an old meta_graph where "local_variables"
# collection is of node_list type works, but cannot build initializer
# with the collection.
graph = ops.Graph()
with self.session(graph=graph) as sess:
meta_graph.import_scoped_meta_graph(
test.test_src_dir_path(
"python/framework/testdata/metrics_export_meta_graph.pb"))
self.assertEqual(len(ops.get_collection(ops.GraphKeys.LOCAL_VARIABLES)),
2)
with self.assertRaisesRegexp(
AttributeError, "'Tensor' object has no attribute 'initializer'"):
initializer = variables.local_variables_initializer()
def testScanCommandFoundDenylistedOp(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
args = self.parser.parse_args(
['scan', '--dir', base_path, '--tag_set', 'serve'])
op_denylist = saved_model_cli._OP_DENYLIST
saved_model_cli._OP_DENYLIST = set(['VariableV2'])
with captured_output() as (out, _):
saved_model_cli.scan(args)
saved_model_cli._OP_DENYLIST = op_denylist
output = out.getvalue().strip()
self.assertTrue('\'VariableV2\'' in output)
def test_graphdef_basic(self):
sm_pb_file = test.test_src_dir_path(
"cc/saved_model/testdata/VarsAndArithmeticObjectGraph/saved_model.pb"
)
with file_io.FileIO(sm_pb_file, "rb") as f:
file_content = f.read()
fingerprint_def = fingerprint_pb2.FingerprintDef()
fingerprint_def.ParseFromString(
fingerprinting.CreateFingerprintDef(file_content))
# We cannot check the value of the graph_def_hash due to non-determinism in
# serialization.
self.assertGreater(fingerprint_def.graph_def_hash, 0)
def testRunCommandInputExamples(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples',
'inputs=[{"x":[8.0],"x2":[5.0]}, {"x":[4.0],"x2":[3.0]}]', '--outdir',
output_dir
])
saved_model_cli.run(args)
y_actual = np.load(os.path.join(output_dir, 'outputs.npy'))
y_expected = np.array([[6.0], [4.0]])
self.assertAllEqual(y_expected, y_actual)
def testAOTCompileCPUWrongSignatureDefKey(self):
if not test.is_built_with_xla():
self.skipTest('Skipping test because XLA is not compiled in.')
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'aot_compile_cpu_dir')
args = self.parser.parse_args([
'aot_compile_cpu', '--dir', base_path, '--tag_set', 'serve',
'--output_prefix', output_dir, '--cpp_class', 'Compiled',
'--signature_def_key', 'MISSING'
])
with self.assertRaisesRegex(ValueError, 'Unable to find signature_def'):
saved_model_cli.aot_compile_cpu(args)
def testRunCommandInputExamples(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x_to_y', '--input_examples',
'inputs=[{"x":[8.0],"x2":[5.0]}, {"x":[4.0],"x2":[3.0]}]', '--outdir',
output_dir
])
saved_model_cli.run(args)
y_actual = np.load(os.path.join(output_dir, 'outputs.npy'))
y_expected = np.array([[6.0], [4.0]])
self.assertAllEqual(y_expected, y_actual)
def testSmileyFace(self):
"""Check warping accuracy by comparing to hardcoded warped images."""
test_data_dir = test.test_src_dir_path('contrib/image/python/'
'kernel_tests/test_data/')
input_file = test_data_dir + 'Yellow_Smiley_Face.png'
with self.test_session() as sess:
input_image = self.load_image(input_file, sess)
control_points = np.asarray([[64, 59], [180 - 64, 59], [39, 111],
[180 - 39, 111], [90, 143], [58, 134],
[180 - 58, 134]]) # pyformat: disable
control_point_displacements = np.asarray([[-10.5, 10.5], [10.5, 10.5],
[0, 0], [0, 0], [0, -10],
[-20, 10.25], [10, 10.75]])
control_points_op = constant_op.constant(
np.expand_dims(np.float32(control_points[:, [1, 0]]), 0))
control_point_displacements_op = constant_op.constant(
np.expand_dims(np.float32(control_point_displacements[:, [1, 0]]),
0))
float_image = np.expand_dims(np.float32(input_image) / 255, 0)
input_image_op = constant_op.constant(float_image)
for interpolation_order in (1, 2, 3):
for num_boundary_points in (0, 1, 4):
warp_op, _ = sparse_image_warp.sparse_image_warp(
input_image_op,
control_points_op,
control_points_op + control_point_displacements_op,
interpolation_order=interpolation_order,
num_boundary_points=num_boundary_points)
with self.test_session() as sess:
warped_image = sess.run(warp_op)
out_image = np.uint8(warped_image[0, :, :, :] * 255)
target_file = (
test_data_dir + 'Yellow_Smiley_Face_Warp-interp' +
'-{}-clamp-{}.png'.format(interpolation_order,
num_boundary_points))
target_image = self.load_image(target_file, sess)
# Check that the target_image and out_image difference is no
# bigger than 2 (on a scale of 0-255). Due to differences in
# floating point computation on different devices, the float
# output in warped_image may get rounded to a different int
# than that in the saved png file loaded into target_image.
self.assertAllClose(target_image,
out_image,
atol=2,
rtol=1e-3)
def testMatrixThatFailsWhenFlushingDenormsToZero(self):
# Test a 32x32 matrix which is known to fail if denorm floats are flushed to
# zero.
matrix = np.genfromtxt(
test.test_src_dir_path(
"python/kernel_tests/testdata/"
"self_adjoint_eig_fail_if_denorms_flushed.txt")).astype(np.float32)
self.assertEqual(matrix.shape, (32, 32))
matrix_tensor = constant_op.constant(matrix)
with self.session(use_gpu=True) as sess:
(e, v) = sess.run(linalg_ops.self_adjoint_eig(matrix_tensor))
self.assertEqual(e.size, 32)
self.assertAllClose(
np.matmul(v, v.transpose()), np.eye(32, dtype=np.float32), atol=2e-3)
self.assertAllClose(matrix,
np.matmul(np.matmul(v, np.diag(e)), v.transpose()))
def testBackwardCompatibility(self):
"""Load and execute a model that was saved in TF2.0."""
model_dir = test.test_src_dir_path(
"python/compiler/tensorrt/test/testdata/tftrt_2.0_saved_model")
saved_model_loaded = load.load(model_dir, tags=[tag_constants.SERVING])
graph_func = saved_model_loaded.signatures[
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
np_input1 = ops.convert_to_tensor(np.ones([4, 1, 1]).astype(np.float32))
np_input2 = ops.convert_to_tensor(np.ones([4, 1, 1]).astype(np.float32))
output = graph_func(input1=np_input1, input2=np_input2)["output_0"]
self.assertEqual(output.shape, (4, 1, 1))
self.assertAllClose(
np.asarray([5.0, 5.0, 5.0, 5.0]).reshape([4, 1, 1]), output)
def testRunCommandOutputFileExistError(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(),
'testRunCommandOutOverwrite_inputs.npz')
np.savez(input_path, x0=x, x1=x_notused)
output_file = os.path.join(test.get_temp_dir(), 'y.npy')
open(output_file, 'a').close()
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
test.get_temp_dir()
])
with self.assertRaises(RuntimeError):
saved_model_cli.run(args)
def testSmileyFace(self):
"""Check warping accuracy by comparing to hardcoded warped images."""
test_data_dir = test.test_src_dir_path('contrib/image/python/'
'kernel_tests/test_data/')
input_file = test_data_dir + 'Yellow_Smiley_Face.png'
with self.test_session() as sess:
input_image = self.load_image(input_file, sess)
control_points = np.asarray([[64, 59], [180 - 64, 59], [39, 111],
[180 - 39, 111], [90, 143], [58, 134],
[180 - 58, 134]]) # pyformat: disable
control_point_displacements = np.asarray(
[[-10.5, 10.5], [10.5, 10.5], [0, 0], [0, 0], [0, -10], [-20, 10.25],
[10, 10.75]])
control_points_op = constant_op.constant(
np.expand_dims(np.float32(control_points[:, [1, 0]]), 0))
control_point_displacements_op = constant_op.constant(
np.expand_dims(np.float32(control_point_displacements[:, [1, 0]]), 0))
float_image = np.expand_dims(np.float32(input_image) / 255, 0)
input_image_op = constant_op.constant(float_image)
for interpolation_order in (1, 2, 3):
for num_boundary_points in (0, 1, 4):
warp_op, _ = sparse_image_warp.sparse_image_warp(
input_image_op,
control_points_op,
control_points_op + control_point_displacements_op,
interpolation_order=interpolation_order,
num_boundary_points=num_boundary_points)
with self.test_session() as sess:
warped_image = sess.run(warp_op)
out_image = np.uint8(warped_image[0, :, :, :] * 255)
target_file = (
test_data_dir +
'Yellow_Smiley_Face_Warp-interp' + '-{}-clamp-{}.png'.format(
interpolation_order, num_boundary_points))
target_image = self.load_image(target_file, sess)
# Check that the target_image and out_image difference is no
# bigger than 2 (on a scale of 0-255). Due to differences in
# floating point computation on different devices, the float
# output in warped_image may get rounded to a different int
# than that in the saved png file loaded into target_image.
self.assertAllClose(target_image, out_image, atol=2, rtol=1e-3)
def testSavedModelBasic(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
ops.reset_default_graph()
sess, meta_graph_def = (
bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
base_path,
tags=[tag_constants.SERVING],
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2})))
self.assertTrue(sess)
# Check basic signature def property.
signature_def = meta_graph_def.signature_def
self.assertEqual(signature_def["regress_x_to_y"].method_name,
signature_constants.REGRESS_METHOD_NAME)
with sess.as_default():
output1 = sess.run(["filename_tensor:0"])
self.assertEqual([compat.as_bytes("foo.txt")], output1)
def testRunCommandOutOverwrite(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(),
'testRunCommandOutOverwrite_inputs.npz')
np.savez(input_path, x0=x, x1=x_notused)
output_file = os.path.join(test.get_temp_dir(), 'y.npy')
open(output_file, 'a').close()
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
test.get_temp_dir(), '--overwrite'
])
saved_model_cli.run(args)
y_actual = np.load(output_file)
y_expected = np.array([[2.5], [3.0]])
self.assertAllClose(y_expected, y_actual)
def testShowCommandSignature(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args(
['show', '--dir', base_path, '--tag_set', 'serve'])
with captured_output() as (out, err):
saved_model_cli.show(args)
output = out.getvalue().strip()
exp_header = ('The given SavedModel MetaGraphDef contains SignatureDefs '
'with the following keys:')
exp_start = 'SignatureDef key: '
exp_keys = [
'"classify_x2_to_y3"', '"classify_x_to_y"', '"regress_x2_to_y3"',
'"regress_x_to_y"', '"regress_x_to_y2"', '"serving_default"'
]
# Order of signatures does not matter
self.assertMultiLineEqual(
output,
'\n'.join([exp_header] + [exp_start + exp_key for exp_key in exp_keys]))
self.assertEqual(err.getvalue().strip(), '')
def testShowCommandInputsOutputs(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
self.parser = saved_model_cli.create_parser()
args = self.parser.parse_args([
'show', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default'
])
with captured_output() as (out, err):
saved_model_cli.show(args)
output = out.getvalue().strip()
expected_output = (
'The given SavedModel SignatureDef contains the following input(s):\n'
' inputs[\'x\'] tensor_info:\n'
' dtype: DT_FLOAT\n shape: (-1, 1)\n name: x:0\n'
'The given SavedModel SignatureDef contains the following output(s):\n'
' outputs[\'y\'] tensor_info:\n'
' dtype: DT_FLOAT\n shape: (-1, 1)\n name: y:0\n'
'Method name is: tensorflow/serving/predict')
self.assertEqual(output, expected_output)
self.assertEqual(err.getvalue().strip(), '')
def testRunCommandExistingOutdir(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(), 'testRunCommand_inputs.npz')
np.savez(input_path, x0=x, x1=x_notused)
output_file = os.path.join(test.get_temp_dir(), 'outputs.npy')
if os.path.exists(output_file):
os.remove(output_file)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'regress_x2_to_y3', '--inputs', 'inputs=' + input_path + '[x0]',
'--outdir',
test.get_temp_dir()
])
saved_model_cli.run(args)
y_actual = np.load(output_file)
y_expected = np.array([[3.5], [4.0]])
self.assertAllClose(y_expected, y_actual)
def testRunCommandNewOutdir(self):
self.parser = saved_model_cli.create_parser()
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
x = np.array([[1], [2]])
x_notused = np.zeros((6, 3))
input_path = os.path.join(test.get_temp_dir(),
'testRunCommandNewOutdir_inputs.npz')
output_dir = os.path.join(test.get_temp_dir(), 'new_dir')
if os.path.isdir(output_dir):
shutil.rmtree(output_dir)
np.savez(input_path, x0=x, x1=x_notused)
args = self.parser.parse_args([
'run', '--dir', base_path, '--tag_set', 'serve', '--signature_def',
'serving_default', '--inputs', 'x=' + input_path + '[x0]', '--outdir',
output_dir
])
saved_model_cli.run(args)
y_actual = np.load(os.path.join(output_dir, 'y.npy'))
y_expected = np.array([[2.5], [3.0]])
self.assertAllClose(y_expected, y_actual)
def testLegacyBasic(self):
base_path = test.test_src_dir_path(SESSION_BUNDLE_PATH)
ops.reset_default_graph()
sess, meta_graph_def = (
bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
base_path,
tags=[""],
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2})))
self.assertTrue(sess)
asset_path = os.path.join(base_path, constants.ASSETS_DIRECTORY)
with sess.as_default():
path1, path2 = sess.run(["filename1:0", "filename2:0"])
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello1.txt")), path1)
self.assertEqual(
compat.as_bytes(os.path.join(asset_path, "hello2.txt")), path2)
collection_def = meta_graph_def.collection_def
signatures_any = collection_def[constants.SIGNATURES_KEY].any_list.value
self.assertEqual(len(signatures_any), 1)
def testSavedModelBasic(self):
base_path = test.test_src_dir_path(SAVED_MODEL_PATH)
ops.reset_default_graph()
sess, meta_graph_def = (
bundle_shim.load_session_bundle_or_saved_model_bundle_from_path(
base_path,
tags=[tag_constants.SERVING],
target="",
config=config_pb2.ConfigProto(device_count={"CPU": 2})))
self.assertTrue(sess)
# Check basic signature def property.
signature_def = meta_graph_def.signature_def
self.assertEqual(len(signature_def), 2)
self.assertEqual(
signature_def[signature_constants.REGRESS_METHOD_NAME].method_name,
signature_constants.REGRESS_METHOD_NAME)
signature = signature_def["tensorflow/serving/regress"]
asset_path = os.path.join(base_path, saved_model_constants.ASSETS_DIRECTORY)
with sess.as_default():
output1 = sess.run(["filename_tensor:0"])
self.assertEqual(["foo.txt"], output1)
