def export(master_spec_path, params_path, export_path, export_moving_averages,
build_runtime_graph):
"""Restores a model and exports it in SavedModel form.
This method loads a graph specified by the spec at master_spec_path and the
params in params_path. It then saves the model in SavedModel format to the
location specified in export_path.
Args:
master_spec_path: Path to a proto-text master spec.
params_path: Path to the parameters file to export.
export_path: Path to export the SavedModel to.
export_moving_averages: Whether to export the moving average parameters.
build_runtime_graph: Whether to build a graph for use by the runtime.
"""
graph = tf.Graph()
master_spec = spec_pb2.MasterSpec()
with tf.gfile.FastGFile(master_spec_path) as fin:
text_format.Parse(fin.read(), master_spec)
# Remove '/' if it exists at the end of the export path, ensuring that
# path utils work correctly.
stripped_path = export_path.rstrip('/')
saver_lib.clean_output_paths(stripped_path)
short_to_original = saver_lib.shorten_resource_paths(master_spec)
saver_lib.export_master_spec(master_spec, graph)
saver_lib.export_to_graph(master_spec, params_path, stripped_path, graph,
export_moving_averages, build_runtime_graph)
saver_lib.export_assets(master_spec, short_to_original, stripped_path)
def export(master_spec_path, params_path, export_path, export_moving_averages,
build_runtime_graph):
"""Restores a model and exports it in SavedModel form.
This method loads a graph specified by the spec at master_spec_path and the
params in params_path. It then saves the model in SavedModel format to the
location specified in export_path.
Args:
master_spec_path: Path to a proto-text master spec.
params_path: Path to the parameters file to export.
export_path: Path to export the SavedModel to.
export_moving_averages: Whether to export the moving average parameters.
build_runtime_graph: Whether to build a graph for use by the runtime.
"""
graph = tf.Graph()
master_spec = spec_pb2.MasterSpec()
with tf.gfile.FastGFile(master_spec_path) as fin:
text_format.Parse(fin.read(), master_spec)
# Remove '/' if it exists at the end of the export path, ensuring that
# path utils work correctly.
stripped_path = export_path.rstrip('/')
saver_lib.clean_output_paths(stripped_path)
short_to_original = saver_lib.shorten_resource_paths(master_spec)
saver_lib.export_master_spec(master_spec, graph)
saver_lib.export_to_graph(master_spec, params_path, stripped_path, graph,
export_moving_averages, build_runtime_graph)
saver_lib.export_assets(master_spec, short_to_original, stripped_path)
def testModelExport(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(), 'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(test_flags.temp_dir(), 'export')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False,
build_runtime_graph=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec,
shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph,
config=restoration_config) as sess:
tf.saved_model.loader.load(sess,
[tf.saved_model.tag_constants.SERVING],
export_path)
averaged_hook_name, non_averaged_hook_name, _ = self.GetHookNodeNames(
master_spec)
# Check that the averaged runtime hook node does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(averaged_hook_name)
# Check that the non-averaged version also does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(non_averaged_hook_name)
def testModelExport(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(),
'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(test_flags.temp_dir(), 'export')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(
master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False,
build_runtime_graph=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec, shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(
log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph, config=restoration_config) as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING],
export_path)
averaged_hook_name, non_averaged_hook_name, _ = self.GetHookNodeNames(
master_spec)
# Check that the averaged runtime hook node does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(averaged_hook_name)
# Check that the non-averaged version also does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(non_averaged_hook_name)
def testModelExportProducesRunnableModel(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(), 'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(test_flags.temp_dir(), 'export')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False,
build_runtime_graph=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec,
shortened_to_original,
export_path)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph,
config=restoration_config) as sess:
tf.saved_model.loader.load(sess,
[tf.saved_model.tag_constants.SERVING],
export_path)
test_doc = sentence_pb2.Sentence()
text_format.Parse(_DUMMY_TEST_SENTENCE, test_doc)
test_reader_string = test_doc.SerializeToString()
test_inputs = [test_reader_string]
tf_out = sess.run('annotation/annotations:0',
feed_dict={
'annotation/ComputeSession/InputBatch:0':
test_inputs
})
# We don't care about accuracy, only that the run sessions don't crash.
del tf_out
def testModelExportProducesRunnableModel(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(),
'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(test_flags.temp_dir(), 'export')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(
master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False,
build_runtime_graph=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec, shortened_to_original,
export_path)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(
log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph, config=restoration_config) as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING],
export_path)
test_doc = sentence_pb2.Sentence()
text_format.Parse(_DUMMY_TEST_SENTENCE, test_doc)
test_reader_string = test_doc.SerializeToString()
test_inputs = [test_reader_string]
tf_out = sess.run(
'annotation/annotations:0',
feed_dict={'annotation/ComputeSession/InputBatch:0': test_inputs})
# We don't care about accuracy, only that the run sessions don't crash.
del tf_out
def testModelExport(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
FLAGS.test_srcdir, 'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(FLAGS.test_tmpdir, 'export')
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec,
shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph,
config=restoration_config) as sess:
tf.saved_model.loader.load(sess,
[tf.saved_model.tag_constants.SERVING],
export_path)
def export(master_spec_path, params_path, resource_path, export_path,
export_moving_averages):
"""Restores a model and exports it in SavedModel form.
This method loads a graph specified by the spec at master_spec_path and the
params in params_path. It then saves the model in SavedModel format to the
location specified in export_path.
Args:
master_spec_path: Path to a proto-text master spec.
params_path: Path to the parameters file to export.
resource_path: Path to resources in the master spec.
export_path: Path to export the SavedModel to.
export_moving_averages: Whether to export the moving average parameters.
"""
# Old CoNLL checkpoints did not need a known-word-map. Create a temporary if
# that file is missing.
if not tf.gfile.Exists(os.path.join(resource_path, 'known-word-map')):
with tf.gfile.FastGFile(os.path.join(resource_path, 'known-word-map'),
'w') as out_file:
out_file.write('This file intentionally left blank.')
graph = tf.Graph()
master_spec = spec_pb2.MasterSpec()
with tf.gfile.FastGFile(master_spec_path) as fin:
text_format.Parse(fin.read(), master_spec)
# This is a workaround for an issue where the segmenter master-spec had a
# spurious resource in it; this resource was not respected in the spec-builder
# and ended up crashing the saver (since it didn't really exist).
for component in master_spec.component:
del component.resource[:]
spec_builder.complete_master_spec(master_spec, None, resource_path)
# Remove '/' if it exists at the end of the export path, ensuring that
# path utils work correctly.
stripped_path = export_path.rstrip('/')
saver_lib.clean_output_paths(stripped_path)
short_to_original = saver_lib.shorten_resource_paths(master_spec)
saver_lib.export_master_spec(master_spec, graph)
saver_lib.export_to_graph(master_spec, params_path, stripped_path, graph,
export_moving_averages)
saver_lib.export_assets(master_spec, short_to_original, stripped_path)
def export(master_spec_path, params_path, resource_path, export_path,
export_moving_averages):
"""Restores a model and exports it in SavedModel form.
This method loads a graph specified by the spec at master_spec_path and the
params in params_path. It then saves the model in SavedModel format to the
location specified in export_path.
Args:
master_spec_path: Path to a proto-text master spec.
params_path: Path to the parameters file to export.
resource_path: Path to resources in the master spec.
export_path: Path to export the SavedModel to.
export_moving_averages: Whether to export the moving average parameters.
"""
# Old CoNLL checkpoints did not need a known-word-map. Create a temporary if
# that file is missing.
if not tf.gfile.Exists(os.path.join(resource_path, 'known-word-map')):
with tf.gfile.FastGFile(os.path.join(resource_path, 'known-word-map'),
'w') as out_file:
out_file.write('This file intentionally left blank.')
graph = tf.Graph()
master_spec = spec_pb2.MasterSpec()
with tf.gfile.FastGFile(master_spec_path) as fin:
text_format.Parse(fin.read(), master_spec)
# This is a workaround for an issue where the segmenter master-spec had a
# spurious resource in it; this resource was not respected in the spec-builder
# and ended up crashing the saver (since it didn't really exist).
for component in master_spec.component:
del component.resource[:]
spec_builder.complete_master_spec(master_spec, None, resource_path)
# Remove '/' if it exists at the end of the export path, ensuring that
# path utils work correctly.
stripped_path = export_path.rstrip('/')
saver_lib.clean_output_paths(stripped_path)
short_to_original = saver_lib.shorten_resource_paths(master_spec)
saver_lib.export_master_spec(master_spec, graph)
saver_lib.export_to_graph(master_spec, params_path, stripped_path, graph,
export_moving_averages)
saver_lib.export_assets(master_spec, short_to_original, stripped_path)
def testModelExport(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
FLAGS.test_srcdir, 'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.)
export_path = os.path.join(FLAGS.test_tmpdir, 'export')
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(
master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=False)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec, shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(
log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph, config=restoration_config) as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING],
export_path)
def testModelExportWithAveragesAndHooks(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(), 'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.) Note that the export
# path must not already exist.
export_path = os.path.join(test_flags.temp_dir(), 'export2')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=True,
build_runtime_graph=True)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec,
shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph,
config=restoration_config) as sess:
tf.saved_model.loader.load(sess,
[tf.saved_model.tag_constants.SERVING],
export_path)
averaged_hook_name, non_averaged_hook_name, cell_subgraph_hook_name = (
self.GetHookNodeNames(master_spec))
# Check that an averaged runtime hook node exists.
restored_graph.get_operation_by_name(averaged_hook_name)
# Check that the non-averaged version does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(non_averaged_hook_name)
# Load the cell subgraph.
cell_subgraph_bytes = restored_graph.get_tensor_by_name(
cell_subgraph_hook_name + ':0')
cell_subgraph_bytes = cell_subgraph_bytes.eval(
feed_dict={'annotation/ComputeSession/InputBatch:0': []})
cell_subgraph_spec = export_pb2.CellSubgraphSpec()
cell_subgraph_spec.ParseFromString(cell_subgraph_bytes)
tf.logging.info('cell_subgraph_spec = %s', cell_subgraph_spec)
# Sanity check inputs.
for cell_input in cell_subgraph_spec.input:
self.assertGreater(len(cell_input.name), 0)
self.assertGreater(len(cell_input.tensor), 0)
self.assertNotEqual(
cell_input.type,
export_pb2.CellSubgraphSpec.Input.TYPE_UNKNOWN)
restored_graph.get_tensor_by_name(
cell_input.tensor) # shouldn't raise
# Sanity check outputs.
for cell_output in cell_subgraph_spec.output:
self.assertGreater(len(cell_output.name), 0)
self.assertGreater(len(cell_output.tensor), 0)
restored_graph.get_tensor_by_name(
cell_output.tensor) # shouldn't raise
# GetHookNames() finds a component with a fixed feature, so at least the
# first feature ID should exist.
self.assertTrue(
any(cell_input.name == 'fixed_channel_0_index_0_ids'
for cell_input in cell_subgraph_spec.input))
# Most dynamic components produce a logits layer.
self.assertTrue(
any(cell_output.name == 'logits'
for cell_output in cell_subgraph_spec.output))
def testModelExportWithAveragesAndHooks(self):
# Get the master spec and params for this graph.
master_spec = self.LoadSpec('ud-hungarian.master-spec')
params_path = os.path.join(
test_flags.source_root(),
'dragnn/python/testdata'
'/ud-hungarian.params')
# Export the graph via SavedModel. (Here, we maintain a handle to the graph
# for comparison, but that's usually not necessary.) Note that the export
# path must not already exist.
export_path = os.path.join(test_flags.temp_dir(), 'export2')
dragnn_model_saver_lib.clean_output_paths(export_path)
saver_graph = tf.Graph()
shortened_to_original = dragnn_model_saver_lib.shorten_resource_paths(
master_spec)
dragnn_model_saver_lib.export_master_spec(master_spec, saver_graph)
dragnn_model_saver_lib.export_to_graph(
master_spec,
params_path,
export_path,
saver_graph,
export_moving_averages=True,
build_runtime_graph=True)
# Export the assets as well.
dragnn_model_saver_lib.export_assets(master_spec, shortened_to_original,
export_path)
# Validate that the assets are all in the exported directory.
path_set = self.ValidateAssetExistence(master_spec, export_path)
# This master-spec has 4 unique assets. If there are more, we have not
# uniquified the assets properly.
self.assertEqual(len(path_set), 4)
# Restore the graph from the checkpoint into a new Graph object.
restored_graph = tf.Graph()
restoration_config = tf.ConfigProto(
log_device_placement=False,
intra_op_parallelism_threads=10,
inter_op_parallelism_threads=10)
with tf.Session(graph=restored_graph, config=restoration_config) as sess:
tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING],
export_path)
averaged_hook_name, non_averaged_hook_name, cell_subgraph_hook_name = (
self.GetHookNodeNames(master_spec))
# Check that an averaged runtime hook node exists.
restored_graph.get_operation_by_name(averaged_hook_name)
# Check that the non-averaged version does not exist.
with self.assertRaises(KeyError):
restored_graph.get_operation_by_name(non_averaged_hook_name)
# Load the cell subgraph.
cell_subgraph_bytes = restored_graph.get_tensor_by_name(
cell_subgraph_hook_name + ':0')
cell_subgraph_bytes = cell_subgraph_bytes.eval(
feed_dict={'annotation/ComputeSession/InputBatch:0': []})
cell_subgraph_spec = export_pb2.CellSubgraphSpec()
cell_subgraph_spec.ParseFromString(cell_subgraph_bytes)
tf.logging.info('cell_subgraph_spec = %s', cell_subgraph_spec)
# Sanity check inputs.
for cell_input in cell_subgraph_spec.input:
self.assertGreater(len(cell_input.name), 0)
self.assertGreater(len(cell_input.tensor), 0)
self.assertNotEqual(cell_input.type,
export_pb2.CellSubgraphSpec.Input.TYPE_UNKNOWN)
restored_graph.get_tensor_by_name(cell_input.tensor) # shouldn't raise
# Sanity check outputs.
for cell_output in cell_subgraph_spec.output:
self.assertGreater(len(cell_output.name), 0)
self.assertGreater(len(cell_output.tensor), 0)
restored_graph.get_tensor_by_name(cell_output.tensor) # shouldn't raise
# GetHookNames() finds a component with a fixed feature, so at least the
# first feature ID should exist.
self.assertTrue(
any(cell_input.name == 'fixed_channel_0_index_0_ids'
for cell_input in cell_subgraph_spec.input))
# Most dynamic components produce a logits layer.
self.assertTrue(
any(cell_output.name == 'logits'
for cell_output in cell_subgraph_spec.output))
