def LoadSpec(self, spec_path):
master_spec = spec_pb2.MasterSpec()
root_dir = os.path.join(test_flags.source_root(), 'dragnn/python')
with open(os.path.join(root_dir, 'testdata', spec_path), 'r') as fin:
text_format.Parse(fin.read().replace('TOPDIR', root_dir),
master_spec)
return master_spec
def LoadSpec(self, spec_path):
master_spec = spec_pb2.MasterSpec()
testdata = os.path.join(test_flags.source_root(),
'dragnn/core/testdata')
with open(os.path.join(testdata, spec_path), 'r') as fin:
text_format.Parse(fin.read().replace('TESTDATA', testdata), master_spec)
return master_spec
def LoadSpec(self, spec_path):
master_spec = spec_pb2.MasterSpec()
testdata = os.path.join(test_flags.source_root(),
'dragnn/core/testdata')
with open(os.path.join(testdata, spec_path), 'r') as fin:
text_format.Parse(fin.read().replace('TESTDATA', testdata), master_spec)
return master_spec
def LoadSpec(self, spec_path):
master_spec = spec_pb2.MasterSpec()
root_dir = os.path.join(test_flags.source_root(),
'dragnn/python')
with open(os.path.join(root_dir, 'testdata', spec_path), 'r') as fin:
text_format.Parse(fin.read().replace('TOPDIR', root_dir), master_spec)
return master_spec
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 setUp(self):
# Creates a task context with the correct testing paths.
initial_task_context = os.path.join(test_flags.source_root(),
'syntaxnet/'
'testdata/context.pbtxt')
self._task_context = os.path.join(test_flags.temp_dir(), 'context.pbtxt')
with open(initial_task_context, 'r') as fin:
with open(self._task_context, 'w') as fout:
fout.write(fin.read().replace('SRCDIR', test_flags.source_root())
.replace('OUTPATH', test_flags.temp_dir()))
# Creates necessary term maps.
with self.test_session() as sess:
gen_parser_ops.lexicon_builder(task_context=self._task_context,
corpus_name='training-corpus').run()
self._num_features, self._num_feature_ids, _, self._num_actions = (
sess.run(gen_parser_ops.feature_size(task_context=self._task_context,
arg_prefix='brain_parser')))
def setUp(self):
# Creates a task context with the correct testing paths.
initial_task_context = os.path.join(test_flags.source_root(),
'syntaxnet/'
'testdata/context.pbtxt')
self._task_context = os.path.join(test_flags.temp_dir(), 'context.pbtxt')
with open(initial_task_context, 'r') as fin:
with open(self._task_context, 'w') as fout:
fout.write(fin.read().replace('SRCDIR', test_flags.source_root())
.replace('OUTPATH', test_flags.temp_dir()))
# Creates necessary term maps.
with self.test_session() as sess:
gen_parser_ops.lexicon_builder(task_context=self._task_context,
corpus_name='training-corpus').run()
self._num_features, self._num_feature_ids, _, self._num_actions = (
sess.run(gen_parser_ops.feature_size(task_context=self._task_context,
arg_prefix='brain_parser')))
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 setUp(self):
# This dataset contains 54 sentences.
self.filepath = os.path.join(test_flags.source_root(),
'syntaxnet/testdata/mini-training-set')
self.batch_size = 20
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))