def test_get_timestamped_export_dir(self):
export_dir_base = tempfile.mkdtemp() + "export/"
export_dir_1 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
time.sleep(1)
export_dir_2 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
time.sleep(1)
export_dir_3 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
# Export directories should be named using a timestamp that is seconds
# since epoch. Such a timestamp is 10 digits long.
time_1 = os.path.basename(export_dir_1)
self.assertEqual(10, len(time_1))
time_2 = os.path.basename(export_dir_2)
self.assertEqual(10, len(time_2))
time_3 = os.path.basename(export_dir_3)
self.assertEqual(10, len(time_3))
self.assertTrue(int(time_1) < int(time_2))
self.assertTrue(int(time_2) < int(time_3))
def test_get_timestamped_export_dir(self):
export_dir_base = tempfile.mkdtemp() + "export/"
export_dir_1 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
time.sleep(1)
export_dir_2 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
time.sleep(1)
export_dir_3 = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
# Export directories should be named using a timestamp that is seconds
# since epoch. Such a timestamp is 10 digits long.
time_1 = os.path.basename(export_dir_1)
self.assertEqual(10, len(time_1))
time_2 = os.path.basename(export_dir_2)
self.assertEqual(10, len(time_2))
time_3 = os.path.basename(export_dir_3)
self.assertEqual(10, len(time_3))
self.assertTrue(int(time_1) < int(time_2))
self.assertTrue(int(time_2) < int(time_3))
def _create_test_export_dir(export_dir_base):
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
gfile.MkDir(export_dir)
time.sleep(1)
return export_dir
def export_fn(estimator, export_dir_base, checkpoint_path=None, eval_result=None):
with ops.Graph().as_default() as g:
contrib_variables.create_global_step(g)
input_ops = feature_transforms.build_csv_serving_tensors_for_training_step(
args.analysis, features, schema, stats, keep_target)
model_fn_ops = estimator._call_model_fn(input_ops.features,
None,
model_fn_lib.ModeKeys.INFER)
output_fetch_tensors = make_prediction_output_tensors(
args=args,
features=features,
input_ops=input_ops,
model_fn_ops=model_fn_ops,
keep_target=keep_target)
# Don't use signature_def_utils.predict_signature_def as that renames
# tensor names if there is only 1 input/output tensor!
signature_inputs = {key: tf.saved_model.utils.build_tensor_info(tensor)
for key, tensor in six.iteritems(input_ops.default_inputs)}
signature_outputs = {key: tf.saved_model.utils.build_tensor_info(tensor)
for key, tensor in six.iteritems(output_fetch_tensors)}
signature_def_map = {
'serving_default':
signature_def_utils.build_signature_def(
signature_inputs,
signature_outputs,
tf.saved_model.signature_constants.PREDICT_METHOD_NAME)}
if not checkpoint_path:
# Locate the latest checkpoint
checkpoint_path = saver.latest_checkpoint(estimator._model_dir)
if not checkpoint_path:
raise ValueError("Couldn't find trained model at %s."
% estimator._model_dir)
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
if (model_fn_ops.scaffold is not None and
model_fn_ops.scaffold.saver is not None):
saver_for_restore = model_fn_ops.scaffold.saver
else:
saver_for_restore = saver.Saver(sharded=True)
with tf_session.Session('') as session:
saver_for_restore.restore(session, checkpoint_path)
init_op = control_flow_ops.group(
variables.local_variables_initializer(),
resources.initialize_resources(resources.shared_resources()),
tf.tables_initializer())
# Perform the export
builder = saved_model_builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(
session, [tag_constants.SERVING],
signature_def_map=signature_def_map,
assets_collection=ops.get_collection(
ops.GraphKeys.ASSET_FILEPATHS),
legacy_init_op=init_op)
builder.save(False)
# Add the extra assets
if assets_extra:
assets_extra_path = os.path.join(compat.as_bytes(export_dir),
compat.as_bytes('assets.extra'))
for dest_relative, source in assets_extra.items():
dest_absolute = os.path.join(compat.as_bytes(assets_extra_path),
compat.as_bytes(dest_relative))
dest_path = os.path.dirname(dest_absolute)
file_io.recursive_create_dir(dest_path)
file_io.copy(source, dest_absolute)
# only keep the last 3 models
saved_model_export_utils.garbage_collect_exports(
export_dir_base,
exports_to_keep=3)
# save the last model to the model folder.
# export_dir_base = A/B/intermediate_models/
if keep_target:
final_dir = os.path.join(args.job_dir, 'evaluation_model')
else:
final_dir = os.path.join(args.job_dir, 'model')
if file_io.is_directory(final_dir):
file_io.delete_recursively(final_dir)
file_io.recursive_create_dir(final_dir)
recursive_copy(export_dir, final_dir)
return export_dir
def _create_test_export_dir(export_dir_base):
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
gfile.MkDir(export_dir)
time.sleep(1)
return export_dir
def export_fn(estimator,
export_dir_base,
checkpoint_path=None,
eval_result=None):
with ops.Graph().as_default() as g:
contrib_variables.create_global_step(g)
input_ops = feature_transforms.build_csv_serving_tensors(
args.output_dir_from_analysis_step, features, schema, stats,
keep_target)
model_fn_ops = estimator._call_model_fn(
input_ops.features, None, model_fn_lib.ModeKeys.INFER)
output_fetch_tensors = make_prediction_output_tensors(
args=args,
features=features,
input_ops=input_ops,
model_fn_ops=model_fn_ops,
keep_target=keep_target)
# Don't use signature_def_utils.predict_signature_def as that renames
# tensor names if there is only 1 input/output tensor!
signature_inputs = {
key: tf.saved_model.utils.build_tensor_info(tensor)
for key, tensor in six.iteritems(input_ops.default_inputs)
}
signature_outputs = {
key: tf.saved_model.utils.build_tensor_info(tensor)
for key, tensor in six.iteritems(output_fetch_tensors)
}
signature_def_map = {
'serving_default':
signature_def_utils.build_signature_def(
signature_inputs, signature_outputs,
tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
}
if not checkpoint_path:
# Locate the latest checkpoint
checkpoint_path = saver.latest_checkpoint(estimator._model_dir)
if not checkpoint_path:
raise ValueError("Couldn't find trained model at %s." %
estimator._model_dir)
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
with tf_session.Session('') as session:
variables.local_variables_initializer()
data_flow_ops.tables_initializer()
saver_for_restore = saver.Saver(variables.global_variables(),
sharded=True)
saver_for_restore.restore(session, checkpoint_path)
init_op = control_flow_ops.group(
variables.local_variables_initializer(),
data_flow_ops.tables_initializer())
# Perform the export
builder = saved_model_builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(
session, [tag_constants.SERVING],
signature_def_map=signature_def_map,
assets_collection=ops.get_collection(
ops.GraphKeys.ASSET_FILEPATHS),
legacy_init_op=init_op)
builder.save(False)
# Add the extra assets
if assets_extra:
assets_extra_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('assets.extra'))
for dest_relative, source in assets_extra.items():
dest_absolute = os.path.join(
compat.as_bytes(assets_extra_path),
compat.as_bytes(dest_relative))
dest_path = os.path.dirname(dest_absolute)
file_io.recursive_create_dir(dest_path)
file_io.copy(source, dest_absolute)
# only keep the last 3 models
saved_model_export_utils.garbage_collect_exports(export_dir_base,
exports_to_keep=3)
# save the last model to the model folder.
# export_dir_base = A/B/intermediate_models/
if keep_target:
final_dir = os.path.join(args.job_dir, 'evaluation_model')
else:
final_dir = os.path.join(args.job_dir, 'model')
if file_io.is_directory(final_dir):
file_io.delete_recursively(final_dir)
file_io.recursive_create_dir(final_dir)
recursive_copy(export_dir, final_dir)
return export_dir
def export_fn(estimator, export_dir_base, checkpoint_path=None, eval_result=None):
with ops.Graph().as_default() as g:
contrib_variables.create_global_step(g)
input_ops = serving_from_csv_input(train_config, args, keep_target)
model_fn_ops = estimator._call_model_fn(input_ops.features,
None,
model_fn_lib.ModeKeys.INFER)
output_fetch_tensors = make_output_tensors(
train_config=train_config,
args=args,
input_ops=input_ops,
model_fn_ops=model_fn_ops,
keep_target=keep_target)
signature_def_map = {
'serving_default': signature_def_utils.predict_signature_def(input_ops.default_inputs,
output_fetch_tensors)
}
if not checkpoint_path:
# Locate the latest checkpoint
checkpoint_path = saver.latest_checkpoint(estimator._model_dir)
if not checkpoint_path:
raise NotFittedError("Couldn't find trained model at %s."
% estimator._model_dir)
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
with tf_session.Session('') as session:
# variables.initialize_local_variables()
variables.local_variables_initializer()
data_flow_ops.tables_initializer()
saver_for_restore = saver.Saver(
variables.global_variables(),
sharded=True)
saver_for_restore.restore(session, checkpoint_path)
init_op = control_flow_ops.group(
variables.local_variables_initializer(),
data_flow_ops.tables_initializer())
# Perform the export
builder = saved_model_builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(
session, [tag_constants.SERVING],
signature_def_map=signature_def_map,
assets_collection=ops.get_collection(
ops.GraphKeys.ASSET_FILEPATHS),
legacy_init_op=init_op)
builder.save(False)
# Add the extra assets
if assets_extra:
assets_extra_path = os.path.join(compat.as_bytes(export_dir),
compat.as_bytes('assets.extra'))
for dest_relative, source in assets_extra.items():
dest_absolute = os.path.join(compat.as_bytes(assets_extra_path),
compat.as_bytes(dest_relative))
dest_path = os.path.dirname(dest_absolute)
gfile.MakeDirs(dest_path)
gfile.Copy(source, dest_absolute)
# only keep the last 3 models
saved_model_export_utils.garbage_collect_exports(
python_portable_string(export_dir_base),
exports_to_keep=3)
# save the last model to the model folder.
# export_dir_base = A/B/intermediate_models/
if keep_target:
final_dir = os.path.join(args.job_dir, 'evaluation_model')
else:
final_dir = os.path.join(args.job_dir, 'model')
if file_io.is_directory(final_dir):
file_io.delete_recursively(final_dir)
file_io.recursive_create_dir(final_dir)
_recursive_copy(export_dir, final_dir)
return export_dir
def export_fn(estimator,
export_dir_base,
checkpoint_path=None,
eval_result=None):
with ops.Graph().as_default() as g:
contrib_variables.create_global_step(g)
input_ops = serving_from_csv_input(train_config, args, keep_target)
model_fn_ops = estimator._call_model_fn(
input_ops.features, None, model_fn_lib.ModeKeys.INFER)
output_fetch_tensors = make_output_tensors(
train_config=train_config,
args=args,
input_ops=input_ops,
model_fn_ops=model_fn_ops,
keep_target=keep_target)
signature_def_map = {
'serving_default':
signature_def_utils.predict_signature_def(
input_ops.default_inputs, output_fetch_tensors)
}
if not checkpoint_path:
# Locate the latest checkpoint
checkpoint_path = saver.latest_checkpoint(estimator._model_dir)
if not checkpoint_path:
raise NotFittedError("Couldn't find trained model at %s." %
estimator._model_dir)
export_dir = saved_model_export_utils.get_timestamped_export_dir(
export_dir_base)
with tf_session.Session('') as session:
# variables.initialize_local_variables()
variables.local_variables_initializer()
data_flow_ops.tables_initializer()
saver_for_restore = saver.Saver(variables.global_variables(),
sharded=True)
saver_for_restore.restore(session, checkpoint_path)
init_op = control_flow_ops.group(
variables.local_variables_initializer(),
data_flow_ops.tables_initializer())
# Perform the export
builder = saved_model_builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(
session, [tag_constants.SERVING],
signature_def_map=signature_def_map,
assets_collection=ops.get_collection(
ops.GraphKeys.ASSET_FILEPATHS),
legacy_init_op=init_op)
builder.save(False)
# Add the extra assets
if assets_extra:
assets_extra_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes('assets.extra'))
for dest_relative, source in assets_extra.items():
dest_absolute = os.path.join(
compat.as_bytes(assets_extra_path),
compat.as_bytes(dest_relative))
dest_path = os.path.dirname(dest_absolute)
gfile.MakeDirs(dest_path)
gfile.Copy(source, dest_absolute)
# only keep the last 3 models
saved_model_export_utils.garbage_collect_exports(
python_portable_string(export_dir_base), exports_to_keep=3)
# save the last model to the model folder.
# export_dir_base = A/B/intermediate_models/
if keep_target:
final_dir = os.path.join(args.job_dir, 'evaluation_model')
else:
final_dir = os.path.join(args.job_dir, 'model')
if file_io.is_directory(final_dir):
file_io.delete_recursively(final_dir)
file_io.recursive_create_dir(final_dir)
_recursive_copy(export_dir, final_dir)
return export_dir
