def app_main(argv):
"""Function passed to absl.app.run."""
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.save_model_path:
save_model_path = FLAGS.save_model_path
else:
save_model_path = tempfile.mktemp(suffix='.saved_model')
sess = tf.Session()
sess.run(tf.initializers.global_variables())
builder = tf.saved_model.builder.SavedModelBuilder(save_model_path)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map,
main_op=init_op,
strip_default_attrs=True)
builder.save()
logging.info('Saved model to: %s', save_model_path)
mlir = pywrap_mlir.experimental_convert_saved_model_v1_to_mlir(
save_model_path, ','.join([tf.saved_model.tag_constants.SERVING]),
show_debug_info)
# We don't strictly need this, but it serves as a handy sanity check
# for that API, which is otherwise a bit annoying to test.
# The canonicalization shouldn't affect these tests in any way.
mlir = pywrap_mlir.experimental_run_pass_pipeline(mlir,
'tf-standard-pipeline',
show_debug_info)
if canonicalize:
mlir = pywrap_mlir.experimental_run_pass_pipeline(mlir, 'canonicalize',
show_debug_info)
print(mlir)
def app_main(argv):
"""Function passed to absl.app.run."""
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.save_model_path:
save_model_path = FLAGS.save_model_path
else:
save_model_path = tempfile.mktemp(suffix='.saved_model')
sess = tf.Session()
sess.run(tf.initializers.global_variables())
builder = tf.saved_model.builder.SavedModelBuilder(save_model_path)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map,
main_op=init_op,
strip_default_attrs=True)
builder.save()
logging.info('Saved model to: %s', save_model_path)
# TODO(b/153507667): Set the following boolean flag once the hoisting
# variables logic from SavedModel importer is removed.
lift_variables = False
upgrade_legacy = True
mlir = pywrap_mlir.experimental_convert_saved_model_v1_to_mlir(
save_model_path, ','.join([tf.saved_model.tag_constants.SERVING]),
lift_variables, upgrade_legacy, show_debug_info)
if canonicalize:
mlir = pywrap_mlir.experimental_run_pass_pipeline(
mlir, 'canonicalize', show_debug_info)
print(mlir)
def app_main(argv):
"""Function passed to absl.app.run."""
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
if FLAGS.save_model_path:
save_model_path = FLAGS.save_model_path
else:
save_model_path = tempfile.mktemp(suffix='.saved_model')
signature_def_map, init_op, assets_collection = create_signature()
sess = tf.Session()
sess.run(tf.initializers.global_variables())
builder = tf.saved_model.builder.SavedModelBuilder(save_model_path)
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map,
main_op=init_op,
assets_collection=assets_collection,
strip_default_attrs=True)
builder.save()
logging.info('Saved model to: %s', save_model_path)
# TODO(b/153507667): Set the following boolean flag once the hoisting
# variables logic from SavedModel importer is removed.
exported_names = ''
lift_variables = False
upgrade_legacy = True
if use_lite:
mlir = pywrap_mlir.experimental_convert_saved_model_v1_to_mlir_lite(
save_model_path, exported_names,
','.join([tf.saved_model.tag_constants.SERVING]),
upgrade_legacy, show_debug_info)
# We don't strictly need this, but it serves as a handy sanity check
# for that API, which is otherwise a bit annoying to test.
# The canonicalization shouldn't affect these tests in any way.
mlir = pywrap_mlir.experimental_run_pass_pipeline(mlir,
'tf-standard-pipeline',
show_debug_info)
else:
mlir = pywrap_mlir.experimental_convert_saved_model_v1_to_mlir(
save_model_path, exported_names,
','.join([tf.saved_model.tag_constants.SERVING]),
lift_variables, upgrade_legacy, show_debug_info)
if canonicalize:
mlir = pywrap_mlir.experimental_run_pass_pipeline(mlir, 'canonicalize',
show_debug_info)
print(mlir)
