def ng_run(self,
tf_target_node,
tf_feed_dict=None,
print_ng_result=False,
verbose=False):
"""
Run and get ngrpah results
Args:
tf_target_node: target node in tf
tf_feed_dict: feed_dict in tf
print_ng_result: prints ng_result if set to True
verbose: prints tf's node_def if set to True
Returns:
ng_result
"""
# init importer, transformer
importer = TFImporter()
importer.import_protobuf(self.pb_txt_path, verbose=verbose)
transformer = ngt.make_transformer()
# set target node
ng_target_node = importer.get_op_handle_by_name(
tf_target_node.name[:-2])
# evaluate ngraph
if tf_feed_dict is not None:
# get targeting nodes for ng, convert tf's feed dict to list
tf_placeholder_nodes = [node for (node, _) in tf_feed_dict.items()]
tf_placeholder_names = [node.name for node in tf_placeholder_nodes]
ng_placeholder_nodes = [
importer.get_op_handle_by_name(name[:-2])
for name in tf_placeholder_names
]
ng_placeholder_vals = [val for (_, val) in tf_feed_dict.items()]
# evaluate ngraph result
ng_result_comp = transformer.computation([ng_target_node],
*ng_placeholder_nodes)
if importer.init_ops:
init_comp = transformer.computation(importer.init_ops)
init_comp()
ng_result = ng_result_comp(*ng_placeholder_vals)[0]
else:
ng_result_comp = transformer.computation([ng_target_node])
if importer.init_ops:
init_comp = transformer.computation(importer.init_ops)
init_comp()
ng_result = ng_result_comp()[0]
if print_ng_result:
print(ng_result)
return ng_result
def ng_run(self,
tf_target_node,
tf_init_op=None,
tf_feed_dict={},
print_ng_result=False,
verbose=False):
"""
Run and get ngraph results
Args:
tf_target_node: target node in tf
tf_feed_dict: feed_dict in tf
print_ng_result: prints ng_result if set to True
verbose: prints tf's node_def if set to True
Returns:
ng_result
"""
# init importer, transformer
importer = TFImporter()
graph_def = tf.GraphDef()
graph_def.ParseFromString(self.graph_string)
importer.import_graph_def(graph_def, verbose=verbose)
# set target node
ng_target_node = importer.get_op_handle_by_name(
tf_target_node.name[:-2])
# get targeting nodes for ng, convert tf's feed dict to list
ng_feed_dict = {
importer.get_op_handle_by_name(node.name[:-2]): val
for (node, val) in tf_feed_dict.items()
}
# evaluate ngraph
with ExecutorFactory() as ex:
ng_result_comp = ex.transformer.computation(
ng_target_node, *ng_feed_dict.keys())
if tf_init_op:
ex.transformer.computation(
importer.get_op_handle(tf_init_op))()
ng_result = ng_result_comp(feed_dict=ng_feed_dict)
return ng_result