def update_body_graph(body_graph: Graph, subgraph_proto: dict,
body_parameter_names: list, body_results: list):
"""
Updates the loop body graph with a sub-graph (for body or condition functions)
:param body_graph: a loop body graph to be updated
:param subgraph_proto: a sub-graph in a protobuf format to be added into the loop body graph
:param body_parameter_names: a (unchanged) list of parameters in the loop body graph
:param body_results: a list of Result nodes that is extended with a list from a sub-graph
"""
# create a map from a node name in original model to a name in a loop body graph assuming
# that names in the original model are unique
# initially, the map contains names for parameters that are common for the body and condition graphs
map_original_name = {}
for idx, pb_node in enumerate(subgraph_proto['input_arg']):
map_original_name[pb_node.name] = body_parameter_names[idx]
# walk through all nodes (non-parameter and non-result nodes) and add into the loop body graph
for pb_node in subgraph_proto['node_def']:
# create an NX node
id = body_graph.unique_id(pb_node.name)
map_original_name[pb_node.name] = id
body_graph.add_node(id, pb=pb_node, kind='op')
if hasattr(body_graph, 'op_names_statistic') and hasattr(
pb_node, 'op'):
body_graph.op_names_statistic[pb_node.op] += 1
# add incoming edges based on data_nodes_map
for dst_port, inp in enumerate(pb_node.input):
orig_src_id = inp.split(":")[0]
# TODO: avoid this temporal workaround for TF 2.4 or higher RNN layers:
# skip control flow dependency
if orig_src_id[0] == '^':
continue
src_id = map_original_name[orig_src_id]
src_port = 0 if len(inp.split(":")) == 1 else int(
inp.split(":")[-1])
assert (body_graph.has_node(src_id))
body_graph.add_edges_from(
[create_tf_edge(src_id + ":" + str(src_port), id, dst_port)])
# create Result nodes in the loop body graph
for output in subgraph_proto['output_arg']:
output_name = subgraph_proto['ret'][output.name]
orig_src_id = output_name.split(":")[0]
src_id = map_original_name[orig_src_id]
src_port = 0 if len(output_name.split(":")) == 1 \
else int(output_name.split(":")[-1])
assert body_graph.has_node(
src_id
), 'The body graph does not contain output with name "{}"'.format(
src_id)
body_results.append(
Node(body_graph, add_opoutput(body_graph, src_id, src_port,
False)))
return True
def load(self, graph: Graph):
argv = graph.graph['cmd_params']
if argv.tensorflow_custom_layer_libraries:
libraries = argv.tensorflow_custom_layer_libraries.split(',')
for library in libraries:
log.info('Loading library "{}" with custom operations'.format(
library))
tf_v1.load_op_library(library)
graph_def, variables_values, framework, inputs_outputs_order = load_tf_graph_def(
graph_file_name=argv.input_model,
is_binary=not argv.input_model_is_text,
checkpoint=argv.input_checkpoint,
user_output_node_names_list=argv.output,
model_dir=argv.saved_model_dir,
meta_graph_file=argv.input_meta_graph,
saved_model_tags=argv.saved_model_tags)
if inputs_outputs_order is not None and isinstance(
inputs_outputs_order, tuple):
graph.inputs_order = inputs_outputs_order[0]
graph.outputs_order = inputs_outputs_order[1]
send_framework_info(framework)
try:
tf_v1.import_graph_def(graph_def, name='')
except:
log.warning(
"TensorFlow post-processing of loaded model was unsuccessful. "
"This is an optional step that Model Optimizer performs for any input model but it is not usually "
"required for all models. "
"It likely means that the original model is ill-formed. "
"Model Optimizer will continue converting this model.")
log.debug("Number of nodes in graph_def: {}".format(len(
graph_def.node))) # pylint: disable=no-member
if argv.tensorboard_logdir:
tensorboard_util.dump_for_tensorboard(graph_def,
argv.tensorboard_logdir)
update_extractors_with_extensions(tf_op_extractors)
try:
protobuf2nx(graph, graph_def)
except Exception as e:
raise Error(
'Cannot pre-process TensorFlow graph after reading from model file "{}". ' \
'File is corrupt or has unsupported format. Details: {}. ' +
refer_to_faq_msg(44),
argv.model_name,
str(e)
) from e
graph.__setattr__('name', argv.model_name)
# 'layout' parameter change may cause an issue in EltwiseInputReshape replacer
# and convert_nhwc_to_nchw(graph)
graph.graph['layout'] = 'NCHW' if argv.disable_nhwc_to_nchw else 'NHWC'
graph.graph['fw'] = 'tf'
graph.graph['variables_values'] = variables_values
del variables_values
used_tensors = restore_edges(graph, get_tf_edges)
# Tensor names information corresponding to a node is stored on outgoing edges.
# As output nodes do not have outgoing edges, fake outputs are required. In the following code
# for each output Identity node is added, and tensor name for the output is kept
# on (output, fake output) edge. After Result nodes adding transformation fake outputs
# are deleted from graph.
add_outputs_identity(
graph, graph.nodes - used_tensors,
lambda g, output, fake_node_name: g.add_edges_from(
[create_tf_edge(output, fake_node_name, 0)]))
remove_control_dependency_inputs(graph)
graph.check_empty_graph(
'protobuf2nx. It may happen due to problems with loaded model')
extract_node_attrs(
graph, lambda node: tf_op_extractor(
node, check_for_duplicates(tf_op_extractors)))
# try to detect layout from the nodes of the graph. If there are no convolution nodes in N(D)HWC layout then we
# consider that the graph is in NCHW layout and no layout conversion should be performed
if not argv.disable_nhwc_to_nchw and not graph_or_sub_graph_has_nhwc_ops(
graph):
if not argv.silent:
log.debug('disable_nhwc_to_nchw" was automatically enabled.')
for_graph_and_each_sub_graph_recursively(
graph, update_cmd_params_and_layout)
send_op_names_info(framework, graph)
send_shapes_info(framework, graph)