def symbol2nx(graph, model_nodes, model_params, input_names: str = ''):
if not input_names:
input_names = ('data',)
else:
input_names = input_names.split(',')
rnn_states = init_rnn_states(model_nodes)
names_rnn_states = list(rnn_states.keys())
# as mxnet contain input layers as index of layer, for correct set up edges, we need provide index of layer with name of graph node
index_node_keys = {}
for i, node in enumerate(model_nodes):
if node['name'] in model_params._arg_params and node['name'] not in input_names:
node['value'] = np.array(model_params._arg_params[node['name']].asnumpy(), dtype=np.float32)
elif node['name'] in model_params._aux_params and node['name'] not in input_names:
node['value'] = np.array(model_params._aux_params[node['name']].asnumpy(), dtype=np.float32)
elif node['name'] in names_rnn_states:
node['value'] = np.zeros(rnn_states[node['name']])
node_name = graph.unique_id(node['name'])
graph.add_node(node_name, **symbol_attrs(node))
graph.node[node_name].update(common_mxnet_fields(Node(graph, node_name)))
index_node_keys[i] = node_name
for i, attrs in enumerate(model_nodes):
node = attrs
edges = get_mxnet_node_edges(node, i, list(model_nodes), index_node_keys)
if len(edges) > 0:
graph.add_edges_from(edges)
return graph
def symbol2nx(graph, model_nodes, model_params, input_names: str = ''):
if not input_names:
input_names = ('data', )
else:
input_names = input_names.split(',')
rnn_states = init_rnn_states(model_nodes)
names_rnn_states = list(rnn_states.keys())
# as mxnet contain input layers as index of layer, for correct set up edges, we need provide index of layer with name of graph node
index_node_keys = {}
fw_name_map = {}
for i, node in enumerate(model_nodes):
if node['name'] in model_params._arg_params and node[
'name'] not in input_names:
node['value'] = np.array(
model_params._arg_params[node['name']].asnumpy(),
dtype=np.float32)
elif node['name'] in model_params._aux_params and node[
'name'] not in input_names:
node['value'] = np.array(
model_params._aux_params[node['name']].asnumpy(),
dtype=np.float32)
elif node['name'] in names_rnn_states:
node['value'] = np.zeros(rnn_states[node['name']])
node_name = graph.unique_id(node['name'])
graph.add_node(node_name, **symbol_attrs(node))
graph.node[node_name].update(
common_mxnet_fields(Node(graph, node_name)))
index_node_keys[i] = node_name
fw_name_map[node_name] = node['name']
used_indices_set = set()
for i, attrs in enumerate(model_nodes):
node = attrs
edges, used_indices = get_mxnet_node_edges(node, i, list(model_nodes),
index_node_keys)
if len(edges) > 0:
graph.add_edges_from(edges)
used_indices_set = used_indices_set.union(used_indices)
output_ids = [
index_node_keys[node_id]
for node_id in set(range(len(model_nodes))) - used_indices_set
]
# 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, output_ids,
lambda g, output_id, fake_node_id, fw_name: g.add_edges_from([
create_mxnet_edge(output_id, fake_node_id, 0, 0, fw_name[output_id]
)
]), {'fw_name': fw_name_map})
return graph
