Fixed framework name attribute in mapping file.

model-optimizer/mo/front/caffe/loader.py

@@ -169,7 +169,7 @@ def caffe_pb_to_nx(graph, proto, model):
     # Blobs in prototxt model can be reused by inplace layer.
     # This requires loading of pb layers in order and tracking the latest
     # layer that writes a particular blob.
-    blob_producers = {}  # maps layer blob name to the layer name and port
+    blob_producers = {}  # maps layer blob name to node id in graph, port and layer name
     proto_layers = get_layers(proto)
     model_layers = None
     if model:
@@ -239,7 +239,7 @@ def caffe_pb_to_nx(graph, proto, model):
         # Input is defined at the top level of proto instead of distinct Input layer
         graph.add_node(input_name, pb=None, model_pb=None, type='GlobalInput', name=input_name, shape=input_dim,
                        kind='op')
-        blob_producers[input_name] = (input_name, 0)
+        blob_producers[input_name] = (input_name, 0, input_name)
 
     used_blobs = set()
     for i, layer in enumerate(proto_layers):
@@ -280,6 +280,7 @@ def caffe_pb_to_nx(graph, proto, model):
                 input_dims.append(np.array(list(dims), dtype=np.int64))
                 input_names.append(layer.name)
 
+        layer_name = layer.name
         layer.name = graph.unique_id(layer.name)
         graph.add_node(layer.name, pb=layer, model_pb=model_layer, kind='op', type='Parameter')
 
@@ -292,7 +293,7 @@ def caffe_pb_to_nx(graph, proto, model):
         for src_port, top in enumerate(layer.top):
             if top in blob_producers:
                 log.debug("Detected reuse of blob {} by layer {}".format(top, layer.name))
-            blob_producers[top] = (layer.name, src_port)
+            blob_producers[top] = (layer.name, src_port, layer_name)
 
     # 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
@@ -320,7 +321,7 @@ def add_edge_caffe(graph: Graph, bottom: str, dst_layer: str, blob_producers: di
         'in': dst_port,
         'name': bottom,
         # debug anchor for a framework name, out port and tensor name
-        'fw_tensor_debug_info': [(src_layer, src_port, bottom)],
+        'fw_tensor_debug_info': [(blob_producers[bottom][2], src_port, bottom)],
         'in_attrs': ['in', 'name'],
         'out_attrs': ['out', 'name'],
         'data_attrs': ['fw_tensor_debug_info']

model-optimizer/mo/front/extractor.py

@@ -753,7 +753,7 @@ def add_outputs_identity(graph: Graph, outputs: list, add_edge: callable, params
     for output in outputs:
         fake_node_name = graph.unique_id(output)
         graph.add_node(fake_node_name, name=fake_node_name, identity=True, kind='op', op='Identity',
-                       infer=None, needs_removal=True)
+                       infer=None, needs_removal=True, symbol_dict={'op': 'Identity'})
         add_edge(graph, output, fake_node_name, **params)
 
 

model-optimizer/mo/front/mxnet/extractors/utils.py

@@ -101,21 +101,25 @@ def has(self, key):
 
 def get_mxnet_node_edges(node: dict, node_id: [int, str], nodes_list: list, index_node_key: dict):
     edge_list = []
+    used_indices = []
     for in_port, src_node_id in enumerate(node['inputs']):
-        src_node = src_node_id[0]
-        dest_port = src_node_id[1]
-        edge_attrs = {
-            'in': in_port,
-            'out': dest_port,
-            # debug anchor for framework name, out port and tensor name
-            'fw_tensor_debug_info': [(index_node_key[src_node], src_node_id[1], nodes_list[src_node]['name'])],
-            'in_attrs': ['in'],
-            'out_attrs': ['out'],
-            'data_attrs': ['fw_tensor_debug_info']
-        }
-        edge = (index_node_key[src_node], index_node_key[node_id], edge_attrs)
+        edge = create_mxnet_edge(index_node_key[src_node_id[0]], index_node_key[node_id], in_port, src_node_id[1],  nodes_list[src_node_id[0]]['name'])
         edge_list.append(edge)
-    return edge_list
+        used_indices.append(src_node_id[0])
+    return edge_list, used_indices
+
+
+def create_mxnet_edge(src_node_id: str, dst_node_id: str, src_port: int, dst_port: int, framework_name: str):
+    edge_attrs = {
+        'in': src_port,
+        'out': dst_port,
+        # debug anchor for framework name, out port and tensor name
+        'fw_tensor_debug_info': [(framework_name, dst_port, framework_name)],
+        'in_attrs': ['in'],
+        'out_attrs': ['out'],
+        'data_attrs': ['fw_tensor_debug_info']
+    }
+    return src_node_id, dst_node_id, edge_attrs
 
 
 def get_mxnet_layer_attrs(json_dic: dict):

model-optimizer/mo/front/mxnet/loader.py

@@ -8,8 +8,9 @@
 import mxnet as mx
 import numpy as np
 
+from mo.front.extractor import add_outputs_identity
 from mo.front.mxnet.extractor import common_mxnet_fields
-from mo.front.mxnet.extractors.utils import get_mxnet_node_edges, load_params, init_rnn_states
+from mo.front.mxnet.extractors.utils import get_mxnet_node_edges, load_params, init_rnn_states, create_mxnet_edge
 from mo.front.mxnet.nd_to_params import build_params_file
 from mo.graph.graph import Node, Graph
 from mo.utils.error import Error
@@ -50,7 +51,8 @@ def parse_input_model(input_model):
     return model_name, iteration_number
 
 
-def load_symbol_def(input_model_name, input_symbol, input_names: str = '', nd_prefix_name: str = '', pretrained_model_name: str = '', legacy_mxnet_model: bool = False):
+def load_symbol_def(input_model_name, input_symbol, input_names: str = '', nd_prefix_name: str = '',
+                    pretrained_model_name: str = '', legacy_mxnet_model: bool = False):
     if not nd_prefix_name and not pretrained_model_name:
         # model name always has extension 'param'
         try:
@@ -95,6 +97,7 @@ def symbol2nx(graph, model_nodes, model_params, input_names: str = ''):
 
     # 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)
@@ -106,12 +109,25 @@ def symbol2nx(graph, model_nodes, model_params, input_names: str = ''):
         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_list = []
     for i, attrs in enumerate(model_nodes):
         node = attrs
-        edges = get_mxnet_node_edges(node, i, list(model_nodes), index_node_keys)
+        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_list += used_indices
+
+    output_ids = [index_node_keys[node_id] for node_id in set(range(1, len(model_nodes))) - set(used_indices_list)]
+
+    # 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
 

model-optimizer/mo/front/onnx/loader.py

@@ -109,7 +109,7 @@ def protobuf2nx(graph: Graph, pb):
                     'out': src_port,
                     'in': 0,
                     'name': out,
-                    'fw_tensor_debug_info': [(id, src_port, out)],
+                    'fw_tensor_debug_info': [(node_id(node), src_port, out)],
                     'in_attrs': ['in', 'name'],
                     'out_attrs': ['out', 'name'],
                     'data_attrs': ['fw_tensor_debug_info']

model-optimizer/mo/graph/graph.py

@@ -1045,12 +1045,8 @@ def add_opoutput(graph: Graph, node_name: str, port: int, cut: bool = True):
     if cut and len(node.out_edges()) != 0:
         opoutput_node = Result(graph).create_node_on_port(node, port, {'name': node_name + '/sink_port_' + str(port)})
     else:
-        tensor_names = None
-        if node.has_valid('op') and port in node.out_ports():
-            tensor_names = node.out_port(port).get_tensor_names()
         opoutput_node = Result(graph).create_node([(node, port)], {'name': node_name + '/sink_port_' + str(port)})
         opoutput_node.in_edge()['data_attrs'] = ['fw_tensor_debug_info']
-        opoutput_node.in_edge()['fw_tensor_debug_info'] = [(node_name, port, tensor_names)]
 
     log.debug('Sink: {} for node {}'.format(opoutput_node.id, node_name))
     log.debug(str(graph.node[opoutput_node.id]))
@@ -1125,8 +1121,7 @@ def set_edge_attribute_between_nodes(node1: Node, node2: Node, attr_name: str, n
         out_port = edge['out']
         out_node = node1.out_node(out_port)
         if out_node.id == node2.id:
-            if attr_name in edge:
-                edge[attr_name] = new_value
+            edge[attr_name] = new_value
 
 # All functions below are deprecated and will be removed in next release
 # Please, use methods from Graph/Node classes instead