Fully connected operator with no bias

python/mxnet/contrib/onnx/mx2onnx/_op_translations.py

@@ -212,20 +212,46 @@ def convert_fully_connected(node, **kwargs):
     """Map MXNet's FullyConnected operator attributes to onnx's Gemm operator
     and return the created node.
     """
-    helper, _, _ = import_onnx_modules()
+    helper, _, mapping = import_onnx_modules()
     name = node["name"]
     inputs = node["inputs"]
+    attrs = node["attrs"]
+    initializer = kwargs["initializer"]
+
+    no_bias = attrs.get("no_bias", "False")
+
     input_node_id = kwargs["index_lookup"][inputs[0][0]]
     weight_node_id = kwargs["index_lookup"][inputs[1][0]]
-    bias_node_id = kwargs["index_lookup"][inputs[2][0]]
+
     proc_nodes = kwargs["proc_nodes"]
-    input_node = proc_nodes[input_node_id]
-    weights_node = proc_nodes[weight_node_id]
-    bias_node = proc_nodes[bias_node_id]
 
+    input_node = proc_nodes[input_node_id]
     input_name = input_node.name
+
+    weights_node = proc_nodes[weight_node_id]
     weights_name = weights_node.name
-    bias_name = bias_node.name
+
+    if no_bias == "False":
+        bias_node_id = kwargs["index_lookup"][inputs[2][0]]
+        bias_node = proc_nodes[bias_node_id]
+        bias_name = bias_node.name
+    else:
+        default_bias = [0]
+        np_arr = np.array(default_bias)
+        data_type = mapping.NP_TYPE_TO_TENSOR_TYPE[np_arr.dtype]
+        dims = np.shape(np_arr)
+
+        bias_name = "bias" + str(kwargs["idx"])
+
+        initializer.append(
+            helper.make_tensor(
+                name=bias_name,
+                data_type=data_type,
+                dims=dims,
+                vals=default_bias,
+                raw=False,
+            )
+        )
 
     node = helper.make_node(
         "Gemm",