Handle TransformGraph for cases where constant outputs where converted to vars.

If the graph has a constant output, it will get converted to a variable. While
dumping graph_defs TransformGraph needs to be able to find that output. So we
teach it to find the newly created variable.

Athos/TFCompiler/DumpTFMtData.py

@@ -31,7 +31,21 @@ def strip_variable_init_constants(graph_def, input_tensor_names, output_tensor_n
    'remove_nodes(op=Identity)', 
    'strip_unused_nodes',
   ]
-  optimized_graph_def = TransformGraph(graph_def, input_tensor_names, output_tensor_names, transforms)
+  # Sanity check if output/input nodes were constant and replaced with variables.
+  all_node_names =  set([i.name for i in graph_def.node])
+  def get_true_names(tensor_names, all_nodes):
+    real_names = []
+    for i in tensor_names:
+      if i not in all_nodes:
+        var_name = i + "_mpc_const_var"
+        if var_name in all_nodes:
+          real_names.append(var_name)
+      else:
+        real_names.append(i)
+    return real_names
+  real_input_names = get_true_names(input_tensor_names, all_node_names)
+  real_output_names = get_true_names(output_tensor_names, all_node_names)
+  optimized_graph_def = TransformGraph(graph_def, real_input_names, real_output_names, transforms)
   return optimized_graph_def
 
 def save_graphdef(graph_def):
@@ -179,6 +193,8 @@ def save_weights(optimized_graph_def, sess, feed_dict, filename, scaling_factor)
   values = sess.run(graph_vars, feed_dict)
   with open(filename, "w") as ff:
     for val in values:
+      if val.shape == (0,): #Empty array, nothing to dump.
+        continue
       for xx in numpy.nditer(val, order="C"):
         ff.write(str(int(xx * (1 << scaling_factor))) + " ")
       ff.write("\n")
\ No newline at end of file