Support run on local PC.

python/tools/tf_converter_lib.py

@@ -398,8 +398,8 @@ class TFConverter(object):
     self.add_tensor(input_names[1], gamma_value.shape,
       gamma_tensor.dtype, offset_value)
 
+    op_def.input.extend([op.inputs[0].name])
     if self.device == 'gpu':
-      op_def.input.extend([op.inputs[0].name])
       for name in input_names:
         output_name = self.add_buffer_to_image(name, "ARGUMENT")
         op_def.input.extend([output_name])
@@ -746,6 +746,16 @@ class TFConverter(object):
     self.add_output_shape(op.outputs, op_def)
     self.resolved_ops[op.name] = 1
 
+  def replace_in_out_name(self, input_name, output_name):
+    input_name = input_name + ":0"
+    output_name = output_name + ":0"
+    for op in self.net_def.op:
+      if len(op.input) > 0 and op.input[0] == input_name:
+        op.input[0] = MACE_INPUT_NODE_NAME + ":0"
+      if len(op.output) > 0 and op.output[0] == output_name:
+        op.output[0] = MACE_OUTPUT_NODE_NAME + ":0"
+
+
   def convert(self, input_node, output_node):
     if self.device == 'gpu':
       self.add_input_transform(input_node)
@@ -807,6 +817,9 @@ class TFConverter(object):
     if self.device == 'gpu':
       self.add_output_transform(output_node)
 
+    if self.device == 'cpu':
+      self.replace_in_out_name(input_node, output_node)
+
     for key in self.resolved_ops:
       if self.resolved_ops[key] != 1:
         print 'Unresolve Op: %s' % key
@@ -935,9 +948,11 @@ def convert_to_mace_pb(input_graph_def, input_node, input_shape, output_node, da
       converter.convert(input_node, output_node)
       optimizer = Optimizer(net_def, device)
       net_def = optimizer.optimize()
-      print "PB Converted, start optimize memory."
-      mem_optimizer = memory_optimizer.MemoryOptimizer(net_def)
-      mem_optimizer.optimize()
-      print "Memory optimization done."
+      print "PB Converted."
+      if device == 'gpu':
+        print "start optimize memory."
+        mem_optimizer = memory_optimizer.MemoryOptimizer(net_def)
+        mem_optimizer.optimize()
+        print "Memory optimization done."
 
   return net_def