Fix tf_converter to support gcn model.

mace/python/tools/tf_converter.py

@@ -24,7 +24,7 @@ def main(unused_args):
       input_graph_def, FLAGS.input_node, FLAGS.output_node, FLAGS.prequantize)
   else:
     output_graph_def = tf_converter_lib.convert_to_mace_pb(
-      input_graph_def)
+      input_graph_def, FLAGS.runtime)
 
   with gfile.GFile(FLAGS.output, "wb") as f:
     f.write(output_graph_def.SerializeToString())

mace/python/tools/tf_converter_lib.py

@@ -13,6 +13,12 @@ pooling_type_mode = {
   'MaxPool': 2
 }
 
+buffer_type_map = {
+  'FILTER' : 0,
+  'IN_OUT' : 1,
+  'ARGUMENT' : 2,
+}
+
 def convert_tensor(op, tensor):
   tf_tensor = op.outputs[0].eval()
   tensor.name = op.outputs[0].name
@@ -36,7 +42,21 @@ def get_input_tensor(op, index):
     input_tensor = get_input_tensor(input_tensor.op, 0)
   return input_tensor
 
-def convert_ops(ops_map, unresolved_ops, net_def):
+def add_buffer_to_image(input_name, input_type, net_def):
+  output_name = input_name[:-2] + "_b2i" + input_name[-2:]
+  op_def = net_def.op.add()
+  op_def.name = output_name
+  op_def.type = 'BufferToImage'
+  op_def.input.extend([input_name])
+  epsilon_arg = op_def.arg.add()
+  epsilon_arg.name = 'buffer_type'
+  epsilon_arg.i = buffer_type_map[input_type]
+  epsilon_arg = op_def.arg.add()
+  epsilon_arg.name = 'mode'
+  epsilon_arg.i = 0
+  return output_name
+
+def convert_ops(unresolved_ops, net_def, device):
   ops_count = len(unresolved_ops)
   resolved_count = 1
 
@@ -54,14 +74,13 @@ def convert_ops(ops_map, unresolved_ops, net_def):
       op_def.type = 'DepthwiseConv2d'
     else:
       op_def.type = first_op.type
-    op_def.input.extend([input.name for input in first_op.inputs])
-    op_def.output.extend([output.name for output in first_op.outputs])
-    output_shapes = []
-    for output in first_op.outputs:
-      output_shape = mace_pb2.OutputShape()
-      output_shape.dims.extend(output.shape.as_list())
-      output_shapes.append(output_shape)
-    op_def.output_shape.extend(output_shapes)
+    if device == 'gpu':
+      op_def.input.extend([first_op.inputs[0].name])
+      output_name = add_buffer_to_image(first_op.inputs[1].name, "FILTER", net_def)
+      op_def.input.extend([output_name])
+    else:
+      op_def.input.extend([input.name for input in first_op.inputs])
+
     padding_arg = op_def.arg.add()
     padding_arg.name = 'padding'
     padding_arg.i = padding_mode[first_op.get_attr('padding')]
@@ -71,27 +90,53 @@ def convert_ops(ops_map, unresolved_ops, net_def):
     data_format_arg = op_def.arg.add()
     data_format_arg.name = 'data_format'
     data_format_arg.s = 'NHWC'
+    final_op = first_op
 
-    if ops_count >= 2 and unresolved_ops[1].type == 'BiasAdd':
-      bias_add_op = unresolved_ops[1]
-      op_def.input.extend([bias_add_op.inputs[1].name])
-      resolved_count = 2
+    if ops_count >= 3 and unresolved_ops[1].type == 'Const' and unresolved_ops[2].type == 'BiasAdd' :
+      bias_tensor = unresolved_ops[1]
+      tensor = net_def.tensors.add()
+      convert_tensor(bias_tensor, tensor)
 
-    if ops_count >= 3 and unresolved_ops[1].type == 'Relu':
-      op_def.name = "FusedConv2D"
+      bias_add_op = unresolved_ops[2]
+      if device == 'gpu':
+        output_name = add_buffer_to_image(bias_add_op.inputs[1].name, "ARGUMENT", net_def)
+        op_def.input.extend([output_name])
+      else:
+        op_def.input.extend([bias_add_op.inputs[1].name])
+      final_op = bias_add_op
       resolved_count = 3
-  elif first_op.type == 'FusedBatchNorm':
-    op_def = net_def.op.add()
-    op_def.name = first_op.name
-    first_op.type = 'BatchNorm'
-    op_def.input.extend([input.name for input in first_op.inputs])
-    op_def.output.extend([output.name for output in first_op.outputs])
+
+    if ops_count >= 4 and unresolved_ops[3].type == 'Relu':
+      relu_op = unresolved_ops[3];
+      op_def.type = "FusedConv2D"
+      final_op = relu_op
+      resolved_count = 4
+
+    op_def.output.extend([output.name for output in final_op.outputs])
     output_shapes = []
-    for output in first_op.outputs:
+    for output in final_op.outputs:
       output_shape = mace_pb2.OutputShape()
       output_shape.dims.extend(output.shape.as_list())
       output_shapes.append(output_shape)
     op_def.output_shape.extend(output_shapes)
+
+  elif first_op.type == 'FusedBatchNorm':
+    op_def = net_def.op.add()
+    op_def.name = first_op.name
+    op_def.type = 'BatchNorm'
+    if device == 'gpu':
+      op_def.input.extend([first_op.inputs[0].name])
+      for i in range(1, len(first_op.inputs)):
+        output_name = add_buffer_to_image(first_op.inputs[i].name, "ARGUMENT", net_def)
+        op_def.input.extend([output_name])
+    else:
+      op_def.input.extend([input.name for input in first_op.inputs])
+    op_def.output.extend([first_op.outputs[0].name])
+
+    output_shape = mace_pb2.OutputShape()
+    output_shape.dims.extend(first_op.outputs[0].shape.as_list())
+    op_def.output_shape.extend([output_shape])
+
     epsilon_arg = op_def.arg.add()
     epsilon_arg.name = 'epsilon'
     epsilon_arg.f = first_op.get_attr('epsilon')
@@ -190,7 +235,7 @@ def convert_ops(ops_map, unresolved_ops, net_def):
     op_def = net_def.op.add()
     op_def.name = first_op.name
     op_def.type = "Concat"
-    op_def.input.extend([first_op.inputs[i] for i in xrange(2)])
+    op_def.input.extend([first_op.inputs[i].name for i in xrange(2)])
     op_def.output.extend([output.name for output in first_op.outputs])
     axis_arg = op_def.arg.add()
     axis_arg.name = 'axis'
@@ -205,19 +250,21 @@ def convert_ops(ops_map, unresolved_ops, net_def):
     op_def = net_def.op.add()
     op_def.name = first_op.name
     op_def.type = "ResizeBilinear"
-    op_def.input.extend(first_op.inputs[0])
+    op_def.input.extend([first_op.inputs[0].name])
     op_def.output.extend([output.name for output in first_op.outputs])
     size_arg = op_def.arg.add()
     size_arg.name = 'size'
     size_arg.ints.extend(get_input_tensor(first_op, 1).eval().astype(np.int32).flat)
+    size_arg = op_def.arg.add()
+    size_arg.name = 'align_corners'
+    size_arg.ints.extend(first_op.get_attr('align_corners'))
     output_shapes = []
     for output in first_op.outputs:
       output_shape = mace_pb2.OutputShape()
       output_shape.dims.extend(output.shape.as_list())
       output_shapes.append(output_shape)
     op_def.output_shape.extend(output_shapes)
-  elif first_op.type in ['Relu', 'SpaceToBatchND',
-                         'BatchToSpaceND', 'BiasAdd', 'FusedBatchNorm']:
+  elif first_op.type in ['Relu', 'SpaceToBatchND', 'BatchToSpaceND', 'BiasAdd']:
     op_def = net_def.op.add()
     op_def.name = first_op.name
     op_def.type = first_op.type
@@ -237,24 +284,17 @@ def convert_ops(ops_map, unresolved_ops, net_def):
     del unresolved_ops[0]
 
 
-def convert_to_mace_pb(input_graph_def):
+def convert_to_mace_pb(input_graph_def, device):
   net_def = mace_pb2.NetDef()
 
   with tf.Session() as session:
     with session.graph.as_default() as graph:
       tf.import_graph_def(input_graph_def, name="")
       ops = graph.get_operations()
-      ops_map = {}
-      for op in ops:
-        if op.name not in ops_map.keys():
-          ops_map[op.name] = op
-        else:
-          raise ValueError("Duplicate op names detected for ", op.name)
-
       unresolved_ops = ops
       while len(unresolved_ops) > 0:
-        convert_ops(ops_map, unresolved_ops, net_def)
+        convert_ops(unresolved_ops, net_def, device)
 
-  print "Done."
+  print "PB Parsed."
 
   return net_def