Added support for PlaceholderWithDefault ops with computed defaults (#1268)

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

README.md

@@ -172,6 +172,10 @@ TensorFlow model's input/output names, which can be found with [summarize graph
 
 By default we preserve the image format of inputs (`nchw` or `nhwc`) as given in the TensorFlow model. If your hosts (for example windows) native format nchw and the model is written for nhwc, ```--inputs-as-nchw``` tensorflow-onnx will transpose the input. Doing so is convenient for the application and the converter in many cases can optimize the transpose away. For example ```--inputs input0:0,input1:0 --inputs-as-nchw input0:0``` assumes that images are passed into ```input0:0``` as nchw while the TensorFlow model given uses nhwc.
 
+#### --ignore_default, --use_default
+
+ONNX requires default values for graph inputs to be constant, while Tensorflow's PlaceholderWithDefault op accepts computed defaults.  To convert such models, pass a comma-separated list of node names to the ignore_default and/or use_default flags.  PlaceholderWithDefault nodes with matching names will be replaced with Placeholder or Identity ops, respectively.
+
 #### --opset
 
 By default we use the opset 8 to generate the graph. By specifying ```--opset``` the user can override the default to generate a graph with the desired opset. For example ```--opset 5``` would create a onnx graph that uses only ops available in opset 5. Because older opsets have in most cases fewer ops, some models might not convert on a older opset.
@@ -289,6 +293,7 @@ tf2onnx.tfonnx.process_tf_graph(tf_graph,
             custom_rewriter=None, extra_opset=None,
             shape_override=None, inputs_as_nchw=None,
             input_names=None, output_names=None,
+            ignore_default=None, use_default=None,
             const_node_values=None):
     """Convert tensorflow graph to onnx graph.
         Args:
@@ -304,6 +309,8 @@ tf2onnx.tfonnx.process_tf_graph(tf_graph,
             inputs_as_nchw: transpose inputs in list from nchw to nchw
             input_names: list of input node names in graph, input name format as node_name:port_id
             output_names: list of output node names in graph, output name format as node_name:port_id
+            ignore_default: list of node names of PlaceholderWithDefault ops to change into Placeholder ops
+            use_default: list of node names of PlaceholderWithDefault ops to change into Identity ops
             const_node_values: an optional dict mapping node names to tensor values
         Return:
             onnx graph

tests/test_backend.py

@@ -23,7 +23,7 @@
 from common import *  # pylint: disable=wildcard-import,unused-wildcard-import
 from tf2onnx import constants, utils
 from tf2onnx.graph_matcher import OpTypePattern, GraphMatcher
-from tf2onnx.tf_loader import is_tf2, tf_placeholder_with_default
+from tf2onnx.tf_loader import is_tf2, tf_placeholder_with_default, tf_placeholder
 from tf2onnx.onnx_opset.signal import make_dft_constant
 
 # pylint: disable=missing-docstring,invalid-name,unused-argument,function-redefined,cell-var-from-loop
@@ -728,6 +728,33 @@ def func():
         x_feed_val = np.array([11.0, 22.0, -33.0, -44.0], dtype=np.float32).reshape((2, 2))
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_feed_val}, as_session=True, premade_placeholders=True)
 
+    def test_placeholder_with_default_computed_use_default(self):
+        x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
+        y_val = np.array([2.0, -4.0, 6.0, -8.0], dtype=np.float32).reshape((2, 2))
+        def func():
+            x = tf_placeholder(tf.float32, x_val.shape, name=_TFINPUT)
+            y = tf_placeholder(tf.float32, y_val.shape, name=_TFINPUT1)
+            total = tf.add(x, y)
+            z = tf_placeholder_with_default(total, x_val.shape, name=_TFINPUT2)
+            total2 = tf.add(total, z)
+            return tf.identity(total2, name=_TFOUTPUT)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val}, as_session=True,
+                            premade_placeholders=True, process_args={'use_default': [_TFINPUT2]})
+
+    def test_placeholder_with_default_computed_ignore_default(self):
+        x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
+        y_val = np.array([2.0, -4.0, 6.0, -8.0], dtype=np.float32).reshape((2, 2))
+        z_val = np.array([3.0, 6.0, 9.0, 10.0], dtype=np.float32).reshape((2, 2))
+        def func():
+            x = tf_placeholder(tf.float32, x_val.shape, name=_TFINPUT)
+            y = tf_placeholder(tf.float32, y_val.shape, name=_TFINPUT1)
+            total = tf.add(x, y)
+            z = tf_placeholder_with_default(total, x_val.shape, name=_TFINPUT2)
+            total2 = tf.add(total, z)
+            return tf.identity(total2, name=_TFOUTPUT)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val, _INPUT1: y_val, _INPUT2: z_val}, as_session=True,
+                            premade_placeholders=True, process_args={'ignore_default': [_TFINPUT2]})
+
     @check_onnxruntime_incompatibility("Add")
     def test_add_bcast(self):
         x1_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))

tf2onnx/convert.py

@@ -59,6 +59,10 @@ def get_args():
     parser.add_argument("--output", help="output model file")
     parser.add_argument("--inputs", help="model input_names")
     parser.add_argument("--outputs", help="model output_names")
+    parser.add_argument("--ignore_default", help="comma-separated list of names of PlaceholderWithDefault "
+                                                 "ops to change into Placeholder ops")
+    parser.add_argument("--use_default", help="comma-separated list of names of PlaceholderWithDefault ops to "
+                                              "change into Identity ops using their default value")
     parser.add_argument("--opset", type=int, default=None, help="opset version to use for onnx domain")
     parser.add_argument("--custom-ops", help="comma-separated map of custom ops to domains in format OpName:domain")
     parser.add_argument("--extra_opset", default=None,
@@ -89,6 +93,10 @@ def get_args():
         args.inputs, args.shape_override = utils.split_nodename_and_shape(args.inputs)
     if args.outputs:
         args.outputs = args.outputs.split(",")
+    if args.ignore_default:
+        args.ignore_default = args.ignore_default.split(",")
+    if args.use_default:
+        args.use_default = args.use_default.split(",")
     if args.inputs_as_nchw:
         args.inputs_as_nchw = args.inputs_as_nchw.split(",")
     if args.target:
@@ -172,6 +180,8 @@ def main():
                              input_names=inputs,
                              output_names=outputs,
                              inputs_as_nchw=args.inputs_as_nchw,
+                             ignore_default=args.ignore_default,
+                             use_default=args.use_default,
                              const_node_values=const_node_values,
                              initialized_tables=initialized_tables)
 

tf2onnx/graph.py

@@ -1077,7 +1077,8 @@ def make_graph(self, doc, graph_name=None, external_tensor_storage=None):
             if op.type == "PlaceholderWithDefault":
                 utils.make_sure(op.inputs[0] is not None, "Cannot find node with output {}".format(op.input[0]))
                 utils.make_sure(op.inputs[0].is_const(),
-                                "non-const default value for PlaceholderWithDefault is not supported.")
+                                "non-const default value for PlaceholderWithDefault node '%s' is not supported. "
+                                "Use the --use_default or --ignore_default flags to convert this node.", op.name)
                 # copy the tensor value, set its name to current node's output, add as initializer
                 value = op.inputs[0].get_tensor_value(as_list=False)
                 tensor = numpy_helper.from_array(value, op.output[0])

tf2onnx/tf_utils.py

@@ -317,7 +317,7 @@ def replace_placeholders_with_tables(graph_def, placeholder_to_table_info):
             n.attr['key_dtype'].type = key_dtype
             n.attr['value_dtype'].type = val_dtype
 
-def tflist_to_onnx(g, shape_override, const_node_values=None):
+def tflist_to_onnx(g, shape_override, const_node_values=None, ignore_default=None, use_default=None):
     """
     Convert the tf-node list into an onnx graph with minimal rewrites so
     we can use the onnx graph as intermediate graph.
@@ -395,11 +395,20 @@ def tflist_to_onnx(g, shape_override, const_node_values=None):
             else:
                 attr[a] = get_tf_node_attr(node, a)
 
+        node_type = node.type
+        input_names = [i.name for i in node.inputs]
+        output_names = [i.name for i in node.outputs]
+
+        if node_type == 'PlaceholderWithDefault':
+            if ignore_default and node.name in ignore_default:
+                node_type = 'Placeholder'
+                input_names = []
+            elif use_default and node.name in use_default:
+                node_type = 'Identity'
+
         if takeit:
             try:
-                input_names = [i.name for i in node.inputs]
-                output_names = [i.name for i in node.outputs]
-                onnx_node = helper.make_node(node.type, input_names, output_names, name=node.name, **attr)
+                onnx_node = helper.make_node(node_type, input_names, output_names, name=node.name, **attr)
                 onnx_nodes.append(onnx_node)
             except Exception as ex:
                 logger.error("pass1 convert failed for %s, ex=%s", node, ex)
@@ -408,8 +417,8 @@ def tflist_to_onnx(g, shape_override, const_node_values=None):
     return onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, functions
 
 
-def tensorflow_to_onnx(graph, shape_override, const_node_values=None):
+def tensorflow_to_onnx(graph, shape_override, const_node_values=None, ignore_default=None, use_default=None):
     """
     Load tensorflow graph and do a conversion.
     """
-    return tflist_to_onnx(graph, shape_override, const_node_values)
+    return tflist_to_onnx(graph, shape_override, const_node_values, ignore_default, use_default)

tf2onnx/tfonnx.py

@@ -367,7 +367,8 @@ def run_rewriters(g, funcs, continue_on_error):
 def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=None,
                      opset=None, custom_op_handlers=None, custom_rewriter=None,
                      extra_opset=None, shape_override=None, inputs_as_nchw=None,
-                     input_names=None, output_names=None, is_subgraph=False, const_node_values=None,
+                     input_names=None, output_names=None, ignore_default=None, use_default=None,
+                     is_subgraph=False, const_node_values=None,
                      initialized_tables=None):
     """Convert tensorflow graph to onnx graph.
         Args:
@@ -383,6 +384,8 @@ def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=No
             inputs_as_nchw: transpose inputs in list from nchw to nhwc
             input_names: list of input node names in graph, input name format as node_name:port_id
             output_names: list of output node names in graph, output name format as node_name:port_id
+            ignore_default: list of node names of PlaceholderWithDefault ops to change into Placeholder ops
+            use_default: list of node names of PlaceholderWithDefault ops to change into Identity ops using the default
             const_node_values: a dict returned by compress_graph_def mapping node names to tensor values
             initialized_tables: mapping from table shared_names to tuple of keys and values of table
         Return:
@@ -416,7 +419,7 @@ def process_tf_graph(tf_graph, continue_on_error=False, verbose=False, target=No
     outputs_to_values, outputs_to_dtypes = compute_const_folding_using_tf(tf_graph, const_node_values, output_names)
 
     onnx_nodes, op_cnt, attr_cnt, output_shapes, dtypes, _ = \
-        tensorflow_to_onnx(tf_graph, shape_override, const_node_values)
+        tensorflow_to_onnx(tf_graph, shape_override, const_node_values, ignore_default, use_default)
     if not is_subgraph:
         # make tf2onnx internal subgraphs from the tensorflow subgraphs
         ordered_func = resolve_functions(tf_graph)