Const fold Ifs and remove keras_learning_phase inputs

tests/test_backend.py

@@ -903,6 +903,18 @@ def func():
         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]})
 
+    def test_fold_cond_keras_learning_phase(self):
+        # keras_learning_phase can slip into frozen graphs and cause huge inefficiencies with If nodes.
+        # Should be removed and Ifs folded.
+        x_val = np.array([1.0, 2.0, -3.0, -4.0], dtype=np.float32).reshape((2, 2))
+        def func():
+            x = tf_placeholder(tf.float32, [None, None], name=_TFINPUT)
+            learning_phase = tf_placeholder_with_default(False, [], name="keras_learning_phase")
+            y = tf.cond(learning_phase, lambda: x * 2, lambda: x * 3)
+            return tf.identity(y, name=_TFOUTPUT)
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, as_session=True, premade_placeholders=True,
+                            graph_validator=lambda g: check_op_count(g, "If", 0, disabled=False))
+
     @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/rewriter/cond_rewriter.py

@@ -89,10 +89,11 @@ def run(self):
                 continue
 
             self._cut_off_connection(cond_context)
-            self._create_if_node(cond_context)
+            if_node = self._create_if_node(cond_context)
             # remove nodes in If branches explicitly
-            for n in list(cond_context.true_branch_context.nodes) + list(cond_context.false_branch_context.nodes):
-                self.g.remove_node(n.name)
+            if if_node is not None:
+                for n in list(cond_context.true_branch_context.nodes) + list(cond_context.false_branch_context.nodes):
+                    self.g.remove_node(n.name)
         logger.debug("cond pre rewrite done")
 
         return self.g.get_nodes()
@@ -136,6 +137,19 @@ def _get_output_shape_dtype(self, cond_context):
 
     def _create_if_node(self, cond_context):
         output_shapes, output_dtypes = self._get_output_shape_dtype(cond_context)
+        pred_node = self.g.get_node_by_output(cond_context.pred_input)
+        while pred_node.type == "Identity":
+            pred_node = pred_node.inputs[0]
+        if pred_node.is_const():
+            # Constant folding for if node
+            if pred_node.get_tensor_value():
+                branch_outputs = cond_context.true_branch_context.output
+            else:
+                branch_outputs = cond_context.false_branch_context.output
+            for merge, out in zip(cond_context.merges, branch_outputs):
+                self.g.replace_all_inputs(merge.output[0], out)
+            return None
+
         true_graph = utils.construct_graph_from_nodes(
             self.g,
             list(cond_context.true_branch_context.nodes),

tf2onnx/tf_utils.py

@@ -438,6 +438,10 @@ def tflist_to_onnx(g, shape_override, const_node_values=None, ignore_default=Non
                 input_names = []
             elif use_default and node.name in use_default:
                 node_type = 'Identity'
+            elif node.name.endswith('keras_learning_phase'):
+                logger.warning("Removing optional input %s that appears to be a keras learning phase parameter. "
+                               "Use --ignore_default to force this into an input.", node.name)
+                node_type = 'Identity'
 
         if takeit:
             try: