Fix bug that resulted in unnecessary placeholders created during functional model cloning when input tensors were specified.

PiperOrigin-RevId: 222424883

tensorflow/python/keras/engine/input_layer.py

@@ -19,12 +19,10 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.python.eager import context
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras import backend
 from tensorflow.python.keras.engine import base_layer
 from tensorflow.python.keras.utils import tf_utils
-from tensorflow.python.ops import array_ops
 from tensorflow.python.util.tf_export import tf_export
 
 
@@ -94,19 +92,19 @@ class InputLayer(base_layer.Layer):
       else:
         batch_input_shape = None
       graph = backend.get_graph()
-      with context.graph_mode():
-        with graph.as_default():
-          # In graph mode, create a graph placeholder to call the layer on.
-          if sparse:
-            input_tensor = array_ops.sparse_placeholder(
-                shape=batch_input_shape,
-                dtype=dtype,
-                name=self.name)
-          else:
-            input_tensor = array_ops.placeholder(
-                shape=batch_input_shape,
-                dtype=dtype,
-                name=self.name)
+      with graph.as_default():
+        # In graph mode, create a graph placeholder to call the layer on.
+        if sparse:
+          input_tensor = backend.placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name,
+              sparse=True)
+        else:
+          input_tensor = backend.placeholder(
+              shape=batch_input_shape,
+              dtype=dtype,
+              name=self.name)
 
       self.is_placeholder = True
       self._batch_input_shape = batch_input_shape

tensorflow/python/keras/models.py

@@ -100,17 +100,19 @@ def _clone_functional_model(model, input_tensors=None):
       input_tensors = list(input_tensors)
     input_tensors = generic_utils.to_list(input_tensors)
     input_tensors_ = []
-    for i, x in enumerate(input_tensors):
-      if not K.is_keras_tensor(x):
-        name = model._input_layers[i].name
-        input_tensor = Input(tensor=x, name='input_wrapper_for_' + name)
+    for i in range(len(input_tensors)):
+      input_tensor = input_tensors[i]
+      if not K.is_keras_tensor(input_tensor):
+        original_input_layer = model._input_layers[i]
+        name = original_input_layer.name
+        input_tensor = Input(tensor=input_tensor,
+                             name='input_wrapper_for_' + name)
         input_tensors_.append(input_tensor)
         # Cache newly created input layer.
-        original_input_layer = x._keras_history[0]
         newly_created_input_layer = input_tensor._keras_history[0]
         layer_map[original_input_layer] = newly_created_input_layer
       else:
-        input_tensors_.append(x)
+        input_tensors_.append(input_tensor)
     input_tensors = input_tensors_
 
   for x, y in zip(model.inputs, input_tensors):

tensorflow/python/keras/models_test.py

@@ -26,10 +26,12 @@ import numpy as np
 from tensorflow.python import keras
 from tensorflow.python.eager import context
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras import backend as K
 from tensorflow.python.keras import metrics
 from tensorflow.python.keras import models
+from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import random_ops
 from tensorflow.python.ops import resource_variable_ops
 from tensorflow.python.platform import test
@@ -219,6 +221,33 @@ class TestModelCloning(test.TestCase):
     with self.assertRaises(ValueError):
       keras.models._clone_sequential_model(seq_model, input_tensors=y)
 
+  def test_functional_cloning_does_not_create_unnecessary_placeholders(self):
+    with ops.Graph().as_default():
+      x = keras.Input((4,))
+      y = keras.layers.Dense(4)(x)
+      model = keras.models.Model(x, y)
+    graph = ops.Graph()
+    with graph.as_default():
+      x = array_ops.ones((10, 4))
+      _ = keras.models.clone_model(model, input_tensors=[x])
+      has_placeholder = _has_placeholder(graph)
+      self.assertFalse(has_placeholder)
+
+  def test_sequential_cloning_does_not_create_unnecessary_placeholders(self):
+    with ops.Graph().as_default():
+      model = keras.models.Sequential([keras.layers.Dense(4)])
+    graph = ops.Graph()
+    with graph.as_default():
+      x = array_ops.ones((10, 4))
+      _ = keras.models.clone_model(model, input_tensors=[x])
+      has_placeholder = _has_placeholder(graph)
+      self.assertFalse(has_placeholder)
+
+
+def _has_placeholder(graph):
+  ops_types = [op.type for op in graph.get_operations()]
+  return any('Placeholder' in s for s in ops_types)
+
 
 class CheckpointingTests(test.TestCase):