Internal change

PiperOrigin-RevId: 308757298
Change-Id: I9ec9dfdd236a1ed796f82fd77e0afee777ca626d

tensorflow/python/keras/layers/convolutional.py

@@ -305,8 +305,9 @@ class Conv(Layer):
     """Recreate conv_op if necessary.
 
     Check if the input_shape in call() is different from that in build().
-    For the values that are not None, if they are different, recreate
-    the _convolution_op to avoid the stateful behavior.
+    If the most-specific input shape describing the build and call shapes is not
+    equal to the shape we currently built with, then we need to rebuild the
+    _convolution_op to avoid incorrect behavior.
 
     Args:
       inputs: The input data to call() method.
@@ -315,12 +316,10 @@ class Conv(Layer):
       `True` or `False` to indicate whether to recreate the conv_op.
     """
     call_input_shape = inputs.get_shape()
-    for axis in range(1, len(call_input_shape)):
-      if (call_input_shape[axis] is not None
-          and self._build_conv_op_input_shape[axis] is not None
-          and call_input_shape[axis] != self._build_conv_op_input_shape[axis]):
-        return True
-    return False
+    # If the most specific compatible shape between _build_input_shape and
+    # call_input_shape is not _build_input_shape then we must re-build.
+    return self._build_conv_op_input_shape.most_specific_compatible_shape(
+        call_input_shape) != self._build_conv_op_input_shape
 
 
 @keras_export('keras.layers.Conv1D', 'keras.layers.Convolution1D')

tensorflow/python/keras/layers/convolutional_test.py

@@ -23,6 +23,8 @@ import numpy as np
 
 from tensorflow.python import keras
 from tensorflow.python.eager import context
+from tensorflow.python.eager import def_function
+from tensorflow.python.framework import tensor_spec
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras import testing_utils
@@ -108,6 +110,26 @@ class Conv1DTest(keras_parameterized.TestCase):
       _ = layer(inpt2).shape
       self.assertEqual(outp1_shape, layer(inpt1).shape)
 
+  def test_conv1d_recreate_conv_unknown_dims(self):
+    with self.cached_session(use_gpu=True):
+      layer = keras.layers.Conv1D(filters=1,
+                                  kernel_size=3,
+                                  strides=1,
+                                  dilation_rate=2,
+                                  padding='causal')
+
+      inpt1 = np.random.normal(size=[1, 9, 1]).astype(np.float32)
+      inpt2 = np.random.normal(size=[1, 2, 1]).astype(np.float32)
+      outp1_shape = layer(inpt1).shape
+
+      @def_function.function(input_signature=[
+          tensor_spec.TensorSpec([1, None, 1])])
+      def fn(inpt):
+        return layer(inpt)
+
+      fn(inpt2)
+      self.assertEqual(outp1_shape, layer(inpt1).shape)
+
 
 @keras_parameterized.run_all_keras_modes
 class Conv2DTest(keras_parameterized.TestCase):