From 9fa88362d4a3f2a122aa36e40275fa37e490be78 Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 8 Jan 2021 12:52:15 -0800
Subject: [PATCH] Internal change

PiperOrigin-RevId: 350819073
Change-Id: I0845756b8d539709152f81d1b485f37d27e0a4f5
---
 .../python/keras/layers/convolutional.py      |  7 ----
 .../python/keras/layers/convolutional_test.py | 42 ++++---------------
 2 files changed, 9 insertions(+), 40 deletions(-)

diff --git a/tensorflow/python/keras/layers/convolutional.py b/tensorflow/python/keras/layers/convolutional.py
index d3ff378e7ae..731b51e2862 100644
--- a/tensorflow/python/keras/layers/convolutional.py
+++ b/tensorflow/python/keras/layers/convolutional.py
@@ -242,8 +242,6 @@ class Conv(Layer):
     self.built = True
 
   def call(self, inputs):
-    input_shape = inputs.shape
-
     if self._is_causal:  # Apply causal padding to inputs for Conv1D.
       inputs = array_ops.pad(inputs, self._compute_causal_padding(inputs))
 
@@ -268,11 +266,6 @@ class Conv(Layer):
           outputs = nn.bias_add(
               outputs, self.bias, data_format=self._tf_data_format)
 
-    if not context.executing_eagerly():
-      # Infer the static output shape:
-      out_shape = self.compute_output_shape(input_shape)
-      outputs.set_shape(out_shape)
-
     if self.activation is not None:
       return self.activation(outputs)
     return outputs
diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py
index 3c099639e08..0bc869160ec 100644
--- a/tensorflow/python/keras/layers/convolutional_test.py
+++ b/tensorflow/python/keras/layers/convolutional_test.py
@@ -174,36 +174,24 @@ class Conv1DTest(keras_parameterized.TestCase):
 @keras_parameterized.run_all_keras_modes
 class Conv2DTest(keras_parameterized.TestCase):
 
-  def _run_test(self, kwargs, expected_output_shape, spatial_shape=(7, 6)):
+  def _run_test(self, kwargs, expected_output_shape):
     num_samples = 2
     stack_size = 3
-    num_row, num_col = spatial_shape
-    input_data = None
-    # Generate valid input data.
-    if None in spatial_shape:
-      input_data_shape = (num_samples, num_row or 7, num_col or 6, stack_size)
-      input_data = 10 * np.random.random(input_data_shape).astype(np.float32)
+    num_row = 7
+    num_col = 6
 
     with self.cached_session(use_gpu=True):
       testing_utils.layer_test(
           keras.layers.Conv2D,
           kwargs=kwargs,
           input_shape=(num_samples, num_row, num_col, stack_size),
-          input_data=input_data,
           expected_output_shape=expected_output_shape)
 
-  def _run_test_extra_batch_dim(self,
-                                kwargs,
-                                expected_output_shape,
-                                spatial_shape=(7, 6)):
+  def _run_test_extra_batch_dim(self, kwargs, expected_output_shape):
     batch_shape = (2, 11)
     stack_size = 3
-    num_row, num_col = spatial_shape
-    input_data = None
-    # Generate valid input data.
-    if None in spatial_shape:
-      input_data_shape = batch_shape + (num_row or 7, num_col or 6, stack_size)
-      input_data = 10 * np.random.random(input_data_shape).astype(np.float32)
+    num_row = 7
+    num_col = 6
 
     with self.cached_session(use_gpu=True):
       if expected_output_shape is not None:
@@ -212,7 +200,6 @@ class Conv2DTest(keras_parameterized.TestCase):
           keras.layers.Conv2D,
           kwargs=kwargs,
           input_shape=batch_shape + (num_row, num_col, stack_size),
-          input_data=input_data,
           expected_output_shape=expected_output_shape)
 
   @parameterized.named_parameters(
@@ -243,24 +230,13 @@ class Conv2DTest(keras_parameterized.TestCase):
           'groups': 3,
           'filters': 6
       }, (None, 5, 4, 6), True),
-      ('dilation_2_unknown_width', {
-          'dilation_rate': (2, 2)
-      }, (None, None, 2, 2), False, (None, 6)),
-      ('dilation_2_unknown_height', {
-          'dilation_rate': (2, 2)
-      }, (None, 3, None, 2), False, (7, None)),
   )
-  def test_conv2d(self,
-                  kwargs,
-                  expected_output_shape=None,
-                  requires_gpu=False,
-                  spatial_shape=(7, 6)):
+  def test_conv2d(self, kwargs, expected_output_shape=None, requires_gpu=False):
     kwargs['filters'] = kwargs.get('filters', 2)
     kwargs['kernel_size'] = (3, 3)
     if not requires_gpu or test.is_gpu_available(cuda_only=True):
-      self._run_test(kwargs, expected_output_shape, spatial_shape)
-      self._run_test_extra_batch_dim(kwargs, expected_output_shape,
-                                     spatial_shape)
+      self._run_test(kwargs, expected_output_shape)
+      self._run_test_extra_batch_dim(kwargs, expected_output_shape)
 
   def test_conv2d_regularizers(self):
     kwargs = {