Adds all Keras modes to `convolutional_test`

PiperOrigin-RevId: 225397991

tensorflow/python/keras/BUILD

@@ -369,7 +369,7 @@ py_test(
     name = "convolutional_test",
     size = "large",
     srcs = ["layers/convolutional_test.py"],
-    shard_count = 4,
+    shard_count = 11,
     srcs_version = "PY2AND3",
     deps = [
         ":keras",

tensorflow/python/keras/backend.py

@@ -568,6 +568,10 @@ def _get_available_gpus():
   Returns:
       A list of available GPU devices.
   """
+  if ops.executing_eagerly_outside_functions():
+    # Returns names of devices directly.
+    return [name for name in context.list_devices() if 'GPU' in name]
+
   global _LOCAL_DEVICES
   if _LOCAL_DEVICES is None:
     _LOCAL_DEVICES = get_session().list_devices()

tensorflow/python/keras/layers/convolutional_test.py

@@ -24,13 +24,13 @@ import numpy as np
 
 from tensorflow.python import keras
 from tensorflow.python.eager import context
-from tensorflow.python.framework import test_util as tf_test_util
+from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.platform import test
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class Convolution1DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class Convolution1DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -100,8 +100,8 @@ class Convolution1DTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class Conv2DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class Conv2DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -175,8 +175,8 @@ class Conv2DTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class Conv2DTransposeTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class Conv2DTransposeTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -267,8 +267,8 @@ class Conv2DTransposeTest(test.TestCase):
                              expected_output=expected_output)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class Conv3DTransposeTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class Conv3DTransposeTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -336,8 +336,8 @@ class Conv3DTransposeTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class SeparableConv1DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class SeparableConv1DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -411,8 +411,8 @@ class SeparableConv1DTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class SeparableConv2DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class SeparableConv2DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -489,8 +489,8 @@ class SeparableConv2DTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class Conv3DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class Conv3DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -557,8 +557,8 @@ class Conv3DTest(test.TestCase):
       self.assertEqual(layer.bias.constraint, b_constraint)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class ZeroPaddingTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class ZeroPaddingTest(keras_parameterized.TestCase):
 
   def test_zero_padding_1d(self):
     num_samples = 2
@@ -726,8 +726,8 @@ class ZeroPaddingTest(test.TestCase):
       keras.layers.ZeroPadding3D(padding=None)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class UpSamplingTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class UpSamplingTest(keras_parameterized.TestCase):
 
   def test_upsampling_1d(self):
     with self.session(use_gpu=True):
@@ -875,8 +875,8 @@ class UpSamplingTest(test.TestCase):
               np.testing.assert_allclose(np_output, expected_out)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class CroppingTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class CroppingTest(keras_parameterized.TestCase):
 
   def test_cropping_1d(self):
     num_samples = 2
@@ -1017,8 +1017,8 @@ class CroppingTest(test.TestCase):
       keras.layers.Cropping3D(cropping=None)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
-class DepthwiseConv2DTest(test.TestCase):
+@keras_parameterized.run_all_keras_modes
+class DepthwiseConv2DTest(keras_parameterized.TestCase):
 
   def _run_test(self, kwargs, arg, values):
     num_samples = 2
@@ -1044,17 +1044,18 @@ class DepthwiseConv2DTest(test.TestCase):
       self._run_test(kwargs, 'data_format', ['channels_first'])
     self._run_test(kwargs, 'depth_multiplier', [1, 2])
 
-    kwargs = {'kernel_size': 3,
-              'padding': 'valid',
-              'data_format': 'channels_first',
-              'activation': None,
-              'depthwise_regularizer': 'l2',
-              'bias_regularizer': 'l2',
-              'activity_regularizer': 'l2',
-              'depthwise_constraint': 'unit_norm',
-              'use_bias': True,
-              'strides': (2, 2),
-             }
+    kwargs = {
+        'kernel_size': 3,
+        'padding': 'valid',
+        'data_format': 'channels_last',
+        'activation': None,
+        'depthwise_regularizer': 'l2',
+        'bias_regularizer': 'l2',
+        'activity_regularizer': 'l2',
+        'depthwise_constraint': 'unit_norm',
+        'use_bias': True,
+        'strides': (2, 2),
+    }
     self._run_test(kwargs, 'depth_multiplier', [1])
 
 if __name__ == '__main__':