Update tests under keras.engine to use combinations.

1. Change all test_util.run_all_in_graph_and_eager_modes to combination.
2. Replace import tensorflow.python.keras with explicit module import.
3. Update BUILD file to not rely on the overall Keras target.

PiperOrigin-RevId: 300985437
Change-Id: Icb9e891619814704996649176360d8fee1d7d863

tensorflow/python/keras/engine/BUILD

@@ -230,8 +230,12 @@ cuda_py_test(
         "nomac",  # TODO(mihaimaruseac): b/127695564
     ],
     deps = [
+        ":engine",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python/keras",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python/keras:backend",
+        "//tensorflow/python/keras:combinations",
+        "//tensorflow/python/keras/layers:convolutional",
         "//third_party/py/numpy",
         "@absl_py//absl/testing:parameterized",
     ],
@@ -282,8 +286,31 @@ tf_py_test(
         "notsan",
     ],
     deps = [
+        ":engine",
+        "//tensorflow/python:array_ops",
         "//tensorflow/python:client_testlib",
-        "//tensorflow/python/keras",
+        "//tensorflow/python:framework_ops",
+        "//tensorflow/python:framework_test_lib",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:nn_ops",
+        "//tensorflow/python:resource_variable_ops",
+        "//tensorflow/python:sparse_ops",
+        "//tensorflow/python:state_ops",
+        "//tensorflow/python:tensor_shape",
+        "//tensorflow/python:training_lib",
+        "//tensorflow/python:variables",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/eager:context",
+        "//tensorflow/python/eager:function",
+        "//tensorflow/python/keras:backend",
+        "//tensorflow/python/keras:callbacks",
+        "//tensorflow/python/keras:combinations",
+        "//tensorflow/python/keras:losses",
+        "//tensorflow/python/keras:metrics",
+        "//tensorflow/python/keras:testing_utils",
+        "//tensorflow/python/keras/layers",
+        "//tensorflow/python/keras/utils:data_utils",
+        "//tensorflow/python/keras/utils:np_utils",
         "//third_party/py/numpy",
         "@absl_py//absl/testing:parameterized",
     ],
@@ -352,8 +379,20 @@ tf_py_test(
         "notsan",
     ],
     deps = [
+        ":engine",
         "//tensorflow/python:client_testlib",
+        "//tensorflow/python:util",
+        "//tensorflow/python/data/ops:dataset_ops",
+        "//tensorflow/python/data/ops:iterator_ops",
+        "//tensorflow/python/eager:context",
         "//tensorflow/python/keras",
+        "//tensorflow/python/keras:combinations",
+        "//tensorflow/python/keras:losses",
+        "//tensorflow/python/keras:metrics",
+        "//tensorflow/python/keras:testing_utils",
+        "//tensorflow/python/keras/layers",
+        "//tensorflow/python/keras/optimizer_v2",
+        "//tensorflow/python/keras/utils:data_utils",
         "//third_party/py/numpy",
         "@absl_py//absl/testing:parameterized",
     ],

tensorflow/python/keras/engine/training_generator_test.py

@@ -23,14 +23,17 @@ import itertools
 from absl.testing import parameterized
 import numpy as np
 
-from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.data.ops import iterator_ops
 from tensorflow.python.eager import context
-from tensorflow.python.framework import test_util as tf_test_util
+from tensorflow.python.keras import combinations
 from tensorflow.python.keras import keras_parameterized
+from tensorflow.python.keras import layers as layers_module
+from tensorflow.python.keras import losses
 from tensorflow.python.keras import metrics as metrics_module
 from tensorflow.python.keras import testing_utils
+from tensorflow.python.keras.engine import input_layer
+from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.engine import training_generator
 from tensorflow.python.keras.optimizer_v2 import rmsprop
 from tensorflow.python.keras.utils import data_utils
@@ -367,11 +370,13 @@ class TestGeneratorMethods(keras_parameterized.TestCase):
         yield pack_and_pad(queue)
 
     model = testing_utils.get_model_from_layers([
-        keras.layers.Embedding(input_dim=len(vocab) + 1, output_dim=4),
-        keras.layers.SimpleRNN(units=1),
-        keras.layers.Activation('sigmoid')], input_shape=(None,))
+        layers_module.Embedding(input_dim=len(vocab) + 1, output_dim=4),
+        layers_module.SimpleRNN(units=1),
+        layers_module.Activation('sigmoid')
+    ],
+                                                input_shape=(None,))
 
-    model.compile(loss=keras.losses.binary_crossentropy, optimizer='sgd')
+    model.compile(loss=losses.binary_crossentropy, optimizer='sgd')
     model.fit(data_gen(), epochs=1, steps_per_epoch=5)
 
 
@@ -471,16 +476,16 @@ class TestGeneratorMethodsWithSequences(keras_parameterized.TestCase):
       def on_epoch_end(self):
         self.epochs += 1
 
-    inputs = keras.Input(10)
-    outputs = keras.layers.Dense(1)(inputs)
-    model = keras.Model(inputs, outputs)
+    inputs = input_layer.Input(10)
+    outputs = layers_module.Dense(1)(inputs)
+    model = training.Model(inputs, outputs)
     model.compile('sgd', 'mse')
     my_seq = MySequence()
     model.fit(my_seq, epochs=2)
     self.assertEqual(my_seq.epochs, 2)
 
 
-@tf_test_util.run_all_in_graph_and_eager_modes
+@combinations.generate(combinations.combine(mode=['graph', 'eager']))
 class TestConvertToGeneratorLike(test.TestCase, parameterized.TestCase):
   simple_inputs = (np.ones((10, 10)), np.ones((10, 1)))
   nested_inputs = ((np.ones((10, 10)), np.ones((10, 20))), (np.ones((10, 1)),

tensorflow/python/keras/engine/training_gpu_test.py

@@ -18,18 +18,21 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl.testing import parameterized
 import numpy as np
 
-from tensorflow.python import keras
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras import combinations
+from tensorflow.python.keras.engine import input_layer
+from tensorflow.python.keras.engine import training
 from tensorflow.python.keras.layers.convolutional import Conv2D
 from tensorflow.python.platform import test
 
 
-class TrainingGPUTest(test.TestCase):
+class TrainingGPUTest(test.TestCase, parameterized.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes
+  @combinations.generate(combinations.combine(mode=['graph', 'eager']))
   def test_model_with_crossentropy_losses_channels_first(self):
     """Tests use of all crossentropy losses with `channels_first`.
 
@@ -63,8 +66,7 @@ class TrainingGPUTest(test.TestCase):
                            activation=activation,
                            kernel_initializer='ones',
                            bias_initializer='ones')(input_tensor)
-      simple_model = keras.models.Model(inputs=input_tensor,
-                                        outputs=predictions)
+      simple_model = training.Model(inputs=input_tensor, outputs=predictions)
       simple_model.compile(optimizer='rmsprop', loss=loss)
       return simple_model
 
@@ -96,7 +98,7 @@ class TrainingGPUTest(test.TestCase):
         data = np.moveaxis(data_channels_first, 1, -1)
         for index, loss_function in enumerate(losses_to_test):
           labels = np.moveaxis(labels_channels_first[index], 1, -1)
-          inputs = keras.Input(shape=(3, 3, 1))
+          inputs = input_layer.Input(shape=(3, 3, 1))
           model = prepare_simple_model(inputs, loss_function, labels)
           loss_channels_last[index] = model.evaluate(x=data, y=labels,
                                                      batch_size=1, verbose=0)
@@ -107,7 +109,7 @@ class TrainingGPUTest(test.TestCase):
         data = data_channels_first
         for index, loss_function in enumerate(losses_to_test):
           labels = labels_channels_first[index]
-          inputs = keras.Input(shape=(1, 3, 3))
+          inputs = input_layer.Input(shape=(1, 3, 3))
           model = prepare_simple_model(inputs, loss_function, labels)
           loss_channels_first[index] = model.evaluate(x=data, y=labels,
                                                       batch_size=1, verbose=0)