Update tests under keras.saving to use combinations.
Change all test_util.run_all_in_graph_and_eager_modes to combination. PiperOrigin-RevId: 301396996 Change-Id: I1d79695f819bb289a428b3fd97965841a873bda9
This commit is contained in:
Scott Zhu
TensorFlower Gardener
parent
8d01f78f82
commit
f7896058b2
@ -92,6 +92,7 @@ tf_py_test(
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deps = [
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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"//third_party/py/numpy",
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"@absl_py//absl/testing:parameterized",
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],
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@ -106,6 +107,7 @@ tf_py_test(
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/feature_column:feature_column_v2",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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"//third_party/py/numpy",
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"@absl_py//absl/testing:parameterized",
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],
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@ -142,6 +144,7 @@ tf_py_test(
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/distribute:mirrored_strategy",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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"//third_party/py/numpy",
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"@absl_py//absl/testing:parameterized",
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],
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@ -156,6 +159,7 @@ tf_py_test(
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deps = [
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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"//third_party/py/numpy",
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"@absl_py//absl/testing:parameterized",
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],
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@ -30,7 +30,7 @@ from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.keras import combinations
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import optimizers
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from tensorflow.python.keras import testing_utils
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@ -51,10 +51,10 @@ except ImportError:
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h5py = None
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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@keras_parameterized.run_with_all_saved_model_formats
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@test_util.run_in_graph_and_eager_modes
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def test_weight_loading(self):
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temp_dir = self.get_temp_dir()
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self.addCleanup(shutil.rmtree, temp_dir)
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@ -83,7 +83,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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y = model.predict(x)
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self.assertAllClose(ref_y, y)
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@test_util.run_in_graph_and_eager_modes
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def test_weight_preprocessing(self):
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input_dim = 3
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output_dim = 3
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@ -210,7 +209,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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for (x, y) in zip(weights1, weights2)
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]
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@test_util.run_in_graph_and_eager_modes
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def test_sequential_weight_loading(self):
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if h5py is None:
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return
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@ -243,7 +241,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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self.assertAllClose(y, ref_y)
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@keras_parameterized.run_with_all_saved_model_formats
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@test_util.run_in_graph_and_eager_modes
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def test_nested_model_weight_loading(self):
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save_format = testing_utils.get_save_format()
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temp_dir = self.get_temp_dir()
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@ -282,7 +279,6 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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self.assertAllClose(y, ref_y)
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@test_util.run_in_graph_and_eager_modes
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def test_sequential_weight_loading_group_name_with_incorrect_length(self):
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if h5py is None:
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return
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@ -314,16 +310,16 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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model.compile(loss=keras.losses.MSE,
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optimizer='rmsprop',
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metrics=[keras.metrics.categorical_accuracy])
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with self.assertRaisesRegexp(ValueError,
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r'Layer #0 \(named \"d1\"\) expects 1 '
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r'weight\(s\), but the saved weights have 2 '
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r'element\(s\)\.'):
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hdf5_format.load_weights_from_hdf5_group_by_name(f_model, model.layers)
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with self.assertRaisesRegexp(ValueError,
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r'Layer #0 \(named \"d1\"\) expects 1 '
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r'weight\(s\), but the saved weights have 2 '
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r'element\(s\)\.'):
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hdf5_format.load_weights_from_hdf5_group_by_name(f_model, model.layers)
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hdf5_format.load_weights_from_hdf5_group_by_name(
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f_model, model.layers, skip_mismatch=True)
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self.assertAllClose(keras.backend.get_value(ref_model.layers[1].kernel),
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keras.backend.get_value(model.layers[1].kernel))
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hdf5_format.load_weights_from_hdf5_group_by_name(
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f_model, model.layers, skip_mismatch=True)
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self.assertAllClose(keras.backend.get_value(ref_model.layers[1].kernel),
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keras.backend.get_value(model.layers[1].kernel))
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def test_sequential_weight_loading_group_name_with_incorrect_shape(self):
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if h5py is None:
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@ -779,7 +775,7 @@ class TestWholeModelSaving(test.TestCase, parameterized.TestCase):
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self.assertRegexpMatches(
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h5file.attrs['keras_version'], r'^[\d]+\.[\d]+\.[\S]+$')
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_functional_model_with_custom_loss_and_metric(self):
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def _make_model():
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inputs = keras.Input(shape=(4,))
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@ -818,7 +814,7 @@ class TestWholeModelSaving(test.TestCase, parameterized.TestCase):
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evaluation_results['sparse_categorical_crossentropy'] +
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evaluation_results['custom_loss'], evaluation_results['loss'], 1e-6)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_save_uncompiled_model_with_optimizer(self):
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with self.cached_session() as session:
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saved_model_dir = self._save_model_dir()
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@ -901,6 +897,7 @@ class _make_subclassed_built(_make_subclassed): # pylint: disable=invalid-name
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self.build((None, input_size))
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class TestWholeModelSavingWithNesting(test.TestCase, parameterized.TestCase):
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"""Tests saving a whole model that contains other models."""
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@ -913,7 +910,6 @@ class TestWholeModelSavingWithNesting(test.TestCase, parameterized.TestCase):
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('subclassed', _make_subclassed),
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('subclassed_built', _make_subclassed_built),
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])
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@test_util.run_in_graph_and_eager_modes
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def test_functional(self, model_fn):
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"""Tests serializing a model that uses a nested model to share weights."""
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if h5py is None:
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@ -926,22 +922,23 @@ class TestWholeModelSavingWithNesting(test.TestCase, parameterized.TestCase):
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outputs = keras.layers.add([base_model(inputs[0]), base_model(inputs[1])])
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return keras.Model(inputs=inputs, outputs=outputs)
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x = (np.random.normal(size=(16, 4)).astype(np.float32),
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np.random.normal(size=(16, 4)).astype(np.float32))
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model = _make_model()
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predictions = model(x)
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# Save and reload.
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model_path = os.path.join(self.get_temp_dir(), 'model.h5')
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model.save(model_path)
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del model
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loaded_model = keras.models.load_model(
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model_path,
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custom_objects={
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'_make_subclassed': _make_subclassed,
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'_make_subclassed_built': _make_subclassed_built,
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},
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compile=False)
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self.assertAllClose(loaded_model(x), predictions, 1e-9)
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with self.cached_session():
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x = (np.random.normal(size=(16, 4)).astype(np.float32),
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np.random.normal(size=(16, 4)).astype(np.float32))
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model = _make_model()
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predictions = model(x)
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# Save and reload.
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model_path = os.path.join(self.get_temp_dir(), 'model.h5')
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model.save(model_path)
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del model
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loaded_model = keras.models.load_model(
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model_path,
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custom_objects={
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'_make_subclassed': _make_subclassed,
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'_make_subclassed_built': _make_subclassed_built,
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},
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compile=False)
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self.assertAllClose(loaded_model(x), predictions, 1e-9)
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class SubclassedModel(training.Model):
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@ -955,7 +952,7 @@ class SubclassedModel(training.Model):
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return self.b_layer(self.x_layer(a))
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class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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class TestWeightSavingAndLoadingTFFormat(test.TestCase, parameterized.TestCase):
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def test_keras_optimizer_warning(self):
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graph = ops.Graph()
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@ -974,7 +971,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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str(mock_log.call_args),
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'Keras optimizer')
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_tensorflow_format_overwrite(self):
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with self.cached_session() as session:
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model = SubclassedModel()
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@ -1025,12 +1022,12 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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model.save_weights(prefix, save_format='tensorflow')
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op_count = len(graph.get_operations())
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model.save_weights(prefix, save_format='tensorflow')
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self.assertEqual(len(graph.get_operations()), op_count)
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self.assertLen(graph.get_operations(), op_count)
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model.load_weights(prefix)
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op_count = len(graph.get_operations())
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model.load_weights(prefix)
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self.assertEqual(len(graph.get_operations()), op_count)
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self.assertLen(graph.get_operations(), op_count)
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def _weight_loading_test_template(self, make_model_fn):
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with self.cached_session():
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@ -1079,7 +1076,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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load_model.train_on_batch(train_x, train_y)
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self.assertAllClose(ref_y_after_train, self.evaluate(load_model(x)))
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_weight_loading_graph_model(self):
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def _make_graph_model():
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a = keras.layers.Input(shape=(2,))
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@ -1089,7 +1086,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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self._weight_loading_test_template(_make_graph_model)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_weight_loading_subclassed_model(self):
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self._weight_loading_test_template(SubclassedModel)
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@ -1127,7 +1124,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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y = self.evaluate(model(x))
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self.assertAllClose(ref_y, y)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_weight_loading_graph_model_added_layer(self):
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def _save_graph_model():
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a = keras.layers.Input(shape=(2,))
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@ -1144,7 +1141,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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self._new_layer_weight_loading_test_template(
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_save_graph_model, _restore_graph_model)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_weight_loading_graph_model_added_no_weight_layer(self):
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def _save_graph_model():
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a = keras.layers.Input(shape=(2,))
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@ -1161,7 +1158,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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self._new_layer_weight_loading_test_template(
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_save_graph_model, _restore_graph_model)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_weight_loading_subclassed_model_added_layer(self):
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class SubclassedModelRestore(training.Model):
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@ -1178,7 +1175,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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self._new_layer_weight_loading_test_template(
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SubclassedModel, SubclassedModelRestore)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_incompatible_checkpoint(self):
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save_path = trackable.Checkpoint().save(
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os.path.join(self.get_temp_dir(), 'ckpt'))
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@ -1191,57 +1188,62 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase):
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AssertionError, 'Nothing except the root object matched'):
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m.load_weights(save_path)
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_directory_passed(self):
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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self.evaluate(v.assign(42.))
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prefix = os.path.join(self.get_temp_dir(), '{}'.format(ops.uid()), 'ckpt/')
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m.save_weights(prefix)
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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with self.cached_session():
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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self.evaluate(v.assign(42.))
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prefix = os.path.join(self.get_temp_dir(),
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'{}'.format(ops.uid()), 'ckpt/')
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m.save_weights(prefix)
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_relative_path(self):
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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os.chdir(self.get_temp_dir())
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with self.cached_session():
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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os.chdir(self.get_temp_dir())
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prefix = 'ackpt'
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self.evaluate(v.assign(42.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('ackpt.index'))
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self.evaluate(v.assign(1.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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prefix = 'ackpt'
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self.evaluate(v.assign(42.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('ackpt.index'))
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self.evaluate(v.assign(1.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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prefix = 'subdir/ackpt'
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self.evaluate(v.assign(43.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('subdir/ackpt.index'))
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(43., self.evaluate(v))
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prefix = 'subdir/ackpt'
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self.evaluate(v.assign(43.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('subdir/ackpt.index'))
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(43., self.evaluate(v))
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prefix = 'ackpt/'
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self.evaluate(v.assign(44.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('ackpt/.index'))
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self.evaluate(v.assign(3.))
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m.load_weights(prefix)
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self.assertEqual(44., self.evaluate(v))
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prefix = 'ackpt/'
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self.evaluate(v.assign(44.))
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m.save_weights(prefix)
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self.assertTrue(file_io.file_exists('ackpt/.index'))
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self.evaluate(v.assign(3.))
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m.load_weights(prefix)
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self.assertEqual(44., self.evaluate(v))
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@test_util.run_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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def test_nonexistent_prefix_directory(self):
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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self.evaluate(v.assign(42.))
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prefix = os.path.join(self.get_temp_dir(), '{}'.format(ops.uid()), 'bckpt')
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m.save_weights(prefix)
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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with self.cached_session():
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m = keras.Model()
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v = m.add_weight(name='v', shape=[])
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self.evaluate(v.assign(42.))
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prefix = os.path.join(self.get_temp_dir(),
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'{}'.format(ops.uid()), 'bckpt')
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m.save_weights(prefix)
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self.evaluate(v.assign(2.))
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m.load_weights(prefix)
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self.assertEqual(42., self.evaluate(v))
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if __name__ == '__main__':
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test.main()
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@ -21,6 +21,7 @@ from __future__ import print_function
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import os
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import sys
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python import keras
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@ -28,6 +29,7 @@ from tensorflow.python.eager import context
|
||||
from tensorflow.python.feature_column import feature_column_lib
|
||||
from tensorflow.python.framework import sparse_tensor
|
||||
from tensorflow.python.framework import test_util
|
||||
from tensorflow.python.keras import combinations
|
||||
from tensorflow.python.keras import testing_utils
|
||||
from tensorflow.python.keras.saving import model_config
|
||||
from tensorflow.python.keras.saving import save
|
||||
@ -43,7 +45,7 @@ except ImportError:
|
||||
h5py = None
|
||||
|
||||
|
||||
class TestSaveModel(test.TestCase):
|
||||
class TestSaveModel(test.TestCase, parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super(TestSaveModel, self).setUp()
|
||||
@ -99,7 +101,7 @@ class TestSaveModel(test.TestCase):
|
||||
save.save_model(self.model, path, save_format='tf')
|
||||
save.load_model(path)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_saving_with_dense_features(self):
|
||||
cols = [
|
||||
feature_column_lib.numeric_column('a'),
|
||||
@ -128,13 +130,14 @@ class TestSaveModel(test.TestCase):
|
||||
inputs_a = np.arange(10).reshape(10, 1)
|
||||
inputs_b = np.arange(10).reshape(10, 1).astype('str')
|
||||
|
||||
# Initialize tables for V1 lookup.
|
||||
if not context.executing_eagerly():
|
||||
self.evaluate(lookup_ops.tables_initializer())
|
||||
with self.cached_session():
|
||||
# Initialize tables for V1 lookup.
|
||||
if not context.executing_eagerly():
|
||||
self.evaluate(lookup_ops.tables_initializer())
|
||||
|
||||
self.assertLen(loaded_model.predict({'a': inputs_a, 'b': inputs_b}), 10)
|
||||
self.assertLen(loaded_model.predict({'a': inputs_a, 'b': inputs_b}), 10)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_saving_with_sequence_features(self):
|
||||
cols = [
|
||||
feature_column_lib.sequence_numeric_column('a'),
|
||||
@ -182,17 +185,18 @@ class TestSaveModel(test.TestCase):
|
||||
inputs_b = sparse_tensor.SparseTensor(indices_b, values_b,
|
||||
(batch_size, timesteps, 1))
|
||||
|
||||
# Initialize tables for V1 lookup.
|
||||
if not context.executing_eagerly():
|
||||
self.evaluate(lookup_ops.tables_initializer())
|
||||
with self.cached_session():
|
||||
# Initialize tables for V1 lookup.
|
||||
if not context.executing_eagerly():
|
||||
self.evaluate(lookup_ops.tables_initializer())
|
||||
|
||||
self.assertLen(
|
||||
loaded_model.predict({
|
||||
'a': inputs_a,
|
||||
'b': inputs_b
|
||||
}, steps=1), batch_size)
|
||||
self.assertLen(
|
||||
loaded_model.predict({
|
||||
'a': inputs_a,
|
||||
'b': inputs_b
|
||||
}, steps=1), batch_size)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_saving_h5_for_rnn_layers(self):
|
||||
# See https://github.com/tensorflow/tensorflow/issues/35731 for details.
|
||||
inputs = keras.Input([10, 91], name='train_input')
|
||||
@ -213,7 +217,7 @@ class TestSaveModel(test.TestCase):
|
||||
rnn_layers[1].kernel.name)
|
||||
self.assertIn('rnn_cell1', rnn_layers[1].kernel.name)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_saving_optimizer_weights(self):
|
||||
|
||||
class MyModel(keras.Model):
|
||||
|
||||
@ -44,7 +44,7 @@ from tensorflow.python.framework import constant_op
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_spec
|
||||
from tensorflow.python.framework import test_util
|
||||
from tensorflow.python.keras import combinations
|
||||
from tensorflow.python.keras import keras_parameterized
|
||||
from tensorflow.python.keras import regularizers
|
||||
from tensorflow.python.keras import testing_utils
|
||||
@ -700,7 +700,7 @@ class TestModelSavingAndLoadingV2(keras_parameterized.TestCase):
|
||||
self.assertAllClose(model(input_arr), loaded(input_arr))
|
||||
|
||||
|
||||
class TestLayerCallTracing(test.TestCase):
|
||||
class TestLayerCallTracing(test.TestCase, parameterized.TestCase):
|
||||
|
||||
def test_functions_have_same_trace(self):
|
||||
|
||||
@ -773,7 +773,7 @@ class TestLayerCallTracing(test.TestCase):
|
||||
|
||||
assert_num_traces(LayerWithChildLayer, training_keyword=False)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_maintains_losses(self):
|
||||
layer = LayerWithLoss()
|
||||
layer(np.ones((2, 3)))
|
||||
@ -786,7 +786,7 @@ class TestLayerCallTracing(test.TestCase):
|
||||
self.assertAllEqual(previous_losses, layer.losses)
|
||||
|
||||
|
||||
@test_util.run_all_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
class MetricTest(test.TestCase, parameterized.TestCase):
|
||||
|
||||
def _save_model_dir(self, dirname='saved_model'):
|
||||
@ -870,28 +870,30 @@ class MetricTest(test.TestCase, parameterized.TestCase):
|
||||
# while returning nothing.
|
||||
super(CustomMetric, self).update_state(*args)
|
||||
|
||||
metric = CustomMetric()
|
||||
save_dir = self._save_model_dir('first_save')
|
||||
with self.cached_session():
|
||||
metric = CustomMetric()
|
||||
save_dir = self._save_model_dir('first_save')
|
||||
|
||||
if requires_build:
|
||||
metric(*self.generate_inputs(num_tensor_args)) # pylint: disable=not-callable
|
||||
if requires_build:
|
||||
metric(*self.generate_inputs(num_tensor_args)) # pylint: disable=not-callable
|
||||
|
||||
self.evaluate([v.initializer for v in metric.variables])
|
||||
self.evaluate([v.initializer for v in metric.variables])
|
||||
|
||||
with self.assertRaisesRegexp(ValueError, 'Unable to restore custom object'):
|
||||
self._test_metric_save_and_load(metric, save_dir, num_tensor_args)
|
||||
with generic_utils.CustomObjectScope({'CustomMetric': CustomMetric}):
|
||||
loaded = self._test_metric_save_and_load(
|
||||
metric,
|
||||
save_dir,
|
||||
num_tensor_args,
|
||||
test_sample_weight=False)
|
||||
with self.assertRaisesRegexp(ValueError,
|
||||
'Unable to restore custom object'):
|
||||
self._test_metric_save_and_load(metric, save_dir, num_tensor_args)
|
||||
with generic_utils.CustomObjectScope({'CustomMetric': CustomMetric}):
|
||||
loaded = self._test_metric_save_and_load(
|
||||
metric,
|
||||
save_dir,
|
||||
num_tensor_args,
|
||||
test_sample_weight=False)
|
||||
|
||||
self._test_metric_save_and_load(
|
||||
loaded,
|
||||
self._save_model_dir('second_save'),
|
||||
num_tensor_args,
|
||||
test_sample_weight=False)
|
||||
self._test_metric_save_and_load(
|
||||
loaded,
|
||||
self._save_model_dir('second_save'),
|
||||
num_tensor_args,
|
||||
test_sample_weight=False)
|
||||
|
||||
def test_custom_metric_wrapped_call(self):
|
||||
|
||||
|
||||
@ -37,6 +37,7 @@ from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.framework import tensor_spec
|
||||
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 import keras_parameterized
|
||||
from tensorflow.python.keras import testing_utils
|
||||
from tensorflow.python.keras.engine import sequential
|
||||
@ -62,7 +63,7 @@ class TraceModelCallTest(keras_parameterized.TestCase):
|
||||
self.assertAllClose(expected, actual)
|
||||
|
||||
@keras_parameterized.run_with_all_model_types
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@keras_parameterized.run_all_keras_modes
|
||||
def test_trace_model_outputs(self):
|
||||
input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
|
||||
model = testing_utils.get_small_mlp(10, 3, input_dim)
|
||||
@ -155,7 +156,7 @@ class TraceModelCallTest(keras_parameterized.TestCase):
|
||||
expected_outputs = {'output_1': outputs[0], 'output_2': outputs[1]}
|
||||
self._assert_all_close(expected_outputs, signature_outputs)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_trace_features_layer(self):
|
||||
columns = [feature_column_lib.numeric_column('x')]
|
||||
model = sequential.Sequential([feature_column_lib.DenseFeatures(columns)])
|
||||
@ -176,7 +177,7 @@ class TraceModelCallTest(keras_parameterized.TestCase):
|
||||
self.assertAllClose({'output_1': [[1., 2.]]},
|
||||
fn({'x': [[1.]], 'y': [[2.]]}))
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_specify_input_signature(self):
|
||||
model = testing_utils.get_small_sequential_mlp(10, 3, None)
|
||||
inputs = array_ops.ones((8, 5))
|
||||
@ -193,7 +194,7 @@ class TraceModelCallTest(keras_parameterized.TestCase):
|
||||
expected_outputs = {'output_1': model(inputs)}
|
||||
self._assert_all_close(expected_outputs, signature_outputs)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
def test_subclassed_model_with_input_signature(self):
|
||||
|
||||
class Model(keras.Model):
|
||||
@ -218,7 +219,7 @@ class TraceModelCallTest(keras_parameterized.TestCase):
|
||||
self._assert_all_close(expected_outputs, signature_outputs)
|
||||
|
||||
@keras_parameterized.run_with_all_model_types
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
@keras_parameterized.run_all_keras_modes
|
||||
def test_model_with_fixed_input_dim(self):
|
||||
"""Ensure that the batch_dim is removed when saving.
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user