Update tests under keras.mixed_precision to use combinations.
Change all test_util.run_all_in_graph_and_eager_modes to combination. PiperOrigin-RevId: 301311255 Change-Id: Icd4f80a3e84ab06ef7d3423298918d172d1338e6
This commit is contained in:
Scott Zhu
TensorFlower Gardener
parent
e473416af2
commit
c608d46195
@ -63,6 +63,7 @@ py_test(
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"//tensorflow/python:client_testlib",
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"//tensorflow/python:platform_test",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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"//tensorflow/python/keras/mixed_precision/experimental:loss_scale_optimizer",
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"//tensorflow/python/keras/optimizer_v2",
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],
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@ -86,6 +87,7 @@ cuda_py_test(
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deps = [
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":device_compatibility_check",
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"//tensorflow/python:client_testlib",
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"//tensorflow/python/keras:combinations",
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],
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)
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@ -137,6 +139,7 @@ py_test(
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"//tensorflow/python:platform_test",
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"//tensorflow/python/distribute:mirrored_strategy",
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"//tensorflow/python/eager:context",
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"//tensorflow/python/keras:combinations",
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"//tensorflow/python/keras/optimizer_v2",
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"@absl_py//absl/testing:parameterized",
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],
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@ -177,6 +180,7 @@ cuda_py_test(
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"//tensorflow/python/distribute:mirrored_strategy",
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"//tensorflow/python/distribute:one_device_strategy",
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"//tensorflow/python/keras",
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"//tensorflow/python/keras:combinations",
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],
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)
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@ -219,6 +223,7 @@ cuda_py_test(
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"//tensorflow/python/distribute:central_storage_strategy",
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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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"@absl_py//absl/testing:parameterized",
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],
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)
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@ -30,7 +30,7 @@ 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 indexed_slices
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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.mixed_precision.experimental import autocast_variable
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from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
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from tensorflow.python.ops import array_ops
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@ -69,7 +69,7 @@ def get_var(val, dtype, name=None):
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return variables.VariableV1(val, use_resource=True, dtype=dtype, name=name)
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@test_util.run_all_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class AutoCastVariableTest(test.TestCase, parameterized.TestCase):
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@parameterized.named_parameters(*TESTCASES)
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@ -156,7 +156,7 @@ class AutoCastVariableTest(test.TestCase, parameterized.TestCase):
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def test_method_delegations(self, distribute):
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# Test AutoCastVariable correctly delegates Variable methods to the
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# underlying variable.
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with get_distribute_scope(distribute):
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with self.test_session(), get_distribute_scope(distribute):
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for read_dtype in (dtypes.float32, dtypes.float16):
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if distribute:
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# MirroredVariable.assign will (incorrectly) return a Mirrored value
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@ -383,18 +383,19 @@ class AutoCastVariableTest(test.TestCase, parameterized.TestCase):
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@parameterized.named_parameters(*TESTCASES)
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def test_checkpoint(self, distribute):
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with get_distribute_scope(distribute):
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x = get_var(1., dtypes.float32)
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x = autocast_variable.create_autocast_variable(x)
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self.evaluate(x.initializer)
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self.evaluate(x.assign(123.))
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with self.test_session():
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with get_distribute_scope(distribute):
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x = get_var(1., dtypes.float32)
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x = autocast_variable.create_autocast_variable(x)
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self.evaluate(x.initializer)
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self.evaluate(x.assign(123.))
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checkpoint = trackable_utils.Checkpoint(x=x)
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prefix = os.path.join(self.get_temp_dir(), 'ckpt')
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save_path = checkpoint.save(prefix)
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self.evaluate(x.assign(234.))
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checkpoint.restore(save_path).assert_consumed().run_restore_ops()
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self.assertEqual(self.evaluate(x), 123.)
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checkpoint = trackable_utils.Checkpoint(x=x)
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prefix = os.path.join(self.get_temp_dir(), 'ckpt')
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save_path = checkpoint.save(prefix)
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self.evaluate(x.assign(234.))
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checkpoint.restore(save_path).assert_consumed().run_restore_ops()
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self.assertEqual(self.evaluate(x), 123.)
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@parameterized.named_parameters(*TESTCASES)
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def test_invalid_wrapped_variable(self, distribute):
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@ -21,7 +21,7 @@ from __future__ import print_function
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import re
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from tensorflow.core.framework import device_attributes_pb2
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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.mixed_precision.experimental import device_compatibility_check
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from tensorflow.python.platform import test
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from tensorflow.python.platform import tf_logging
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@ -42,7 +42,7 @@ def _get_device_attrs(device_type, device_name=None, cc_major=None,
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device_type='GPU', physical_device_desc=physical_device_desc)
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@test_util.run_all_in_graph_and_eager_modes
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class DeviceCompatibilityCheckTest(test.TestCase):
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def _test_compat_check(self, device_attr_list, should_warn, expected_regex,
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@ -31,8 +31,8 @@ from tensorflow.python.eager import context
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from tensorflow.python.eager import def_function
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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 test_util
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from tensorflow.python.keras import backend
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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 layers
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from tensorflow.python.keras import models
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@ -119,11 +119,11 @@ TESTCASES = ({
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})
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class KerasLayerTest(keras_parameterized.TestCase):
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"""Test mixed precision with Keras layers."""
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_mixed_policies_(self, strategy_fn):
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for dtype in 'float16', 'bfloat16':
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x = constant_op.constant([1.])
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@ -142,7 +142,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.evaluate(variables.global_variables_initializer())
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self.assertEqual(self.evaluate(y), 1.)
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@test_util.run_in_graph_and_eager_modes
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def test_layer_with_int_variable(self):
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class LayerWithIntVar(base_layer.Layer):
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@ -159,7 +158,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(layer(x).dtype, 'int32')
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_layer_with_non_autocast_variable(self, strategy_fn):
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x = constant_op.constant([1.])
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with strategy_fn().scope():
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@ -172,7 +170,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(self.evaluate(y), 1.)
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_layer_calling_tf_function(self, strategy_fn):
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x = constant_op.constant([1.])
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with strategy_fn().scope():
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@ -185,7 +182,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(self.evaluate(y), 1.)
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_layer_regularizer_runs_in_var_dtype(self, strategy_fn):
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x = constant_op.constant([1.])
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with strategy_fn().scope():
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@ -211,7 +207,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(self.evaluate(regularizer_loss), 1.)
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_passing_policy_to_layer(self, strategy_fn):
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x = constant_op.constant([1.], dtype=dtypes.float16)
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with strategy_fn().scope():
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@ -230,7 +225,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(layer(x).dtype, dtypes.float64)
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self.assertEqual(layer.v.dtype, dtypes.float64)
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@test_util.run_in_graph_and_eager_modes
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def test_error_passing_policy_string_to_layer(self):
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with self.assertRaisesRegexp(
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TypeError, "Cannot convert value 'mixed_float16' to a "
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@ -240,7 +234,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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mp_test_util.MultiplyLayer(dtype='mixed_float16')
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_gradient(self, strategy_fn):
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x = constant_op.constant([1.])
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with strategy_fn().scope() as strategy:
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@ -308,17 +301,19 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(self.evaluate(layer(x)), 100.)
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_checkpointing_layer_weights(self, strategy_fn):
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=True, mixed_prec_when_loading=True)
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=True, mixed_prec_when_loading=False)
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=False, mixed_prec_when_loading=True)
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with self.test_session():
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=True,
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mixed_prec_when_loading=True)
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=True,
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mixed_prec_when_loading=False)
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self._test_checkpointing_layer_weights(
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strategy_fn, mixed_prec_when_saving=False,
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mixed_prec_when_loading=True)
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def test_config(self, strategy_fn):
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x = constant_op.constant([1.], dtype=dtypes.float16)
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with strategy_fn().scope():
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@ -397,7 +392,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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self.assertEqual(layer(x).dtype, 'float16')
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self.assertEqual(layer.v.dtype, 'float16')
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@test_util.run_in_graph_and_eager_modes
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def test_delete_variable(self):
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layer = base_layer.Layer(dtype=policy.Policy('mixed_float16'))
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layer.x = layer.add_weight('x')
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@ -405,7 +399,6 @@ class KerasLayerTest(keras_parameterized.TestCase):
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del layer.x
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self.assertEqual(layer.trainable_weights, [])
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@test_util.run_in_graph_and_eager_modes
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def test_build_and_call_layer_in_function(self):
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layer = mp_test_util.MultiplyLayer(dtype=policy.Policy('mixed_float16'))
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@def_function.function
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@ -833,7 +826,7 @@ class KerasModelTest(keras_parameterized.TestCase):
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model.fit(dataset)
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self.assertEqual(backend.eval(layer.v), -3)
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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_loss_scale_optimizer_overrides_policy_loss_scale(self):
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with policy.policy_scope(policy.Policy('float32', loss_scale=10.)):
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opt = gradient_descent.SGD(1.)
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@ -844,7 +837,7 @@ class KerasModelTest(keras_parameterized.TestCase):
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model.compile(opt, loss='mse')
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self.assertEqual(self.evaluate(model.optimizer.loss_scale()), 5.)
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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_pass_invalid_optimizer_with_loss_scaling(self):
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with policy.policy_scope(policy.Policy('float32', loss_scale=10.)):
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x = layers.Input(shape=(1,))
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@ -857,7 +850,7 @@ class KerasModelTest(keras_parameterized.TestCase):
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with self.assertRaisesRegexp(ValueError, error_msg):
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model.compile(optimizers.SGD(1.), 'mse')
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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_loss_dtype(self):
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with policy.policy_scope('float16'):
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x = layers.Input(shape=(1,))
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@ -867,6 +860,7 @@ class KerasModelTest(keras_parameterized.TestCase):
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# The loss should not be casted to the policy's dtype.
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self.assertEqual(model.losses[0].dtype, 'float32')
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters(
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{
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'testcase_name': 'base',
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@ -883,7 +877,6 @@ class KerasModelTest(keras_parameterized.TestCase):
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'strategy_fn': create_mirrored_strategy,
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'h5': True,
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})
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@test_util.run_in_graph_and_eager_modes
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def test_save_weights_with_autocast_vars(self, strategy_fn, h5=False):
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with strategy_fn().scope():
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with policy.policy_scope('mixed_float16'):
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@ -28,6 +28,7 @@ from tensorflow.python.distribute import mirrored_strategy
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from tensorflow.python.eager import context
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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 optimizers
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from tensorflow.python.keras.mixed_precision.experimental import loss_scale_optimizer
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from tensorflow.python.keras.mixed_precision.experimental import test_util as mp_test_util
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@ -68,6 +69,7 @@ TESTCASES = ({
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@test_util.with_control_flow_v2
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@combinations.generate(combinations.combine(mode=['graph', 'eager']))
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class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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def _run_if_in_graph_mode(self, val):
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@ -85,7 +87,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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return lambda: opt.minimize(loss, var_list=[var])
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def testFixedLossScaleAppliedToLossWithMinimize(self, strategy_fn):
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with strategy_fn().scope() as strategy:
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var = variables.Variable([5.0])
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@ -119,7 +120,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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# mp_test_util.create_identity_with_grad_check_fn added an assertion op.
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self.evaluate(run_op)
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@test_util.run_in_graph_and_eager_modes
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def testGetScaledLoss(self):
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opt = gradient_descent.SGD(2.0)
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opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale=2.)
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@ -130,7 +130,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(10., self.evaluate(opt.get_scaled_loss(loss)))
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self.assertEqual(10., self.evaluate(opt.get_scaled_loss(lambda: loss)()))
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@test_util.run_in_graph_and_eager_modes
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def testGetUnscaledGradients(self):
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opt = gradient_descent.SGD(2.0)
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opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale=2)
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@ -142,7 +141,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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grads = [self.evaluate(g) if g is not None else g for g in grads]
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self.assertEqual([1.5, None, -2.], grads)
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@test_util.run_in_graph_and_eager_modes
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def testGetUnscaledSparseGradients(self):
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opt = gradient_descent.SGD(2.0)
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opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale=2)
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@ -156,7 +154,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.evaluate(sparse_grad.values))
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def testDynamicLossScale(self, strategy_fn):
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strategy = strategy_fn()
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learning_rate = 2.
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@ -190,7 +187,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertAllClose([1.], self.evaluate(var))
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def testDynamicUpdate(self, strategy_fn):
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with strategy_fn().scope() as strategy:
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var = variables.Variable([1.0, 2.0])
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@ -221,7 +217,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(2., self.evaluate(opt.loss_scale()))
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def testDynamicLossScaleWithFloat16Loss(self, strategy_fn):
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strategy = strategy_fn()
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learning_rate = 2.
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@ -243,7 +238,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertAllClose([3.], self.evaluate(var))
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@parameterized.named_parameters(*TESTCASES)
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@test_util.run_in_graph_and_eager_modes
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def testDynamicLossScaleWithSlots(self, strategy_fn):
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strategy_obj = strategy_fn()
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if (isinstance(strategy_obj, mirrored_strategy.MirroredStrategy) and
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@ -283,7 +277,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(opt.get_slot_names(), ['momentum'])
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@test_util.run_in_graph_and_eager_modes
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def testIterations(self):
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opt = gradient_descent.SGD(2.0)
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lso = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale=10.)
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@ -291,27 +284,26 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(lso.iterations, 7)
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self.assertEqual(opt.iterations, 7)
|
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|
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@test_util.run_in_graph_and_eager_modes
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def testWeightMethods(self):
|
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var = variables.Variable([1.0])
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opt = gradient_descent.SGD(1.0)
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initial_loss_scale = 2.
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loss_scale = loss_scale_module.DynamicLossScale(
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initial_loss_scale=initial_loss_scale, increment_period=1,
|
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multiplier=4)
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opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale)
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run_op = opt.minimize(lambda: var * 2, [var])
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self.evaluate(variables.global_variables_initializer())
|
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self._run_if_in_graph_mode(run_op)
|
||||
with self.test_session():
|
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var = variables.Variable([1.0])
|
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opt = gradient_descent.SGD(1.0)
|
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initial_loss_scale = 2.
|
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loss_scale = loss_scale_module.DynamicLossScale(
|
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initial_loss_scale=initial_loss_scale, increment_period=1,
|
||||
multiplier=4)
|
||||
opt = loss_scale_optimizer.LossScaleOptimizer(opt, loss_scale)
|
||||
run_op = opt.minimize(lambda: var * 2, [var])
|
||||
self.evaluate(variables.global_variables_initializer())
|
||||
self._run_if_in_graph_mode(run_op)
|
||||
|
||||
self.assertLen(opt.weights, 1) # The 'iterations' weight
|
||||
self.assertEqual(self.evaluate(opt.weights[0]), 1)
|
||||
self.assertEqual(opt.get_weights()[0], 1)
|
||||
self.assertEqual(self.evaluate(opt.variables()[0]), 1)
|
||||
opt.set_weights([np.array(2.)])
|
||||
self.assertEqual(self.evaluate(opt.variables()[0]), 2)
|
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self.assertLen(opt.weights, 1) # The 'iterations' weight
|
||||
self.assertEqual(self.evaluate(opt.weights[0]), 1)
|
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self.assertEqual(opt.get_weights()[0], 1)
|
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self.assertEqual(self.evaluate(opt.variables()[0]), 1)
|
||||
opt.set_weights([np.array(2.)])
|
||||
self.assertEqual(self.evaluate(opt.variables()[0]), 2)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testSlotMethodErrors(self):
|
||||
opt = gradient_descent.SGD(1.0, momentum=1.0)
|
||||
opt = loss_scale_optimizer.LossScaleOptimizer(opt, 'dynamic')
|
||||
@ -332,9 +324,8 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
loss_scale_optimizer.LossScaleOptimizer(opt, None)
|
||||
|
||||
@parameterized.named_parameters(*TESTCASES)
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testGettingAndSettingLearningRate(self, strategy_fn):
|
||||
with strategy_fn().scope() as strategy:
|
||||
with self.test_session(), strategy_fn().scope() as strategy:
|
||||
var = variables.Variable([5.0])
|
||||
opt = adam.Adam(learning_rate=1.0)
|
||||
loss = lambda: var * 2.0
|
||||
@ -357,7 +348,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
with self.assertRaises(AttributeError):
|
||||
opt.not_an_attr += 3
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testArbitraryAttributesNotExposed(self):
|
||||
opt = adam.Adam(learning_rate=1.0)
|
||||
# Test that Adam has attributes 'epsilon' and 'beta1'
|
||||
@ -375,7 +365,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
"'LossScaleOptimizer' object has no attribute 'beta_1'"):
|
||||
opt.beta_1 # pylint: disable=pointless-statement
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testApplyGradientsGetsUnwrappedTensors(self):
|
||||
# Tests that gradients passed to apply_gradients are not wrapped in a
|
||||
# DistributionStrategy wrapper, such as PerReplica, but instead are raw
|
||||
@ -403,7 +392,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
strategy.experimental_run(run_fn)
|
||||
|
||||
@parameterized.named_parameters(*TESTCASES)
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testCheckpoint(self, strategy_fn):
|
||||
strategy = strategy_fn()
|
||||
if (isinstance(strategy, mirrored_strategy.MirroredStrategy) and
|
||||
@ -447,7 +435,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
self.assertEqual(self.evaluate(loss_scale._num_good_steps), 1)
|
||||
self.assertAlmostEqual(self.evaluate(slot_var).item(), slot_value)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testGetConfig(self):
|
||||
opt = gradient_descent.SGD(2., momentum=0.5)
|
||||
loss_scale = loss_scale_module.DynamicLossScale(
|
||||
@ -466,7 +453,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
self.assertEqual(opt.loss_scale.increment_period, 3.)
|
||||
self.assertEqual(opt.loss_scale.multiplier, 4.)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testSerializationWithBuiltInOptimizer(self):
|
||||
opt = gradient_descent.SGD(2., momentum=0.5)
|
||||
loss_scale = loss_scale_module.DynamicLossScale(
|
||||
@ -485,7 +471,6 @@ class LossScaleOptimizerTest(test.TestCase, parameterized.TestCase):
|
||||
self.assertEqual(opt.loss_scale.increment_period, 3.)
|
||||
self.assertEqual(opt.loss_scale.multiplier, 4.)
|
||||
|
||||
@test_util.run_in_graph_and_eager_modes
|
||||
def testSerializationWithCustomOptimizer(self):
|
||||
class MySGD(gradient_descent.SGD):
|
||||
|
||||
|
||||
@ -18,10 +18,12 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from absl.testing import parameterized
|
||||
|
||||
from tensorflow.python.eager import context
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import test_util
|
||||
from tensorflow.python.keras import combinations
|
||||
from tensorflow.python.keras import testing_utils
|
||||
from tensorflow.python.keras.engine import base_layer_utils
|
||||
from tensorflow.python.keras.mixed_precision.experimental import device_compatibility_check
|
||||
@ -33,8 +35,8 @@ from tensorflow.python.training.experimental import loss_scale as loss_scale_mod
|
||||
from tensorflow.python.training.experimental import mixed_precision
|
||||
|
||||
|
||||
@test_util.run_all_in_graph_and_eager_modes
|
||||
class PolicyTest(test.TestCase):
|
||||
@combinations.generate(combinations.combine(mode=['graph', 'eager']))
|
||||
class PolicyTest(test.TestCase, parameterized.TestCase):
|
||||
"""Tests Policies."""
|
||||
|
||||
@testing_utils.enable_v2_dtype_behavior
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user