Merge pull request #35066 from tensorflow/layer_imports_internal
[r2.1 Cherrypick] Unify V1/2 layer naming in internal imports
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Goldie Gadde
GitHub
commit
03a3020934
@ -18,8 +18,11 @@ from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python import tf2
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# Generic layers.
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# pylint: disable=g-bad-import-order
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# pylint: disable=g-import-not-at-top
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from tensorflow.python.keras.engine.input_layer import Input
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from tensorflow.python.keras.engine.input_layer import InputLayer
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from tensorflow.python.keras.engine.input_spec import InputSpec
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@ -27,10 +30,20 @@ from tensorflow.python.keras.engine.base_layer import Layer
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from tensorflow.python.keras.engine.base_preprocessing_layer import PreprocessingLayer
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# Preprocessing layers.
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from tensorflow.python.keras.layers.preprocessing.normalization import Normalization
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from tensorflow.python.keras.layers.preprocessing.normalization_v1 import Normalization as NormalizationV1
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from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization as TextVectorizationV1
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if tf2.enabled():
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from tensorflow.python.keras.layers.preprocessing.normalization import Normalization
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from tensorflow.python.keras.layers.preprocessing.normalization_v1 import Normalization as NormalizationV1
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NormalizationV2 = Normalization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization as TextVectorizationV1
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TextVectorizationV2 = TextVectorization
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else:
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from tensorflow.python.keras.layers.preprocessing.normalization_v1 import Normalization
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from tensorflow.python.keras.layers.preprocessing.normalization import Normalization as NormalizationV2
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NormalizationV1 = Normalization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization_v1 import TextVectorization
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from tensorflow.python.keras.layers.preprocessing.text_vectorization import TextVectorization as TextVectorizationV2
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TextVectorizationV1 = TextVectorization
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# Advanced activations.
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from tensorflow.python.keras.layers.advanced_activations import LeakyReLU
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@ -121,8 +134,14 @@ from tensorflow.python.keras.layers.noise import GaussianDropout
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# Normalization layers.
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from tensorflow.python.keras.layers.normalization import LayerNormalization
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from tensorflow.python.keras.layers.normalization import BatchNormalization
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from tensorflow.python.keras.layers.normalization_v2 import BatchNormalization as BatchNormalizationV2
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if tf2.enabled():
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from tensorflow.python.keras.layers.normalization_v2 import BatchNormalization
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from tensorflow.python.keras.layers.normalization import BatchNormalization as BatchNormalizationV1
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BatchNormalizationV2 = BatchNormalization
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else:
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from tensorflow.python.keras.layers.normalization import BatchNormalization
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from tensorflow.python.keras.layers.normalization_v2 import BatchNormalization as BatchNormalizationV2
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BatchNormalizationV1 = BatchNormalization
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# Kernelized layers.
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from tensorflow.python.keras.layers.kernelized import RandomFourierFeatures
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@ -163,14 +182,32 @@ from tensorflow.python.keras.layers.recurrent import SimpleRNNCell
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from tensorflow.python.keras.layers.recurrent import PeepholeLSTMCell
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from tensorflow.python.keras.layers.recurrent import SimpleRNN
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from tensorflow.python.keras.layers.recurrent import GRU
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from tensorflow.python.keras.layers.recurrent import GRUCell
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from tensorflow.python.keras.layers.recurrent import LSTM
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from tensorflow.python.keras.layers.recurrent import LSTMCell
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from tensorflow.python.keras.layers.recurrent_v2 import GRU as GRU_v2
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from tensorflow.python.keras.layers.recurrent_v2 import GRUCell as GRUCell_v2
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from tensorflow.python.keras.layers.recurrent_v2 import LSTM as LSTM_v2
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from tensorflow.python.keras.layers.recurrent_v2 import LSTMCell as LSTMCell_v2
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if tf2.enabled():
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from tensorflow.python.keras.layers.recurrent_v2 import GRU
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from tensorflow.python.keras.layers.recurrent_v2 import GRUCell
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from tensorflow.python.keras.layers.recurrent_v2 import LSTM
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from tensorflow.python.keras.layers.recurrent_v2 import LSTMCell
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from tensorflow.python.keras.layers.recurrent import GRU as GRUV1
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from tensorflow.python.keras.layers.recurrent import GRUCell as GRUCellV1
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from tensorflow.python.keras.layers.recurrent import LSTM as LSTMV1
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from tensorflow.python.keras.layers.recurrent import LSTMCell as LSTMCellV1
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GRUV2 = GRU
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GRUCellV2 = GRUCell
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LSTMV2 = LSTM
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LSTMCellV2 = LSTMCell
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else:
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from tensorflow.python.keras.layers.recurrent import GRU
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from tensorflow.python.keras.layers.recurrent import GRUCell
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from tensorflow.python.keras.layers.recurrent import LSTM
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from tensorflow.python.keras.layers.recurrent import LSTMCell
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from tensorflow.python.keras.layers.recurrent_v2 import GRU as GRUV2
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from tensorflow.python.keras.layers.recurrent_v2 import GRUCell as GRUCellV2
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from tensorflow.python.keras.layers.recurrent_v2 import LSTM as LSTMV2
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from tensorflow.python.keras.layers.recurrent_v2 import LSTMCell as LSTMCellV2
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GRUV1 = GRU
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GRUCellV1 = GRUCell
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LSTMV1 = LSTM
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LSTMCellV1 = LSTMCell
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# Convolutional-recurrent layers.
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from tensorflow.python.keras.layers.convolutional_recurrent import ConvLSTM2D
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@ -460,7 +460,7 @@ class CuDNNV1OnlyTest(keras_parameterized.TestCase):
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input_shape = (3, 5)
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def gru(cudnn=False, **kwargs):
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layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRU
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layer_class = keras.layers.CuDNNGRU if cudnn else keras.layers.GRUV1
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return layer_class(2, input_shape=input_shape, **kwargs)
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def get_layer_weights(layer):
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@ -256,7 +256,7 @@ class RNNCellWrapperTest(test.TestCase, parameterized.TestCase):
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with self.assertRaisesRegexp(ValueError, "does not work with "):
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wrapper_cls(cell)
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cell = layers.LSTMCell_v2(10)
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cell = layers.LSTMCellV2(10)
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with self.assertRaisesRegexp(ValueError, "does not work with "):
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wrapper_cls(cell)
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@ -145,7 +145,7 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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(None, input_dim, 4, 4, 4),
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],
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[
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(keras.layers.GRU(output_dim)),
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(keras.layers.GRUV1(output_dim)),
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[np.random.random((input_dim, output_dim)),
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np.random.random((output_dim, output_dim)),
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np.random.random((output_dim,)),
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@ -158,7 +158,7 @@ class TestWeightSavingAndLoading(test.TestCase, parameterized.TestCase):
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(None, 4, input_dim),
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],
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[
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(keras.layers.LSTM(output_dim)),
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(keras.layers.LSTMV1(output_dim)),
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[np.random.random((input_dim, output_dim)),
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np.random.random((output_dim, output_dim)),
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np.random.random((output_dim,)),
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@ -20,7 +20,7 @@ from __future__ import division
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from __future__ import print_function
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from tensorflow.python.keras import layers as keras_layers
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from tensorflow.python.keras.layers import normalization as keras_normalization
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from tensorflow.python.layers import base
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from tensorflow.python.ops import init_ops
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from tensorflow.python.util import deprecation
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@ -28,7 +28,7 @@ from tensorflow.python.util.tf_export import tf_export
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@tf_export(v1=['layers.BatchNormalization'])
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class BatchNormalization(keras_layers.BatchNormalization, base.Layer):
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class BatchNormalization(keras_normalization.BatchNormalization, base.Layer):
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"""Batch Normalization layer from http://arxiv.org/abs/1502.03167.
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"Batch Normalization: Accelerating Deep Network Training by Reducing
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@ -170,7 +170,7 @@ class BatchNormalization(keras_layers.BatchNormalization, base.Layer):
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@deprecation.deprecated(
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date=None, instructions='Use keras.layers.BatchNormalization instead. In '
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'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
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'be used (consult the `tf.keras.layers.batch_normalization` '
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'be used (consult the `tf.keras.layers.BatchNormalization` '
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'documentation).')
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@tf_export(v1=['layers.batch_normalization'])
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def batch_normalization(inputs,
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