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Make TensorFlow imports compatible with TF 1.14.

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PiperOrigin-RevId: 289424199
This commit is contained in:
Augustin Zidek
2020-01-13 13:45:38 +00:00
committed by Diego de Las Casas
parent 3c6b08d3ca
commit 25a0bc935f
12 changed files with 26 additions and 35 deletions
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alphafold_casp13/asa_output.py
+3 -5
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@@ -14,8 +14,7 @@
# limitations under the License.
"""Class for predicting Accessible Surface Area."""
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
class ASAOutputLayer(object):
@@ -26,9 +25,8 @@ class ASAOutputLayer(object):
def compute_asa_output(self, activations):
"""Just compute the logits and outputs given activations."""
asa_logits = contrib_layers.linear(
activations,
1,
asa_logits = tf.contrib.layers.linear(
activations, 1,
weights_initializer=tf.random_uniform_initializer(-0.01, 0.01),
scope='ASALogits')
self.asa_output = tf.nn.relu(asa_logits, name='ASA_output_relu')
alphafold_casp13/contacts.py
+1 -1
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@@ -24,7 +24,7 @@ from absl import logging
import numpy as np
import six
import sonnet as snt
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import config_dict
from alphafold_casp13 import contacts_experiment
alphafold_casp13/contacts_dataset.py
+1 -1
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@@ -18,7 +18,7 @@ import collections
import enum
import json
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
_ProteinDescription = collections.namedtuple(
alphafold_casp13/contacts_experiment.py
+1 -1
View File
@@ -15,7 +15,7 @@
"""Contact prediction convnet experiment example."""
from absl import logging
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import contacts_dataset
from alphafold_casp13 import contacts_network
alphafold_casp13/contacts_network.py
+11 -16
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@@ -16,13 +16,12 @@
from absl import logging
import sonnet
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import asa_output
from alphafold_casp13 import secstruct
from alphafold_casp13 import two_dim_convnet
from alphafold_casp13 import two_dim_resnet
from tensorflow.contrib import layers as contrib_layers
def call_on_tuple(f):
@@ -194,27 +193,23 @@ class ContactsNet(sonnet.AbstractModule):
axis=join_dim)],
axis=collapse_dim) # Join the two crops together.
if self._collapsed_batch_norm:
embedding_1d = contrib_layers.batch_norm(
embedding_1d,
is_training=use_on_the_fly_stats,
fused=True,
decay=0.999,
scope='collapsed_batch_norm',
embedding_1d = tf.contrib.layers.batch_norm(
embedding_1d, is_training=use_on_the_fly_stats,
fused=True, decay=0.999, scope='collapsed_batch_norm',
data_format='NHWC')
for i, nfil in enumerate(self._filters_1d):
embedding_1d = contrib_layers.fully_connected(
embedding_1d = tf.contrib.layers.fully_connected(
embedding_1d,
num_outputs=nfil,
normalizer_fn=(contrib_layers.batch_norm
if self._collapsed_batch_norm else None),
normalizer_params={
'is_training': use_on_the_fly_stats,
'updates_collections': None
},
normalizer_fn=(
tf.contrib.layers.batch_norm if self._collapsed_batch_norm
else None),
normalizer_params={'is_training': use_on_the_fly_stats,
'updates_collections': None},
scope='collapsed_embed_%d' % i)
if self.torsion_multiplier > 0:
self.torsion_logits = contrib_layers.fully_connected(
self.torsion_logits = tf.contrib.layers.fully_connected(
embedding_1d,
num_outputs=self._torsion_bins * self._torsion_bins,
activation_fn=None,
alphafold_casp13/distogram_io.py
+1 -1
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@@ -22,7 +22,7 @@ import os
import numpy as np
import six.moves.cPickle as pickle
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
RR_FORMAT = """PFRMAT RR
alphafold_casp13/ensemble_contact_maps.py
+1 -1
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@@ -24,7 +24,7 @@ import os
from absl import app
from absl import flags
from absl import logging
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import distogram_io
from alphafold_casp13 import parsers
alphafold_casp13/parsers.py
+1 -1
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@@ -16,7 +16,7 @@
import pickle
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
def distance_histogram_dict(f):
alphafold_casp13/paste_contact_maps.py
+1 -1
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@@ -21,7 +21,7 @@ from absl import flags
from absl import logging
import numpy as np
import six
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import distogram_io
from alphafold_casp13 import parsers
alphafold_casp13/secstruct.py
+2 -3
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@@ -18,8 +18,7 @@ import os
from absl import logging
import numpy as np
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
# 8-class classes (Q8)
@@ -57,7 +56,7 @@ class Secstruct(object):
"""Make the layer."""
with tf.variable_scope(self.name, reuse=tf.AUTO_REUSE):
logging.info('Creating secstruct %s', activations)
self.logits = contrib_layers.linear(activations, self._dimension)
self.logits = tf.contrib.layers.linear(activations, self._dimension)
self.ss_q8_probs = tf.nn.softmax(self.logits)
self.ss_q3_probs = tf.matmul(
self.ss_q8_probs, tf.constant(self.q3_map_matrix, dtype=tf.float32))
alphafold_casp13/two_dim_convnet.py
+2 -3
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@@ -15,8 +15,7 @@
"""Two dimensional convolutional neural net layers."""
from absl import logging
import tensorflow.compat.v1 as tf
from tensorflow.contrib import layers as contrib_layers
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
def weight_variable(shape, stddev=0.01):
@@ -92,7 +91,7 @@ def make_conv_sep2d_layer(input_node,
def batch_norm_layer(h_conv, layer_name, is_training=True, data_format='NCHW'):
"""Batch norm layer."""
logging.vlog(1, 'batch norm for layer %s', layer_name)
return contrib_layers.batch_norm(
return tf.contrib.layers.batch_norm(
h_conv,
is_training=is_training,
fused=True,
alphafold_casp13/two_dim_resnet.py
+1 -1
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@@ -15,7 +15,7 @@
"""2D Resnet."""
from absl import logging
import tensorflow.compat.v1 as tf
import tensorflow as tf # pylint: disable=g-explicit-tensorflow-version-import
from alphafold_casp13 import two_dim_convnet