From 2160fc7f174a2d0ffe79aef5ca1b59419e4fafa1 Mon Sep 17 00:00:00 2001 From: Augustin Zidek Date: Wed, 8 Jan 2020 10:28:00 +0000 Subject: [PATCH] Update requirements versions, change tf.compat.v1. usages to tf. PiperOrigin-RevId: 288659456 --- alphafold_casp13/contacts.py | 6 +++--- alphafold_casp13/contacts_dataset.py | 4 ++-- alphafold_casp13/contacts_experiment.py | 6 +++--- alphafold_casp13/contacts_network.py | 8 ++++---- alphafold_casp13/requirements.txt | 9 +++++---- alphafold_casp13/secstruct.py | 2 +- alphafold_casp13/two_dim_convnet.py | 12 ++++++------ 7 files changed, 24 insertions(+), 23 deletions(-) diff --git a/alphafold_casp13/contacts.py b/alphafold_casp13/contacts.py index 2210505..1fe45ca 100644 --- a/alphafold_casp13/contacts.py +++ b/alphafold_casp13/contacts.py @@ -71,10 +71,10 @@ def evaluate(crop_size_x, crop_size_y, feature_normalization, checkpoint_path, normalization_exclusion=normalization_exclusion) checkpoint = snt.get_saver(experiment.model, collections=[ - tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, - tf.compat.v1.GraphKeys.MOVING_AVERAGE_VARIABLES]) + tf.GraphKeys.GLOBAL_VARIABLES, + tf.GraphKeys.MOVING_AVERAGE_VARIABLES]) - with tf.compat.v1.train.SingularMonitoredSession(hooks=[]) as sess: + with tf.train.SingularMonitoredSession(hooks=[]) as sess: logging.info('Restoring from checkpoint %s', checkpoint_path) checkpoint.restore(sess, checkpoint_path) diff --git a/alphafold_casp13/contacts_dataset.py b/alphafold_casp13/contacts_dataset.py index c16b49d..6a8dad0 100644 --- a/alphafold_casp13/contacts_dataset.py +++ b/alphafold_casp13/contacts_dataset.py @@ -167,7 +167,7 @@ def parse_tfexample(raw_data, features): for k, v in parsed_features.items(): new_shape = shape(feature_name=k, num_residues=num_residues) # Make sure the feature we are reshaping is not empty. - assert_non_empty = tf.compat.v1.assert_greater( + assert_non_empty = tf.assert_greater( tf.size(v), 0, name='assert_%s_non_empty' % k, message='The feature %s is not set in the tf.Example. Either do not ' 'request the feature or use a tf.Example that has the feature set.' % k) @@ -245,7 +245,7 @@ def normalize_from_stats_file( train_mean = tf.cast(norm_stats['mean'][key], dtype=tf.float32) train_range = tf.sqrt(tf.cast(norm_stats['var'][key], dtype=tf.float32)) value -= train_mean - value = tf.compat.v1.where( + value = tf.where( train_range > range_epsilon, value / train_range, value) features[key] = value else: diff --git a/alphafold_casp13/contacts_experiment.py b/alphafold_casp13/contacts_experiment.py index d464722..46b0e3e 100644 --- a/alphafold_casp13/contacts_experiment.py +++ b/alphafold_casp13/contacts_experiment.py @@ -22,12 +22,12 @@ from alphafold_casp13 import contacts_network def _int_ph(shape, name): - return tf.compat.v1.placeholder( + return tf.placeholder( dtype=tf.int32, shape=shape, name=('%s_placeholder' % name)) def _float_ph(shape, name): - return tf.compat.v1.placeholder( + return tf.placeholder( dtype=tf.float32, shape=shape, name=('%s_placeholder' % name)) @@ -102,7 +102,7 @@ class Contacts(object): dataset = dataset.batch(1) # Get a batch of tensors in the legacy ProteinsDataset format. - iterator = tf.compat.v1.data.make_one_shot_iterator(dataset) + iterator = tf.data.make_one_shot_iterator(dataset) self._input_batch = iterator.get_next() self.num_eval_examples = sum( diff --git a/alphafold_casp13/contacts_network.py b/alphafold_casp13/contacts_network.py index 1826413..02cc1d3 100644 --- a/alphafold_casp13/contacts_network.py +++ b/alphafold_casp13/contacts_network.py @@ -41,7 +41,7 @@ def call_on_tuple(f): class ContactsNet(sonnet.AbstractModule): - """A network to go from sequence to secondary structure.""" + """A network to go from sequence to distance histograms.""" def __init__(self, binary_code_bits, @@ -102,7 +102,7 @@ class ContactsNet(sonnet.AbstractModule): if self.asa_multiplier > 0: self._asa = asa_output.ASAOutputLayer() if self._position_specific_bias_size: - self._position_specific_bias = tf.compat.v1.get_variable( + self._position_specific_bias = tf.get_variable( 'position_specific_bias', [self._position_specific_bias_size, self._num_bins or 1], initializer=tf.zeros_initializer()) @@ -338,7 +338,7 @@ class ContactsNet(sonnet.AbstractModule): layers_forward = None if config_2d_deep.extra_blocks: # Optionally put some extra double-size blocks at the beginning. - with tf.compat.v1.variable_scope('Deep2DExtra'): + with tf.variable_scope('Deep2DExtra'): hidden_2d = two_dim_resnet.make_two_dim_resnet( input_node=hidden_2d, num_residues=None, # Unused @@ -362,7 +362,7 @@ class ContactsNet(sonnet.AbstractModule): if features_forward is not None: hidden_2d = tf.concat([hidden_2d, features_forward], 1 if data_format == 'NCHW' else 3) - with tf.compat.v1.variable_scope('Deep2D'): + with tf.variable_scope('Deep2D'): logging.info('2d hidden shape is %s', str(hidden_2d.shape.as_list())) contact_pre_logits = two_dim_resnet.make_two_dim_resnet( input_node=hidden_2d, diff --git a/alphafold_casp13/requirements.txt b/alphafold_casp13/requirements.txt index 90eeae0..b810cbb 100644 --- a/alphafold_casp13/requirements.txt +++ b/alphafold_casp13/requirements.txt @@ -1,6 +1,7 @@ -absl-py>=0.8.0 -numpy>=1.16 -six>=1.12 -dm-sonnet>=1.35 +setuptools==41.0.0 +absl-py==0.8.1 +numpy==1.16 +six==1.12 +dm-sonnet==1.35 tensorflow==1.14 tensorflow-probability==0.7.0 diff --git a/alphafold_casp13/secstruct.py b/alphafold_casp13/secstruct.py index 7178521..169eb4b 100644 --- a/alphafold_casp13/secstruct.py +++ b/alphafold_casp13/secstruct.py @@ -55,7 +55,7 @@ class Secstruct(object): def make_layer_new(self, activations): """Make the layer.""" - with tf.compat.v1.variable_scope(self.name, reuse=tf.AUTO_REUSE): + 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.ss_q8_probs = tf.nn.softmax(self.logits) diff --git a/alphafold_casp13/two_dim_convnet.py b/alphafold_casp13/two_dim_convnet.py index 1914098..92acb66 100644 --- a/alphafold_casp13/two_dim_convnet.py +++ b/alphafold_casp13/two_dim_convnet.py @@ -22,12 +22,12 @@ from tensorflow.contrib import layers as contrib_layers def weight_variable(shape, stddev=0.01): """Returns the weight variable.""" logging.vlog(1, 'weight init for shape %s', str(shape)) - return tf.compat.v1.get_variable( + return tf.get_variable( 'w', shape, initializer=tf.random_normal_initializer(stddev=stddev)) def bias_variable(shape): - return tf.compat.v1.get_variable( + return tf.get_variable( 'b', shape, initializer=tf.zeros_initializer()) @@ -61,12 +61,12 @@ def make_conv_sep2d_layer(input_node, filter_size_2 = filter_size logging.vlog(1, 'layer %s in %d out %d chan mult %d', layer_name, in_channels, out_channels, channel_multiplier) - with tf.compat.v1.variable_scope(layer_name): - with tf.compat.v1.variable_scope('depthwise'): + with tf.variable_scope(layer_name): + with tf.variable_scope('depthwise'): w_depthwise = weight_variable( [filter_size, filter_size_2, in_channels, channel_multiplier], stddev=stddev) - with tf.compat.v1.variable_scope('pointwise'): + with tf.variable_scope('pointwise'): w_pointwise = weight_variable( [1, 1, in_channels * channel_multiplier, out_channels], stddev=stddev) h_conv = tf.nn.separable_conv2d( @@ -119,7 +119,7 @@ def make_conv_layer(input_node, filter_size_2 = filter_size logging.vlog( 1, 'layer %s in %d out %d', layer_name, in_channels, out_channels) - with tf.compat.v1.variable_scope(layer_name): + with tf.variable_scope(layer_name): w_conv = weight_variable( [filter_size, filter_size_2, in_channels, out_channels], stddev=stddev) h_conv = conv2d(