Explicitly replace "import tensorflow" with "tensorflow.compat.v1"

PiperOrigin-RevId: 287568660

Author: Deepmind Team

alphafold_casp13/README.md

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+# AlphaFold
+
+This package provides an implementation of the contact prediction network,
+associated model weights and CASP13 dataset as published in Nature.
+
+Any publication that discloses findings arising from using this source code must
+cite *AlphaFold: Protein structure prediction using potentials from deep
+learning* by Andrew W. Senior, Richard Evans, John Jumper, James Kirkpatrick,
+Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander W. R. Nelson,
+Alex Bridgland, Hugo Penedones, Stig Petersen, Karen Simonyan, Steve Crossan,
+Pushmeet Kohli, David T. Jones, David Silver, Koray Kavukcuoglu, Demis Hassabis.
+
+## Setup
+
+### Dependencies
+
+*   Python 3.6+.
+*   [Abseil 0.8.0+](https://github.com/abseil/abseil-py)
+*   [Numpy 1.16+](https://numpy.org)
+*   [Six 1.12+](https://pypi.org/project/six/)
+*   [Sonnet 1.35+](https://github.com/deepmind/sonnet)
+*   [TensorFlow 1.14](https://tensorflow.org). Not compatible with TensorFlow
+    2.0+.
+*   [TensorFlow Probability 0.7.0](https://www.tensorflow.org/probability)
+
+You can set up Python virtual environment with these dependencies inside the
+forked `deepmind_research` repository using:
+
+```shell
+python3 -m venv alphafold_venv
+source alphafold_venv/bin/activate
+pip install -r alphafold_casp13/requirements.txt
+```
+
+### Input data
+
+The dataset can be downloaded from
+[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
+
+Download it e.g. using `wget`:
+
+```shell
+wget https://storage.googleapis.com/alphafold_casp13_data/casp13_data.zip
+```
+
+The zip file contains 1 directory for each CASP13 target and a `LICENSE.md`
+file. Each target directory contains the following files:
+
+1.  `TARGET.tfrec` file. This is a
+    [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) file
+    with serialized tf.train.Example protocol buffers that contain the features
+    needed to run the model.
+1.  `contacts/TARGET.pickle` file(s) with the predicted distogram.
+1.  `contacts/TARGET.rr` file(s) with the contact map derived from the predicted
+    distogram. The RR format is described on the
+    [CASP website](http://predictioncenter.org/casp13/index.cgi?page=format#RR).
+
+Note that for **T0999** the target was manually split based on hits in HHSearch
+into 5 sub-targets, hence there are 5 distograms
+(`contacts/T0999s{1,2,3,4,5}.pickle`) and 5 RR files
+(`contacts/T0999s{1,2,3,4,5}.rr`).
+
+The `contacts/` folder is not needed to run the model, these files are included
+only for convenience so that you don't need to run the inference for CASP13
+targets to get the contact map.
+
+### Model checkpoints
+
+The model checkpoints can be downloaded from
+[Google Cloud Storage](https://console.cloud.google.com/storage/browser/alphafold_casp13_data).
+
+Download them e.g. using `wget`:
+
+```shell
+wget https://storage.googleapis.com/alphafold_casp13_data/alphafold_casp13_weights.zip
+```
+
+The zip file contains:
+
+1.  A directory `873731`. This contains the weights for the distogram model.
+1.  A directory `916425`. This contains the weights for the background distogram
+    model.
+1.  A directory `941521`. This contains the weights for the torsion model.
+1.  `LICENSE.md`. The model checkpoints have a non-commercial license which is
+    defined in this file.
+
+Each directory with model weights contains a number of different model
+configurations. Each model has a config file and associated weights. There is
+only one torsion model. Each model directory also contains a stats file that is
+used for feature normalization specific to that model.
+
+## Distogram prediction
+
+### Running the system
+
+You can use the `run_eval.sh` script to run the entire Distogram prediction
+system. There are a few steps you need to start with:
+
+1.  Download the input data as described above. Unpack the data in the
+    directory with the code.
+1.  Download the model checkpoints as described above. Unpack the data.
+1.  In `run_eval.sh` set the following:
+    *   `DISTOGRAM_MODEL` to the path to the directory with the distogram model.
+    *   `BACKGROUND_MODEL` to the path to the directory with the background
+        model.
+    *   `TORSION_MODEL` to the path to the directory with the torsion model.
+    *   `TARGET` to the path to the directory with the target input data.
+
+Then run `alphafold_casp13/run_eval.sh` from the `deepmind_research` parent
+directory (you will get errors if you try running `run_eval.sh` directly from
+the `alphafold_casp13` directory).
+
+The contact prediction works in the following way:
+
+1.  4 replicas (by *replica* we mean a configuration file describing the network
+    architecture and a snapshot with the network weights), each with slightly
+    different model configuration, are launched to predict the distogram.
+1.  4 replicas, each with slightly different model configuration are launched to
+    predict the background distogram.
+1.  1 replica is launched to predict the torsions.
+1.  The predictions from the different replicas are averaged together using
+    `ensemble_contact_maps.py`.
+1.  The predictions for the 64 × 64 distogram crops are pasted together using
+    `paste_contact_maps.py`.
+
+When running `run_eval.sh` the output has the following directory structure:
+
+*   **distogram/**: Contains 4 subfolders, one for each replica. Each of these
+    contain the predicted ASA, secondary structure and a pickle file with the
+    distogram for each crop. It also contains an `ensemble` directory with the
+    ensembled distograms.
+*   **background_distogram/**: Contains 4 subfolders, one for each replica. Each
+    of these contain a pickle file with the background distogram for each crop.
+    It also contains an `ensemble` directory with the ensembled background
+    distograms.
+*   **torsion/**: Contains 1 subfolder as there was only a single replica. This
+    folder contains contains the predicted ASA, secondary structure, backbone
+    torsions and a pickle file with the distogram for each crop. It also
+    contains an `ensemble` directory with the ensembled torsions.
+*   **pasted/**: Contains distograms obtained from the ensembled distograms by
+    pasting. An RR contact map file is computed from this pasted distogram.
+    **This is the final distogram that was used in the subsequent AlphaFold
+    folding pipeline in CASP13.**
+
+## Data splits
+
+We used a version of [PDB](https://www.rcsb.org/) downloaded on 2018-03-15. The
+train/test split can be found in the `train_domains.txt` and `test_domains.txt`
+files.
+
+Disclaimer: This is not an official Google product.

alphafold_casp13/asa_output.py

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+# Lint as: python3.
+# Copyright 2019 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Class for predicting Accessible Surface Area."""
+
+import tensorflow.compat.v1 as tf
+from tensorflow.contrib import layers as contrib_layers
+
+
+class ASAOutputLayer(object):
+  """An output layer to predict Accessible Surface Area."""
+
+  def __init__(self, name='asa'):
+    self.name = name
+
+  def compute_asa_output(self, activations):
+    """Just compute the logits and outputs given activations."""
+    asa_logits = 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')
+
+    return asa_logits

alphafold_casp13/config_dict.py

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+# Lint as: python3.
+# Copyright 2019 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Utilities for storing configuration flags."""
+
+import json
+
+
+class ConfigDict(dict):
+  """Configuration dictionary with convenient dot element access."""
+
+  def __init__(self, *args, **kwargs):
+    super(ConfigDict, self).__init__(*args, **kwargs)
+    for arg in args:
+      if isinstance(arg, dict):
+        for key, value in arg.items():
+          self._add(key, value)
+    for key, value in kwargs.items():
+      self._add(key, value)
+
+  def _add(self, key, value):
+    if isinstance(value, dict):
+      self[key] = ConfigDict(value)
+    else:
+      self[key] = value
+
+  def __getattr__(self, attr):
+    try:
+      return self[attr]
+    except KeyError as e:
+      raise AttributeError(e)
+
+  def __setattr__(self, key, value):
+    self.__setitem__(key, value)
+
+  def __setitem__(self, key, value):
+    super(ConfigDict, self).__setitem__(key, value)
+    self.__dict__.update({key: value})
+
+  def __delattr__(self, item):
+    self.__delitem__(item)
+
+  def __delitem__(self, key):
+    super(ConfigDict, self).__delitem__(key)
+    del self.__dict__[key]
+
+  def to_json(self):
+    return json.dumps(self)
+
+  @classmethod
+  def from_json(cls, json_string):
+    return cls(json.loads(json_string))