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104 lines
3.9 KiB
Bash
Executable File
104 lines
3.9 KiB
Bash
Executable File
#!/bin/bash
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# Copyright 2019 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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set -e
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# We assume the script is being run from the deepmind_research parent directory.
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DISTOGRAM_MODEL="alphafold_casp13/873731" # Path to the directory with the distogram model.
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BACKGROUND_MODEL="alphafold_casp13/916425" # Path to the directory with the background model.
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TORSION_MODEL="alphafold_casp13/941521" # Path to the directory with the torsion model.
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TARGET="T1019s2" # The name of the target.
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TARGET_PATH="alphafold_casp13/${TARGET}" # Path to the directory with the target input data.
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# Set up the virtual environment and install dependencies.
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python3 -m venv alphafold_venv
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source alphafold_venv/bin/activate
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pip install wheel
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pip install -r alphafold_casp13/requirements.txt
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# Create the output directory.
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OUTPUT_DIR="${HOME}/contacts_${TARGET}_$(date +%Y_%m_%d_%H_%M_%S)"
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mkdir -p "${OUTPUT_DIR}"
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echo "Saving output to ${OUTPUT_DIR}/"
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# Run contact prediction over 4 replicas.
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for replica in 0 1 2 3; do
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echo "Launching all models for replica ${replica}"
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# Run the distogram model.
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python3 -m alphafold_casp13.contacts \
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--logtostderr \
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--cpu=true \
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--config_path="${DISTOGRAM_MODEL}/${replica}/config.json" \
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--checkpoint_path="${DISTOGRAM_MODEL}/${replica}/tf_graph_data/tf_graph_data.ckpt" \
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--output_path="${OUTPUT_DIR}/distogram/${replica}" \
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--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
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--stats_file="${DISTOGRAM_MODEL}/stats_train_s35.json" &
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# Run the background model.
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python3 -m alphafold_casp13.contacts \
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--logtostderr \
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--cpu=true \
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--config_path="${BACKGROUND_MODEL}/${replica}/config.json" \
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--checkpoint_path="${BACKGROUND_MODEL}/${replica}/tf_graph_data/tf_graph_data.ckpt" \
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--output_path="${OUTPUT_DIR}/background_distogram/${replica}" \
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--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
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--stats_file="${BACKGROUND_MODEL}/stats_train_s35.json" &
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done
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# Run the torsion model, but only 1 replica.
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python3 -m alphafold_casp13.contacts \
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--logtostderr \
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--cpu=true \
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--config_path="${TORSION_MODEL}/0/config.json" \
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--checkpoint_path="${TORSION_MODEL}/0/tf_graph_data/tf_graph_data.ckpt" \
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--output_path="${OUTPUT_DIR}/torsion/0" \
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--eval_sstable="${TARGET_PATH}/${TARGET}.tfrec" \
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--stats_file="${TORSION_MODEL}/stats_train_s35.json" &
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echo "All models running, waiting for them to complete"
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wait
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echo "Ensembling all replica outputs"
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# Run the ensembling jobs for distograms, background distograms.
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for output_dir in "${OUTPUT_DIR}/distogram" "${OUTPUT_DIR}/background_distogram"; do
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pickle_dirs="${output_dir}/0/pickle_files/,${output_dir}/1/pickle_files/,${output_dir}/2/pickle_files/,${output_dir}/3/pickle_files/"
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# Ensemble distograms.
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python3 -m alphafold_casp13.ensemble_contact_maps \
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--logtostderr \
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--pickle_dirs="${pickle_dirs}" \
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--output_dir="${output_dir}/ensemble/"
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done
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# Only ensemble single replica distogram for torsions.
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python3 -m alphafold_casp13.ensemble_contact_maps \
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--logtostderr \
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--pickle_dirs="${OUTPUT_DIR}/torsion/0/pickle_files/" \
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--output_dir="${OUTPUT_DIR}/torsion/ensemble/"
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echo "Pasting contact maps"
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python3 -m alphafold_casp13.paste_contact_maps \
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--logtostderr \
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--pickle_input_dir="${OUTPUT_DIR}/distogram/ensemble/" \
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--output_dir="${OUTPUT_DIR}/pasted/" \
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--tfrecord_path="${TARGET_PATH}/${TARGET}.tfrec"
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echo "Done"
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