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# Copyright 2021 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.
"""Feature pre-processing input pipeline for AlphaFold."""
from alphafold.model.tf import data_transforms
from alphafold.model.tf import shape_placeholders
import tensorflow.compat.v1 as tf
import tree
# Pylint gets confused by the curry1 decorator because it changes the number
# of arguments to the function.
# pylint:disable=no-value-for-parameter
NUM_RES = shape_placeholders.NUM_RES
NUM_MSA_SEQ = shape_placeholders.NUM_MSA_SEQ
NUM_EXTRA_SEQ = shape_placeholders.NUM_EXTRA_SEQ
NUM_TEMPLATES = shape_placeholders.NUM_TEMPLATES
def nonensembled_map_fns(data_config):
"""Input pipeline functions which are not ensembled."""
common_cfg = data_config.common
map_fns = [
data_transforms.correct_msa_restypes,
data_transforms.add_distillation_flag(False),
data_transforms.cast_64bit_ints,
data_transforms.squeeze_features,
# Keep to not disrupt RNG.
data_transforms.randomly_replace_msa_with_unknown(0.0),
data_transforms.make_seq_mask,
data_transforms.make_msa_mask,
# Compute the HHblits profile if it's not set. This has to be run before
# sampling the MSA.
data_transforms.make_hhblits_profile,
data_transforms.make_random_crop_to_size_seed,
]
if common_cfg.use_templates:
map_fns.extend([
data_transforms.fix_templates_aatype,
data_transforms.make_template_mask,
data_transforms.make_pseudo_beta('template_')
])
map_fns.extend([
data_transforms.make_atom14_masks,
])
return map_fns
def ensembled_map_fns(data_config):
"""Input pipeline functions that can be ensembled and averaged."""
common_cfg = data_config.common
eval_cfg = data_config.eval
map_fns = []
if common_cfg.reduce_msa_clusters_by_max_templates:
pad_msa_clusters = eval_cfg.max_msa_clusters - eval_cfg.max_templates
else:
pad_msa_clusters = eval_cfg.max_msa_clusters
max_msa_clusters = pad_msa_clusters
max_extra_msa = common_cfg.max_extra_msa
map_fns.append(
data_transforms.sample_msa(
max_msa_clusters,
keep_extra=True))
if 'masked_msa' in common_cfg:
# Masked MSA should come *before* MSA clustering so that
# the clustering and full MSA profile do not leak information about
# the masked locations and secret corrupted locations.
map_fns.append(
data_transforms.make_masked_msa(common_cfg.masked_msa,
eval_cfg.masked_msa_replace_fraction))
if common_cfg.msa_cluster_features:
map_fns.append(data_transforms.nearest_neighbor_clusters())
map_fns.append(data_transforms.summarize_clusters())
# Crop after creating the cluster profiles.
if max_extra_msa:
map_fns.append(data_transforms.crop_extra_msa(max_extra_msa))
else:
map_fns.append(data_transforms.delete_extra_msa)
map_fns.append(data_transforms.make_msa_feat())
crop_feats = dict(eval_cfg.feat)
if eval_cfg.fixed_size:
map_fns.append(data_transforms.select_feat(list(crop_feats)))
map_fns.append(data_transforms.random_crop_to_size(
eval_cfg.crop_size,
eval_cfg.max_templates,
crop_feats,
eval_cfg.subsample_templates))
map_fns.append(data_transforms.make_fixed_size(
crop_feats,
pad_msa_clusters,
common_cfg.max_extra_msa,
eval_cfg.crop_size,
eval_cfg.max_templates))
else:
map_fns.append(data_transforms.crop_templates(eval_cfg.max_templates))
return map_fns
def process_tensors_from_config(tensors, data_config):
"""Apply filters and maps to an existing dataset, based on the config."""
def wrap_ensemble_fn(data, i):
"""Function to be mapped over the ensemble dimension."""
d = data.copy()
fns = ensembled_map_fns(data_config)
fn = compose(fns)
d['ensemble_index'] = i
return fn(d)
eval_cfg = data_config.eval
tensors = compose(
nonensembled_map_fns(
data_config))(
tensors)
tensors_0 = wrap_ensemble_fn(tensors, tf.constant(0))
num_ensemble = eval_cfg.num_ensemble
if data_config.common.resample_msa_in_recycling:
# Separate batch per ensembling & recycling step.
num_ensemble *= data_config.common.num_recycle + 1
if isinstance(num_ensemble, tf.Tensor) or num_ensemble > 1:
fn_output_signature = tree.map_structure(
tf.TensorSpec.from_tensor, tensors_0)
tensors = tf.map_fn(
lambda x: wrap_ensemble_fn(tensors, x),
tf.range(num_ensemble),
parallel_iterations=1,
fn_output_signature=fn_output_signature)
else:
tensors = tree.map_structure(lambda x: x[None],
tensors_0)
return tensors
@data_transforms.curry1
def compose(x, fs):
for f in fs:
x = f(x)
return x
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