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# Copyright 2024 ByteDance and/or its affiliates.
#
# 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.
import os
from pathlib import Path
import numpy as np
import torch
from biotite.structure import AtomArray
from protenix.data.utils import save_structure_cif
from protenix.utils.file_io import save_json
from protenix.utils.torch_utils import round_values
def get_clean_full_confidence(full_confidence_dict: dict) -> dict:
"""
Clean and format the full confidence dictionary by removing unnecessary keys and rounding values.
Args:
full_confidence_dict (dict): The dictionary containing full confidence data.
Returns:
dict: The cleaned and formatted dictionary.
"""
# Remove atom_coordinate
full_confidence_dict.pop("atom_coordinate")
# Remove atom_is_polymer
full_confidence_dict.pop("atom_is_polymer")
# Keep two decimal places
full_confidence_dict = round_values(full_confidence_dict)
return full_confidence_dict
class DataDumper:
def __init__(
self,
base_dir,
need_atom_confidence: bool = False,
sorted_by_ranking_score: bool = True,
) -> None:
self.base_dir = base_dir
self.need_atom_confidence = need_atom_confidence
self.sorted_by_ranking_score = sorted_by_ranking_score
def dump(
self,
dataset_name: str,
pdb_id: str,
seed: int,
pred_dict: dict,
atom_array: AtomArray,
entity_poly_type: dict[str, str],
saved_path=None
):
"""
Dump the predictions and related data to the specified directory.
Args:
dataset_name (str): The name of the dataset.
pdb_id (str): The PDB ID of the sample.
seed (int): The seed used for randomization.
pred_dict (dict): The dictionary containing the predictions.
atom_array (AtomArray): The AtomArray object containing the structure data.
entity_poly_type (dict[str, str]): The entity poly type information.
"""
#dump_dir = self._get_dump_dir(dataset_name, pdb_id, seed)
dump_dir = saved_path
Path(dump_dir).mkdir(parents=True, exist_ok=True)
self.dump_predictions(
pred_dict=pred_dict,
dump_dir=dump_dir,
pdb_id=pdb_id,
atom_array=atom_array,
entity_poly_type=entity_poly_type,
seed=seed,
)
def _get_dump_dir(self, dataset_name: str, sample_name: str, seed: int) -> str:
"""
Generate the directory path for dumping data based on the dataset name, sample name, and seed.
"""
dump_dir = os.path.join(
self.base_dir, dataset_name, sample_name, f"seed_{seed}"
)
return dump_dir
def dump_predictions(
self,
pred_dict: dict,
dump_dir: str,
pdb_id: str,
atom_array: AtomArray,
entity_poly_type: dict[str, str],
seed: int,
):
"""
Dump raw predictions from the model:
structure: Save the predicted coordinates as CIF files.
confidence: Save the confidence data as JSON files.
"""
prediction_save_dir = os.path.join(dump_dir, "predictions")
os.makedirs(prediction_save_dir, exist_ok=True)
# Dump structure
b_factor = None
if "full_data" in pred_dict:
all_atom_plddt = []
# len(pred_dict["full_data"]) == N_sample
for each_sample_dict in pred_dict["full_data"]:
if "atom_plddt" in each_sample_dict:
# atom_plddt.shape == [N_atom]
atom_plddt = each_sample_dict["atom_plddt"]
if atom_plddt.dtype == torch.bfloat16:
atom_plddt = atom_plddt.to(torch.float32)
all_atom_plddt.append(atom_plddt.cpu().numpy() * 100.0)
if len(all_atom_plddt) == len(pred_dict["full_data"]):
b_factor = all_atom_plddt
sorted_indices = self._get_ranker_indices(data=pred_dict)
self._save_structure(
pred_coordinates=pred_dict["coordinate"],
prediction_save_dir=prediction_save_dir,
sample_name=pdb_id,
atom_array=atom_array,
entity_poly_type=entity_poly_type,
seed=seed,
sorted_indices=sorted_indices,
b_factor=b_factor,
)
if "coordinate_orig" in pred_dict:
os.makedirs(os.path.join(dump_dir, "predictions_orig"), exist_ok=True)
self._save_structure(
pred_coordinates=pred_dict["coordinate_orig"],
prediction_save_dir=os.path.join(dump_dir, "predictions_orig"),
sample_name=pdb_id,
atom_array=atom_array,
entity_poly_type=entity_poly_type,
seed=seed,
sorted_indices=sorted_indices,
b_factor=b_factor,
)
# Dump confidence
self._save_confidence(
data=pred_dict,
prediction_save_dir=prediction_save_dir,
sample_name=pdb_id,
seed=seed,
sorted_indices=sorted_indices,
)
def _save_structure(
self,
pred_coordinates: torch.Tensor,
prediction_save_dir: str,
sample_name: str,
atom_array: AtomArray,
entity_poly_type: dict[str, str],
seed: int,
sorted_indices: None,
b_factor: torch.Tensor = None,
):
assert atom_array is not None
N_sample = pred_coordinates.shape[0]
if sorted_indices is None:
sorted_indices = range(N_sample) # do not rank the output file
for idx, rank in enumerate(sorted_indices):
output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_seed_{seed}_sample_{rank}.cif",
)
if b_factor is not None:
# b_factor.shape == [N_sample, N_atom]
atom_array.set_annotation("b_factor", np.round(b_factor[idx], 2))
save_structure_cif(
atom_array=atom_array,
pred_coordinate=pred_coordinates[idx],
output_fpath=output_fpath,
entity_poly_type=entity_poly_type,
pdb_id=sample_name,
)
def _get_ranker_indices(self, data: dict):
N_sample = len(data["summary_confidence"])
if self.sorted_by_ranking_score:
value = torch.tensor(
[
data["summary_confidence"][i]["ranking_score"]
for i in range(N_sample)
]
)
sorted_indices = [
i for i in torch.argsort(torch.argsort(value, descending=True))
]
else:
sorted_indices = [i for i in range(N_sample)]
return sorted_indices
def _save_confidence(
self,
data: dict,
prediction_save_dir: str,
sample_name: str,
seed: int,
sorted_indices: None,
):
N_sample = len(data["summary_confidence"])
for idx in range(N_sample):
if self.need_atom_confidence:
data["full_data"][idx] = get_clean_full_confidence(
data["full_data"][idx]
)
if sorted_indices is None:
sorted_indices = range(N_sample)
for idx, rank in enumerate(sorted_indices):
output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_seed_{seed}_summary_confidence_sample_{rank}.json",
)
save_json(data["summary_confidence"][idx], output_fpath, indent=4)
if self.need_atom_confidence:
output_fpath = os.path.join(
prediction_save_dir,
f"{sample_name}_full_data_sample_{rank}.json",
)
save_json(data["full_data"][idx], output_fpath, indent=None)
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