mirror of
https://github.com/baker-laboratory/RoseTTAFold-All-Atom.git
synced 2024-11-04 22:25:42 +00:00
91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
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import torch
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import numpy as np
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import hydra
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import os
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from rf2aa.training.EMA import EMA
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from rf2aa.model.RoseTTAFoldModel import RoseTTAFoldModule
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from rf2aa.util_module import XYZConverter
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from rf2aa.chemical import ChemicalData as ChemData
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#TODO: control environment variables from config
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# limit thread counts
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os.environ['OMP_NUM_THREADS'] = '4'
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os.environ['OPENBLAS_NUM_THREADS'] = '4'
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#os.environ['PYTORCH_CUDA_ALLOC_CONF'] = "max_split_size_mb:512"
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## To reproduce errors
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import random
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def seed_all(seed=0):
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random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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np.random.seed(seed)
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torch.set_num_threads(4)
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#torch.autograd.set_detect_anomaly(True)
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class Trainer:
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def __init__(self, config) -> None:
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self.config = config
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assert self.config.ddp_params.batch_size == 1, "batch size is assumed to be 1"
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if self.config.experiment.output_dir is not None:
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self.output_dir = self.config.experiment.output_dir
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else:
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self.output_dir = "models/"
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if not os.path.exists(self.output_dir):
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os.makedirs(self.output_dir)
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def move_constants_to_device(self, gpu):
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self.fi_dev = ChemData().frame_indices.to(gpu)
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self.xyz_converter = XYZConverter().to(gpu)
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self.l2a = ChemData().long2alt.to(gpu)
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self.aamask = ChemData().allatom_mask.to(gpu)
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self.num_bonds = ChemData().num_bonds.to(gpu)
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self.atom_type_index = ChemData().atom_type_index.to(gpu)
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self.ljlk_parameters = ChemData().ljlk_parameters.to(gpu)
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self.lj_correction_parameters = ChemData().lj_correction_parameters.to(gpu)
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self.hbtypes = ChemData().hbtypes.to(gpu)
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self.hbbaseatoms = ChemData().hbbaseatoms.to(gpu)
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self.hbpolys = ChemData().hbpolys.to(gpu)
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self.cb_len = ChemData().cb_length_t.to(gpu)
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self.cb_ang = ChemData().cb_angle_t.to(gpu)
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self.cb_tor = ChemData().cb_torsion_t.to(gpu)
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class LegacyTrainer(Trainer):
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def __init__(self, config) -> None:
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super().__init__(config)
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def construct_model(self, device="cpu"):
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self.model = RoseTTAFoldModule(
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**self.config.legacy_model_param,
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aamask = ChemData().allatom_mask.to(device),
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atom_type_index = ChemData().atom_type_index.to(device),
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ljlk_parameters = ChemData().ljlk_parameters.to(device),
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lj_correction_parameters = ChemData().lj_correction_parameters.to(device),
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num_bonds = ChemData().num_bonds.to(device),
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cb_len = ChemData().cb_length_t.to(device),
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cb_ang = ChemData().cb_angle_t.to(device),
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cb_tor = ChemData().cb_torsion_t.to(device),
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).to(device)
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if self.config.training_params.EMA is not None:
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self.model = EMA(self.model, self.config.training_params.EMA)
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@hydra.main(version_base=None, config_path='config/train')
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def main(config):
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seed_all()
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trainer = trainer_factory[config.experiment.trainer](config=config)
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trainer_factory = {
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"legacy": LegacyTrainer,
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}
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if __name__ == "__main__":
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main()
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