plus_embedding.py

# Written by Seonwoo Min, Seoul National University (mswzeus@gmail.com)
# PLUS

import os
import sys
import argparse

import torch

import plus.config as config
from plus.data.alphabets import Protein
import plus.data.dataset as dataset
import plus.model.plus_rnn as plus_rnn
import plus.model.plus_tfm as plus_tfm
import plus.model.p_elmo as p_elmo
from plus.data.fasta import load_fasta
from plus.train import Trainer
from plus.utils import Print, set_seeds, set_output, load_models


parser = argparse.ArgumentParser('Protein sequence embedding with PLUS models')
parser.add_argument('--data-config',  help='path for data configuration file')
parser.add_argument('--model-config', help='path for model configuration file')
parser.add_argument('--lm-model-config', help='path for lm-model configuration file (for P-ELMo)')
parser.add_argument('--run-config', help='path for run configuration file')
parser.add_argument('--pretrained-model', help='path for pretrained model file')
parser.add_argument('--pretrained-lm-model', help='path for pretrained lm-model file (for P-ELMo)')
parser.add_argument('--device', help='device to use; multi-GPU if given multiple GPUs sperated by comma (default: cpu)')
parser.add_argument('--output-path', help='path for outputs (default: stdout and without saving)')
parser.add_argument('--output-index', help='index for outputs')
parser.add_argument('--sanity-check', default=False, action='store_true', help='sanity check flag')


def main():
    set_seeds(2020)
    args = vars(parser.parse_args())

    alphabet = Protein()
    cfgs = []
    data_cfg  = config.DataConfig(args["data_config"]);   cfgs.append(data_cfg)
    if args["lm_model_config"] is None:
        model_cfg = config.ModelConfig(args["model_config"], input_dim=len(alphabet))
        cfgs += [model_cfg]
    else:
        lm_model_cfg = config.ModelConfig(args["lm_model_config"], idx="lm_model_config", input_dim=len(alphabet))
        model_cfg = config.ModelConfig(args["model_config"], input_dim=len(alphabet),
                                       lm_dim=lm_model_cfg.num_layers * lm_model_cfg.hidden_dim * 2)
        cfgs += [model_cfg, lm_model_cfg]
    run_cfg = config.RunConfig(args["run_config"], sanity_check=args["sanity_check"]);  cfgs.append(run_cfg)
    output, save_prefix = set_output(args, "embedding_log", embedding=True)
    os.environ['CUDA_VISIBLE_DEVICES'] = args["device"] if args["device"] is not None else ""
    device, data_parallel = torch.device("cuda" if torch.cuda.is_available() else "cpu"), torch.cuda.device_count() > 1
    config.print_configs(args, cfgs, device, output)
    flag_rnn = (model_cfg.model_type == "RNN")
    flag_lm_model = (args["lm_model_config"] is not None)

    ## load test datasets
    start = Print(" ".join(['start loading a dataset:', data_cfg.path["test"]]), output)
    test_dataset = load_fasta(data_cfg, "test", alphabet, sanity_check=args["sanity_check"])
    test_dataset = dataset.Embedding_dataset(test_dataset, alphabet, run_cfg, flag_rnn)
    collate_fn = dataset.collate_sequences_for_embedding if flag_rnn else None
    iterator_test = torch.utils.data.DataLoader(test_dataset, run_cfg.batch_size_eval, collate_fn=collate_fn)
    end = Print(" ".join(['loaded', str(len(test_dataset)), 'sequences']), output)
    Print(" ".join(['elapsed time:', str(end - start)]), output, newline=True)

    ## initialize a model
    start = Print('start initializing a model', output)
    models_list = [] # list of lists [model, idx, flag_frz, flag_clip_grad, flag_clip_weight]
    ### model
    if not flag_rnn:                model = plus_tfm.PLUS_TFM(model_cfg)
    elif not flag_lm_model:         model = plus_rnn.PLUS_RNN(model_cfg)
    else:                           model = p_elmo.P_ELMo(model_cfg)
    models_list.append([model, "", True, False, False])
    ### lm_model
    if flag_lm_model:
        lm_model = p_elmo.P_ELMo_lm(lm_model_cfg)
        models_list.append([lm_model, "lm", True, False, False])
    load_models(args, models_list, device, data_parallel, output, tfm_cls=flag_rnn)
    get_loss = plus_rnn.get_embedding if flag_rnn else plus_tfm.get_embedding
    end = Print('end initializing a model', output)
    Print("".join(['elapsed time:', str(end - start)]), output, newline=True)

    ## setup trainer configurations
    start = Print('start setting trainer configurations', output)
    tasks_list = [["", [], []]] # list of lists [idx, metrics_train, metrics_eval]
    trainer = Trainer(models_list, get_loss, run_cfg, tasks_list)
    trainer_args = {"data_parallel": data_parallel}
    end = Print('end setting trainer configurations', output)
    Print("".join(['elapsed time:', str(end - start)]), output, newline=True)

    ## evaluate a model
    start = Print('start embedding protein sequences', output)

    ### evaluate cls
    for b, batch in enumerate(iterator_test):
        batch = [t.to(device) if type(t) is torch.Tensor else t for t in batch]
        trainer.embed(batch, trainer_args)
        if b % 10 == 0: print('# cls {:.1%} loss={:.4f}'.format(
            b / len(iterator_test), trainer.loss_eval), end='\r', file=sys.stderr)
    print(' ' * 150, end='\r', file=sys.stderr)

    trainer.save_embeddings(save_prefix)
    trainer.reset()

    end = Print('end embedding protein sequences', output)
    Print("".join(['elapsed time:', str(end - start)]), output, newline=True)
    output.close()


if __name__ == '__main__':
    main()