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import argparse
def get_parser(parser=None):
if parser is None:
parser = argparse.ArgumentParser()
# Model
#model_arg = parser.add_argument_group('Model')
parser.add_argument('--n_head',
type=int, default=8,
help='GPT number of heads')
parser.add_argument('--n_layer',
type=int, default=12,
help='GPT number of layers')
parser.add_argument('--q_dropout',
type=float, default=0.5,
help='Encoder layers dropout')
parser.add_argument('--d_dropout',
type=float, default=0.1,
help='Decoder layers dropout')
parser.add_argument('--n_embd',
type=int, default=768,
help='Latent vector dimensionality')
parser.add_argument('--fc_h',
type=int, default=512,
help='Fully connected hidden dimensionality')
# Train
#train_arg = parser.add_argument_group('Train')
parser.add_argument('--n_batch',
type=int, default=512,
help='Batch size')
parser.add_argument('--unlike_alpha',
type=float, default=1.0,
help='unlikelihood loss alpha weight')
parser.add_argument('--from_scratch',
action='store_true', default=False,
help='train on qm9 from scratch')
parser.add_argument('--unlikelihood',
action='store_true', default=False,
help='use unlikelihood loss with gpt pretrain')
parser.add_argument('--grad_acc',
type=int, default=1,
help='number of batches to accumulate gradients')
parser.add_argument('--checkpoint_every',
type=int, default=1000,
help='save checkpoint every x iterations')
parser.add_argument('--clip_grad',
type=int, default=50,
help='Clip gradients to this value')
parser.add_argument('--lr_start',
type=float, default=3 * 1e-4,
help='Initial lr value')
parser.add_argument('--lr_end',
type=float, default=3 * 1e-4,
help='Maximum lr weight value')
parser.add_argument('--lr_multiplier',
type=int, default=1,
help='lr weight multiplier')
parser.add_argument('--n_last',
type=int, default=1000,
help='Number of iters to smooth loss calc')
parser.add_argument('--n_jobs',
type=int, default=1,
help='Number of threads')
parser.add_argument('--accelerator',
type=str, default='ddp',
help='The accelerator backend to use (previously known as distributed_backend)')
parser.add_argument('--num_nodes',
type=int, default=1,
help='number of GPU nodes for distributed training')
parser.add_argument('--device',
type=str, default='cuda',
help='Device to run: "cpu" or "cuda:<device number>"')
parser.add_argument('--seed',
type=int, default=12345,
help='Seed')
parser.add_argument('--init_params_from',
type=str, default='',
help='Path to a ckpt used to initialize the parameters if no restart_path is provided')
parser.add_argument('--train_decoder_every',
type=int, default=10,
help='Optimize decoder params every n batches')
parser.add_argument('--lr_decoder',
type=float, default=1e-4,
help='Learning rate for decoder part')
parser.add_argument("--local_rank", type=int, default=-1, help="local_rank for distributed training on gpus")
parser.add_argument('--gpu', default=None, type=int, help='GPU id to use.')
parser.add_argument('--dist-backend', default='nccl', type=str, help='distributed backend')
parser.add_argument('--save_checkpoint_path', default='/data', help='checkpoint saving path')
parser.add_argument('--load_checkpoint_path', default='', help='checkpoint loading path')
#common_arg = parser.add_argument_group('Common')
parser.add_argument('--vocab_load',
type=str, required=False,
help='Where to load the vocab')
parser.add_argument('--n_samples',
type=int, required=False,
help='Number of samples to sample')
parser.add_argument('--gen_save',
type=str, required=False,
help='Where to save the gen molecules')
parser.add_argument("--max_len",
type=int, default=100,
help="Max of length of SMILES")
parser.add_argument('--train_load',
type=str, required=False,
help='Where to load the model')
parser.add_argument('--val_load',
type=str, required=False,
help='Where to load the model')
parser.add_argument('--n_workers',
type=int, required=False, default=1,
help='Where to load the model')
#beam search hyper parameters
parser.add_argument('--beam_size', type=int, default=0,
help="Number of beams to generate")
parser.add_argument('--num_seq_returned', type=int, default=0,
help="number of beams to be returned (must be <= beam_size")
parser.add_argument('--min_len', type=int, default=1,
help="minimum length to be generated")
parser.add_argument('--nucleus_thresh', type=float, default=.9,
help="nucleus sampling threshold")
parser.add_argument('--finetune_path',
type=str, default="",
help='path to trainer file to continue training')
parser.add_argument('--restart_path',
type=str, default="",
help='path to trainer file to continue training')
parser.add_argument('--data_path',
type=str, default="",
help='path to pubchem file')
parser.add_argument('--pretext_size',
type=int, default=0,
help='number of k-mers to pretext')
parser.add_argument('--model_save_dir',
type=str, required=False, default='./models_dump/',
help='Where to save the models/log/config/vocab')
parser.add_argument('--model_save',
type=str, required=False, default='model.pt',
help='Where to save the model')
#parser.add_argument('--save_frequency',
# type=int, default=20,
# help='How often to save the model')
parser.add_argument('--num_epoch',
type=int, default=1,
help='number of epochs to train')
#parser.add_argument('--num_iter',
# type=int, default=-1,
# help='how many itersations per epoch (for unlikelihood tuning)')
parser.add_argument('--log_file',
type=str, required=False,
help='Where to save the log')
parser.add_argument('--tb_loc',
type=str, required=False,
help='Where to save the tensorflow location')
parser.add_argument('--config_save',
type=str, required=False,
help='Where to save the config')
parser.add_argument('--vocab_save',
type=str,
help='Where to save the vocab')
# resume_arg = parser.add_argument_group('Resume')
parser.add_argument('--debug',
default=False, action='store_true',
help='do not erase cache at end of program')
parser.add_argument('--fast_dev_run',
default=False,
help='This flag runs a “unit test” by running n if set to n (int) else 1 if set to True training and validation batch(es).')
parser.add_argument('--freeze_model',
default=False, action='store_true',
help='freeze weights of bert model during fine tuning')
parser.add_argument('--resume',
default=False, action='store_true',
help='Resume from a saved model')
parser.add_argument('--rotate',
default=False, action='store_true',
help='use rotational relative embedding')
parser.add_argument('--model_load',
type=str, required=False,
help='Where to load the model')
parser.add_argument('--root_dir',
type=str, required=False, default='.',
help='location of root dir')
parser.add_argument('--config_load',
type=str, required=False,
help='Where to load the config')
parser.add_argument('--gpus',
type=int, required=False, default=1,
help='number of gpus to use')
#parser.add_argument('--start_epoch',
# type=int, required=False, default=0,
# help='Where to load the config')
parser.add_argument('--model_arch',
type=str, required=False,
help='used to teack model arch in params')
parser.add_argument('--eval_every',
type=int, default=50000,
help='run evaluation every x iterations')
parser.add_argument('--num_feats',
type=int, required=False, default=32,
help='number of random reatures for FAVOR+')
parser.add_argument('--max_epochs',
type=int, required=False, default=1,
help='max number of epochs')
# debug() FINE TUNEING
# parser.add_argument('--save_dir', type=str, required=True)
parser.add_argument('--mode',
type=str, default='cls',
help='type of pooling to use')
parser.add_argument("--dataset_length", type=int, default=None, required=False)
parser.add_argument("--num_workers", type=int, default=0, required=False)
parser.add_argument("--dropout", type=float, default=0.1, required=False)
#parser.add_argument("--dims", type=int, nargs="*", default="", required=False)
parser.add_argument(
"--smiles_embedding",
type=str,
default="/dccstor/medscan7/smallmolecule/runs/ba-predictor/small-data/embeddings/protein/ba_embeddings_tanh_512_2986138_2.pt",
)
# parser.add_argument("--train_pct", type=str, required=False, default="95")
#parser.add_argument("--aug", type=int, required=True)
parser.add_argument("--dataset_name", type=str, required=False, default="sol")
parser.add_argument("--measure_name", type=str, required=False, default="measure")
parser.add_argument("--smi_ted_version", type=str, required=True, default="v1")
#parser.add_argument("--emb_type", type=str, required=True)
#parser.add_argument("--checkpoints_folder", type=str, required=True)
#parser.add_argument("--results_dir", type=str, required=True)
#parser.add_argument("--patience_epochs", type=int, required=True)
parser.add_argument(
"--data_root",
type=str,
required=False,
default="/dccstor/medscan7/smallmolecule/runs/ba-predictor/small-data/affinity",
)
# parser.add_argument("--use_bn", type=int, default=0)
parser.add_argument("--use_linear", type=int, default=0)
parser.add_argument("--lr", type=float, default=0.001)
# parser.add_argument("--weight_decay", type=float, default=5e-4)
# parser.add_argument("--val_check_interval", type=float, default=1.0)
parser.add_argument("--batch_size", type=int, default=64)
return parser
def parse_args():
parser = get_parser()
args = parser.parse_args()
return args
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