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import argparse
import uuid
import logging
import time
import os
import sys
from utils.logger import create_exp_dir
import glob
class ArgsInit(object):
def __init__(self):
parser = argparse.ArgumentParser(description="DeeperGCN")
# ===================== DATASET =====================
parser.add_argument(
"--seed", type=int,
default=1, help="Seed for numpy and torch"
)
parser.add_argument(
"--num_workers", type=int,
default=0, help="number of workers (default: 0)"
)
parser.add_argument(
"--batch_size", type=int,
default=5120, help="input batch size for training (default: 5120)",
)
parser.add_argument(
"--feature", type=str,
default="full", help="two options: full or simple"
)
parser.add_argument(
"--add_virtual_node",
action="store_true"
)
# ===================== TRAIN & EVAL =====================
parser.add_argument(
"--use_gpu", action="store_true"
)
parser.add_argument(
"--device", type=int,
default=0, help="which gpu to use if any (default: 0)"
)
parser.add_argument(
"--epochs", type=int,
default=20, help="number of epochs to train (default: 300)",
)
parser.add_argument(
"--lr", type=float,
default=5e-5, help="learning rate set for optimizer (default: 5e-5)"
)
parser.add_argument(
"--dropout", type=float,
default=0.2, help="Dropout rate layer (default: 0.2)"
)
# model
parser.add_argument(
"--num_layers",
type=int,
default=20,
help="the number of layers of the networks",
)
parser.add_argument(
"--mlp_layers",
type=int,
default=3,
help="the number of layers of mlp in conv",
)
parser.add_argument(
"--hidden_channels",
type=int,
default=128,
help="the dimension of embeddings of nodes and edges",
)
parser.add_argument(
"--block",
default="res+",
type=str,
help="graph backbone block type {res+, res, dense, plain}",
)
parser.add_argument("--conv", type=str, default="gen", help="the type of GCNs")
parser.add_argument(
"--gcn_aggr",
type=str,
default="softmax",
help="the aggregator of GENConv [mean, max, add, softmax, softmax_sg, power]",
)
parser.add_argument(
"--norm", type=str, default="batch", help="the type of normalization layer"
)
parser.add_argument(
"--num_tasks", type=int, default=1, help="the number of prediction tasks"
)
# learnable parameters
parser.add_argument(
"--t", type=float, default=1.0, help="the temperature of SoftMax"
)
parser.add_argument(
"--p", type=float, default=1.0, help="the power of PowerMean"
)
parser.add_argument("--learn_t", action="store_true")
parser.add_argument("--learn_p", action="store_true")
# message norm
parser.add_argument("--msg_norm", action="store_true")
parser.add_argument("--learn_msg_scale", action="store_true")
# encode edge in conv
parser.add_argument("--conv_encode_edge", action="store_true")
# graph pooling type
parser.add_argument(
"--graph_pooling", type=str, default="mean", help="graph pooling method"
)
# save model
parser.add_argument(
"--model_save_path",
type=str,
default="model_ckpt",
help="the directory used to save models",
)
parser.add_argument("--save", type=str, default="EXP", help="experiment name")
# load pre-trained model
parser.add_argument(
"--model_load_init_path",
type=str,
default="/media/SSD5/pruiz/home/Best_Models",
help="the directory to load adversarial weights",
)
parser.add_argument(
"--model_load_prot_init_path",
type=str,
default="/media/SSD5/pruiz/home/Best_Models",
help="the directory to load protein weights",
)
parser.add_argument(
"--model_load_path",
type=str,
default="Checkpoint_Last_model.pth",
help="the path of pre-trained model",
)
parser.add_argument(
"--resume",
action="store_true",
default=False,
help="resume training from a previous model",
)
# data partition
parser.add_argument("--cross_val", type=int, default=0, help="Data partition")
# Task
parser.add_argument(
"--task_type", type=str, default="classification", help="Choose task type"
)
parser.add_argument(
"--binary",
action="store_true",
default=False,
help="Performed de binary task",
)
parser.add_argument(
"--balanced_loader",
action="store_true",
default=False,
help="Balance the dataloader",
)
parser.add_argument(
"--target", type=str, default=None, help="Target for the binary task"
)
parser.add_argument(
"--nclasses", type=int, default=102, help="number of target classes"
)
parser.add_argument(
"--num_features",
type=int,
default=2,
help="Num of features used for simple classification",
)
# PROTEIN MODEL
parser.add_argument(
"--LMPM",
action="store_true",
default=False,
help="Initialize training the LM and PM jointly.",
)
parser.add_argument(
"--PLANET",
action="store_true",
default=False,
help="Initialize training PLANET.",
)
parser.add_argument(
"--use_prot", action="store_true", default=False, help="Use protein info"
)
parser.add_argument(
"--freeze_molecule",
action="store_true",
default=False,
help="Whether to freeze molecule network",
)
parser.add_argument(
"--num_layers_prot",
type=int,
default=20,
help="the number of layers of the networks",
)
parser.add_argument(
"--mlp_layers_prot",
type=int,
default=3,
help="the number of layers of mlp in conv",
)
parser.add_argument(
"--hidden_channels_prot",
type=int,
default=128,
help="the dimension of embeddings of nodes and edges",
)
parser.add_argument("--msg_norm_prot", action="store_true", default=False)
parser.add_argument(
"--learn_msg_scale_prot", action="store_true", default=False
)
parser.add_argument(
"--conv_encode_edge_prot", action="store_true", default=False
)
parser.add_argument("--use_prot_metadata", action="store_true", default=False)
parser.add_argument(
"--num_metadata",
type=int,
default=240,
help="Number of metadata of the protein.",
)
parser.add_argument(
"--scalar",
action="store_true",
default=False,
help="Use same multiplier factor for all metadata",
)
# CONCATENATION MULTIPLIER
parser.add_argument(
"--multi_concat",
action="store_true",
default=False,
help="Use a multiplier to concant info",
)
# CONCATENATION MLP
parser.add_argument(
"--MLP",
action="store_true",
default=False,
help="Use a multiplier to concant info",
)
# ADVERSARIAL AUGMENTATION TRAINING
parser.add_argument(
"--init_adv_training",
action="store_true",
default=False,
help="Initialize training with adversarial molecules",
)
parser.add_argument(
"--advs",
action="store_true",
default=False,
help="Training with adversarial molecules",
)
parser.add_argument(
"--saliency",
action="store_true",
default=False,
help="Allow backpropagation through atom features.",
)
parser.add_argument(
"--inference_path",
type=str,
help='path model for inference',
default=None
)
parser.add_argument(
"--target_checkpoint_path",
type=str,
help='path to the target checkpoints',
default=None
)
parser.add_argument(
"--target_list",
type=str,
help='path to the target list mappings',
default=None
)
parser.add_argument(
"--input_file_smiles",
type=str,
help='path input file with smiles for inference',
default=None
)
parser.add_argument(
"--output_file",
type=str,
help='path output file with predictions',
default=None
)
self.args = parser.parse_args()
def save_exp(self):
self.args.save = "{}/Fold{}".format(self.args.save, str(self.args.cross_val))
self.args.save = "log/{}".format(self.args.save)
self.args.model_save_path = os.path.join(
self.args.save, self.args.model_save_path
)
create_exp_dir(self.args.save, scripts_to_save=glob.glob("*.py"))
log_format = "%(asctime)s %(message)s"
logging.basicConfig(
stream=sys.stdout,
level=logging.INFO,
format=log_format,
datefmt="%m/%d %I:%M:%S %p",
)
fh = logging.FileHandler(os.path.join(self.args.save, "log.txt"))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
return self.args
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