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args.py
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args.py
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import argparse
import sys
import yaml
from configs import parser as _parser
args = None
def parse_arguments():
parser = argparse.ArgumentParser(description="PyTorch SBT Training")
# General Config
parser.add_argument(
"--data", help="path to dataset base directory", default="/s/lovelace/c/nobackup/iray/mgorb/luffy-data"
#default="/s/luffy/b/nobackup/mgorb/data"
)
parser.add_argument("--optimizer", help="Which optimizer to use", default="adam")
parser.add_argument("--entity", help="num of entity", type=int, default=None)
parser.add_argument("--dmodel", help="num of entity", type=int, default=None)
parser.add_argument("--dataset", help="dataset", type=str, default="")
parser.add_argument(
"--config", help="Config file to use (see configs dir)", default=None
)
parser.add_argument(
"--log-dir", help="Where to save the runs. If None use ./runs", default=None
)
parser.add_argument(
"--window_size", help="WS", type=int,default=None
)
parser.add_argument(
"--rerand_epoch_freq", type=int,default=None
)
parser.add_argument(
"--rerand_rate", type=float,default=None
)
parser.add_argument(
"-j",
"--workers",
default=20,
type=int,
metavar="N",
help="number of data loading workers (default: 20)",
)
parser.add_argument(
"--epochs",
default=90,
type=int,
metavar="N",
help="number of total epochs to run",
)
parser.add_argument(
"-b",
"--batch-size",
default=64,
type=int,
metavar="N",
help="mini-batch size (default: 256), this is the total "
"batch size of all GPUs on the current node when "
"using Data Parallel or Distributed Data Parallel",
)
parser.add_argument(
"--lr",
"--learning-rate",
default=0.001,
type=float,
metavar="LR",
help="initial learning rate",
dest="lr",
)
parser.add_argument("--model_runs", help="number of times to run model", default=5, type=int)
parser.add_argument("--forecast", help="forecast", default=False)
parser.add_argument("--es_epochs", help="earlystopping epochs", default=None)
parser.add_argument("--scale_fan", default=False, type=bool)
parser.add_argument("--lin_prune_rate", default=1, type=float)
parser.add_argument("--attention_prune_rate", default=1, type=float)
parser.add_argument("--scheduler", default=False, type=bool)
parser.add_argument("--mode", default="fan_in", help="Weight initialization mode")
parser.add_argument("--nonlinearity", default="relu", help="Nonlinearity used by initialization")
parser.add_argument("--has_src_mask", default=False, help="Weight initialization mode")
parser.add_argument("--ablation", default=False, type=bool)
parser.add_argument("--forecasting_steps", default=1, type=int)
parser.add_argument(
"-p",
"--print-freq",
default=50,
type=int,
metavar="N",
help="print frequency (default: 10)",
)
parser.add_argument("--num-classes", default=10, type=int)
parser.add_argument(
"--resume",
default="",
type=str,
metavar="PATH",
help="path to latest checkpoint (default: none)",
)
parser.add_argument(
"-e",
"--evaluate",
dest="evaluate",
action="store_true",
help="evaluate model on validation set",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
default=None,
type=str,
help="use pre-trained model",
)
parser.add_argument(
"--gpu", default=1, type=int, help="gpu id"
)
parser.add_argument(
"--name", default=None, type=str, help="Experiment name to append to filepath"
)
parser.add_argument(
"--save_every", default=-1, type=int, help="Save every ___ epochs"
)
parser.add_argument(
"--conv-type", type=str, default=None, help="What kind of sparsity to use"
)
parser.add_argument(
"--freeze-weights",
action="store_true",
help="Whether or not to train only subnet (this freezes weights)",
)
parser.add_argument(
"--weight_init", default=None, help="Weight initialization modifications"
)
parser.add_argument(
"--score_init", default=None, help="Weight initialization modifications"
)
parser.add_argument(
"--weight_seed", default=0, help="Weight initialization modifications"
)
parser.add_argument(
"--score_seed", default=0, help="Weight initialization modifications"
)
parser.add_argument(
"--seed", default=0, type=int, help="seed for initializing training. "
)
args = parser.parse_args()
# Allow for use from notebook without config file
if len(sys.argv) > 1:
get_config(args)
return args
def get_config(args):
# get commands from command line
override_args = _parser.argv_to_vars(sys.argv)
# load yaml file
yaml_txt = open(args.config).read()
# override args
loaded_yaml = yaml.load(yaml_txt, Loader=yaml.FullLoader)
for v in override_args:
loaded_yaml[v] = getattr(args, v)
print(f"=> Reading YAML config from {args.config}")
args.__dict__.update(loaded_yaml)
def run_args():
global args
if args is None:
args = parse_arguments()
run_args()