File size: 6,983 Bytes
95456ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import sys
import os
import argparse
import datetime
import time
from ScanDL2.CONSTANTS import (
COMPLETE_SCANDL_MODULE_TRAIN_PATH_BSC,
COMPLETE_SCANDL_MODULE_TRAIN_PATH_CELER,
COMPLETE_SCANDL_MODULE_TRAIN_PATH_EMTEC,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="training args.")
parser.add_argument(
"--noise_schedule",
type=str,
default="sqrt",
choices=["linear", "cosine", "sqrt", "trunc_cos", "trunc_lin", "pw_lin"],
help="the distribution of noises",
)
parser.add_argument("--diff_steps", type=int, default=2000, help="diffusion steps")
parser.add_argument(
"--schedule_sampler",
type=str,
default="lossaware",
choices=["uniform", "lossaware", "fixstep"],
help="schedule sampler of timesteps",
)
parser.add_argument("--seq_len", type=int, default=128, help="max len of input sequence")
parser.add_argument(
"--hidden_t_dim", type=int, default=128, help="hidden size of time embedding"
)
parser.add_argument(
"--hidden_dim",
type=int,
default=768,
help="hidden size of word embedding and transformer hidden size",
)
parser.add_argument("--learning_steps", type=int, default=60000, help="total steps of learning")
parser.add_argument("--save_interval", type=int, default=2000, help="save step")
parser.add_argument(
"--resume_checkpoint",
type=str,
default="none",
help="path to resume checkpoint, like xxx/xxx.pt",
)
parser.add_argument("--lr", type=float, default=1e-04, help="learning rate")
parser.add_argument("--bsz", type=int, default=64, help="batch size")
parser.add_argument("--microbatch", type=int, default=64, help="microbatch size")
parser.add_argument("--seed", type=int, default=101, help="random seed")
parser.add_argument(
"--config_name", type=str, default="bert-base-cased", help="config of pre-trained models"
)
parser.add_argument(
"--vocab",
type=str,
default="bert",
help="use bert vocab or load external vocab dict if given as path",
)
parser.add_argument(
"--use_plm_init",
type=str,
default="no",
choices=["no", "bert"],
help="load init parameter from the pre-trained lm",
)
parser.add_argument("--log_interval", type=int, default=200, required=False)
parser.add_argument("--eval_interval", type=int, default=500, required=False)
parser.add_argument(
"--notes",
type=str,
default="-",
help="as training notes or specifical args",
required=False,
)
parser.add_argument("--app", type=str, default="", help="other input args")
# further arguments
parser.add_argument(
"--data_split_criterion",
type=str,
help="how to split the data into train, val, test:"
" scanpath (random), reader, sentence, combined",
required=False,
default="reader",
)
parser.add_argument(
"--num_transformer_layers",
type=int,
default=4,
required=False,
help="the number of encoder layers",
)
parser.add_argument(
"--num_transformer_heads",
type=int,
default=8,
required=False,
help="the number of attention heads",
)
parser.add_argument(
"--celer_only_L1",
required=False,
action="store_true",
help="if given, all celer speakers are used" "as opposed to only L1 speakers",
)
parser.add_argument(
"--corpus",
type=str,
help="the eye-tracking corpus to use for training.",
required=False,
default="celer",
choices=["celer", "zuco", "emtec", "bsc"],
)
parser.add_argument(
"--inference",
required=False,
default="cv",
choices=["cv", "zuco", "in-corpus"],
help="if zuco, inference is performed on zuco while trained on celer; if cv, inference is"
"done in k-fold Cross-Validation; if in-corpus, the training corpus is simply split into"
"train and test.",
)
parser.add_argument(
"--mask_padding",
action="store_false",
required=False,
help="if given, padding will not be masked in transformer attention. if not given, mask_padding"
"is stored as True; padding will be masked.",
)
parser.add_argument(
"--load_train_data",
type=str,
default="-",
help="if given, previously saved train data is loaded from the specified checkpoint path",
)
args = parser.parse_args()
# set working dir to the upper folder
abspath = os.path.abspath(sys.argv[0])
dname = os.path.dirname(abspath)
dname = os.path.dirname(dname)
os.chdir(dname)
if args.corpus == "emtec":
model_file = COMPLETE_SCANDL_MODULE_TRAIN_PATH_EMTEC
elif args.corpus == "bsc":
model_file = COMPLETE_SCANDL_MODULE_TRAIN_PATH_BSC
elif args.corpus == "celer":
model_file = COMPLETE_SCANDL_MODULE_TRAIN_PATH_CELER
else:
raise NotImplementedError(f"Corpus {args.corpus} not implemented.")
if int(os.environ["LOCAL_RANK"]) == 0:
if not os.path.exists(model_file):
os.makedirs(model_file)
COMMANDLINE = (
f"TOKENIZERS_PARALLELISM=FALSE "
f"python -m scripts.sp_train "
f"--checkpoint_path {model_file} "
f"--vocab {args.vocab} "
f"--use_plm_init {args.use_plm_init} "
f"--lr {args.lr} "
f"--batch_size {args.bsz} "
f"--microbatch {args.microbatch} "
f"--diffusion_steps {args.diff_steps} "
f"--noise_schedule {args.noise_schedule} "
f"--schedule_sampler {args.schedule_sampler} "
f"--seq_len {args.seq_len} "
f"--resume_checkpoint {args.resume_checkpoint} "
f"--hidden_t_dim {args.hidden_t_dim} "
f"--seed {args.seed} "
f"--hidden_dim {args.hidden_dim} "
f"--learning_steps {args.learning_steps} "
f"--save_interval {args.save_interval} "
f"--config_name {args.config_name} "
f"--notes {args.notes} "
f"--data_split_criterion {args.data_split_criterion} "
f"--num_transformer_layers {args.num_transformer_layers} "
f"--num_transformer_heads {args.num_transformer_heads} "
f"--corpus {args.corpus} "
f"--inference {args.inference} "
f"--load_train_data {args.load_train_data}"
)
if int(os.environ["LOCAL_RANK"]) == 0:
with open(os.path.join(model_file, "saved_bash.sh"), "w") as f:
print(COMMANDLINE, file=f)
print(COMMANDLINE)
os.system(COMMANDLINE)
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