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run.py
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run.py
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import argparse
import torch
from exp.exp_sup import Exp_All_Task as Exp_All_Task_SUP
import random
import numpy as np
import wandb
from utils.ddp import is_main_process, init_distributed_mode
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='UniTS supervised training')
# basic config
parser.add_argument('--task_name', type=str, required=False, default='ALL_task',
help='task name')
parser.add_argument('--is_training', type=int,
required=True, default=1, help='status')
parser.add_argument('--model_id', type=str, required=True,
default='test', help='model id')
parser.add_argument('--model', type=str, required=True, default='UniTS',
help='model name')
# data loader
parser.add_argument('--data', type=str, required=False,
default='All', help='dataset type')
parser.add_argument('--features', type=str, default='M',
help='forecasting task, options:[M, S, MS]; M:multivariate predict multivariate, S:univariate predict univariate, MS:multivariate predict univariate')
parser.add_argument('--target', type=str, default='OT',
help='target feature in S or MS task')
parser.add_argument('--freq', type=str, default='h',
help='freq for time features encoding, options:[s:secondly, t:minutely, h:hourly, d:daily, b:business days, w:weekly, m:monthly], you can also use more detailed freq like 15min or 3h')
parser.add_argument('--task_data_config_path', type=str,
default='exp/all_task.yaml', help='root path of the task and data yaml file')
parser.add_argument('--subsample_pct', type=float,
default=None, help='subsample percent')
# ddp
parser.add_argument('--local-rank', type=int, help='local rank')
parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up
distributed training; see https://pytorch.org/docs/stable/distributed.html""")
parser.add_argument('--num_workers', type=int, default=0,
help='data loader num workers')
parser.add_argument("--memory_check", action="store_true", default=True)
parser.add_argument("--large_model", action="store_true", default=True)
# optimization
parser.add_argument('--itr', type=int, default=1, help='experiments times')
parser.add_argument('--train_epochs', type=int,
default=10, help='train epochs')
parser.add_argument("--prompt_tune_epoch", type=int, default=0)
parser.add_argument('--warmup_epochs', type=int,
default=0, help='warmup epochs')
parser.add_argument('--batch_size', type=int, default=32,
help='batch size of train input data')
parser.add_argument('--acc_it', type=int, default=1,
help='acc iteration to enlarge batch size')
parser.add_argument('--learning_rate', type=float,
default=0.0001, help='optimizer learning rate')
parser.add_argument('--min_lr', type=float, default=None,
help='optimizer min learning rate')
parser.add_argument('--weight_decay', type=float,
default=0.0, help='optimizer weight decay')
parser.add_argument('--layer_decay', type=float,
default=None, help='optimizer layer decay')
parser.add_argument('--des', type=str, default='test',
help='exp description')
parser.add_argument('--lradj', type=str,
default='supervised', help='adjust learning rate')
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--dropout', type=float, default=0.1, help='dropout')
parser.add_argument('--checkpoints', type=str, default='./checkpoints/',
help='save location of model checkpoints')
parser.add_argument('--pretrained_weight', type=str, default=None,
help='location of pretrained model checkpoints')
parser.add_argument('--debug', type=str,
default='enabled', help='disabled')
parser.add_argument('--project_name', type=str,
default='tsfm-multitask', help='wandb project name')
# model settings
parser.add_argument('--d_model', type=int, default=512,
help='dimension of model')
parser.add_argument('--n_heads', type=int, default=8, help='num of heads')
parser.add_argument('--e_layers', type=int, default=2,
help='num of encoder layers')
parser.add_argument("--share_embedding",
action="store_true", default=False)
parser.add_argument("--patch_len", type=int, default=16)
parser.add_argument("--stride", type=int, default=8)
parser.add_argument("--prompt_num", type=int, default=5)
parser.add_argument('--fix_seed', type=int, default=None, help='seed')
# task related settings
# forecasting task
parser.add_argument('--inverse', action='store_true',
help='inverse output data', default=False)
# inputation task
parser.add_argument('--mask_rate', type=float,
default=0.25, help='mask ratio')
# anomaly detection task
parser.add_argument('--anomaly_ratio', type=float,
default=1.0, help='prior anomaly ratio (%)')
# zero-shot-forecast-new-length
parser.add_argument("--offset", type=int, default=0)
parser.add_argument("--max_offset", type=int, default=0)
parser.add_argument('--zero_shot_forecasting_new_length',
type=str, default=None, help='unify')
args = parser.parse_args()
init_distributed_mode(args)
if args.fix_seed is not None:
random.seed(args.fix_seed)
torch.manual_seed(args.fix_seed)
np.random.seed(args.fix_seed)
print('Args in experiment:')
print(args)
exp_name = '{}_{}_{}_{}_ft{}_dm{}_el{}_{}'.format(
args.task_name,
args.model_id,
args.model,
args.data,
args.features,
args.d_model,
args.e_layers,
args.des)
if int(args.prompt_tune_epoch) != 0:
exp_name = 'Ptune'+str(args.prompt_tune_epoch)+'_'+exp_name
print(exp_name)
if is_main_process():
wandb.init(
name=exp_name,
# set the wandb project where this run will be logged
project=args.project_name,
# track hyperparameters and run metadata
config=args,
mode=args.debug,
)
Exp = Exp_All_Task_SUP
if args.is_training:
for ii in range(args.itr):
# setting record of experiments
setting = '{}_{}_{}_{}_ft{}_dm{}_el{}_{}_{}'.format(
args.task_name,
args.model_id,
args.model,
args.data,
args.features,
args.d_model,
args.e_layers,
args.des, ii)
exp = Exp(args) # set experiments
print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
exp.train(setting)
else:
ii = 0
setting = '{}_{}_{}_{}_ft{}_dm{}_el{}_{}_{}'.format(
args.task_name,
args.model_id,
args.model,
args.data,
args.features,
args.d_model,
args.e_layers,
args.des, ii)
exp = Exp(args) # set experiments
print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
exp.test(setting, load_pretrain=True)
torch.cuda.empty_cache()