-
Notifications
You must be signed in to change notification settings - Fork 63
/
run.py
179 lines (160 loc) · 7.76 KB
/
run.py
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
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()