main_cat.py

import argparse
import os
import torch

from exp.exp_cat import Exp_CAT

parser = argparse.ArgumentParser(description='Content-Aware Transformer')
parser.add_argument('--data', type=str, required=True, default='HDFS', help='data')
parser.add_argument('--root_path', type=str, default='./data/HDFS/', help='root path of the data file')
parser.add_argument('--checkpoints', type=str, default='./checkpoints/', help='location of model checkpoints')
parser.add_argument('--seq_len', type=int, default=4, help='input sequence length of CAT encoder')
parser.add_argument('--label_len', type=int, default=4, help='start token length of CAT decoder')
parser.add_argument('--pred_len', type=int, default=8, help='prediction sequence length')
# CAT decoder input: concat[start token series(label_len), zero padding series(pred_len)]
parser.add_argument('--enc_in', type=int, default=1, help='encoder input size')
parser.add_argument('--dec_in', type=int, default=1, help='decoder input size')
parser.add_argument('--c_out', type=int, default=1, help='output size')
parser.add_argument('--d_model', type=int, default=32, 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('--d_layers', type=int, default=1, help='num of decoder layers')
parser.add_argument('--d_ff', type=int, default=2048, help='dimension of fcn')
parser.add_argument('--factor', type=int, default=5, help='probsparse attn factor')
parser.add_argument('--padding', type=int, default=0, help='padding type')
parser.add_argument('--distil', action='store_false', help='whether to use distilling in encoder, using this argument means not using distilling', default=True)
parser.add_argument('--dropout', type=float, default=0.05, help='dropout')
parser.add_argument('--attn', type=str, default='prob', help='attention used in encoder, options:[prob, full]')
parser.add_argument('--embed', type=str, default='timeF', help='time features encoding, options:[timeF, fixed, learned]')
parser.add_argument('--activation', type=str, default='gelu',help='activation')
parser.add_argument('--output_attention', action='store_true', help='whether to output attention in ecoder')
parser.add_argument('--do_predict', action='store_true', help='whether to predict unseen future data')
parser.add_argument('--mix', action='store_false', help='use mix attention in generative decoder', default=True)
parser.add_argument('--cols', type=str, nargs='+', help='certain cols from the data files as the input features')
parser.add_argument('--num_workers', type=int, default=0, help='data loader num workers')
parser.add_argument('--itr', type=int, default=1, help='experiments times')
parser.add_argument('--train_epochs', type=int, default=100, help='train epochs')
parser.add_argument('--batch_size', type=int, default=32, help='batch size of train input data')
parser.add_argument('--patience', type=int, default=3, help='early stopping patience')
parser.add_argument('--learning_rate', type=float, default=0.0001, help='optimizer learning rate')
parser.add_argument('--des', type=str, default='test',help='exp description')
parser.add_argument('--loss', type=str, default='mse',help='loss function')
parser.add_argument('--lradj', type=str, default='type1',help='adjust learning rate')
parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision training', default=False)
parser.add_argument('--inverse', action='store_true', help='inverse output data', default=False)
parser.add_argument('--use_gpu', type=bool, default=True, help='use gpu')
parser.add_argument('--gpu', type=int, default=0, help='gpu')
parser.add_argument('--use_multi_gpu', action='store_true', help='use multiple gpus', default=False)
parser.add_argument('--devices', type=str, default='0,1,2,3',help='device ids of multile gpus')

args = parser.parse_args()

args.use_gpu = True if torch.cuda.is_available() and args.use_gpu else False

if args.use_gpu and args.use_multi_gpu:
    args.devices = args.devices.replace(' ','')
    device_ids = args.devices.split(',')
    args.device_ids = [int(id_) for id_ in device_ids]
    args.gpu = args.device_ids[0]

print('Args in experiment:')
print(args)

Exp = Exp_CAT

for ii in range(args.itr):
    # setting record of experiments
    setting = '{}_sl{}_ll{}_pl{}_dm{}_nh{}_el{}_dl{}_df{}_at{}_fc{}_eb{}_dt{}_mx{}_{}_{}'.format(args.data, 
                args.seq_len, args.label_len, args.pred_len,
                args.d_model, args.n_heads, args.e_layers, args.d_layers, args.d_ff, args.attn, args.factor, 
                args.embed, args.distil, args.mix, args.des, ii)
    
    exp = Exp(args) # set experiments
    print('>>>>>>>start training : {}>>>>>>>>>>>>>>>>>>>>>>>>>>'.format(setting))
    exp.train(setting)
    
    print('>>>>>>>testing : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
    exp.test(setting)

    if args.do_predict:
        print('>>>>>>>predicting : {}<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<'.format(setting))
        exp.predict(setting, True)

    torch.cuda.empty_cache()