测试文件 #13
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你问的可能是测试函数，在源文件main.py中没有执行test()函数，你可以在最后加上一句test()来执行test函数。 |
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为啥测试文件执行不了 |
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***@***.***这几天在期末考试，回复可能不及时。
import argparse
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from data.data_loader import Dataset_ECG, Dataset_Dhfm, Dataset_Solar, Dataset_Wiki,Dataset_Electricity
from model.FourierGNN import FGN
import time
import os
import numpy as np
from utils.utils import save_model, load_model, evaluate
# main settings can be seen in markdown file (README.md)
parser = argparse.ArgumentParser(description='fourier graph network for multivariate time series forecasting')
parser.add_argument('--data', type=str, default='metr', help='data set') #ECG
parser.add_argument('--feature_size', type=int, default='207', help='feature size') #140
parser.add_argument('--seq_length', type=int, default=12, help='inout length') #每个窗口的T是固定的
parser.add_argument('--pre_length', type=int, default=12, help='predict length')
parser.add_argument('--embed_size', type=int, default=20, help='hidden dimensions') #128
parser.add_argument('--hidden_size', type=int, default=256, help='hidden dimensions')
parser.add_argument('--train_epochs', type=int, default=1, help='train epochs') #100
parser.add_argument('--batch_size', type=int, default=32, help='input data batch size')
parser.add_argument('--learning_rate', type=float, default=0.00001, help='optimizer learning rate')
parser.add_argument('--exponential_decay_step', type=int, default=5)
parser.add_argument('--validate_freq', type=int, default=1)
parser.add_argument('--early_stop', type=bool, default=False)
parser.add_argument('--decay_rate', type=float, default=0.5)
parser.add_argument('--train_ratio', type=float, default=0.7)
parser.add_argument('--val_ratio', type=float, default=0.2)
parser.add_argument('--device', type=str, default='cuda:0', help='device')
args = parser.parse_args()
print(f'Training configs: {args}')
# create output dir
result_train_file = os.path.join('output', args.data, 'train')
result_test_file = os.path.join('output', args.data, 'test')
if not os.path.exists(result_train_file):
os.makedirs(result_train_file)
if not os.path.exists(result_test_file):
os.makedirs(result_test_file)
# data set
data_parser = {
'traffic':{'root_path':'data/traffic.npy', 'type':'0'},
'ECG':{'root_path':'data/ECG_data.csv', 'type':'1'},
'COVID':{'root_path':'data/covid.csv', 'type':'1'},
'electricity':{'root_path':'data/electricity.txt', 'type':'1'},
'solar':{'root_path':'/data/solar', 'type':'1'},
'metr':{'root_path':'data/metr.csv', 'type':'1'},
'wiki':{'root_path':'data/wiki.csv', 'type':'1'},
}
# data process
if args.data in data_parser.keys():
data_info = data_parser[args.data] #'root_path':'data/electricity.csv', 'type':'1'
data_dict = {
'ECG': Dataset_ECG,
'COVID': Dataset_ECG,
'traffic': Dataset_Dhfm,
'solar': Dataset_Solar,
'wiki': Dataset_Wiki,
'electricity': Dataset_Electricity,
'metr': Dataset_ECG
}
Data = data_dict[args.data] #Dataset_Electricity
# train val test
train_set = Data(root_path=data_info['root_path'], flag='train', seq_len=args.seq_length, pre_len=args.pre_length, type=data_info['type'], train_ratio=args.train_ratio, val_ratio=args.val_ratio)
test_set = Data(root_path=data_info['root_path'], flag='test', seq_len=args.seq_length, pre_len=args.pre_length, type=data_info['type'], train_ratio=args.train_ratio, val_ratio=args.val_ratio)
val_set = Data(root_path=data_info['root_path'], flag='val', seq_len=args.seq_length, pre_len=args.pre_length, type=data_info['type'], train_ratio=args.train_ratio, val_ratio=args.val_ratio)
train_dataloader = DataLoader(
train_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False
)
test_dataloader = DataLoader(
test_set,
batch_size=args.batch_size,
shuffle=False,
num_workers=0,
drop_last=False
)
val_dataloader = DataLoader(
val_set,
batch_size=args.batch_size,
shuffle=True,
num_workers=0,
drop_last=False
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = FGN(pre_length=args.pre_length, embed_size=args.embed_size, feature_size=args.feature_size, seq_length=args.seq_length, hidden_size=args.hidden_size)
my_optim = torch.optim.RMSprop(params=model.parameters(), lr=args.learning_rate, eps=1e-08)
my_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer=my_optim, gamma=args.decay_rate)
forecast_loss = nn.MSELoss(reduction='mean').to(device)
def validate(model, vali_loader):
model.eval()
cnt = 0
loss_total = 0
preds = []
trues = []
for i, (x, y) in enumerate(vali_loader):
cnt += 1
y = y.float().to("cuda:0")
x = x.float().to("cuda:0")
forecast = model(x)
y = y.permute(0, 2, 1).contiguous()
loss = forecast_loss(forecast, y)
loss_total += float(loss)
forecast = forecast.detach().cpu().numpy() # .squeeze()
y = y.detach().cpu().numpy() # .squeeze()
preds.append(forecast)
trues.append(y)
preds = np.concatenate(preds, axis=0)
trues = np.concatenate(trues, axis=0)
score = evaluate(trues, preds)
print(f'RAW : MAPE {score[0]:7.9%}; MAE {score[1]:7.9f}; RMSE {score[2]:7.9f}; RSE {score[3]:7.9f}; CORR {score[4]:7.9f}')
model.train()
return loss_total/cnt
def test():
result_test_file = 'output/'+args.data+'/train'
model = load_model(result_test_file, epoch)
#print(model)
#print(type(model))
model.eval()
preds = []
trues = []
sne = []
for index, (x, y) in enumerate(test_dataloader):
y = y.float().to("cuda:0")
x = x.float().to("cuda:0")
forecast = model(x)
y = y.permute(0, 2, 1).contiguous()
forecast = forecast.detach().cpu().numpy() # .squeeze()
y = y.detach().cpu().numpy() # .squeeze()
preds.append(forecast)
trues.append(y)
preds = np.concatenate(preds, axis=0)
trues = np.concatenate(trues, axis=0)
score = evaluate(trues, preds)
print(f'RAW : MAPE {score[0]:7.9%}; MAE {score[1]:7.9f}; RMSE {score[2]:7.9f}; RSE {score[3]:7.9f}; CORR {score[4]:7.9f}')
if __name__ == '__main__':
for epoch in range(args.train_epochs):
epoch_start_time = time.time()
model.train()
loss_total = 0
cnt = 0
for index, (x, y) in enumerate(train_dataloader):
cnt += 1
y = y.float().to("cuda:0")
x = x.float().to("cuda:0")
forecast = model(x)
y = y.permute(0, 2, 1).contiguous()
loss = forecast_loss(forecast, y)
loss.backward()
my_optim.step()
loss_total += float(loss)
if (epoch + 1) % args.exponential_decay_step == 0:
my_lr_scheduler.step()
if (epoch + 1) % args.validate_freq == 0:
val_loss = validate(model, val_dataloader)
print('| end of epoch {:3d} | time: {:5.2f}s | train_total_loss {:5.4f} | val_loss {:5.4f}'.format(
epoch, (time.time() - epoch_start_time), loss_total / cnt, val_loss))
save_model(model, result_train_file, epoch)
test()
…________________________________
发件人: zhurong-dot ***@***.***>
发送时间: 2024年12月30日 17:03
收件人: aikunyi/FourierGNN ***@***.***>
抄送: Solaaaaa ***@***.***>; Comment ***@***.***>
主题: Re: [aikunyi/FourierGNN] 测试文件 (Issue #13)
为啥测试文件执行不了
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请问哪个是测试文件
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