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test.py
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test.py
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from torch.nn.modules.module import T
import tool
import data
import models
import train
import informer
from constants import *
import numpy as np
import torch
def test_score():
y = np.random.rand(50, 24)
y_hat = np.random.rand(50, 24)
score = tool.score(y, y_hat)
# y = torch.from_numpy(y)
# y_hat = torch.from_numpy(y_hat)
score1 = tool.evaluate_metrics(y,y_hat)
print(score, score1)
def test_read_data():
dataloader, train_loader = data.read_data()
for i, (y, x) in enumerate(dataloader):
print(y.shape, x.shape)
break
dataloader, train_loader = data.read_data(mean=False)
for y, x in dataloader:
print(y.shape, x.shape)
break
def test_read_test_data():
datas, label = data.read_test_data()
print(datas.shape, label.shape)
def test_model():
model_ = models.build_model()
datas, label = data.read_test_data()
label = label.detach().numpy()
y = model_(datas).detach().numpy()
# print(y.shape, label.shape)
print(tool.score(label, y))
def test_informer():
model = informer.build_model().double()
dataloader = data.read_data(in_range=True,mean=True, val=False, start_random=True,dataset="CMIP")
testloader = data.read_data(in_range=True,mean=True, val=False, start_random=True,dataset="SODA")
train.train(model, dataloader, testloader)
def test_train():
model_ = models.build_model()
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
device = DEVICE
dataloader = data.read_data(mean=False,in_range=False,val=False, start_random=True)
train_loader = data.read_data(mean=False,in_range=False,dataset="SODA",val=False)
datas, label = data.read_test_data(mean=False,in_range=False)
datas = datas.to(device)
label = label.detach().numpy()
model_.to(device)
model_.eval()
y = model_(datas).cpu().detach().numpy()
print(tool.score(label, y))
model_.train()
train.train(model_, dataloader, train_loader)
model_.eval()
y = model_(datas).cpu().detach().numpy()
print(tool.score(label, y))
print(np.abs(y-label))
# print(y.shape, label.shape)
if __name__ == "__main__":
tool.set_seed()
## test tool
# test_score()
## test data
# test_read_data()
# test_read_test_data()
## test model
# test_model()
# test_train()
test_informer()