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import numpy as np
import torch
import matplotlib.pyplot as plt
import torch.nn as nn
import time
from util.time import *
from util.env import *
import argparse
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import pandas as pd
import torch.nn.functional as F
from util.data import *
from util.preprocess import *
def test(model, dataloader):
# test
loss_func = nn.MSELoss(reduction='mean')
device = get_device()
test_loss_list = []
now = time.time()
test_predicted_list = []
test_ground_list = []
test_labels_list = []
t_test_predicted_list = []
t_test_ground_list = []
t_test_labels_list = []
test_len = len(dataloader)
model.eval()
i = 0
acu_loss = 0
for x, y, labels, edge_index in dataloader:
x, y, labels, edge_index = [item.to(device).float() for item in [x, y, labels, edge_index]]
with torch.no_grad():
predicted = model(x, edge_index).float().to(device)
loss = loss_func(predicted, y)
labels = labels.unsqueeze(1).repeat(1, predicted.shape[1])
if len(t_test_predicted_list) <= 0:
t_test_predicted_list = predicted
t_test_ground_list = y
t_test_labels_list = labels
else:
t_test_predicted_list = torch.cat((t_test_predicted_list, predicted), dim=0)
t_test_ground_list = torch.cat((t_test_ground_list, y), dim=0)
t_test_labels_list = torch.cat((t_test_labels_list, labels), dim=0)
test_loss_list.append(loss.item())
acu_loss += loss.item()
i += 1
if i % 10000 == 1 and i > 1:
print(timeSincePlus(now, i / test_len))
test_predicted_list = t_test_predicted_list.tolist()
test_ground_list = t_test_ground_list.tolist()
test_labels_list = t_test_labels_list.tolist()
avg_loss = sum(test_loss_list)/len(test_loss_list)
return avg_loss, [test_predicted_list, test_ground_list, test_labels_list]