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Runtime error
| 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] | |