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- import torch
- from torch.autograd import Variable
- from shutil import copyfile
- from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
- def test(dataloader, net, criterion, optimizer, opt):
- test_loss = 0
- correct = 0
- net.eval()
- all_targets = []
- all_predicted = []
- for i, (adj_matrix, embedding_matrix, target) in enumerate(dataloader, 0):
- # padding = torch.zeros(len(annotation), opt.n_node, opt.state_dim - opt.annotation_dim).double()
- # init_input = torch.cat((annotation, padding), 2)
- # init_input = torch.zeros(len(adj_matrix), opt.n_node, opt.state_dim).double()
- init_input = embedding_matrix
- if opt.cuda:
- init_input = init_input.cuda()
- adj_matrix = adj_matrix.cuda()
- # annotation = annotation.cuda()
- target = target.cuda()
- init_input = Variable(init_input)
- adj_matrix = Variable(adj_matrix)
- # annotation = Variable(annotation)
- target = Variable(target)
- # print(target)
- output = net(init_input, adj_matrix)
- # print(output)
- # test_loss += criterion(output, target).data[0]
- test_loss += criterion(output, target).item()
- pred = output.data.max(1, keepdim=True)[1]
- # print(pred)
- all_predicted.extend(pred.data.view_as(target).cpu().numpy())
- all_targets.extend(target.cpu().numpy())
- correct += pred.eq(target.data.view_as(pred)).cpu().sum()
- test_loss /= len(dataloader.dataset)
- print('Accuracy:', accuracy_score(all_targets, all_predicted))
- print(classification_report(all_targets, all_predicted))
- print(confusion_matrix(all_targets, all_predicted))
- print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'.format(
- test_loss, correct, len(dataloader.dataset),
- 100. * correct / len(dataloader.dataset)))
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