PointNet

# 학습 

for epoch in range(opt.nepoch):
    for i, data in enumerate(dataloader, 0):
        points, target = data
        points, target = Variable(points), Variable(target[:,0])
        points = points.transpose(2,1)
        points, target = points.cuda(), target.cuda()
        optimizer.zero_grad()
        classifier = classifier.train()
        pred, _ = classifier(points)
        loss = F.nll_loss(pred, target)
        loss.backward()
        optimizer.step()
        pred_choice = pred.data.max(1)[1]
        correct = pred_choice.eq(target.data).cpu().sum()
        print('[%d: %d/%d] train loss: %f accuracy: %f' %(epoch, i, num_batch, loss.item(),correct.item() / float(opt.batchSize)))

        if i % 10 == 0:
            j, data = next(enumerate(testdataloader, 0))
            points, target = data
            points, target = Variable(points), Variable(target[:,0])
            points = points.transpose(2,1)
            points, target = points.cuda(), target.cuda()
            classifier = classifier.eval()
            pred, _ = classifier(points)
            loss = F.nll_loss(pred, target)
            pred_choice = pred.data.max(1)[1]
            correct = pred_choice.eq(target.data).cpu().sum()
            print('[%d: %d/%d] %s loss: %f accuracy: %f' %(epoch, i, num_batch, blue('test'), loss.item(), correct.item()/float(opt.batchSize)))

    torch.save(classifier.state_dict(), '%s/cls_model_%d.pth' % (opt.outf, epoch))

# 테스트 
for i, data in enumerate(testdataloader, 0):
    points, target = data
    points, target = Variable(points), Variable(target[:, 0])
    points = points.transpose(2, 1)
    points, target = points.cuda(), target.cuda()
    pred, _ = classifier(points)
    loss = F.nll_loss(pred, target)

    pred_choice = pred.data.max(1)[1]
    correct = pred_choice.eq(target.data).cpu().sum()
    print('i:%d  loss: %f accuracy: %f' %(i, loss.data[0], correct/float(32)))