Function generate

test_generation.py:442–482

(model, opt)

def generate(model, opt):

    _, test_dataset = get_dataset(opt.dataroot, opt.npoints, opt.category)

    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batch_size,
                                                  shuffle=False, num_workers=int(opt.workers), drop_last=False)

    with torch.no_grad():

        samples = []
        ref = []

        for i, data in tqdm(enumerate(test_dataloader), total=len(test_dataloader), desc='Generating Samples'):

            x = data['test_points'].transpose(1,2)
            m, s = data['mean'].float(), data['std'].float()

            gen = model.gen_samples(x.shape,
                                       'cuda', clip_denoised=False).detach().cpu()

            gen = gen.transpose(1,2).contiguous()
            x = x.transpose(1,2).contiguous()



            gen = gen * s + m
            x = x * s + m
            samples.append(gen)
            ref.append(x)

            visualize_pointcloud_batch(os.path.join(str(Path(opt.eval_path).parent), 'x.png'), gen[:64], None,
                                       None, None)

        samples = torch.cat(samples, dim=0)
        ref = torch.cat(ref, dim=0)

        torch.save(samples, opt.eval_path)



    return ref


def main(opt):