+def save_examplar_vignettes(data_set, nb, name):
+ n = torch.randperm(data_set.nb_samples).narrow(0, 0, nb)
+
+ for k in range(0, nb):
+ b = n[k] // data_set.batch_size
+ m = n[k] % data_set.batch_size
+ i, t = data_set.get_batch(b)
+ i = i[m].float()
+ i.sub_(i.min())
+ i.div_(i.max())
+ if k == 0: patchwork = Tensor(nb, 1, i.size(1), i.size(2))
+ patchwork[k].copy_(i)
+
+ torchvision.utils.save_image(patchwork, name)
+