def draw_interpolation(generator, num_steps):
cuda = True if torch.cuda.is_available() else False
if cuda:
generator.cuda()
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
p1 = np.random.normal(0, 1, opt.latent_dim)
p2 = np.random.normal(0, 1, opt.latent_dim)
batch = get_interpolation(num_steps, p1, p2)
batch = Variable(Tensor(batch).transpose_(0, 1))
ecgs_fake_varied = generator(batch).detach().cpu().numpy()
some_num = random.randint(0, 1111)
save_ecgs_varied_one_plot(ecgs_fake_varied, "INTERPOL" + str(some_num))
save_ecgs_varied_several_plots(ecgs_fake_varied,
"INTERPOL" + str(some_num))
def get_same_class_codei_vary(self, class_id, code_i, steps):
# батч длиной steps
# класс фиксирован
# переменная ci (i-тая по счету среди непрерывных) пробегает диапазон
# остальные c равны нулю
z = np.random.normal(0, 1, (steps, self.latent_dim))
label = to_categorical(np.array([class_id]), self.n_classes)
labels_one_hot = np.repeat(label, steps, axis=0)
p1 = np.array([0 for _ in range(self.code_dim)])
p1[code_i] = -1
p2 = np.array([0 for _ in range(self.code_dim)])
p2[code_i] = 1
code_input = get_interpolation(steps, p1, p2)
code_input = code_input.transpose((1, 0))
return z, code_input, labels_one_hot