def sample(network, save_manager, latent_size, name="", nb_samples=10):
mp.offline()
example = torch.ones(latent_size)
samples = []
for i in range(nb_samples):
samples.append(torch.rand_like(example))
samples = torch.stack(samples, dim=0)
out_points_list, out_batch_list = network.decode(samples)
out_points = out_points_list[0]
out_batch = out_batch_list[0]
plot = None
for i in range(nb_samples):
cloud_out = out_points[out_batch == i].detach().numpy()
if plot is None:
plot = mp.subplot(cloud_out,
c=cloud_out[:, 0],
s=[nb_samples, 1, i],
shading={"point_size": 0.2})
else:
mp.subplot(cloud_out,
c=cloud_out[:, 0],
data=plot,
s=[nb_samples, 1, i],
shading={"point_size": 0.2})
save_manager.save_mesh_plot(plot, "sample_{}".format(name))
def sample_dataset(save_manager, data_generator, nb_files=10):
mp.offline()
train_loader, val_loader = data_generator.generate_loaders()
i = 0
for batch_obj in train_loader:
if i == nb_files:
break
points = batch_obj.pos
batch = batch_obj.batch
plot = None
nb_clouds = max(batch) + 1
for j in range(nb_clouds):
current_points = points[batch == j].detach().numpy()
if plot is None:
plot = mp.subplot(
current_points, c=current_points[:, 0],
s=[nb_clouds, 1, j], shading={"point_size": 0.2}
)
else:
mp.subplot(
current_points, c=current_points[:, 0],
data=plot, s=[nb_clouds, 1, j], shading={"point_size": 0.2}
)
i += 1
save_manager.save_mesh_plot(plot, "dataset_samples{}".format(i))
def morph(network, data_generator, save_manager, name="", nb_steps=5):
mp.offline()
train_loader, val_loader = data_generator.generate_loaders()
for batch in val_loader:
in_points_list, in_batch_list, mean, variance = network.encode(batch)
print(mean.size(0))
for i in range(mean.size(0) - 1):
mean1 = mean[i]
for j in range(mean.size(0) - 1 - i):
mean2 = mean[i + 1 + j]
interpol_means = []
for alpha in np.arange(0.0, 1.0 + 1 / nb_steps, 1 / nb_steps):
if alpha >= 1.001:
break
interpol_means.append((1 - alpha) * mean1 + alpha * mean2)
interpol_stack = torch.stack(interpol_means, dim=0)
out_points_list, out_batch_list = network.decode(
interpol_stack)
batch_size = torch.max(out_batch_list[0]) + 1
print(batch_size)
plot = None
for plot_nb in range(batch_size):
cloud_out = out_points_list[0][out_batch_list[0] ==
plot_nb].detach().numpy()
if plot is None:
plot = mp.subplot(cloud_out,
c=cloud_out[:, 0],
s=[batch_size, 1, plot_nb],
shading={"point_size": 0.2})
else:
mp.subplot(cloud_out,
c=cloud_out[:, 0],
data=plot,
s=[batch_size, 1, plot_nb],
shading={"point_size": 0.2})
print(i)
print(j)
nb = (mean.size(0) - 1) * i + j
print(nb)
save_manager.save_mesh_plot(plot,
"morph_{}_{}".format(name, nb))
break
def vae_clouds(network, data_generator, save_manager, name=""):
mp.offline()
train_loader, val_loader = data_generator.generate_loaders()
for batch in val_loader:
in_points_list, in_batch_list, mean, variance = network.encode(batch)
out_points_list, out_batch_list = network.decode(mean)
batch_size = torch.max(in_batch_list[0]) + 1
plot = None
for j in range(batch_size):
cloud_in = in_points_list[0][in_batch_list[0] == j].detach().numpy()
cloud_out = out_points_list[0][out_batch_list[0] == j].detach().numpy()
if plot is None:
plot = mp.subplot(
cloud_in, c=cloud_in[:, 0],
s=[6*batch_size, 1, 6*j], shading={"point_size": 0.2}
)
else:
mp.subplot(
cloud_in, c=cloud_in[:, 0],
data=plot, s=[6*batch_size, 1, 6*j], shading={"point_size": 0.2}
)
mp.subplot(
cloud_out, c=cloud_out[:, 0],
data=plot, s=[6*batch_size, 1, 6*j+1], shading={"point_size": 0.2}
)
cloud_in = in_points_list[1][in_batch_list[1] == j].detach().numpy()
cloud_out = out_points_list[1][out_batch_list[1] == j].detach().numpy()
mp.subplot(
cloud_in, c=cloud_in[:, 0],
data=plot, s=[6 * batch_size, 1, 6 * j + 2], shading={"point_size": 0.2}
)
mp.subplot(
cloud_out, c=cloud_out[:, 0],
data=plot, s=[6 * batch_size, 1, 6 * j + 3], shading={"point_size": 0.2}
)
cloud_in = in_points_list[2][in_batch_list[2] == j].detach().numpy()
cloud_out = out_points_list[2][out_batch_list[2] == j].detach().numpy()
mp.subplot(
cloud_in, c=cloud_in[:, 0],
data=plot, s=[6 * batch_size, 1, 6 * j + 4], shading={"point_size": 0.2}
)
mp.subplot(
cloud_out, c=cloud_out[:, 0],
data=plot, s=[6 * batch_size, 1, 6 * j + 5], shading={"point_size": 0.2}
)
save_manager.save_mesh_plot(plot, "auto_encode_clouds_{}".format(name))