def show_models():
TRANSITION_TIME = 2
viewer = MeshRenderer()
while True:
for sample_index in range(SAMPLE_COUNT):
try:
start = time.perf_counter()
end = start + TRANSITION_TIME
while time.perf_counter() < end:
progress = min(
(time.perf_counter() - start) / TRANSITION_TIME, 1.0)
if ROTATE_MODEL:
viewer.rotation = (
147 +
(sample_index + progress) / SAMPLE_COUNT * 360 * 6,
40)
code = torch.tensor(spline(float(sample_index) + progress),
dtype=torch.float32,
device=device)
viewer.set_mesh(
sdf_net.get_mesh(code,
voxel_resolution=64,
sphere_only=False,
level=SURFACE_LEVEL))
except KeyboardInterrupt:
viewer.stop()
return
indices[BATCH_SIZE // 2:] = torch.tensor(np.random.choice(
negative_indices, BATCH_SIZE // 2),
device=device)
sdf_net.zero_grad()
predicted_sdf = sdf_net(points[indices, :], latent_code)
batch_sdf = sdf[indices]
loss = torch.mean(torch.abs(predicted_sdf - batch_sdf))
loss.backward()
optimizer.step()
if loss.item() < error_targets[image_index]:
try:
viewer.set_mesh(
sdf_net.get_mesh(latent_code[0, :],
voxel_resolution=64,
raise_on_empty=True))
if save_images:
image = viewer.get_image(flip_red_blue=True)
cv2.imwrite("images/frame-{:05d}.png".format(image_index),
image)
image_index += 1
except ValueError:
pass
step += 1
print('Step {:04d}, Image {:04d}, loss: {:.6f}, target: {:.6f}'.format(
step, image_index, loss.item(), error_targets[image_index]))
except KeyboardInterrupt:
viewer.stop()
break