Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix gradient missing during densify&prune #828

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
+10 −13
Open

fix gradient missing during densify&prune #828

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
23 changes: 10 additions & 13 deletions scene/gaussian_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -277,15 +277,14 @@ def _prune_optimizer(self, mask):
if stored_state is not None:
stored_state["exp_avg"] = stored_state["exp_avg"][mask]
stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][mask]

del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter((group["params"][0][mask].requires_grad_(True)))
self.optimizer.state[group['params'][0]] = stored_state

grad = group["params"][0].grad[mask].clone()
group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
group["params"][0].grad = grad
optimizable_tensors[group["name"]] = group["params"][0]

optimizable_tensors[group["name"]] = group["params"][0]
else:
group["params"][0] = nn.Parameter(group["params"][0][mask].requires_grad_(True))
optimizable_tensors[group["name"]] = group["params"][0]
return optimizable_tensors

def prune_points(self, mask):
Expand Down Expand Up @@ -314,15 +313,13 @@ def cat_tensors_to_optimizer(self, tensors_dict):

stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(extension_tensor)), dim=0)
stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(extension_tensor)), dim=0)

del self.optimizer.state[group['params'][0]]
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
self.optimizer.state[group['params'][0]] = stored_state

optimizable_tensors[group["name"]] = group["params"][0]
else:
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
optimizable_tensors[group["name"]] = group["params"][0]
grad = torch.cat((group["params"][0].grad, torch.zeros_like(extension_tensor)), dim=0).clone()
group["params"][0] = nn.Parameter(torch.cat((group["params"][0], extension_tensor), dim=0).requires_grad_(True))
group["params"][0].grad = grad
optimizable_tensors[group["name"]] = group["params"][0]

return optimizable_tensors

Expand Down Expand Up @@ -389,7 +386,7 @@ def densify_and_clone(self, grads, grad_threshold, scene_extent):
def densify_and_prune(self, max_grad, min_opacity, extent, max_screen_size):
grads = self.xyz_gradient_accum / self.denom
grads[grads.isnan()] = 0.0

self.densify_and_clone(grads, max_grad, extent)
self.densify_and_split(grads, max_grad, extent)

Expand Down