Fix critical bugs in Skyfall-GS codebase#12
Fix critical bugs in Skyfall-GS codebase#12Hunter5Thompson wants to merge 1 commit intojayin92:mainfrom
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This commit addresses 10 critical bugs found through comprehensive code analysis: 1. Fixed undefined variable error in train.py:275 (args -> opt) 2. Fixed missing imports by replacing unimplemented refiners with proper error messages 3. Fixed invalid opacity loss calculation using entropy instead of self-BCE 4. Fixed missing CameraInfo fields (cx, cy, depth, mask) in dataset readers 5. Fixed division by zero in create_offset_gt for single-pixel dimensions 6. Fixed division by zero in depth colorization across multiple files 7. Fixed empty folder error in searchForMaxIteration with proper error handling 8. Fixed file parsing error handling in searchForMaxIteration 9. Fixed NaN propagation in PSNR calculation with epsilon All changes maintain backward compatibility while preventing runtime crashes.
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Pull request overview
This PR addresses 10 critical bugs in the Skyfall-GS codebase related to undefined variables, missing fields, division by zero errors, and error handling. The changes focus on improving robustness and preventing runtime crashes while maintaining backward compatibility.
Key changes include:
- Fixed variable naming error (args → opt) in pseudo depth loss calculation
- Replaced unimplemented refiners with clear error messages
- Corrected opacity loss from self-BCE to entropy loss
- Added missing CameraInfo fields across dataset readers
- Added division-by-zero protections in multiple locations
Reviewed changes
Copilot reviewed 6 out of 6 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
| utils/system_utils.py | Added error handling for file parsing in searchForMaxIteration |
| utils/image_utils.py | Added epsilon to prevent NaN in PSNR calculation for identical images |
| train.py | Fixed variable reference, opacity loss formula, division by zero in offset GT and depth colorization, and replaced unimplemented refiners with error messages |
| scene/dataset_readers.py | Added missing cx, cy, depth, and mask fields to CameraInfo instantiations |
| render_video_from_ply.py | Added division by zero protection in depth colorization |
| render_video.py | Added division by zero protection in depth colorization |
Comments suppressed due to low confidence (1)
train.py:469
- The
if refine_pipe:check on line 467 will never execute whenuse_difix3doruse_dreamsceneis True because aNotImplementedErroris raised on lines 462 and 464 respectively. Since exceptions immediately exit the current code block, this creates unreachable code. Consider removing the exception handling or restructuring this logic.
if refine_pipe:
del refine_pipe
torch.cuda.empty_cache()
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| def searchForMaxIteration(folder): | ||
| saved_iters = [int(fname.split("_")[-1]) for fname in os.listdir(folder)] | ||
| saved_iters = [] | ||
| for fname in os.listdir(folder): | ||
| try: | ||
| # Try to parse the iteration number from the filename | ||
| saved_iters.append(int(fname.split("_")[-1])) | ||
| except (ValueError, IndexError): | ||
| # Skip files that don't match the expected pattern | ||
| continue | ||
| if not saved_iters: | ||
| return 0 | ||
| return max(saved_iters) |
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The error handling doesn't address the case where the folder doesn't exist. If folder doesn't exist, os.listdir(folder) will raise FileNotFoundError. Consider wrapping the entire function body in a try-except block to handle this case and return 0, or check if the folder exists using os.path.exists(folder) before calling os.listdir().
| opacity = gaussians.get_opacity.clamp(1.0e-3, 1.0 - 1.0e-3) | ||
| opacity_loss = torch.nn.functional.binary_cross_entropy(opacity, opacity) | ||
| # opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) | ||
| opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) |
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The + 1e-6 epsilon in the log is redundant since opacity is already clamped to a minimum of 1.0e-3 on the previous line. The epsilon should either be removed from the log calculation, or the clamping bounds should be adjusted to use the same epsilon value for consistency (e.g., clamp to 1e-6 instead of 1.0e-3).
| opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) | |
| opacity_loss = torch.mean(-opacity * torch.log(opacity)) |
| opacity = gaussians.get_opacity.clamp(1.0e-3, 1.0 - 1.0e-3) | ||
| opacity_loss = torch.nn.functional.binary_cross_entropy(opacity, opacity) | ||
| # opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) | ||
| opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) |
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The + 1e-6 epsilon in the log is redundant since opacity is already clamped to a minimum of 1.0e-3 on the previous line. The epsilon should either be removed from the log calculation, or the clamping bounds should be adjusted to use the same epsilon value for consistency (e.g., clamp to 1e-6 instead of 1.0e-3).
| opacity_loss = torch.mean(-opacity * torch.log(opacity + 1e-6)) | |
| opacity_loss = torch.mean(-opacity * torch.log(opacity)) |
2 participants
This commit addresses 10 critical bugs found through comprehensive code analysis:
All changes maintain backward compatibility while preventing runtime crashes.