Rename variables, remove debugging flag

gaussian_splatting/arguments/__init__.py

@@ -68,7 +68,6 @@ class PipelineParams(ParamGroup):
     def __init__(self, parser=None):
         self.convert_SHs_python = False
         self.compute_cov3D_python = False
-        self.debug = False
         super().__init__(parser, "Pipeline Parameters")
 
 class OptimizationParams(ParamGroup):

gaussian_splatting/gaussian_renderer/__init__.py

@@ -45,7 +45,7 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
         sh_degree=pc.active_sh_degree,
         campos=viewpoint_camera.camera_center,
         prefiltered=False,
-        debug=pipe.debug
+        debug=False
     )
 
     rasterizer = GaussianRasterizer(raster_settings=raster_settings)

gaussian_splatting/training.py

@@ -19,8 +19,7 @@ def __init__(
         testing_iterations=None,
         saving_iterations=None,
         checkpoint_iterations=None,
-        checkpoint=None,
-        debug_from=-1,
+        checkpoint_path=None,
         quiet=False,
         detect_anomaly=False
     ):
@@ -36,8 +35,7 @@ def __init__(
             checkpoint_iterations = []
         self._checkpoint_iterations = checkpoint_iterations
 
-        self._checkpoint = checkpoint
-        self._debug_from = debug_from
+        self._checkpoint_path = checkpoint_path
 
         safe_state(quiet)
 
@@ -54,8 +52,9 @@ def run(
         gaussians = GaussianModel(dataset.sh_degree)
         scene = Scene(dataset, gaussians)
         gaussians.training_setup(opt)
-        if self._checkpoint:
-            (model_params, first_iter) = torch.load(self._checkpoint)
+
+        if self._checkpoint_path:
+            (model_params, first_iter) = torch.load(self._checkpoint_path)
             gaussians.restore(model_params, opt)
 
         bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
@@ -82,10 +81,6 @@ def run(
                 viewpoint_stack = scene.getTrainCameras().copy()
             viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))
 
-            # Render
-            if (iteration - 1) == self._debug_from:
-                pipe.debug = True
-
             bg = torch.rand((3), device="cuda") if opt.random_background else background
 
             render_pkg = render(viewpoint_cam, gaussians, pipe, bg)

scripts/train.py

@@ -20,22 +20,20 @@
     lp = ModelParams(parser)
     op = OptimizationParams(parser)
     pp = PipelineParams(parser)
-    parser.add_argument('--debug_from', type=int, default=-1)
     parser.add_argument('--detect_anomaly', action='store_true', default=False)
     parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
     parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
     parser.add_argument("--quiet", action="store_true")
     parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
-    parser.add_argument("--start_checkpoint", type=str, default = None)
+    parser.add_argument("--checkpoint_path", type=str, default = None)
     args = parser.parse_args(sys.argv[1:])
     args.save_iterations.append(args.iterations)
 
     trainer = Trainer(
         testing_iterations=args.test_iterations,
         saving_iterations=args.save_iterations,
         checkpoint_iterations=args.checkpoint_iterations,
-        checkpoint=args.start_checkpoint,
-        debug_from=args.debug_from,
+        checkpoint_path=args.checkpoint_path,
         quiet=args.quiet,
         detect_anomaly=args.detect_anomaly
     )

scripts/train_modal.py

@@ -65,7 +65,6 @@ class Pipeline():
     def __init__(self):
         self.convert_SHs_python = False
         self.compute_cov3D_python = False
-        self.debug = False
 
 class Optimization():
     def __init__(self):