remove resolution model params

gaussian_splatting/arguments/__init__.py

@@ -59,7 +59,6 @@ def __init__(self, parser=None, source_path="", sentinel=False):
         self._source_path = source_path
         self._model_path = ""
         self._images = "images"
-        self._resolution = -1
         self.eval = False
         super().__init__(parser, "Loading Parameters", sentinel)
 

gaussian_splatting/scene/__init__.py

@@ -31,6 +31,7 @@ def __init__(
         gaussians: GaussianModel,
         load_iteration=None,
         shuffle=True,
+        resolution=-1,
         resolution_scales=[1.0],
     ):
         """b
@@ -86,11 +87,11 @@ def __init__(
         for resolution_scale in resolution_scales:
             print("Loading Training Cameras")
             self.train_cameras[resolution_scale] = cameraList_from_camInfos(
-                scene_info.train_cameras, resolution_scale, args
+                scene_info.train_cameras, resolution_scale, resolution
             )
             print("Loading Test Cameras")
             self.test_cameras[resolution_scale] = cameraList_from_camInfos(
-                scene_info.test_cameras, resolution_scale, args
+                scene_info.test_cameras, resolution_scale, resolution
             )
 
         if self.loaded_iter:

gaussian_splatting/training.py

@@ -16,13 +16,16 @@
 class Trainer:
     def __init__(
         self,
+        resolution=-1,
         testing_iterations=None,
         saving_iterations=None,
         checkpoint_iterations=None,
         checkpoint_path=None,
         quiet=False,
         detect_anomaly=False,
     ):
+        self._resolution = resolution
+
         if testing_iterations is None:
             testing_iterations = [7_000, 30_000]
         self._testing_iterations = testing_iterations
@@ -48,7 +51,7 @@ def run(
     ):
         first_iter = 0
         gaussians = GaussianModel(dataset.sh_degree)
-        scene = Scene(dataset, gaussians)
+        scene = Scene(dataset, gaussians, resolution=self._resolution)
         gaussians.training_setup(opt)
 
         if self._checkpoint_path:

gaussian_splatting/utils/camera.py

@@ -18,15 +18,15 @@
 WARNED = False
 
 
-def loadCam(args, id, cam_info, resolution_scale):
+def load_camera(resolution, cam_id, cam_info, resolution_scale):
     orig_w, orig_h = cam_info.image.size
 
-    if args.resolution in [1, 2, 4, 8]:
-        resolution = round(orig_w / (resolution_scale * args.resolution)), round(
-            orig_h / (resolution_scale * args.resolution)
+    if resolution in [1, 2, 4, 8]:
+        resolution = round(orig_w / (resolution_scale * resolution)), round(
+            orig_h / (resolution_scale * resolution)
         )
     else:  # should be a type that converts to float
-        if args.resolution == -1:
+        if resolution == -1:
             if orig_w > 1600:
                 global WARNED
                 if not WARNED:
@@ -39,7 +39,7 @@ def loadCam(args, id, cam_info, resolution_scale):
             else:
                 global_down = 1
         else:
-            global_down = orig_w / args.resolution
+            global_down = orig_w / resolution
 
         scale = float(global_down) * float(resolution_scale)
         resolution = (int(orig_w / scale), int(orig_h / scale))
@@ -61,15 +61,15 @@ def loadCam(args, id, cam_info, resolution_scale):
         image=gt_image,
         gt_alpha_mask=loaded_mask,
         image_name=cam_info.image_name,
-        uid=id,
+        uid=cam_id,
     )
 
 
-def cameraList_from_camInfos(cam_infos, resolution_scale, args):
+def cameraList_from_camInfos(cam_infos, resolution_scale, resolution):
     camera_list = []
 
-    for id, c in enumerate(cam_infos):
-        camera_list.append(loadCam(args, id, c, resolution_scale))
+    for cam_id, c in enumerate(cam_infos):
+        camera_list.append(load_camera(resolution, cam_id, c, resolution_scale))
 
     return camera_list
 

scripts/render.py

@@ -42,11 +42,21 @@ def render_set(model_path, name, iteration, views, gaussians):
 
 
 def render_sets(
-    dataset: ModelParams, iteration: int, skip_train: bool, skip_test: bool
+    dataset: ModelParams,
+    iteration: int,
+    skip_train: bool,
+    skip_test: bool,
+    resolution: int = -1,
 ):
     with torch.no_grad():
         gaussians = GaussianModel(dataset.sh_degree)
-        scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
+        scene = Scene(
+            dataset,
+            gaussians,
+            load_iteration=iteration,
+            shuffle=False,
+            resolution=resolution,
+        )
 
         if not skip_train:
             render_set(

scripts/train.py

@@ -29,10 +29,12 @@
     parser.add_argument("--quiet", action="store_true")
     parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
     parser.add_argument("--checkpoint_path", type=str, default=None)
+    parser.add_argument("--resolution", default=-1, type=int)
     args = parser.parse_args(sys.argv[1:])
     args.save_iterations.append(args.iterations)
 
     trainer = Trainer(
+        resolution=args.resolution,
         testing_iterations=args.test_iterations,
         saving_iterations=args.save_iterations,
         checkpoint_iterations=args.checkpoint_iterations,

scripts/train_modal.py

@@ -45,7 +45,6 @@ def __init__(
         self.source_path = "/workspace/data/phil_open/5"
         self.model_path = ""
         self.images = "images"
-        self.resolution = -1
         self.eval = False