added support for setting floating point range

Users may want to reduce their memory consumption by using fp16.
However, in my tests, such attempts will result in lower quality renders.
Some data type conversions did not have any impact, so I removed them completely.

README.md

@@ -194,6 +194,8 @@ python train.py -s <path to COLMAP or NeRF Synthetic dataset>
   Influence of SSIM on total loss from 0 to 1, ```0.2``` by default. 
   #### --percent_dense
   Percentage of scene extent (0--1) a point must exceed to be forcibly densified, ```0.01``` by default.
+  #### --data_dtype
+  The data type (float32, float16) in which images are stored when computing the loss. ```float32``` by default.
 
 </details>
 <br>

arguments/__init__.py

@@ -53,6 +53,7 @@ class ModelParams(ParamGroup):
         self._resolution = -1
         self._white_background = False
         self.data_device = "cuda"
+        self.data_dtype = "float32"
         self.eval = False
         super().__init__(parser, "Loading Parameters", sentinel)
 

gaussian_renderer/__init__.py

@@ -92,6 +92,10 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
         rotations = rotations,
         cov3D_precomp = cov3D_precomp)
 
+    # after rasterization, we convert the resulting image to the target dtype
+    # The rasterizer expects parameters as float32, so the result is also float32.
+    rendered_image = rendered_image.to(viewpoint_camera.original_image.dtype)
+
     # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
     # They will be excluded from value updates used in the splitting criteria.
     return {"render": rendered_image,

render.py

@@ -34,13 +34,13 @@ def render_set(model_path, name, iteration, views, gaussians, pipeline, backgrou
         torchvision.utils.save_image(rendering, os.path.join(render_path, '{0:05d}'.format(idx) + ".png"))
         torchvision.utils.save_image(gt, os.path.join(gts_path, '{0:05d}'.format(idx) + ".png"))
 
-def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool):
+def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool, dtype=torch.float32):
     with torch.no_grad():
-        gaussians = GaussianModel(dataset.sh_degree)
+        gaussians = GaussianModel(dataset.sh_degree, dtype=dtype)
         scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
 
         bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
-        background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
+        background = torch.tensor(bg_color, dtype=dtype, device="cuda")
 
         if not skip_train:
              render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background)
@@ -62,5 +62,7 @@ if __name__ == "__main__":
 
     # Initialize system state (RNG)
     safe_state(args.quiet)
+    
+    dtype = torch.float32
 
-    render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test)
+    render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test, dtype)

scene/cameras.py

@@ -17,7 +17,7 @@ from utils.graphics_utils import getWorld2View2, getProjectionMatrix
 class Camera(nn.Module):
     def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask,
                  image_name, uid,
-                 trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda"
+                 trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda", data_dtype=torch.float32
                  ):
         super(Camera, self).__init__()
 
@@ -28,6 +28,7 @@ class Camera(nn.Module):
         self.FoVx = FoVx
         self.FoVy = FoVy
         self.image_name = image_name
+        self.data_dtype = data_dtype
 
         try:
             self.data_device = torch.device(data_device)
@@ -36,12 +37,12 @@ class Camera(nn.Module):
             print(f"[Warning] Custom device {data_device} failed, fallback to default cuda device" )
             self.data_device = torch.device("cuda")
 
-        self.original_image = image.clamp(0.0, 1.0).to(self.data_device)
+        self.original_image = image.clamp(0.0, 1.0).to(self.data_dtype).to(self.data_device)
         self.image_width = self.original_image.shape[2]
         self.image_height = self.original_image.shape[1]
 
         if gt_alpha_mask is not None:
-            self.original_image *= gt_alpha_mask.to(self.data_device)
+            self.original_image *= gt_alpha_mask.to(self.data_dtype).to(self.data_device)
         else:
             self.original_image *= torch.ones((1, self.image_height, self.image_width), device=self.data_device)
 

scene/gaussian_model.py

@@ -23,11 +23,11 @@ from utils.general_utils import strip_symmetric, build_scaling_rotation
 
 class GaussianModel:
 
-    def setup_functions(self):
+    def setup_functions(self, dtype):
         def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
-            L = build_scaling_rotation(scaling_modifier * scaling, rotation)
+            L = build_scaling_rotation(scaling_modifier * scaling, rotation, dtype)
             actual_covariance = L @ L.transpose(1, 2)
-            symm = strip_symmetric(actual_covariance)
+            symm = strip_symmetric(actual_covariance, dtype)
             return symm
         
         self.scaling_activation = torch.exp
@@ -41,7 +41,7 @@ class GaussianModel:
         self.rotation_activation = torch.nn.functional.normalize
 
 
-    def __init__(self, sh_degree : int):
+    def __init__(self, sh_degree : int, dtype=torch.float32):
         self.active_sh_degree = 0
         self.max_sh_degree = sh_degree  
         self._xyz = torch.empty(0)
@@ -56,7 +56,8 @@ class GaussianModel:
         self.optimizer = None
         self.percent_dense = 0
         self.spatial_lr_scale = 0
-        self.setup_functions()
+        self.dtype = dtype
+        self.setup_functions(dtype)
 
     def capture(self):
         return (
@@ -136,7 +137,7 @@ class GaussianModel:
         rots = torch.zeros((fused_point_cloud.shape[0], 4), device="cuda")
         rots[:, 0] = 1
 
-        opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=torch.float, device="cuda"))
+        opacities = inverse_sigmoid(0.1 * torch.ones((fused_point_cloud.shape[0], 1), dtype=self.dtype, device="cuda"))
 
         self._xyz = nn.Parameter(fused_point_cloud.requires_grad_(True))
         self._features_dc = nn.Parameter(features[:,:,0:1].transpose(1, 2).contiguous().requires_grad_(True))
@@ -144,7 +145,7 @@ class GaussianModel:
         self._scaling = nn.Parameter(scales.requires_grad_(True))
         self._rotation = nn.Parameter(rots.requires_grad_(True))
         self._opacity = nn.Parameter(opacities.requires_grad_(True))
-        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda")
+        self.max_radii2D = torch.zeros((self.get_xyz.shape[0]), device="cuda", dtype=self.dtype)
 
     def training_setup(self, training_args):
         self.percent_dense = training_args.percent_dense
@@ -246,12 +247,12 @@ class GaussianModel:
         for idx, attr_name in enumerate(rot_names):
             rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
 
-        self._xyz = nn.Parameter(torch.tensor(xyz, dtype=torch.float, device="cuda").requires_grad_(True))
+        self._xyz = nn.Parameter(torch.tensor(xyz, dtype=self.dtype, device="cuda").requires_grad_(True))
-        self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
+        self._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=self.dtype, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
-        self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=torch.float, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
+        self._features_rest = nn.Parameter(torch.tensor(features_extra, dtype=self.dtype, device="cuda").transpose(1, 2).contiguous().requires_grad_(True))
-        self._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
+        self._opacity = nn.Parameter(torch.tensor(opacities, dtype=self.dtype, device="cuda").requires_grad_(True))
-        self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
+        self._scaling = nn.Parameter(torch.tensor(scales, dtype=self.dtype, device="cuda").requires_grad_(True))
-        self._rotation = nn.Parameter(torch.tensor(rots, dtype=torch.float, device="cuda").requires_grad_(True))
+        self._rotation = nn.Parameter(torch.tensor(rots, dtype=self.dtype, device="cuda").requires_grad_(True))
 
         self.active_sh_degree = self.max_sh_degree
 

train.py

@@ -16,7 +16,7 @@ from utils.loss_utils import l1_loss, ssim
 from gaussian_renderer import render, network_gui
 import sys
 from scene import Scene, GaussianModel
-from utils.general_utils import safe_state
+from utils.general_utils import get_data_dtype, safe_state
 import uuid
 from tqdm import tqdm
 from utils.image_utils import psnr
@@ -216,6 +216,7 @@ if __name__ == "__main__":
     # Start GUI server, configure and run training
     network_gui.init(args.ip, args.port)
     torch.autograd.set_detect_anomaly(args.detect_anomaly)
+    
     training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)
 
     # All done

utils/camera_utils.py

@@ -11,7 +11,7 @@
 
 from scene.cameras import Camera
 import numpy as np
-from utils.general_utils import PILtoTorch
+from utils.general_utils import PILtoTorch, get_data_dtype
 from utils.graphics_utils import fov2focal
 
 WARNED = False
@@ -39,6 +39,8 @@ def loadCam(args, id, cam_info, resolution_scale):
         resolution = (int(orig_w / scale), int(orig_h / scale))
 
     resized_image_rgb = PILtoTorch(cam_info.image, resolution)
+    
+    # resized_image_rgb = resized_image_rgb.to(get_data_dtype(args.data_dtype))
 
     gt_image = resized_image_rgb[:3, ...]
     loaded_mask = None
@@ -49,7 +51,8 @@ def loadCam(args, id, cam_info, resolution_scale):
     return Camera(colmap_id=cam_info.uid, R=cam_info.R, T=cam_info.T, 
                   FoVx=cam_info.FovX, FoVy=cam_info.FovY, 
                   image=gt_image, gt_alpha_mask=loaded_mask,
-                  image_name=cam_info.image_name, uid=id, data_device=args.data_device)
+                  image_name=cam_info.image_name, uid=id, data_device=args.data_device, 
+                  data_dtype=get_data_dtype(args.data_dtype))
 
 def cameraList_from_camInfos(cam_infos, resolution_scale, args):
     camera_list = []

utils/general_utils.py

@@ -61,8 +61,8 @@ def get_expon_lr_func(
 
     return helper
 
-def strip_lowerdiag(L):
+def strip_lowerdiag(L, dtype=torch.float32):
-    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
+    uncertainty = torch.zeros((L.shape[0], 6), dtype=dtype, device="cuda")
 
     uncertainty[:, 0] = L[:, 0, 0]
     uncertainty[:, 1] = L[:, 0, 1]
@@ -72,8 +72,8 @@ def strip_lowerdiag(L):
     uncertainty[:, 5] = L[:, 2, 2]
     return uncertainty
 
-def strip_symmetric(sym):
+def strip_symmetric(sym, dtype=torch.float32):
-    return strip_lowerdiag(sym)
+    return strip_lowerdiag(sym, dtype=dtype)
 
 def build_rotation(r):
     norm = torch.sqrt(r[:,0]*r[:,0] + r[:,1]*r[:,1] + r[:,2]*r[:,2] + r[:,3]*r[:,3])
@@ -98,8 +98,8 @@ def build_rotation(r):
     R[:, 2, 2] = 1 - 2 * (x*x + y*y)
     return R
 
-def build_scaling_rotation(s, r):
+def build_scaling_rotation(s, r, dtype=torch.float32):
-    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
+    L = torch.zeros((s.shape[0], 3, 3), dtype=dtype, device="cuda")
     R = build_rotation(r)
 
     L[:,0,0] = s[:,0]
@@ -131,3 +131,12 @@ def safe_state(silent):
     np.random.seed(0)
     torch.manual_seed(0)
     torch.cuda.set_device(torch.device("cuda:0"))
+
+def get_data_dtype(dtype):
+    if dtype == "float32":
+        return torch.float32
+    elif dtype == "float64":
+        return torch.float64
+    elif dtype == "float16":
+        return torch.float16
+    return torch.float32