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.

Author: PerhapsS44

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 @@ def __init__(self, parser, sentinel=False):
         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 @@ def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParam
 
     # 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 @@
 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 @@ def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask,
         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 @@ def __init__(self, colmap_id, R, T, FoVx, FoVy, image, gt_alpha_mask,
             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 @@
 
 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 @@ def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
         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 @@ def __init__(self, sh_degree : int):
         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 (
@@ -136,15 +137,15 @@ def create_from_pcd(self, pcd : BasicPointCloud, spatial_lr_scale : float):
         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))
         self._features_rest = nn.Parameter(features[:,:,1:].transpose(1, 2).contiguous().requires_grad_(True))
         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 @@ def load_ply(self, path):
         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._features_dc = nn.Parameter(torch.tensor(features_dc, dtype=torch.float, 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._opacity = nn.Parameter(torch.tensor(opacities, dtype=torch.float, device="cuda").requires_grad_(True))
-        self._scaling = nn.Parameter(torch.tensor(scales, dtype=torch.float, device="cuda").requires_grad_(True))
-        self._rotation = nn.Parameter(torch.tensor(rots, 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=self.dtype, 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=self.dtype, 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=self.dtype, device="cuda").requires_grad_(True))
 
         self.active_sh_degree = self.max_sh_degree
 

train.py

@@ -16,7 +16,7 @@
 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 @@ def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_i
     # 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 helper(step):
 
     return helper
 
-def strip_lowerdiag(L):
-    uncertainty = torch.zeros((L.shape[0], 6), dtype=torch.float, device="cuda")
+def strip_lowerdiag(L, dtype=torch.float32):
+    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):
-    return strip_lowerdiag(sym)
+def strip_symmetric(sym, dtype=torch.float32):
+    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):
-    L = torch.zeros((s.shape[0], 3, 3), dtype=torch.float, device="cuda")
+def build_scaling_rotation(s, r, dtype=torch.float32):
+    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 flush(self):
     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