From b5a5f72eda16752559d07e574db0c50499168d5a Mon Sep 17 00:00:00 2001 From: Stefan Saraev Date: Thu, 2 May 2024 14:48:56 +0300 Subject: [PATCH] load speedup: refactored image loading Images are now loaded on the target device as uint8s. Then they are converted to the target data type (eg. fp32 or fp16). This speeds up the loading time. Also, users can opt to store the image as uint8 or as target data type. This will further reduce memory usage. --- README.md | 2 ++ arguments/__init__.py | 1 + gaussian_renderer/__init__.py | 2 +- render.py | 10 ++++----- scene/cameras.py | 38 +++++++++++++++++++++++++++-------- utils/camera_utils.py | 3 ++- utils/general_utils.py | 2 +- 7 files changed, 41 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 28f2dee..14278cd 100644 --- a/README.md +++ b/README.md @@ -196,6 +196,8 @@ python train.py -s 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. + #### --store_images_as_uint8 + Flag that describes how to store images in memory. If set, the images will be stored as uint8, and will be converted to the target data type on demand.
diff --git a/arguments/__init__.py b/arguments/__init__.py index 3cad0b3..fdfe3fc 100644 --- a/arguments/__init__.py +++ b/arguments/__init__.py @@ -54,6 +54,7 @@ class ModelParams(ParamGroup): self._white_background = False self.data_device = "cuda" self.data_dtype = "float32" + self.store_images_as_uint8 = False self.eval = False super().__init__(parser, "Loading Parameters", sentinel) diff --git a/gaussian_renderer/__init__.py b/gaussian_renderer/__init__.py index e8af831..3efab6e 100644 --- a/gaussian_renderer/__init__.py +++ b/gaussian_renderer/__init__.py @@ -94,7 +94,7 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor, # 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) + rendered_image = rendered_image.to(viewpoint_camera.data_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. diff --git a/render.py b/render.py index 70f85cb..fc54831 100644 --- a/render.py +++ b/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, dtype=torch.float32): +def render_sets(dataset : ModelParams, iteration : int, pipeline : PipelineParams, skip_train : bool, skip_test : bool): with torch.no_grad(): - gaussians = GaussianModel(dataset.sh_degree, dtype=dtype) + gaussians = GaussianModel(dataset.sh_degree) 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=dtype, device="cuda") + background = torch.tensor(bg_color, dtype=torch.float32, device="cuda") if not skip_train: render_set(dataset.model_path, "train", scene.loaded_iter, scene.getTrainCameras(), gaussians, pipeline, background) @@ -63,6 +63,4 @@ 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, dtype) \ No newline at end of file + render_sets(model.extract(args), args.iteration, pipeline.extract(args), args.skip_train, args.skip_test) \ No newline at end of file diff --git a/scene/cameras.py b/scene/cameras.py index 0609d0a..5264a04 100644 --- a/scene/cameras.py +++ b/scene/cameras.py @@ -17,7 +17,8 @@ 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", data_dtype=torch.float32 + trans=np.array([0.0, 0.0, 0.0]), scale=1.0, data_device = "cuda", data_dtype=torch.float32, + store_images_as_uint8=True, ): super(Camera, self).__init__() @@ -37,14 +38,18 @@ 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_dtype).to(self.data_device) - self.image_width = self.original_image.shape[2] - self.image_height = self.original_image.shape[1] + self.store_images_as_uint8 = store_images_as_uint8 - if gt_alpha_mask is not None: - 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) + self._original_image = image.to(self.data_device) + self._gt_alpha_mask = gt_alpha_mask + if self._gt_alpha_mask is not None: + self._gt_alpha_mask = self._gt_alpha_mask.to(self.data_device) + + if not store_images_as_uint8: + self._original_image = self.convert_image(self._original_image) + + self.image_width = self._original_image.shape[2] + self.image_height = self._original_image.shape[1] self.zfar = 100.0 self.znear = 0.01 @@ -57,6 +62,23 @@ class Camera(nn.Module): self.full_proj_transform = (self.world_view_transform.unsqueeze(0).bmm(self.projection_matrix.unsqueeze(0))).squeeze(0) self.camera_center = self.world_view_transform.inverse()[3, :3] + def convert_image(self, image): + image = (image / 255.0).clamp(0.0, 1.0).to(self.data_dtype) + gt_alpha_mask = self._gt_alpha_mask + + if gt_alpha_mask is not None: + gt_alpha_mask = gt_alpha_mask / 255.0 + image *= gt_alpha_mask.to(self.data_dtype) + + return image + + @property + def original_image(self): + if self.store_images_as_uint8: + return self.convert_image(self._original_image) + else: + return self._original_image + class MiniCam: def __init__(self, width, height, fovy, fovx, znear, zfar, world_view_transform, full_proj_transform): self.image_width = width diff --git a/utils/camera_utils.py b/utils/camera_utils.py index 6e762a5..400e7b6 100644 --- a/utils/camera_utils.py +++ b/utils/camera_utils.py @@ -52,7 +52,8 @@ def loadCam(args, id, cam_info, resolution_scale): 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, - data_dtype=get_data_dtype(args.data_dtype)) + data_dtype=get_data_dtype(args.data_dtype), + store_images_as_uint8=args.store_images_as_uint8) def cameraList_from_camInfos(cam_infos, resolution_scale, args): camera_list = [] diff --git a/utils/general_utils.py b/utils/general_utils.py index ed7f0a6..4b0c53a 100644 --- a/utils/general_utils.py +++ b/utils/general_utils.py @@ -20,7 +20,7 @@ def inverse_sigmoid(x): def PILtoTorch(pil_image, resolution): resized_image_PIL = pil_image.resize(resolution) - resized_image = torch.from_numpy(np.array(resized_image_PIL)) / 255.0 + resized_image = torch.from_numpy(np.array(resized_image_PIL))# / 255.0 if len(resized_image.shape) == 3: return resized_image.permute(2, 0, 1) else: