sky init without densify

scene/gaussian_model.py

@@ -229,7 +229,7 @@ class GaussianModel:
             xyz = torch.concat((groundbox_xyz.cuda(), xyz), dim=0)
             fused_color = torch.concat((groundbox_color.cuda(), fused_color), dim=0)
             
-            debug_xy = True
+            debug_xy = False
             if debug_xy:
                 folder = "/home/qinllgroup/hongxiangyu/git_project/gaussian-splatting-xy/data/tree_01_debug/mini3/outputs_debug"
                 sky_ply_path = os.path.join(folder, "skybox_scene_init.ply")

train-densify.py

@@ -0,0 +1,285 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import os
+import torch
+from random import randint
+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, get_expon_lr_func
+import uuid
+from tqdm import tqdm
+from utils.image_utils import psnr
+from argparse import ArgumentParser, Namespace
+from arguments import ModelParams, PipelineParams, OptimizationParams
+try:
+    from torch.utils.tensorboard import SummaryWriter
+    TENSORBOARD_FOUND = True
+except ImportError:
+    TENSORBOARD_FOUND = False
+
+try:
+    from fused_ssim import fused_ssim
+    FUSED_SSIM_AVAILABLE = True
+except:
+    FUSED_SSIM_AVAILABLE = False
+
+try:
+    from diff_gaussian_rasterization import SparseGaussianAdam
+    SPARSE_ADAM_AVAILABLE = True
+except:
+    SPARSE_ADAM_AVAILABLE = False
+
+def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
+
+    if not SPARSE_ADAM_AVAILABLE and opt.optimizer_type == "sparse_adam":
+        sys.exit(f"Trying to use sparse adam but it is not installed, please install the correct rasterizer using pip install [3dgs_accel].")
+
+    first_iter = 0
+    tb_writer = prepare_output_and_logger(dataset)
+    gaussians = GaussianModel(dataset.sh_degree, opt.optimizer_type)
+    scene = Scene(dataset, gaussians)
+    gaussians.training_setup(opt)
+    if checkpoint:
+        (model_params, first_iter) = torch.load(checkpoint)
+        gaussians.restore(model_params, opt)
+
+    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
+    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
+
+    iter_start = torch.cuda.Event(enable_timing = True)
+    iter_end = torch.cuda.Event(enable_timing = True)
+
+    use_sparse_adam = opt.optimizer_type == "sparse_adam" and SPARSE_ADAM_AVAILABLE 
+    depth_l1_weight = get_expon_lr_func(opt.depth_l1_weight_init, opt.depth_l1_weight_final, max_steps=opt.iterations)
+
+    viewpoint_stack = scene.getTrainCameras().copy()
+    viewpoint_indices = list(range(len(viewpoint_stack)))
+    ema_loss_for_log = 0.0
+    ema_Ll1depth_for_log = 0.0
+
+    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
+    first_iter += 1
+    for iteration in range(first_iter, opt.iterations + 1):
+        if network_gui.conn == None:
+            network_gui.try_connect()
+        while network_gui.conn != None:
+            try:
+                net_image_bytes = None
+                custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive()
+                if custom_cam != None:
+                    net_image = render(custom_cam, gaussians, pipe, background, scaling_modifier=scaling_modifer, use_trained_exp=dataset.train_test_exp, separate_sh=SPARSE_ADAM_AVAILABLE)["render"]
+                    net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy())
+                network_gui.send(net_image_bytes, dataset.source_path)
+                if do_training and ((iteration < int(opt.iterations)) or not keep_alive):
+                    break
+            except Exception as e:
+                network_gui.conn = None
+
+        iter_start.record()
+
+        gaussians.update_learning_rate(iteration)
+
+        # Every 1000 its we increase the levels of SH up to a maximum degree
+        if iteration % 1000 == 0:
+            gaussians.oneupSHdegree()
+
+        # Pick a random Camera
+        if not viewpoint_stack:
+            viewpoint_stack = scene.getTrainCameras().copy()
+            viewpoint_indices = list(range(len(viewpoint_stack)))
+        rand_idx = randint(0, len(viewpoint_indices) - 1)
+        viewpoint_cam = viewpoint_stack.pop(rand_idx)
+        vind = viewpoint_indices.pop(rand_idx)
+
+        # Render
+        if (iteration - 1) == 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, use_trained_exp=dataset.train_test_exp, separate_sh=SPARSE_ADAM_AVAILABLE)
+        image, viewspace_point_tensor, visibility_filter, radii = render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]
+
+        if viewpoint_cam.alpha_mask is not None:
+            alpha_mask = viewpoint_cam.alpha_mask.cuda()
+            image *= alpha_mask
+
+        # Loss
+        gt_image = viewpoint_cam.original_image.cuda()
+        Ll1 = l1_loss(image, gt_image)
+        if FUSED_SSIM_AVAILABLE:
+            ssim_value = fused_ssim(image.unsqueeze(0), gt_image.unsqueeze(0))
+        else:
+            ssim_value = ssim(image, gt_image)
+
+        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim_value)
+
+        # Depth regularization
+        Ll1depth_pure = 0.0
+        if depth_l1_weight(iteration) > 0 and viewpoint_cam.depth_reliable:
+            invDepth = render_pkg["depth"]
+            mono_invdepth = viewpoint_cam.invdepthmap.cuda()
+            depth_mask = viewpoint_cam.depth_mask.cuda()
+
+            Ll1depth_pure = torch.abs((invDepth  - mono_invdepth) * depth_mask).mean()
+            Ll1depth = depth_l1_weight(iteration) * Ll1depth_pure 
+            loss += Ll1depth
+            Ll1depth = Ll1depth.item()
+        else:
+            Ll1depth = 0
+
+        loss.backward()
+
+        iter_end.record()
+
+        with torch.no_grad():
+            # Progress bar
+            ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
+            ema_Ll1depth_for_log = 0.4 * Ll1depth + 0.6 * ema_Ll1depth_for_log
+
+            if iteration % 10 == 0:
+                progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{7}f}", "Depth Loss": f"{ema_Ll1depth_for_log:.{7}f}"})
+                progress_bar.update(10)
+            if iteration == opt.iterations:
+                progress_bar.close()
+
+            # Log and save
+            training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background, 1., SPARSE_ADAM_AVAILABLE, None, dataset.train_test_exp), dataset.train_test_exp)
+            if (iteration in saving_iterations):
+                print("\n[ITER {}] Saving Gaussians".format(iteration))
+                scene.save(iteration)
+
+            # Densification
+            if iteration < opt.densify_until_iter:
+                # Keep track of max radii in image-space for pruning
+                gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
+                gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)
+
+                if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
+                    size_threshold = 20 if iteration > opt.opacity_reset_interval else None
+                    gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold, radii)
+                
+                if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
+                    gaussians.reset_opacity()
+
+            # Optimizer step
+            if iteration < opt.iterations:
+                gaussians.exposure_optimizer.step()
+                gaussians.exposure_optimizer.zero_grad(set_to_none = True)
+                if use_sparse_adam:
+                    visible = radii > 0
+                    gaussians.optimizer.step(visible, radii.shape[0])
+                    gaussians.optimizer.zero_grad(set_to_none = True)
+                else:
+                    gaussians.optimizer.step()
+                    gaussians.optimizer.zero_grad(set_to_none = True)
+
+            if (iteration in checkpoint_iterations):
+                print("\n[ITER {}] Saving Checkpoint".format(iteration))
+                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
+
+def prepare_output_and_logger(args):    
+    if not args.model_path:
+        if os.getenv('OAR_JOB_ID'):
+            unique_str=os.getenv('OAR_JOB_ID')
+        else:
+            unique_str = str(uuid.uuid4())
+        args.model_path = os.path.join("./output/", unique_str[0:10])
+        
+    # Set up output folder
+    print("Output folder: {}".format(args.model_path))
+    os.makedirs(args.model_path, exist_ok = True)
+    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
+        cfg_log_f.write(str(Namespace(**vars(args))))
+
+    # Create Tensorboard writer
+    tb_writer = None
+    if TENSORBOARD_FOUND:
+        tb_writer = SummaryWriter(args.model_path)
+    else:
+        print("Tensorboard not available: not logging progress")
+    return tb_writer
+
+def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs, train_test_exp):
+    if tb_writer:
+        tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration)
+        tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
+        tb_writer.add_scalar('iter_time', elapsed, iteration)
+
+    # Report test and samples of training set
+    if iteration in testing_iterations:
+        torch.cuda.empty_cache()
+        validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()}, 
+                              {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})
+
+        for config in validation_configs:
+            if config['cameras'] and len(config['cameras']) > 0:
+                l1_test = 0.0
+                psnr_test = 0.0
+                for idx, viewpoint in enumerate(config['cameras']):
+                    image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0)
+                    gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
+                    if train_test_exp:
+                        image = image[..., image.shape[-1] // 2:]
+                        gt_image = gt_image[..., gt_image.shape[-1] // 2:]
+                    if tb_writer and (idx < 5):
+                        tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
+                        if iteration == testing_iterations[0]:
+                            tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration)
+                    l1_test += l1_loss(image, gt_image).mean().double()
+                    psnr_test += psnr(image, gt_image).mean().double()
+                psnr_test /= len(config['cameras'])
+                l1_test /= len(config['cameras'])          
+                print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
+                if tb_writer:
+                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
+                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)
+
+        if tb_writer:
+            tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
+            tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
+        torch.cuda.empty_cache()
+
+if __name__ == "__main__":
+    # Set up command line argument parser
+    parser = ArgumentParser(description="Training script parameters")
+    lp = ModelParams(parser)
+    op = OptimizationParams(parser)
+    pp = PipelineParams(parser)
+    parser.add_argument('--ip', type=str, default="127.0.0.1")
+    parser.add_argument('--port', type=int, default=6009)
+    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('--disable_viewer', action='store_true', default=False)
+    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
+    parser.add_argument("--start_checkpoint", type=str, default = None)
+    args = parser.parse_args(sys.argv[1:])
+    args.save_iterations.append(args.iterations)
+    
+    print("Optimizing " + args.model_path)
+
+    # Initialize system state (RNG)
+    safe_state(args.quiet)
+
+    # Start GUI server, configure and run training
+    if not args.disable_viewer:
+        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
+    print("\nTraining complete.")