""" Copyright (c) 2022 Ruilong Li, UC Berkeley. """ import argparse import math import pathlib import time import imageio import numpy as np import torch import torch.nn.functional as F import tqdm from lpips import LPIPS from radiance_fields.ngp import NGPRadianceField from examples.utils import ( MIPNERF360_UNBOUNDED_SCENES, NERF_SYNTHETIC_SCENES, render_image_with_occgrid, render_image_with_occgrid_test, set_random_seed, ) from nerfacc.estimators.occ_grid import OccGridEstimator def run(args): device = "cuda:0" set_random_seed(42) if args.scene in MIPNERF360_UNBOUNDED_SCENES: from datasets.nerf_360_v2 import SubjectLoader # training parameters max_steps = 20000 init_batch_size = 1024 target_sample_batch_size = 1 << 18 weight_decay = 0.0 # scene parameters aabb = torch.tensor([-1.0, -1.0, -1.0, 1.0, 1.0, 1.0], device=device) near_plane = 0.2 far_plane = 1.0e10 # dataset parameters train_dataset_kwargs = {"color_bkgd_aug": "random", "factor": 4} test_dataset_kwargs = {"factor": 4} # model parameters grid_resolution = 128 grid_nlvl = 4 # render parameters render_step_size = 1e-3 alpha_thre = 1e-2 cone_angle = 0.004 else: from datasets.nerf_synthetic import SubjectLoader # training parameters max_steps = 20000 init_batch_size = 1024 target_sample_batch_size = 1 << 18 weight_decay = ( 1e-5 if args.scene in ["materials", "ficus", "drums"] else 1e-6 ) # scene parameters aabb = torch.tensor([-1.5, -1.5, -1.5, 1.5, 1.5, 1.5], device=device) near_plane = 0.0 far_plane = 1.0e10 # dataset parameters train_dataset_kwargs = {} test_dataset_kwargs = {} # model parameters grid_resolution = 128 grid_nlvl = 1 # render parameters render_step_size = 5e-3 alpha_thre = 0.0 cone_angle = 0.0 train_dataset = SubjectLoader( subject_id=args.scene, root_fp=args.data_root, split=args.train_split, num_rays=init_batch_size, device=device, **train_dataset_kwargs, ) test_dataset = SubjectLoader( subject_id=args.scene, root_fp=args.data_root, split="test", num_rays=None, device=device, **test_dataset_kwargs, ) estimator = OccGridEstimator( roi_aabb=aabb, resolution=grid_resolution, levels=grid_nlvl ).to(device) # setup the radiance field we want to train. grad_scaler = torch.cuda.amp.GradScaler(2**10) radiance_field = NGPRadianceField(aabb=estimator.aabbs[-1]).to(device) optimizer = torch.optim.Adam( radiance_field.parameters(), lr=1e-2, eps=1e-15, weight_decay=weight_decay ) scheduler = torch.optim.lr_scheduler.ChainedScheduler( [ torch.optim.lr_scheduler.LinearLR( optimizer, start_factor=0.01, total_iters=100 ), torch.optim.lr_scheduler.MultiStepLR( optimizer, milestones=[ max_steps // 2, max_steps * 3 // 4, max_steps * 9 // 10, ], gamma=0.33, ), ] ) lpips_net = LPIPS(net="vgg").to(device) lpips_norm_fn = lambda x: x[None, ...].permute(0, 3, 1, 2) * 2 - 1 lpips_fn = lambda x, y: lpips_net(lpips_norm_fn(x), lpips_norm_fn(y)).mean() # training tic = time.time() for step in range(max_steps + 1): radiance_field.train() estimator.train() i = torch.randint(0, len(train_dataset), (1,)).item() data = train_dataset[i] render_bkgd = data["color_bkgd"] rays = data["rays"] pixels = data["pixels"] def occ_eval_fn(x): density = radiance_field.query_density(x) return density * render_step_size # update occupancy grid estimator.update_every_n_steps( step=step, occ_eval_fn=occ_eval_fn, occ_thre=1e-2, ) # render rgb, acc, depth, n_rendering_samples = render_image_with_occgrid( radiance_field, estimator, rays, # rendering options near_plane=near_plane, render_step_size=render_step_size, render_bkgd=render_bkgd, cone_angle=cone_angle, alpha_thre=alpha_thre, ) if n_rendering_samples == 0: continue if target_sample_batch_size > 0: # dynamic batch size for rays to keep sample batch size constant. num_rays = len(pixels) num_rays = int( num_rays * (target_sample_batch_size / float(n_rendering_samples)) ) train_dataset.update_num_rays(num_rays) # compute loss loss = F.smooth_l1_loss(rgb, pixels) optimizer.zero_grad() # do not unscale it because we are using Adam. grad_scaler.scale(loss).backward() optimizer.step() scheduler.step() if step % 10000 == 0: elapsed_time = time.time() - tic loss = F.mse_loss(rgb, pixels) psnr = -10.0 * torch.log(loss) / np.log(10.0) print( f"elapsed_time={elapsed_time:.2f}s | step={step} | " f"loss={loss:.5f} | psnr={psnr:.2f} | " f"n_rendering_samples={n_rendering_samples:d} | num_rays={len(pixels):d} | " f"max_depth={depth.max():.3f} | " ) if step > 0 and step % max_steps == 0: # evaluation radiance_field.eval() estimator.eval() psnrs = [] lpips = [] with torch.no_grad(): for i in tqdm.tqdm(range(len(test_dataset))): data = test_dataset[i] render_bkgd = data["color_bkgd"] rays = data["rays"] pixels = data["pixels"] # rendering # rgb, acc, depth, _ = render_image_with_occgrid_test( # 1024, # # scene # radiance_field, # estimator, # rays, # # rendering options # near_plane=near_plane, # render_step_size=render_step_size, # render_bkgd=render_bkgd, # cone_angle=cone_angle, # alpha_thre=alpha_thre, # ) rgb, acc, depth, _ = render_image_with_occgrid( radiance_field, estimator, rays, # rendering options near_plane=near_plane, render_step_size=render_step_size, render_bkgd=render_bkgd, cone_angle=cone_angle, alpha_thre=alpha_thre, ) mse = F.mse_loss(rgb, pixels) psnr = -10.0 * torch.log(mse) / np.log(10.0) psnrs.append(psnr.item()) lpips.append(lpips_fn(rgb, pixels).item()) # if i == 0: # imageio.imwrite( # "rgb_test.png", # (rgb.cpu().numpy() * 255).astype(np.uint8), # ) # imageio.imwrite( # "rgb_error.png", # ( # (rgb - pixels).norm(dim=-1).cpu().numpy() * 255 # ).astype(np.uint8), # ) psnr_avg = sum(psnrs) / len(psnrs) lpips_avg = sum(lpips) / len(lpips) print(f"evaluation: psnr_avg={psnr_avg}, lpips_avg={lpips_avg}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--data_root", type=str, # default=str(pathlib.Path.cwd() / "data/360_v2"), default=str(pathlib.Path.cwd() / "data/nerf_synthetic"), help="the root dir of the dataset", ) parser.add_argument( "--train_split", type=str, default="train", choices=["train", "trainval"], help="which train split to use", ) parser.add_argument( "--scene", type=str, default="lego", choices=NERF_SYNTHETIC_SCENES + MIPNERF360_UNBOUNDED_SCENES, help="which scene to use", ) args = parser.parse_args() run(args)