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| import torch | |
| import argparse | |
| from nerf.provider import NeRFDataset | |
| from nerf.utils import * | |
| from optimizer import Shampoo | |
| from nerf.gui import NeRFGUI | |
| # torch.autograd.set_detect_anomaly(True) | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--text', default=None, help="text prompt") | |
| parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --dir_text") | |
| parser.add_argument('-O2', action='store_true', help="equals --fp16 --dir_text") | |
| parser.add_argument('--test', action='store_true', help="test mode") | |
| parser.add_argument('--save_mesh', action='store_true', help="export an obj mesh with texture") | |
| parser.add_argument('--eval_interval', type=int, default=10, help="evaluate on the valid set every interval epochs") | |
| parser.add_argument('--workspace', type=str, default='workspace') | |
| parser.add_argument('--guidance', type=str, default='stable-diffusion', help='choose from [stable-diffusion, clip]') | |
| parser.add_argument('--seed', type=int, default=0) | |
| ### training options | |
| parser.add_argument('--iters', type=int, default=10000, help="training iters") | |
| parser.add_argument('--lr', type=float, default=1e-3, help="initial learning rate") | |
| parser.add_argument('--ckpt', type=str, default='latest') | |
| parser.add_argument('--cuda_ray', action='store_true', help="use CUDA raymarching instead of pytorch") | |
| parser.add_argument('--max_steps', type=int, default=1024, help="max num steps sampled per ray (only valid when using --cuda_ray)") | |
| parser.add_argument('--num_steps', type=int, default=64, help="num steps sampled per ray (only valid when not using --cuda_ray)") | |
| parser.add_argument('--upsample_steps', type=int, default=64, help="num steps up-sampled per ray (only valid when not using --cuda_ray)") | |
| parser.add_argument('--update_extra_interval', type=int, default=16, help="iter interval to update extra status (only valid when using --cuda_ray)") | |
| parser.add_argument('--max_ray_batch', type=int, default=4096, help="batch size of rays at inference to avoid OOM (only valid when not using --cuda_ray)") | |
| parser.add_argument('--albedo_iters', type=int, default=1000, help="training iters that only use albedo shading") | |
| # model options | |
| parser.add_argument('--bg_radius', type=float, default=1.4, help="if positive, use a background model at sphere(bg_radius)") | |
| parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied") | |
| # network backbone | |
| parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training") | |
| parser.add_argument('--backbone', type=str, default='grid', help="nerf backbone, choose from [grid, tcnn, vanilla]") | |
| # rendering resolution in training, decrease this if CUDA OOM. | |
| parser.add_argument('--w', type=int, default=64, help="render width for NeRF in training") | |
| parser.add_argument('--h', type=int, default=64, help="render height for NeRF in training") | |
| parser.add_argument('--jitter_pose', action='store_true', help="add jitters to the randomly sampled camera poses") | |
| ### dataset options | |
| parser.add_argument('--bound', type=float, default=1, help="assume the scene is bounded in box(-bound, bound)") | |
| parser.add_argument('--dt_gamma', type=float, default=0, help="dt_gamma (>=0) for adaptive ray marching. set to 0 to disable, >0 to accelerate rendering (but usually with worse quality)") | |
| parser.add_argument('--min_near', type=float, default=0.1, help="minimum near distance for camera") | |
| parser.add_argument('--radius_range', type=float, nargs='*', default=[1.0, 1.5], help="training camera radius range") | |
| parser.add_argument('--fovy_range', type=float, nargs='*', default=[40, 70], help="training camera fovy range") | |
| parser.add_argument('--dir_text', action='store_true', help="direction-encode the text prompt, by appending front/side/back/overhead view") | |
| parser.add_argument('--angle_overhead', type=float, default=30, help="[0, angle_overhead] is the overhead region") | |
| parser.add_argument('--angle_front', type=float, default=60, help="[0, angle_front] is the front region, [180, 180+angle_front] the back region, otherwise the side region.") | |
| parser.add_argument('--lambda_entropy', type=float, default=1e-4, help="loss scale for alpha entropy") | |
| parser.add_argument('--lambda_opacity', type=float, default=0, help="loss scale for alpha value") | |
| parser.add_argument('--lambda_orient', type=float, default=1e-2, help="loss scale for orientation") | |
| ### GUI options | |
| parser.add_argument('--gui', action='store_true', help="start a GUI") | |
| parser.add_argument('--W', type=int, default=800, help="GUI width") | |
| parser.add_argument('--H', type=int, default=800, help="GUI height") | |
| parser.add_argument('--radius', type=float, default=3, help="default GUI camera radius from center") | |
| parser.add_argument('--fovy', type=float, default=60, help="default GUI camera fovy") | |
| parser.add_argument('--light_theta', type=float, default=60, help="default GUI light direction in [0, 180], corresponding to elevation [90, -90]") | |
| parser.add_argument('--light_phi', type=float, default=0, help="default GUI light direction in [0, 360), azimuth") | |
| parser.add_argument('--max_spp', type=int, default=1, help="GUI rendering max sample per pixel") | |
| opt = parser.parse_args() | |
| if opt.O: | |
| opt.fp16 = True | |
| opt.dir_text = True | |
| # use occupancy grid to prune ray sampling, faster rendering. | |
| opt.cuda_ray = True | |
| # opt.lambda_entropy = 1e-4 | |
| # opt.lambda_opacity = 0 | |
| elif opt.O2: | |
| opt.fp16 = True | |
| opt.dir_text = True | |
| opt.lambda_entropy = 1e-4 # necessary to keep non-empty | |
| opt.lambda_opacity = 3e-3 # no occupancy grid, so use a stronger opacity loss. | |
| if opt.backbone == 'vanilla': | |
| from nerf.network import NeRFNetwork | |
| elif opt.backbone == 'tcnn': | |
| from nerf.network_tcnn import NeRFNetwork | |
| elif opt.backbone == 'grid': | |
| from nerf.network_grid import NeRFNetwork | |
| else: | |
| raise NotImplementedError(f'--backbone {opt.backbone} is not implemented!') | |
| print(opt) | |
| seed_everything(opt.seed) | |
| model = NeRFNetwork(opt) | |
| print(model) | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| if opt.test: | |
| guidance = None # no need to load guidance model at test | |
| trainer = Trainer('df', opt, model, guidance, device=device, workspace=opt.workspace, fp16=opt.fp16, use_checkpoint=opt.ckpt) | |
| if opt.gui: | |
| gui = NeRFGUI(opt, trainer) | |
| gui.render() | |
| else: | |
| test_loader = NeRFDataset(opt, device=device, type='test', H=opt.H, W=opt.W, size=100).dataloader() | |
| trainer.test(test_loader) | |
| if opt.save_mesh: | |
| trainer.save_mesh(resolution=256) | |
| else: | |
| if opt.guidance == 'stable-diffusion': | |
| from nerf.sd import StableDiffusion | |
| guidance = StableDiffusion(device) | |
| elif opt.guidance == 'clip': | |
| from nerf.clip import CLIP | |
| guidance = CLIP(device) | |
| else: | |
| raise NotImplementedError(f'--guidance {opt.guidance} is not implemented.') | |
| optimizer = lambda model: torch.optim.Adam(model.get_params(opt.lr), betas=(0.9, 0.99), eps=1e-15) | |
| # optimizer = lambda model: Shampoo(model.get_params(opt.lr)) | |
| train_loader = NeRFDataset(opt, device=device, type='train', H=opt.h, W=opt.w, size=100).dataloader() | |
| scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 0.1 ** min(iter / opt.iters, 1)) | |
| # scheduler = lambda optimizer: optim.lr_scheduler.OneCycleLR(optimizer, max_lr=opt.lr, total_steps=opt.iters, pct_start=0.1) | |
| trainer = Trainer('df', opt, model, guidance, device=device, workspace=opt.workspace, optimizer=optimizer, ema_decay=None, fp16=opt.fp16, lr_scheduler=scheduler, use_checkpoint=opt.ckpt, eval_interval=opt.eval_interval, scheduler_update_every_step=True) | |
| if opt.gui: | |
| trainer.train_loader = train_loader # attach dataloader to trainer | |
| gui = NeRFGUI(opt, trainer) | |
| gui.render() | |
| else: | |
| valid_loader = NeRFDataset(opt, device=device, type='val', H=opt.H, W=opt.W, size=5).dataloader() | |
| max_epoch = np.ceil(opt.iters / len(train_loader)).astype(np.int32) | |
| trainer.train(train_loader, valid_loader, max_epoch) | |
| # also test | |
| test_loader = NeRFDataset(opt, device=device, type='test', H=opt.H, W=opt.W, size=100).dataloader() | |
| trainer.test(test_loader) | |
| if opt.save_mesh: | |
| trainer.save_mesh(resolution=256) |