main.py

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)