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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=0.95, 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)
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