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main_insta.py
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import argparse
from functools import partial
from insta.gui import NeRFGUI
from insta.provider import NeRFDataset
from insta.utils import *
from loss import huber_loss
# torch.autograd.set_detect_anomaly(True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
parser.add_argument('-O', action='store_true', help="equals --fp16 --cuda_ray --preload")
parser.add_argument('--test', action='store_true', help="test mode")
parser.add_argument('--workspace', type=str, default='workspace')
parser.add_argument('--seed', type=int, default=49)
### training options
parser.add_argument('--iters', type=int, default=100000, help="training iters")
parser.add_argument('--val_freq', type=int, default=10, help="training iters")
parser.add_argument('--lr', type=float, default=0.0025, help="initial learning rate")
# parser.add_argument('--lr', type=float, default=1e-2, help="initial learning rate")
parser.add_argument('--ckpt', type=str, default='latest')
parser.add_argument('--num_rays', type=int, default=4096, help="num rays sampled per image for each training step")
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=1024, help="num steps sampled per ray (only valid when NOT using --cuda_ray)")
parser.add_argument('--upsample_steps', type=int, default=0, 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('--patch_size', type=int, default=1, help="[experimental] render patches in training, so as to apply LPIPS loss. 1 means disabled, use [64, 32, 16] to enable")
### network backbone options
parser.add_argument('--fp16', action='store_true', help="use amp mixed precision training")
parser.add_argument('--ff', action='store_true', help="use fully-fused MLP")
parser.add_argument('--tcnn', action='store_true', help="use TCNN backend")
### dataset options
parser.add_argument('--color_space', type=str, default='srgb', help="Color space, supports (linear, srgb)")
parser.add_argument('--preload', action='store_true', help="preload all data into GPU, accelerate training but use more GPU memory")
# (the default value is for the fox dataset)
parser.add_argument('--bound', type=float, default=2, help="assume the scene is bounded in box[-bound, bound]^3, if > 1, will invoke adaptive ray marching.")
parser.add_argument('--scale', type=float, default=1, help="scale camera location into box[-bound, bound]^3")
parser.add_argument('--offset', type=float, nargs='*', default=[0, 0, 0], help="offset of camera location")
parser.add_argument('--dt_gamma', type=float, default=1 / 256, 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=0.01, help="minimum near distance for camera")
parser.add_argument('--density_thresh', type=float, default=10, help="threshold for density grid to be occupied")
parser.add_argument('--bg_radius', type=float, default=-1, help="if positive, use a background model at sphere(bg_radius)")
### GUI options
parser.add_argument('--gui', action='store_true', help="start a GUI")
parser.add_argument('--W', type=int, default=512, help="GUI width")
parser.add_argument('--H', type=int, default=512, help="GUI height")
parser.add_argument('--radius', type=float, default=5, help="default GUI camera radius from center")
parser.add_argument('--fovy', type=float, default=50, help="default GUI camera fovy")
parser.add_argument('--max_spp', type=int, default=64, help="GUI rendering max sample per pixel")
### experimental
parser.add_argument('--error_map', action='store_true', help="use error map to sample rays")
parser.add_argument('--clip_text', type=str, default='', help="text input for CLIP guidance")
parser.add_argument('--rand_pose', type=int, default=-1, help="<0 uses no rand pose, =0 only uses rand pose, >0 sample one rand pose every $ known poses")
opt = parser.parse_args()
if opt.O:
opt.fp16 = True
opt.cuda_ray = True
opt.preload = True
if opt.patch_size > 1:
opt.error_map = False # do not use error_map if use patch-based training
# assert opt.patch_size > 16, "patch_size should > 16 to run LPIPS loss."
assert opt.num_rays % (opt.patch_size ** 2) == 0, "patch_size ** 2 should be dividable by num_rays."
if opt.ff:
opt.fp16 = True
assert opt.bg_radius <= 0, "background model is not implemented for --ff"
from insta.network_ff import NeRFNetwork
elif opt.tcnn:
opt.fp16 = True
assert opt.bg_radius <= 0, "background model is not implemented for --tcnn"
from insta.network_tcnn import NeRFNetwork
else:
from insta.network import NeRFNetwork
print(opt)
seed_everything(opt.seed)
model = NeRFNetwork(
encoding="hashgrid",
bound=opt.bound,
cuda_ray=opt.cuda_ray,
density_scale=1,
min_near=opt.min_near,
density_thresh=opt.density_thresh,
bg_radius=opt.bg_radius,
)
print(model)
# criterion = torch.nn.MSELoss(reduction='none')
criterion = partial(huber_loss, reduction='none')
# criterion = torch.nn.HuberLoss(reduction='none', beta=0.1) # only available after torch 1.10 ?
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if opt.test:
metrics = [PSNRMeter(), LPIPSMeter(device=device)]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, criterion=criterion, fp16=opt.fp16, metrics=metrics, use_checkpoint=opt.ckpt)
if opt.gui:
gui = NeRFGUI(opt, trainer)
gui.render()
else:
test_dataset = NeRFDataset(opt, device=device, type='test').dataloader()
test_loader = test_dataset.dataloader()
model.canonical_triangles = test_dataset.canonical_triangles
model.topology = test_dataset.topology
model.edge_face_mask = test_dataset.edge_face_mask
if test_loader.has_gt:
trainer.evaluate(test_loader) # blender has gt, so evaluate it.
trainer.test(test_loader, write_video=True) # test and save video
trainer.save_mesh(resolution=256, threshold=10)
else:
optimizer = lambda model: torch.optim.Adam(model.get_params(opt.lr), betas=(0.9, 0.99), eps=1e-15)
train_dataset = NeRFDataset(opt, device=device, type='train')
train_loader = train_dataset.dataloader()
model.canonical_triangles = train_dataset.canonical_triangles
model.topology = train_dataset.topology
model.edge_face_mask = train_dataset.edge_face_mask
# decay to 0.1 * init_lr at last iter step
scheduler = None
# scheduler = lambda optimizer: optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 0.1 ** min(iter / opt.iters, 1))
metrics = [PSNRMeter(), LPIPSMeter(device=device)]
trainer = Trainer('ngp', opt, model, device=device, workspace=opt.workspace, lr_scheduler=scheduler, optimizer=optimizer, criterion=criterion, ema_decay=0.95, fp16=opt.fp16, scheduler_update_every_step=True, metrics=metrics, use_checkpoint=opt.ckpt, eval_interval=opt.val_freq)
if opt.gui:
gui = NeRFGUI(opt, trainer, train_loader)
gui.render()
else:
valid_loader = NeRFDataset(opt, device=device, type='val', downscale=1).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').dataloader()
# if test_loader.has_gt:
# trainer.evaluate(test_loader) # blender has gt, so evaluate it.
trainer.test(test_loader, write_video=True) # test and save video
# trainer.save_mesh(resolution=256, threshold=10)