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opt.py
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opt.py
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# Copyright 2021 Alex Yu
# First, install svox2
# Then, python opt.py <path_to>/nerf_synthetic/<scene> -t ckpt/<some_name>
# or use launching script: sh launch.sh <EXP_NAME> <GPU> <DATA_DIR>
import torch
import torch.cuda
import torch.optim
import torch.nn.functional as F
import svox2
import json
import imageio
import os
from os import path
import shutil
import gc
import numpy as np
import math
import argparse
import cv2
from util.dataset import datasets
from util.util import Timing, get_expon_lr_func, generate_dirs_equirect, viridis_cmap
from util import config_util
from warnings import warn
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from typing import NamedTuple, Optional, Union
device = "cuda" if torch.cuda.is_available() else "cpu"
parser = argparse.ArgumentParser()
config_util.define_common_args(parser)
group = parser.add_argument_group("general")
group.add_argument('--train_dir', '-t', type=str, default='ckpt',
help='checkpoint and logging directory')
group.add_argument('--reso',
type=str,
default=
"[[256, 256, 256], [512, 512, 512]]",
help='List of grid resolution (will be evaled as json);'
'resamples to the next one every upsamp_every iters, then ' +
'stays at the last one; ' +
'should be a list where each item is a list of 3 ints or an int')
group.add_argument('--upsamp_every', type=int, default=
3 * 12800,
help='upsample the grid every x iters')
group.add_argument('--init_iters', type=int, default=
0,
help='do not upsample for first x iters')
group.add_argument('--upsample_density_add', type=float, default=
0.0,
help='add the remaining density by this amount when upsampling')
group.add_argument('--basis_type',
choices=['sh', '3d_texture', 'mlp'],
default='sh',
help='Basis function type')
group.add_argument('--basis_reso', type=int, default=32,
help='basis grid resolution (only for learned texture)')
group.add_argument('--sh_dim', type=int, default=9, help='SH/learned basis dimensions (at most 10)')
group.add_argument('--mlp_posenc_size', type=int, default=4, help='Positional encoding size if using MLP basis; 0 to disable')
group.add_argument('--mlp_width', type=int, default=32, help='MLP width if using MLP basis')
group.add_argument('--background_nlayers', type=int, default=0,#32,
help='Number of background layers (0=disable BG model)')
group.add_argument('--background_reso', type=int, default=512, help='Background resolution')
group = parser.add_argument_group("optimization")
group.add_argument('--n_iters', type=int, default=10 * 12800, help='total number of iters to optimize for')
group.add_argument('--batch_size', type=int, default=
5000,
#100000,
# 2000,
help='batch size')
# TODO: make the lr higher near the end
group.add_argument('--sigma_optim', choices=['sgd', 'rmsprop'], default='rmsprop', help="Density optimizer")
group.add_argument('--lr_sigma', type=float, default=3e1, help='SGD/rmsprop lr for sigma')
group.add_argument('--lr_sigma_final', type=float, default=5e-2)
group.add_argument('--lr_sigma_decay_steps', type=int, default=250000)
group.add_argument('--lr_sigma_delay_steps', type=int, default=15000,
help="Reverse cosine steps (0 means disable)")
group.add_argument('--lr_sigma_delay_mult', type=float, default=1e-2)#1e-4)#1e-4)
group.add_argument('--sh_optim', choices=['sgd', 'rmsprop'], default='rmsprop', help="SH optimizer")
group.add_argument('--lr_sh', type=float, default=
1e-2,
help='SGD/rmsprop lr for SH')
group.add_argument('--lr_sh_final', type=float,
default=
5e-6
)
group.add_argument('--lr_sh_decay_steps', type=int, default=250000)
group.add_argument('--lr_sh_delay_steps', type=int, default=0, help="Reverse cosine steps (0 means disable)")
group.add_argument('--lr_sh_delay_mult', type=float, default=1e-2)
# BG LRs
group.add_argument('--bg_optim', choices=['sgd', 'rmsprop'], default='rmsprop', help="Background optimizer")
group.add_argument('--lr_sigma_bg', type=float, default=3e0,
help='SGD/rmsprop lr for background')
group.add_argument('--lr_sigma_bg_final', type=float, default=3e-3,
help='SGD/rmsprop lr for background')
group.add_argument('--lr_sigma_bg_decay_steps', type=int, default=250000)
group.add_argument('--lr_sigma_bg_delay_steps', type=int, default=0, help="Reverse cosine steps (0 means disable)")
group.add_argument('--lr_sigma_bg_delay_mult', type=float, default=1e-2)
group.add_argument('--lr_color_bg', type=float, default=1e-1,
help='SGD/rmsprop lr for background')
group.add_argument('--lr_color_bg_final', type=float, default=5e-6,#1e-4,
help='SGD/rmsprop lr for background')
group.add_argument('--lr_color_bg_decay_steps', type=int, default=250000)
group.add_argument('--lr_color_bg_delay_steps', type=int, default=0, help="Reverse cosine steps (0 means disable)")
group.add_argument('--lr_color_bg_delay_mult', type=float, default=1e-2)
# END BG LRs
group.add_argument('--basis_optim', choices=['sgd', 'rmsprop'], default='rmsprop', help="Learned basis optimizer")
group.add_argument('--lr_basis', type=float, default=#2e6,
1e-6,
help='SGD/rmsprop lr for SH')
group.add_argument('--lr_basis_final', type=float,
default=
1e-6
)
group.add_argument('--lr_basis_decay_steps', type=int, default=250000)
group.add_argument('--lr_basis_delay_steps', type=int, default=0,#15000,
help="Reverse cosine steps (0 means disable)")
group.add_argument('--lr_basis_begin_step', type=int, default=0)#4 * 12800)
group.add_argument('--lr_basis_delay_mult', type=float, default=1e-2)
group.add_argument('--rms_beta', type=float, default=0.95, help="RMSProp exponential averaging factor")
group.add_argument('--print_every', type=int, default=20, help='print every')
group.add_argument('--save_every', type=int, default=5,
help='save every x epochs')
group.add_argument('--eval_every', type=int, default=1,
help='evaluate every x epochs')
group.add_argument('--init_sigma', type=float,
default=0.1,
help='initialization sigma')
group = parser.add_argument_group("misc experiments")
group.add_argument('--thresh_type',
choices=["weight", "sigma"],
default="weight",
help='Upsample threshold type')
group.add_argument('--weight_thresh', type=float,
default=0.0005 * 512,
# default=0.025 * 512,
help='Upsample weight threshold; will be divided by resulting z-resolution')
group.add_argument('--density_thresh', type=float,
default=5.0,
help='Upsample sigma threshold')
group.add_argument('--background_density_thresh', type=float,
default=0.25,
help='Background sigma threshold for sparsification')
group.add_argument('--max_grid_elements', type=int,
default=44_000_000,
help='Max items to store after upsampling '
'(the number here is given for 22GB memory)')
group.add_argument('--tune_mode', action='store_true', default=False,
help='hypertuning mode (do not save, for speed)')
group.add_argument('--tune_nosave', action='store_true', default=False,
help='do not save any checkpoint even at the end')
group = parser.add_argument_group("losses")
# Foreground TV
group.add_argument('--lambda_tv', type=float, default=1e-5)
group.add_argument('--tv_sparsity', type=float, default=0.01)
group.add_argument('--tv_logalpha', action='store_true', default=False,
help='Use log(1-exp(-delta * sigma)) as in neural volumes')
group.add_argument('--lambda_tv_sh', type=float, default=1e-3)
group.add_argument('--tv_sh_sparsity', type=float, default=0.01)
group.add_argument('--lambda_tv_lumisphere', type=float, default=0.0)#1e-2)#1e-3)
group.add_argument('--tv_lumisphere_sparsity', type=float, default=0.01)
group.add_argument('--tv_lumisphere_dir_factor', type=float, default=0.0)
group.add_argument('--lambda_l2_sh', type=float, default=0.0)#1e-4)
group.add_argument('--tv_early_only', type=int, default=1, help="Turn off TV regularization after the first split/prune")
group.add_argument('--tv_contiguous', type=int, default=1,
help="Apply TV only on contiguous link chunks, which is faster")
# End Foreground TV
group.add_argument('--lambda_sparsity', type=float, default=
0.0,
help="Weight for sparsity loss as in SNeRG/PlenOctrees " +
"(but applied on the ray)")
group.add_argument('--lambda_beta', type=float, default=
0.0,
help="Weight for beta distribution sparsity loss as in neural volumes")
# Background TV
group.add_argument('--lambda_tv_background_sigma', type=float, default=1e-2)
group.add_argument('--lambda_tv_background_color', type=float, default=1e-2)
group.add_argument('--tv_background_sparsity', type=float, default=0.01)
# End Background TV
# Basis TV
group.add_argument('--lambda_tv_basis', type=float, default=0.0,
help='Learned basis total variation loss')
# End Basis TV
group.add_argument('--weight_decay_sigma', type=float, default=1.0)
group.add_argument('--weight_decay_sh', type=float, default=1.0)
group.add_argument('--lr_decay', action='store_true', default=True)
group.add_argument('--n_train', type=int, default=None, help='Number of training images. Defaults to use all avaiable.')
args = parser.parse_args()
config_util.maybe_merge_config_file(args)
assert args.lr_sigma_final <= args.lr_sigma, "lr_sigma must be >= lr_sigma_final"
assert args.lr_sh_final <= args.lr_sh, "lr_sh must be >= lr_sh_final"
assert args.lr_basis_final <= args.lr_basis, "lr_basis must be >= lr_basis_final"
os.makedirs(args.train_dir, exist_ok=True)
summary_writer = SummaryWriter(args.train_dir)
reso_list = json.loads(args.reso)
reso_id = 0
with open(path.join(args.train_dir, 'args.json'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
# Changed name to prevent errors
shutil.copyfile(__file__, path.join(args.train_dir, 'opt_frozen.py'))
torch.manual_seed(20200823)
np.random.seed(20200823)
factor = 1
dset = datasets[args.dataset_type](
args.data_dir,
split="train",
device=device,
factor=factor,
n_images=args.n_train,
**config_util.build_data_options(args))
if args.background_nlayers > 0 and not dset.should_use_background:
warn('Using a background model for dataset type ' + str(type(dset)) + ' which typically does not use background')
dset_test = datasets[args.dataset_type](
args.data_dir, split="test", **config_util.build_data_options(args))
grid = svox2.SparseGrid(reso=reso_list[reso_id],
center=dset.scene_center,
radius=dset.scene_radius,
use_sphere_bound=dset.use_sphere_bound,
basis_dim=args.sh_dim,
use_z_order=True,
device=device,
basis_reso=args.basis_reso,
basis_type=svox2.__dict__['BASIS_TYPE_' + args.basis_type.upper()],
mlp_posenc_size=args.mlp_posenc_size,
mlp_width=args.mlp_width,
background_nlayers=args.background_nlayers,
background_reso=args.background_reso)
# DC -> gray; mind the SH scaling!
grid.sh_data.data[:] = 0.0
grid.density_data.data[:] = args.init_sigma
if grid.use_background:
grid.background_data.data[..., -1] = args.init_sigma
grid.background_data.data[..., :-1] = 0.0 / svox2.utils.SH_C0
# grid.sh_data.data[:, 0] = 4.0
# osh = grid.density_data.data.shape
# den = grid.density_data.data.view(grid.links.shape)
# # den[:] = 0.00
# # den[:, :256, :] = 1e9
# # den[:, :, 0] = 1e9
# grid.density_data.data = den.view(osh)
optim_basis_mlp = None
if grid.basis_type == svox2.BASIS_TYPE_3D_TEXTURE:
grid.reinit_learned_bases(init_type='sh')
# grid.reinit_learned_bases(init_type='fourier')
# grid.reinit_learned_bases(init_type='sg', upper_hemi=True)
# grid.basis_data.data.normal_(mean=0.28209479177387814, std=0.001)
elif grid.basis_type == svox2.BASIS_TYPE_MLP:
# MLP!
optim_basis_mlp = torch.optim.Adam(
grid.basis_mlp.parameters(),
lr=args.lr_basis
)
grid.requires_grad_(True)
config_util.setup_render_opts(grid.opt, args)
print('Render options', grid.opt)
gstep_id_base = 0
resample_cameras = [
svox2.Camera(c2w.to(device=device),
dset.intrins.fx,
dset.intrins.fy,
dset.intrins.cx,
dset.intrins.cy,
width=dset.w,
height=dset.h,
ndc_coeffs=dset.ndc_coeffs) for c2w in dset.c2w
]
ckpt_path = path.join(args.train_dir, 'ckpt.npz')
lr_sigma_func = get_expon_lr_func(args.lr_sigma, args.lr_sigma_final, args.lr_sigma_delay_steps,
args.lr_sigma_delay_mult, args.lr_sigma_decay_steps)
lr_sh_func = get_expon_lr_func(args.lr_sh, args.lr_sh_final, args.lr_sh_delay_steps,
args.lr_sh_delay_mult, args.lr_sh_decay_steps)
lr_basis_func = get_expon_lr_func(args.lr_basis, args.lr_basis_final, args.lr_basis_delay_steps,
args.lr_basis_delay_mult, args.lr_basis_decay_steps)
lr_sigma_bg_func = get_expon_lr_func(args.lr_sigma_bg, args.lr_sigma_bg_final, args.lr_sigma_bg_delay_steps,
args.lr_sigma_bg_delay_mult, args.lr_sigma_bg_decay_steps)
lr_color_bg_func = get_expon_lr_func(args.lr_color_bg, args.lr_color_bg_final, args.lr_color_bg_delay_steps,
args.lr_color_bg_delay_mult, args.lr_color_bg_decay_steps)
lr_sigma_factor = 1.0
lr_sh_factor = 1.0
lr_basis_factor = 1.0
last_upsamp_step = args.init_iters
if args.enable_random:
warn("Randomness is enabled for training (normal for LLFF & scenes with background)")
epoch_id = -1
while True:
dset.shuffle_rays()
epoch_id += 1
epoch_size = dset.rays.origins.size(0)
batches_per_epoch = (epoch_size-1)//args.batch_size+1
# Test
def eval_step():
# Put in a function to avoid memory leak
print('Eval step')
with torch.no_grad():
stats_test = {'psnr' : 0.0, 'mse' : 0.0}
# Standard set
N_IMGS_TO_SAVE = min(5, dset_test.n_images) if not args.tune_mode else 1
N_IMGS_TO_EVAL = min(20 if epoch_id > 0 else 5, dset_test.n_images)
img_eval_interval = dset_test.n_images // N_IMGS_TO_EVAL
img_save_interval = (N_IMGS_TO_EVAL // N_IMGS_TO_SAVE)
img_ids = range(0, dset_test.n_images, img_eval_interval)
# Special 'very hard' specular + fuzz set
# img_ids = [2, 5, 7, 9, 21,
# 44, 45, 47, 49, 56,
# 80, 88, 99, 115, 120,
# 154]
img_save_interval = 1
n_images_gen = 0
for i, img_id in tqdm(enumerate(img_ids), total=len(img_ids)):
c2w = dset_test.c2w[img_id].to(device=device)
cam = svox2.Camera(c2w,
dset_test.intrins.fx,
dset_test.intrins.fy,
dset_test.intrins.cx,
dset_test.intrins.cy,
width=dset_test.w,
height=dset_test.h,
ndc_coeffs=dset_test.ndc_coeffs)
rgb_pred_test = grid.volume_render_image(cam, use_kernel=True)
rgb_gt_test = dset_test.gt[img_id].to(device=device)
all_mses = ((rgb_gt_test - rgb_pred_test) ** 2).cpu()
if i % img_save_interval == 0:
img_pred = rgb_pred_test.cpu()
img_pred.clamp_max_(1.0)
summary_writer.add_image(f'test/image_{img_id:04d}',
img_pred, global_step=gstep_id_base, dataformats='HWC')
mse_img = all_mses / all_mses.max()
summary_writer.add_image(f'test/mse_map_{img_id:04d}',
mse_img, global_step=gstep_id_base, dataformats='HWC')
rgb_pred_test = rgb_gt_test = None
mse_num : float = all_mses.mean().item()
psnr = -10.0 * math.log10(mse_num)
if math.isnan(psnr):
print('NAN PSNR', i, img_id, mse_num)
assert False
stats_test['mse'] += mse_num
stats_test['psnr'] += psnr
n_images_gen += 1
if grid.basis_type == svox2.BASIS_TYPE_3D_TEXTURE or \
grid.basis_type == svox2.BASIS_TYPE_MLP:
# Add spherical map visualization
EQ_RESO = 256
eq_dirs = generate_dirs_equirect(EQ_RESO * 2, EQ_RESO)
eq_dirs = torch.from_numpy(eq_dirs).to(device=device).view(-1, 3)
if grid.basis_type == svox2.BASIS_TYPE_MLP:
sphfuncs = grid._eval_basis_mlp(eq_dirs)
else:
sphfuncs = grid._eval_learned_bases(eq_dirs)
sphfuncs = sphfuncs.view(EQ_RESO, EQ_RESO*2, -1).permute([2, 0, 1]).cpu().numpy()
stats = [(sphfunc.min(), sphfunc.mean(), sphfunc.max())
for sphfunc in sphfuncs]
sphfuncs_cmapped = [viridis_cmap(sphfunc) for sphfunc in sphfuncs]
for im, (minv, meanv, maxv) in zip(sphfuncs_cmapped, stats):
cv2.putText(im, f"{minv=:.4f} {meanv=:.4f} {maxv=:.4f}", (10, 20),
0, 0.5, [255, 0, 0])
sphfuncs_cmapped = np.concatenate(sphfuncs_cmapped, axis=0)
summary_writer.add_image(f'test/spheric',
sphfuncs_cmapped, global_step=gstep_id_base, dataformats='HWC')
# END add spherical map visualization
stats_test['mse'] /= n_images_gen
stats_test['psnr'] /= n_images_gen
for stat_name in stats_test:
summary_writer.add_scalar('test/' + stat_name,
stats_test[stat_name], global_step=gstep_id_base)
summary_writer.add_scalar('epoch_id', float(epoch_id), global_step=gstep_id_base)
print('eval stats:', stats_test)
if epoch_id % max(factor, args.eval_every) == 0:
eval_step()
gc.collect()
def train_step():
print('Train step')
pbar = tqdm(enumerate(range(0, epoch_size, args.batch_size)), total=batches_per_epoch)
stats = {"mse" : 0.0, "psnr" : 0.0, "invsqr_mse" : 0.0}
for iter_id, batch_begin in pbar:
gstep_id = iter_id + gstep_id_base
lr_sigma = lr_sigma_func(gstep_id) * lr_sigma_factor
lr_sh = lr_sh_func(gstep_id) * lr_sh_factor
lr_basis = lr_basis_func(gstep_id - args.lr_basis_begin_step) * lr_basis_factor
lr_sigma_bg = lr_sigma_bg_func(gstep_id - args.lr_basis_begin_step) * lr_basis_factor
lr_color_bg = lr_color_bg_func(gstep_id - args.lr_basis_begin_step) * lr_basis_factor
if not args.lr_decay:
lr_sigma = args.lr_sigma * lr_sigma_factor
lr_sh = args.lr_sh * lr_sh_factor
lr_basis = args.lr_basis * lr_basis_factor
batch_end = min(batch_begin + args.batch_size, epoch_size)
batch_origins = dset.rays.origins[batch_begin: batch_end]
batch_dirs = dset.rays.dirs[batch_begin: batch_end]
# batch_dirs.normal_() # FIXME
# batch_dirs /= batch_dirs.norm(dim=-1).unsqueeze(-1) # FIXME
rgb_gt = dset.rays.gt[batch_begin: batch_end]
# rgb_gt[:] = 0 # FIXME
rays = svox2.Rays(batch_origins, batch_dirs)
# with Timing("volrend_fused"):
rgb_pred = grid.volume_render_fused(rays, rgb_gt,
beta_loss=args.lambda_beta,
sparsity_loss=args.lambda_sparsity,
randomize=args.enable_random)
# with Timing("loss_comp"):
mse = F.mse_loss(rgb_gt, rgb_pred)
# Stats
mse_num : float = mse.detach().item()
psnr = -10.0 * math.log10(mse_num)
stats['mse'] += mse_num
stats['psnr'] += psnr
stats['invsqr_mse'] += 1.0 / mse_num ** 2
if (iter_id + 1) % args.print_every == 0:
# Print averaged stats
pbar.set_description(f'epoch {epoch_id} psnr={psnr:.2f}')
for stat_name in stats:
stat_val = stats[stat_name] / args.print_every
summary_writer.add_scalar(stat_name, stat_val, global_step=gstep_id)
stats[stat_name] = 0.0
# if args.lambda_tv > 0.0:
# with torch.no_grad():
# tv = grid.tv(logalpha=args.tv_logalpha, ndc_coeffs=dset.ndc_coeffs)
# summary_writer.add_scalar("loss_tv", tv, global_step=gstep_id)
# if args.lambda_tv_sh > 0.0:
# with torch.no_grad():
# tv_sh = grid.tv_color()
# summary_writer.add_scalar("loss_tv_sh", tv_sh, global_step=gstep_id)
# with torch.no_grad():
# tv_basis = grid.tv_basis()
# summary_writer.add_scalar("loss_tv_basis", tv_basis, global_step=gstep_id)
summary_writer.add_scalar("lr_sh", lr_sh, global_step=gstep_id)
summary_writer.add_scalar("lr_sigma", lr_sigma, global_step=gstep_id)
if grid.basis_type == svox2.BASIS_TYPE_3D_TEXTURE:
summary_writer.add_scalar("lr_basis", lr_basis, global_step=gstep_id)
if grid.use_background:
summary_writer.add_scalar("lr_sigma_bg", lr_sigma_bg, global_step=gstep_id)
summary_writer.add_scalar("lr_color_bg", lr_color_bg, global_step=gstep_id)
if args.weight_decay_sh < 1.0:
grid.sh_data.data *= args.weight_decay_sigma
if args.weight_decay_sigma < 1.0:
grid.density_data.data *= args.weight_decay_sh
# Apply TV/Sparsity regularizers
if args.lambda_tv > 0.0:
# with Timing("tv_inpl"):
grid.inplace_tv_grad(grid.density_data.grad,
scaling=args.lambda_tv,
sparse_frac=args.tv_sparsity,
logalpha=args.tv_logalpha,
ndc_coeffs=dset.ndc_coeffs,
contiguous=args.tv_contiguous)
if args.lambda_tv_sh > 0.0:
# with Timing("tv_color_inpl"):
grid.inplace_tv_color_grad(grid.sh_data.grad,
scaling=args.lambda_tv_sh,
sparse_frac=args.tv_sh_sparsity,
ndc_coeffs=dset.ndc_coeffs,
contiguous=args.tv_contiguous)
if args.lambda_tv_lumisphere > 0.0:
grid.inplace_tv_lumisphere_grad(grid.sh_data.grad,
scaling=args.lambda_tv_lumisphere,
dir_factor=args.tv_lumisphere_dir_factor,
sparse_frac=args.tv_lumisphere_sparsity,
ndc_coeffs=dset.ndc_coeffs)
if args.lambda_l2_sh > 0.0:
grid.inplace_l2_color_grad(grid.sh_data.grad,
scaling=args.lambda_l2_sh)
if grid.use_background and (args.lambda_tv_background_sigma > 0.0 or args.lambda_tv_background_color > 0.0):
grid.inplace_tv_background_grad(grid.background_data.grad,
scaling=args.lambda_tv_background_color,
scaling_density=args.lambda_tv_background_sigma,
sparse_frac=args.tv_background_sparsity,
contiguous=args.tv_contiguous)
if args.lambda_tv_basis > 0.0:
tv_basis = grid.tv_basis()
loss_tv_basis = tv_basis * args.lambda_tv_basis
loss_tv_basis.backward()
# print('nz density', torch.count_nonzero(grid.sparse_grad_indexer).item(),
# ' sh', torch.count_nonzero(grid.sparse_sh_grad_indexer).item())
# Manual SGD/rmsprop step
grid.optim_density_step(lr_sigma, beta=args.rms_beta, optim=args.sigma_optim)
grid.optim_sh_step(lr_sh, beta=args.rms_beta, optim=args.sh_optim)
if grid.use_background:
grid.optim_background_step(lr_sigma_bg, lr_color_bg, beta=args.rms_beta, optim=args.bg_optim)
if gstep_id >= args.lr_basis_begin_step:
if grid.basis_type == svox2.BASIS_TYPE_3D_TEXTURE:
grid.optim_basis_step(lr_basis, beta=args.rms_beta, optim=args.basis_optim)
elif grid.basis_type == svox2.BASIS_TYPE_MLP:
optim_basis_mlp.step()
optim_basis_mlp.zero_grad()
train_step()
gc.collect()
gstep_id_base += batches_per_epoch
# ckpt_path = path.join(args.train_dir, f'ckpt_{epoch_id:05d}.npz')
# Overwrite prev checkpoints since they are very huge
if args.save_every > 0 and (epoch_id + 1) % max(
factor, args.save_every) == 0 and not args.tune_mode:
print('Saving', ckpt_path)
grid.save(ckpt_path)
if (gstep_id_base - last_upsamp_step) >= args.upsamp_every:
last_upsamp_step = gstep_id_base
if reso_id < len(reso_list) - 1:
print('* Upsampling from', reso_list[reso_id], 'to', reso_list[reso_id + 1])
if args.tv_early_only > 0:
print('turning off TV regularization')
args.lambda_tv = 0.0
args.lambda_tv_sh = 0.0
reso_id += 1
use_sparsify = True
z_reso = reso_list[reso_id] if isinstance(reso_list[reso_id], int) else reso_list[reso_id][2]
grid.resample(reso=reso_list[reso_id],
sigma_thresh=args.density_thresh,
weight_thresh=args.weight_thresh / z_reso if use_sparsify else 0.0,
dilate=2, #use_sparsify,
cameras=resample_cameras if args.thresh_type == 'weight' else None,
max_elements=args.max_grid_elements)
if grid.use_background and reso_id <= 1:
grid.sparsify_background(args.background_density_thresh)
if args.upsample_density_add:
grid.density_data.data[:] += args.upsample_density_add
if factor > 1 and reso_id < len(reso_list) - 1:
print('* Using higher resolution images due to large grid; new factor', factor)
factor //= 2
dset.gen_rays(factor=factor)
dset.shuffle_rays()
if gstep_id_base >= args.n_iters:
print('* Final eval and save')
eval_step()
if not args.tune_nosave:
grid.save(ckpt_path)
break