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run_nerf.py
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run_nerf.py
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import os, sys
import numpy as np
import imageio
import json
import random
import time
import torch
from run_nerf_helpers import *
from Spline import *
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm, trange
import matplotlib.pyplot as plt
from load_llff import load_llff_data
import torchvision.transforms.functional as torchvision_F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
np.random.seed(0)
DEBUG = False
def config_parser():
import configargparse
parser = configargparse.ArgumentParser()
parser.add_argument('--config', is_config_file=True, default='configs/cozy2room.txt',
help='config file path')
parser.add_argument("--expname", type=str,
help='experiment name')
parser.add_argument("--basedir", type=str, default='./logs/',
help='where to store ckpts and logs')
parser.add_argument("--datadir", type=str, default='./data/llff/fern',
help='input data directory')
# training options
parser.add_argument("--N_iters", type=int, default=200000,
help='the number of sharp images one blur image corresponds to')
parser.add_argument("--deblur_images", type=int, default=5,
help='the number of sharp images one blur image corresponds to')
parser.add_argument("--skip", type=int, default=8,
help='original llffhold before concatenate images')
parser.add_argument("--netdepth", type=int, default=8,
help='layers in network')
parser.add_argument("--netwidth", type=int, default=256,
help='channels per layer')
parser.add_argument("--netdepth_fine", type=int, default=8,
help='layers in fine network')
parser.add_argument("--netwidth_fine", type=int, default=256,
help='channels per layer in fine network')
parser.add_argument("--N_rand", type=int, default=32*32*4,
help='batch size (number of random rays per gradient step)')
parser.add_argument("--lrate", type=float, default=5e-4,
help='learning rate')
parser.add_argument("--pose_lrate", type=float, default=1e-3,
help='learning rate')
parser.add_argument("--lrate_decay", type=int, default=200,
help='exponential learning rate decay (in 1000 steps)')
parser.add_argument("--chunk", type=int, default=1024*2,
help='number of rays processed in parallel, decrease if running out of memory')
parser.add_argument("--netchunk", type=int, default=1024*32,
help='number of pts sent through network in parallel, decrease if running out of memory')
parser.add_argument("--no_batching", action='store_true',
help='only take random rays from 1 image at a time')
parser.add_argument("--no_reload", action='store_true',
help='do not reload weights from saved ckpt')
parser.add_argument("--ft_path", type=str, default=None,
help='specific weights npy file to reload for coarse network')
# rendering options
parser.add_argument("--N_samples", type=int, default=64,
help='number of coarse samples per ray')
parser.add_argument("--N_importance", type=int, default=0,
help='number of additional fine samples per ray')
parser.add_argument("--perturb", type=float, default=1.,
help='set to 0. for no jitter, 1. for jitter')
parser.add_argument("--use_viewdirs", action='store_true',
help='use full 5D input instead of 3D')
parser.add_argument("--i_embed", type=int, default=0,
help='set 0 for default positional encoding, -1 for none')
parser.add_argument("--multires", type=int, default=10,
help='log2 of max freq for positional encoding (3D location)')
parser.add_argument("--multires_views", type=int, default=4,
help='log2 of max freq for positional encoding (2D direction)')
parser.add_argument("--raw_noise_std", type=float, default=0.,
help='std dev of noise added to regularize sigma_a output, 1e0 recommended')
parser.add_argument("--render_only", action='store_true',
help='do not optimize, reload weights and render out render_poses path')
parser.add_argument("--render_test", action='store_true',
help='render the test set instead of render_poses path')
parser.add_argument("--render_factor", type=int, default=0,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
parser.add_argument("--ndc", type=bool, default=True,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
# training options
parser.add_argument("--precrop_iters", type=int, default=0,
help='number of steps to train on central crops')
parser.add_argument("--precrop_frac", type=float,
default=.5, help='fraction of img taken for central crops')
# dataset options
parser.add_argument("--dataset_type", type=str, default='llff',
help='options: llff / blender / deepvoxels')
parser.add_argument("--testskip", type=int, default=8,
help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')
## deepvoxels flags
parser.add_argument("--shape", type=str, default='greek',
help='options : armchair / cube / greek / vase')
## blender flags
parser.add_argument("--white_bkgd", action='store_true',
help='set to render synthetic data on a white bkgd (always use for dvoxels)')
parser.add_argument("--half_res", action='store_true',
help='load blender synthetic data at 400x400 instead of 800x800')
## llff flags
parser.add_argument("--factor", type=int, default=8,
help='downsample factor for LLFF images')
parser.add_argument("--no_ndc", action='store_true',
help='do not use normalized device coordinates (set for non-forward facing scenes)')
parser.add_argument("--lindisp", action='store_true',
help='sampling linearly in disparity rather than depth')
parser.add_argument("--spherify", action='store_true',
help='set for spherical 360 scenes')
parser.add_argument("--llffhold", type=int, default=8,
help='will take every 1/N images as LLFF test set, paper uses 8')
# logging/saving options
parser.add_argument("--i_print", type=int, default=100,
help='frequency of console printout and metric loggin')
parser.add_argument("--i_img", type=int, default=15000,
help='frequency of tensorboard image logging')
parser.add_argument("--i_weights", type=int, default=10000,
help='frequency of weight ckpt saving')
parser.add_argument("--i_testset", type=int, default=50000,
help='frequency of testset saving')
parser.add_argument("--i_video", type=int, default=30000,
help='frequency of render_poses video saving')
parser.add_argument("--load_weights", action='store_true',
help='frequency of weight ckpt loading')
# barf: up & down
parser.add_argument("--barf", action='store_true',
help='barf')
parser.add_argument("--barf_start", type=float, default=0.1,
help='barf start')
parser.add_argument("--barf_end", type=float, default=0.9,
help='barf start')
# test option
parser.add_argument("--only_optim_one", action='store_true', default=False,
help='frequency of weight ckpt loading')
parser.add_argument("--split_train_data", action='store_true', default=False,
help='frequency of weight ckpt loading')
parser.add_argument("--weight_iter", type=int, default=20000,
help='weight_iter')
# pose noise
parser.add_argument("--pose_noise", type=float, default=0.1,
help='random noise of pose')
# linear
parser.add_argument("--linear", action='store_true', default=False,
help='linear or cubic spline')
# novel view
parser.add_argument("--novel_view", action='store_true', default=False,
help='novel view test')
parser.add_argument("--i_novel_view", type=int, default=200000,
help='novel view iter')
parser.add_argument("--factor_pose_novel", type=float, default=2.0,
help='factor of learning rate')
parser.add_argument("--N_novel_view", type=int, default=20000,
help='novel view iter for optimizing poses')
return parser