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render_histogram.py
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render_histogram.py
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# This is the code for rendering histograms after training.
# usage:
# python render_histogram.py --config configs/test/zaragoza_bunny.txt
import os, sys
import numpy as np
import json
import random
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm, trange
import scipy.io
from math import ceil
from run_netf_helpers import *
from load_nlos import *
from fields import *
def config_parser():
import configargparse
parser = configargparse.ArgumentParser()
parser.add_argument('--config', is_config_file=True,
help='config file path')
parser.add_argument("--nlos_file", type=str, default=None,
help='input data path')
parser.add_argument("--dataset_type", type=str, default='zaragoza256',
help='options: zaragoza256 / fk')
# NeTF arguments
parser.add_argument("--num_sampling_points", type=int, default=16,
help='number of sampling points in one direction, so the number of all sampling points is the square of this value')
parser.add_argument("--histogram_batchsize", type=int, default=1,
help='the batchsize of histogram')
parser.add_argument("--start", type=int, default=100,
help='the start point of histogram')
parser.add_argument("--end", type=int, default=300,
help='the end point of histogram')
parser.add_argument("--gt_times", type=float, default=100,
help='scaling factor of histogram')
parser.add_argument("--num_epochs", type=int, default=10,
help='number of training epochs')
parser.add_argument("--rng", type=int, default=1,
help='random seed')
# our options
parser.add_argument("--init_lr", type=float, default=1e-4,
help="initial learning rate")
parser.add_argument("--weight_h", type=float, default=1.,
help="weight for histogram loss")
parser.add_argument("--weight_ei", type=float, default=1e-1,
help="weight for eikonal loss")
parser.add_argument("--weight_z", type=float, default=0.,
help="weight for zero-sdf loss")
parser.add_argument("--weight_f", type=float, default=0.,
help="weight for freespace loss")
parser.add_argument("--weight_en", type=float, default=0.,
help="weight for entropy loss")
parser.add_argument("--save_m", type=int, default=16,
help="model save interval")
parser.add_argument("--transient_threshold", type=float, default=0,
help="threshold value for transient mask")
parser.add_argument("--geometric_init", type=int, default=1,
help="require geometric initialization (1) or not (0)")
parser.add_argument("--num_sampling_zero_pts", type=int, default=32,
help="number of sampling points on each sphere for zero-sdf loss")
parser.add_argument("--num_sampling_eik_pts", type=int, default=4096,
help="number of sampling points for eikonal loss")
parser.add_argument("--num_sampling_lb_pts", type=int, default=4096,
help="number of sampling points for freespace loss")
parser.add_argument("--render_background", type=int, default=0,
help="render background (1) or not (0)")
parser.add_argument("--test_epoch", type=int, default=5)
parser.add_argument("--test_m", type=int, default=0)
parser.add_argument("--test_volume_size", type=int, default=256)
parser.add_argument("--num_iters", type=int, default=100)
parser.add_argument("--require_mask", type=int, default=1)
parser.add_argument("--remove_mesh_th", type=float, default=0.1)
return parser
def train():
parser = config_parser()
args = parser.parse_args()
seed = args.rng
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# load data
if args.dataset_type == "zaragoza256":
nlos_data, camera_position, camera_grid_size, camera_grid_positions, camera_grid_points, volume_position, volume_size, deltaT, c = load_zaragoza256_data(args.nlos_file)
elif args.dataset_type == "fk":
nlos_data, camera_position, camera_grid_size, camera_grid_positions, camera_grid_points, volume_position, volume_size, deltaT, c = load_fk_data(args.nlos_file)
# target volume is centered at the origin
camera_grid_positions = camera_grid_positions - volume_position[:,None]
volume_position = np.zeros(3)
vmin = volume_position - volume_size / 2
vmax = volume_position + volume_size / 2
scene = os.path.splitext(os.path.basename(args.config))[0]
create_dir("{}_histogram".format(scene))
nlos_data = torch.Tensor(nlos_data).to(device)
L,M,N = nlos_data.shape
# create output dir
dir_param_list = [scene,
args.weight_h, args.weight_ei, args.weight_z, args.weight_f, args.weight_en,
args.transient_threshold,
args.num_sampling_zero_pts,
args.render_background,
]
out_dir = os.path.join("out", "_".join([str(param) for param in dir_param_list]))
# create models
if args.geometric_init == 1:
geometric_init = True
elif args.geometric_init == 0:
geometric_init = False
sdf_network = SDFNetwork(d_in=3,
d_out=257,
d_hidden=256,
n_layers=8,
skip_in=[4],
multires=6,
bias=0.5,
scale=1.0,
geometric_init=geometric_init,
weight_norm=True,
inside_outside=False)
color_network = RenderingNetwork(d_feature=256,
mode="no_normal",
d_in=6,
d_out=1,
d_hidden=256,
n_layers=4,
weight_norm=True,
multires_view=4,
squeeze_out=False)
deviation_network = SingleVarianceNetwork(init_val=0.3)
load_model(sdf_network, "sdf", out_dir, args.test_epoch, args.test_m)
load_model(color_network, "color", out_dir, args.test_epoch, args.test_m)
load_model(deviation_network, "deviation", out_dir, args.test_epoch, args.test_m)
if args.render_background == 1:
background_network = BackgroundNetwork(d_out=1,
d_hidden=16,
n_layers=3,
multires_pts=3, multires_time=3,
squeeze_out=False)
load_model(background_network, "background", out_dir, args.test_epoch, args.test_m)
else:
background_network = None
criterion = torch.nn.MSELoss(reduction='mean')
# shuffle data
nlos_data, camera_grid_positions, index = shuffle_data(nlos_data, camera_grid_positions)
current_nlos_data = nlos_data
current_camera_grid_positions = camera_grid_positions
with torch.no_grad():
total_loss = test(args, criterion,
sdf_network, color_network, deviation_network, background_network,
current_nlos_data, current_camera_grid_positions, index,
volume_position, volume_size, c, deltaT, out_dir = "{}_histogram".format(scene))
def test(args, criterion,
sdf_network, color_network, deviation_network, background_network,
current_nlos_data, current_camera_grid_positions, index,
volume_position, volume_size, c, deltaT,
out_dir="histogram"):
L,M,N = current_nlos_data.shape
total_loss = torch.zeros(M * N)
print("run test:")
for m in tqdm(range(0, M, 1)):
# batchsize is 1
for n in range(0, N, 1):
# minibatch
for j in range(0, 1, 1):
loss, _, _, gt_histogram, pred_histogram, background_histogram = compute_histogram_loss(args,
M,m,N,n,j,L,
criterion,
sdf_network, color_network, deviation_network, background_network,
current_camera_grid_positions,
current_nlos_data,
volume_position, volume_size, c, deltaT)
total_loss[index[m * N + n]] = loss.item() / (torch.mean(gt_histogram) + 1e-8)
np.save(os.path.join(out_dir, "gt_hist_{}".format(index[m * N + n])), gt_histogram.cpu().numpy())
np.save(os.path.join(out_dir, "pred_hist_{}".format(index[m * N + n])), pred_histogram.cpu().numpy())
if args.render_background == 1:
np.save(os.path.join(out_dir, "background_hist_{}".format(index[m * N + n])), background_histogram.cpu().numpy())
exit()
return total_loss
if __name__=='__main__':
torch.set_default_tensor_type('torch.cuda.FloatTensor')
train()