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run.py
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run.py
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# -*- encoding: utf-8 -*-
import os, sys, copy, time, random, argparse, pdb
from tqdm import tqdm, trange
import mmcv, shutil
import imageio
import numpy as np
import torch
import torch.nn.functional as F
from pytorch_msssim import ms_ssim
from lib import utils
from lib import V4D as V4D
from lib.load_data import load_data
def config_parser():
'''Define command line arguments
'''
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', required=True,
help='config file path')
parser.add_argument("--seed", type=int, default=777,
help='Random seed')
parser.add_argument("--no_reload", action='store_true',
help='do not reload weights from saved ckpt')
parser.add_argument("--no_reload_optimizer", action='store_true',
help='do not reload optimizer state from saved ckpt')
parser.add_argument("--ft_path", type=str, default='',
help='specific weights npy file to reload for coarse network')
parser.add_argument("--export_bbox_and_cams_only", type=str, default='',
help='export scene bbox and camera poses for debugging and 3d visualization')
parser.add_argument("--export_coarse_only", type=str, default='')
# testing options
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')
parser.add_argument("--render_train", action='store_true')
parser.add_argument("--render_video", action='store_true')
parser.add_argument("--render_video_factor", type=int, default=0,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
# logging/saving options
parser.add_argument("--i_print", type=int, default=500,
help='frequency of console printout and metric loggin')
parser.add_argument("--i_weights", type=int, default=100000,
help='frequency of weight ckpt saving')
parser.add_argument("--i_vis_weight", type=int, default=1,
help='frequency of weight ckpt saving')
# experiment setting
parser.add_argument("--density_feat_size", type=int, default=12, help='number of the voxel feature')
parser.add_argument("--rgbnet_width", type=int, default=256, help='width of the mlp')
parser.add_argument("--N_rand", type=int, default=4196, help='batch size (number of random rays per optimization step)')
parser.add_argument("--rgbnet_depth", type=int, default=5, help='depth of the mlp')
parser.add_argument("--savename", type=str, action='append', default=[],
help='discard')
parser.add_argument("--weight_density_tv", type=float, default=0.1, help='weight_density_tv')
parser.add_argument("--weight_rgb_tv", type=float, default=0.1, help='weight_rgb_tv')
parser.add_argument("--weight_main0", type=float, default=0.0, help='weight for the lut')
parser.add_argument("--weight_main1", type=float, default=1.0, help='weight for the lut')
parser.add_argument("--tv_decay_factor", type=float, default=0.005, help='decay factor for weight_density_tv')
parser.add_argument("--tv_from", type=int, default=1, help='tv loss from N iters')
parser.add_argument("--tv_every", type=int, default=1, help='tv loss interval')
parser.add_argument("--cuda_tv", type=lambda x: x.lower() == 'true', default=False, help='enable the cuda_tv')
parser.add_argument("--decay_density_tv", type=lambda x: x.lower() == 'true', default=True,
help='decay loss weight to tv_decay_factor')
parser.add_argument("--decay_rgb_tv", type=lambda x: x.lower() == 'true', default=True,
help='decay loss weight to tv_decay_factor')
parser.add_argument("--feature_shift", type=lambda x: x.lower() == 'true', default=True,
help='use feature_shift or not')
parser.add_argument("--feature_t_shift", type=lambda x: x.lower() == 'true', default=True,
help='use feature_t_shift or not')
parser.add_argument("--search_geometry", type=lambda x: x.lower() == 'true', default=False,
help='use search_geometry or not')
parser.add_argument("--dual_voxel", type=lambda x: x.lower() == 'true', default=True,
help='use dual_voxel or not, separate the density voxel and the flow and rgb voxel')
parser.add_argument("--viewbase_pe", type=int, default=4, help='position encoding for the view direction ')
parser.add_argument("--view_direction", type=lambda x: x.lower() == 'true', default=True, help='use view_direction or not')
# loss
parser.add_argument("--voxel_lr", type=float, default=0.1, help='use two_rgb_loss in lut or not')
parser.add_argument("--basedir", type=str, default='./logs',
help='basedir for save the network')
parser.add_argument("--coarse_iters", type=int, default=10000, help='number of coarse_iters')
parser.add_argument("--fine_iters", type=int, default=250000, help='number of fine_iters')
parser.add_argument("--total_data_device", type=str, default='cuda', help='load total dynamic data to gpu or cpu ')
parser.add_argument("--static_type", type=str, default='dynamic', help='dynamic')
parser.add_argument("--coarse_index", type=int, default=9, help='time interval for the fine bounding box')
parser.add_argument("--view_dependent", type=str, default='post', help='using [pre, post] view dependent in rgb')
parser.add_argument("--video_only", type=str, default='no', help='the path of the pretrain model [no, ]')
return parser
@torch.no_grad()
def render_viewpoints(model, render_poses, HW, Ks, ndc, render_kwargs,
gt_imgs=None, savedir=None, render_factor=0,
eval_ssim=False, eval_lpips_alex=False, eval_lpips_vgg=False, eval_mse=False, times=None,
data_record=None, render_type='test'):
'''Render images for the given viewpoints; run evaluation if gt given.
'''
assert len(render_poses) == len(HW) and len(HW) == len(Ks)
if render_factor != 0:
HW = np.copy(HW)
Ks = np.copy(Ks)
HW //= render_factor
Ks[:, :2, :3] //= render_factor
rgbs = []
normals = []
disps = []
psnrs = []
ssims = []
lpips_alex = []
lpips_vgg = []
mse = []
total_inference_time = []
render_poses = render_poses.to('cuda')
times = times.to('cuda')
for i, c2w in enumerate(tqdm(render_poses)):
H, W = HW[i]
K = Ks[i]
# start counting time
eps_time = time.time()
rays_o, rays_d, viewdirs = V4D.get_rays_of_a_view(
H, W, K, c2w, ndc, inverse_y=render_kwargs['inverse_y'],
flip_x=cfg.data.flip_x, flip_y=cfg.data.flip_y)
times_test = torch.ones([H, W, 1]) * times[i]
keys = ['rgb_marched', 'disp']
print('testing i:', i)
# global_step = -1
render_result_chunks = [
{k: v for k, v in model(ro, rd, vd, rt, global_step=0, mode='test', **render_kwargs).items() if k in keys}
for ro, rd, vd, rt in
zip(rays_o.split(16, 0), rays_d.split(16, 0), viewdirs.split(16, 0), times_test.split(16, 0))]
render_result = {
k: torch.cat([ret[k][-1] for ret in render_result_chunks])
for k in render_result_chunks[0].keys()
}
eps_time = time.time() - eps_time
total_inference_time.append(eps_time)
print('single image inference: finish (eps time:', eps_time, 'secs)')
rgb = render_result['rgb_marched'].cpu().numpy()
disp = render_result['disp'].cpu().numpy()
rgbs.append(rgb)
disps.append(disp)
if i == 0:
print('Testing', rgb.shape, disp.shape)
if gt_imgs is not None and render_factor == 0:
mse_i = np.square(rgb - gt_imgs[i])
p = -10. * np.log10(np.mean(mse_i))
psnrs.append(p)
if eval_ssim:
ssims.append(utils.rgb_ssim(rgb, gt_imgs[i], max_val=1))
if eval_lpips_alex:
lpips_alex.append(utils.rgb_lpips(rgb, gt_imgs[i], net_name='alex', device=c2w.device))
if eval_lpips_vgg:
lpips_vgg.append(utils.rgb_lpips(rgb, gt_imgs[i], net_name='vgg', device=c2w.device))
if eval_mse:
mse.append(mse_i)
if savedir is not None:
rgb8 = utils.to8b(rgbs[-1])
filename = os.path.join(savedir, '{:03d}.png'.format(i))
imageio.imwrite(filename, rgb8)
rgbs = np.array(rgbs)
disps = np.array(disps)
if len(psnrs):
mean_psnr = np.mean(psnrs)
mean_total_inference_time = np.mean(total_inference_time)
print('{} psnr avg = {}'.format(render_type, mean_psnr))
print('{} inference time: avg = {}'.format(render_type, mean_total_inference_time))
data_record.write('\n {} psnr avg = {} \n'.format(render_type, mean_psnr))
data_record.write('{} inference time: avg = {} \n'.format(render_type, mean_total_inference_time))
if eval_ssim:
mean_ssim = np.mean(ssims)
data_record.write('{} ssim avg = {} \n'.format(render_type, mean_ssim))
print('{} ssim avg = {}'.format(render_type, mean_ssim))
if eval_lpips_vgg:
mean_lpips_vgg = np.mean(lpips_vgg)
data_record.write('{} lpips_vgg avg = {} \n'.format(render_type, mean_lpips_vgg))
print('{} lpips_vgg avg = {}'.format(render_type, mean_lpips_vgg))
if eval_lpips_alex:
mean_lpips_alex = np.mean(lpips_alex)
data_record.write('{} lpips_alex avg = {} \n'.format(render_type, mean_lpips_alex))
print('{} lpips_alex avg = {}'.format(render_type, mean_lpips_alex))
if mse:
mean_mse = np.mean(mse)
data_record.write('{} mse avg = {} \n'.format(render_type, mean_mse))
print('{} mse avg = {}'.format(render_type, mean_mse))
return rgbs, disps
@torch.no_grad()
def render_viewpoints_hyper(model, data_class, render_kwargs, ndc,
savedir=None, render_factor=0,
eval_ssim=False, eval_lpips_alex=False, eval_lpips_vgg=False, eval_mse=False,
data_record=None, render_type='test'):
'''Render images for the given viewpoints; run evaluation if gt given.
'''
rgbs = []
disps = []
psnrs = []
ssims = []
lpips_alex = []
lpips_vgg = []
mse = []
total_inference_time = []
rgbs_tensor = []
rgbs_gt_tensor = []
H = data_class['data_class'].h
W = data_class['data_class'].w
for i, j in enumerate(data_class['data_class'].i_test):
rays_o, rays_d, viewdirs, gt_imgs, times = data_class['data_class'].load_idx_1(j, not_dic=True, ndc=ndc)
# pdb.set_trace()
times_test = torch.ones([H, W, 1], device='cuda') * times
# start counting time
eps_time = time.time()
keys = ['rgb_marched', 'disp']
render_result_chunks = [
{k: v for k, v in model(ro, rd, vd, rt, global_step=0, mode='test', **render_kwargs).items() if k in keys}
for ro, rd, vd, rt in
zip(rays_o.split(16, 0), rays_d.split(16, 0), viewdirs.split(16, 0), times_test.split(16, 0))]
render_result = {
k: torch.cat([ret[k][-1] for ret in render_result_chunks])
for k in render_result_chunks[0].keys()
}
eps_time = time.time() - eps_time
total_inference_time.append(eps_time)
print('single image inference: finish (eps time:', eps_time, 'secs)')
rgb = render_result['rgb_marched'].cpu().numpy()
disp = render_result['disp'].cpu().numpy()
rgbs.append(rgb)
disps.append(disp)
if i == 0:
print('Testing', rgb.shape, disp.shape)
if gt_imgs is not None and render_factor == 0:
gt_imgs = gt_imgs.cpu().numpy()
mse_i = np.square(rgb - gt_imgs)
p = -10. * np.log10(np.mean(mse_i))
psnrs.append(p)
rgbs_tensor.append(torch.from_numpy(np.clip(rgb, 0, 1)).permute(2,0,1))
rgbs_gt_tensor.append(torch.from_numpy(np.clip(gt_imgs, 0, 1)).permute(2,0,1))
# pdb.set_trace()
if eval_ssim:
ssims.append(utils.rgb_ssim(rgb, gt_imgs, max_val=1))
if eval_lpips_alex:
lpips_alex.append(utils.rgb_lpips(rgb, gt_imgs, net_name='alex', device=times_test.device))
if eval_lpips_vgg:
lpips_vgg.append(utils.rgb_lpips(rgb, gt_imgs, net_name='vgg', device=times_test.device))
if eval_mse:
mse.append(mse_i)
if savedir is not None:
rgb8 = utils.to8b(rgbs[-1])
filename = os.path.join(savedir, '{:03d}.png'.format(i))
imageio.imwrite(filename, rgb8)
rgbs = np.array(rgbs)
disps = np.array(disps)
if 1:
rgbs_tensor = torch.stack(rgbs_tensor, 0)
rgbs_gt_tensor = torch.stack(rgbs_gt_tensor, 0)
ms_ssims = ms_ssim(rgbs_gt_tensor, rgbs_tensor, data_range=1, size_average=True)
if len(psnrs):
mean_psnr = np.mean(psnrs)
mean_total_inference_time = np.mean(total_inference_time)
print('{} psnr avg = {}'.format(render_type, mean_psnr))
print('{} inference time: avg = {}'.format(render_type, mean_total_inference_time))
data_record.write('\n {} psnr avg = {} \n'.format(render_type, mean_psnr))
data_record.write('{} inference time: avg = {} \n'.format(render_type, mean_total_inference_time))
if eval_ssim:
mean_ssim = np.mean(ssims)
data_record.write('{} ssim avg = {} \n'.format(render_type, mean_ssim))
print('{} ssim avg = {}'.format(render_type, mean_ssim))
if ms_ssims:
data_record.write('{} ms_ssims avg = {} \n'.format(render_type, ms_ssims))
print('{} ms_ssims avg = {}'.format(render_type, ms_ssims))
if eval_lpips_vgg:
mean_lpips_vgg = np.mean(lpips_vgg)
data_record.write('{} lpips_vgg avg = {} \n'.format(render_type, mean_lpips_vgg))
print('{} lpips_vgg avg = {}'.format(render_type, mean_lpips_vgg))
if eval_lpips_alex:
mean_lpips_alex = np.mean(lpips_alex)
data_record.write('{} lpips_alex avg = {} \n'.format(render_type, mean_lpips_alex))
print('{} lpips_alex avg = {}'.format(render_type, mean_lpips_alex))
if mse:
mean_mse = np.mean(mse)
data_record.write('{} mse avg = {} \n'.format(render_type, mean_mse))
print('{} mse avg = {}'.format(render_type, mean_mse))
return rgbs, disps
def seed_everything():
'''Seed everything for better reproducibility.
(some pytorch operation is non-deterministic like the backprop of grid_samples)
'''
torch.manual_seed(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
def load_everything(args, cfg):
'''Load images / poses / camera settings / data split.
'''
data_dict = load_data(cfg.data)
if cfg.data.dataset_type == 'hyper_dataset':
kept_keys = {
'data_class',
'near', 'far',
'i_train', 'i_val', 'i_test',}
for k in list(data_dict.keys()):
if k not in kept_keys:
data_dict.pop(k)
# pdb.set_trace()
return data_dict
# remove useless field
kept_keys = {
'hwf', 'HW', 'Ks', 'near', 'far',
'i_train', 'i_val', 'i_test', 'irregular_shape',
'poses', 'render_poses', 'images', 'times', 'render_times'}
for k in list(data_dict.keys()):
if k not in kept_keys:
data_dict.pop(k)
# construct data tensor
if data_dict['irregular_shape']:
data_dict['images'] = [torch.FloatTensor(im, device='cpu') for im in data_dict['images']]
else:
data_dict['images'] = torch.FloatTensor(data_dict['images'], device='cpu')
data_dict['poses'] = torch.tensor(data_dict['poses'], device='cpu')
data_dict['render_times'] = torch.tensor(data_dict['render_times'], device='cpu')
data_dict['times'] = torch.tensor(data_dict['times'], device='cpu')
return data_dict
def compute_bbox_by_cam_frustrm(args, cfg, HW, Ks, poses, i_train, near, far, **kwargs):
print('compute_bbox_by_cam_frustrm: start')
xyz_min = torch.Tensor([np.inf, np.inf, np.inf])
xyz_max = -xyz_min
for (H, W), K, c2w in zip(HW[i_train], Ks[i_train], poses[i_train]):
rays_o, rays_d, viewdirs = V4D.get_rays_of_a_view(
H=H, W=W, K=K, c2w=c2w,
ndc=cfg.data.ndc, inverse_y=cfg.data.inverse_y,
flip_x=cfg.data.flip_x, flip_y=cfg.data.flip_y)
pts_nf = torch.stack([rays_o + viewdirs * near, rays_o + viewdirs * far])
pts_nf = pts_nf.to('cuda')
xyz_min = torch.minimum(xyz_min, pts_nf.amin((0, 1, 2)))
xyz_max = torch.maximum(xyz_max, pts_nf.amax((0, 1, 2)))
print('compute_bbox_by_cam_frustrm: xyz_min', xyz_min)
print('compute_bbox_by_cam_frustrm: xyz_max', xyz_max)
print('compute_bbox_by_cam_frustrm: finish')
return xyz_min, xyz_max
def compute_bbox_by_cam_frustrm_hyper(args, cfg, data_class):
print('compute_bbox_by_cam_frustrm: start')
xyz_min = torch.Tensor([np.inf, np.inf, np.inf])
xyz_max = -xyz_min
for i in data_class.i_train:
rays_o, rays_d, viewdirs, _ = data_class.load_idx(i,not_dic=True)
# pdb.set_trace()
if data_class.near == 0:
pts_nf = torch.stack([rays_o + rays_d * data_class.near, rays_o + rays_d * data_class.far])
else:
pts_nf = torch.stack([rays_o+viewdirs*data_class.near, rays_o+viewdirs*data_class.far])
xyz_min = torch.minimum(xyz_min, pts_nf.amin((0,1,2)))
xyz_max = torch.maximum(xyz_max, pts_nf.amax((0,1,2)))
print('compute_bbox_by_cam_frustrm: xyz_min', xyz_min)
print('compute_bbox_by_cam_frustrm: xyz_max', xyz_max)
# pdb.set_trace()
print('compute_bbox_by_cam_frustrm: finish')
return xyz_min, xyz_max
@torch.no_grad()
def compute_bbox_by_coarse_geo(model_class, model_path, thres, time_concat, cfg):
print('compute_bbox_by_coarse_geo: start')
eps_time = time.time()
model = utils.load_model(model_class, model_path)
interp = torch.stack(torch.meshgrid(
torch.linspace(0, 1, model.density.get_dense_grid().shape[2]),
torch.linspace(0, 1, model.density.get_dense_grid().shape[3]),
torch.linspace(0, 1, model.density.get_dense_grid().shape[4]),
), -1)
dense_xyz = model.xyz_min * (1 - interp) + model.xyz_max * interp
time_index = [i / (cfg.coarse_index) for i in range(cfg.coarse_index)]
time_index.append(1)
xyz_min_total = torch.zeros(len(time_index), 3)
xyz_max_total = torch.zeros(len(time_index), 3)
for i, times in enumerate(time_index):
ray_t = torch.ones(dense_xyz[:, :, :, 1].shape).unsqueeze(3) * times
density = model.get_occupancy_alpha_coarse_geo(dense_xyz, ray_t)
alpha = model.activate_density(density)
mask = (alpha > thres)
active_xyz = dense_xyz[mask]
xyz_min = active_xyz.amin(0)
xyz_max = active_xyz.amax(0)
xyz_min_total[i] = xyz_min
xyz_max_total[i] = xyz_max
print('compute_bbox_by_coarse_geo: xyz_min', xyz_min)
print('compute_bbox_by_coarse_geo: xyz_max', xyz_max)
xyz_min, _ = torch.min(xyz_min_total, dim=0)
xyz_max, _ = torch.max(xyz_max_total, dim=0)
eps_time = time.time() - eps_time
print('compute_bbox_by_coarse_geo: finish (eps time:', eps_time, 'secs)')
return xyz_min, xyz_max
def scene_rep_reconstruction(args, cfg, cfg_model, cfg_train, xyz_min, xyz_max, xyz_min_coarse, xyz_max_coarse,
data_dict, stage, data_record=None, coarse_ckpt_path=None):
# init
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
total_data_device = torch.device(cfg.total_data_device)
if abs(cfg_model.world_bound_scale - 1) > 1e-9:
xyz_shift = (xyz_max - xyz_min) * (cfg_model.world_bound_scale - 1) / 2
xyz_min -= xyz_shift
xyz_max += xyz_shift
if cfg.data.dataset_type =='hyper_dataset':
data_class = data_dict['data_class']
near = data_class.near
far = data_class.far
i_train = data_class.i_train
i_test = data_class.i_test
else:
HW, Ks, near, far, i_train, i_val, i_test, poses, render_poses, images, times, render_times = [
data_dict[k] for k in [
'HW', 'Ks', 'near', 'far', 'i_train', 'i_val', 'i_test', 'poses', 'render_poses', 'images', 'times',
'render_times']]
# find whether there is existing checkpoint path
last_ckpt_path = os.path.join(cfg.save_path, f'{stage}_last.tar')
if args.no_reload:
reload_ckpt_path = None
elif args.ft_path:
reload_ckpt_path = args.ft_path
elif os.path.isfile(last_ckpt_path):
reload_ckpt_path = last_ckpt_path
else:
reload_ckpt_path = None
# init model
model_kwargs = copy.deepcopy(cfg_model)
num_voxels = model_kwargs.pop('num_voxels')
if len(cfg_train.pg_scale) and reload_ckpt_path is None:
num_voxels = int(num_voxels / (2 ** len(cfg_train.pg_scale)))
if stage == 'coarse':
model = V4D.v4d(
xyz_min=xyz_min, xyz_max=xyz_max, num_voxels=num_voxels, rgbnet_width=cfg.rgbnet_width,
N_iters=cfg_train.N_iters, density_feat_size=cfg.density_feat_size, norm_xyz=True, **model_kwargs)
else:
model = V4D.v4d(
xyz_min=xyz_min, xyz_max=xyz_max, lut_from=cfg_train.lut_from, viewbase_pe=cfg.viewbase_pe, lut_dim=cfg_train.lut_dim,
num_voxels=num_voxels, view_direction=cfg.view_direction,
dual_voxel=cfg.dual_voxel, feature_t_shift=cfg.feature_t_shift,
norm_xyz=False, rgbnet_depth=cfg.rgbnet_depth, rgbnet_width=cfg.rgbnet_width,
feature_shift=cfg.feature_shift, N_iters=cfg_train.N_iters,
density_feat_size=cfg.density_feat_size, lut_field=cfg_train.lut_field, lut_pe=cfg_train.lut_pe,
lut_iter=cfg_train.lut_iter,
pertur_surf=cfg_train.pertur_surf, xyz_min_coarse=xyz_min_coarse, xyz_max_coarse=xyz_max_coarse,
view_dependent=cfg.view_dependent, **model_kwargs)
model = model.to(device)
# init optimizer
optimizer = utils.create_optimizer_or_freeze_model(model, cfg_train, global_step=0)
# load checkpoint if there is
if reload_ckpt_path is None:
print(f'scene_rep_reconstruction ({stage}): train from scratch')
start = 0
else:
print(f'scene_rep_reconstruction ({stage}): reload from {reload_ckpt_path}')
model, optimizer, start = utils.load_checkpoint(
model, optimizer, reload_ckpt_path, args.no_reload_optimizer)
# init rendering setup
render_kwargs = {
'near': data_dict['near'],
'far': data_dict['far'],
'bg': 1 if cfg.data.white_bkgd else 0,
'stepsize': cfg_model.stepsize,
'inverse_y': cfg.data.inverse_y,
'flip_x': cfg.data.flip_x,
'flip_y': cfg.data.flip_y,}
# init batch rays sampler
def gather_training_rays():
if data_dict['irregular_shape']:
rgb_tr_ori = [images[i].to(total_data_device) for i in i_train]
else:
rgb_tr_ori = images[i_train].to(total_data_device)
# pdb.set_trace()
if cfg_train.ray_sampler == 'in_maskcache':
rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz = V4D.get_training_rays_in_maskcache_sampling(
rgb_tr_ori=rgb_tr_ori,
train_poses=poses[i_train],
HW=HW[i_train], Ks=Ks[i_train],
ndc=cfg.data.ndc, inverse_y=cfg.data.inverse_y,
flip_x=cfg.data.flip_x, flip_y=cfg.data.flip_y,
model=model, render_kwargs=render_kwargs)
elif cfg_train.ray_sampler == 'flatten':
rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, times_tr = V4D.get_training_rays_flatten(
rgb_tr_ori=rgb_tr_ori,
train_poses=poses[i_train],
HW=HW[i_train], Ks=Ks[i_train], ndc=cfg.data.ndc, inverse_y=cfg.data.inverse_y,
flip_x=cfg.data.flip_x, flip_y=cfg.data.flip_y, times=times[i_train])
else:
rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, times_tr = V4D.get_training_rays(
rgb_tr=rgb_tr_ori,
train_poses=poses[i_train],
HW=HW[i_train], Ks=Ks[i_train], ndc=cfg.data.ndc, inverse_y=cfg.data.inverse_y,
flip_x=cfg.data.flip_x, flip_y=cfg.data.flip_y, times=times[i_train])
index_generator = V4D.batch_indices_generator(len(rgb_tr), cfg_train.N_rand)
batch_index_sampler = lambda: next(index_generator)
return rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, batch_index_sampler, times_tr
def gather_training_rays_hyper():
now_device = 'cuda' #total_data_device # if cfg.data.load2gpu_on_the_fly else device
H = data_class.h
W = data_class.w
rgb_tr = torch.zeros([len(data_class.i_train), H, W, 3], device=now_device)
rays_o_tr = torch.zeros([len(data_class.i_train), H, W, 3], device=now_device)
rays_d_tr = torch.zeros([len(data_class.i_train), H, W, 3], device=now_device)
viewdirs_tr = torch.zeros([len(data_class.i_train), H, W, 3], device=now_device)
times_tr = torch.zeros([len(data_class.i_train), H, W, 1], device=now_device)
imsz = [1] * len(data_class.i_train)
for i, j in enumerate(data_class.i_train):
rays_o, rays_d, viewdirs, rgb, times = data_class.load_idx_1(j, not_dic=True, ndc = cfg.data.ndc)
rgb_tr[i].copy_(rgb.to(now_device))
rays_o_tr[i].copy_(rays_o.to(now_device))
rays_d_tr[i].copy_(rays_d.to(now_device))
viewdirs_tr[i].copy_(viewdirs.to(now_device))
# pdb.set_trace()
times_id = torch.ones([H, W, 1], device=now_device) * times.cpu()
times_tr[i].copy_(times_id.to(now_device))
del rays_o, rays_d, viewdirs, times_id
index_generator = V4D.batch_indices_generator(data_class.i_train, cfg_train.N_rand)
batch_index_sampler = lambda: next(index_generator)
return rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, times_tr, batch_index_sampler
if cfg.data.dataset_type == 'hyper_dataset':
rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, times_tr, batch_index_sampler = gather_training_rays_hyper()
else:
rgb_tr, rays_o_tr, rays_d_tr, viewdirs_tr, imsz, batch_index_sampler, times_tr = gather_training_rays()
# view-count-based learning rate # coarse train true, fine train false
if cfg_train.pervoxel_lr:
def per_voxel_init():
cnt = model.voxel_count_views(
rays_o_tr=rays_o_tr, rays_d_tr=rays_d_tr, imsz=imsz, near=near, far=far,
stepsize=cfg_model.stepsize, downrate=cfg_train.pervoxel_lr_downrate,
irregular_shape=data_dict['irregular_shape'])
optimizer.set_pervoxel_lr(cnt)
per_voxel_init()
# GOGO
torch.cuda.empty_cache()
psnr_lst0 = []
psnr_lst1 = []
time0 = time.time()
for global_step in trange(1 + start, 1 + cfg_train.N_iters):
torch.cuda.empty_cache()
# progress scaling checkpoint
if global_step in cfg_train.pg_scale:
ori_world_size, new_world_size = model.scale_volume_grid(model.num_voxels * 2)
data_record.write(
'v4d: scale_volume_grid scale world_size from {} to {} \n'.format(ori_world_size, new_world_size))
optimizer = utils.create_optimizer_or_freeze_model(model, cfg_train, global_step=0)
# reset the lr after the scale
# random sample rays
if cfg_train.ray_sampler in ['flatten', 'in_maskcache']:
sel_i = batch_index_sampler()
target = rgb_tr[sel_i]
rays_o = rays_o_tr[sel_i]
rays_d = rays_d_tr[sel_i]
viewdirs = viewdirs_tr[sel_i]
times_o = times_tr[sel_i]
elif cfg_train.ray_sampler == 'random':
sel_b = torch.randint(rgb_tr.shape[0], [cfg_train.N_rand])
sel_r = torch.randint(rgb_tr.shape[1], [cfg_train.N_rand])
sel_c = torch.randint(rgb_tr.shape[2], [cfg_train.N_rand])
target = rgb_tr[sel_b, sel_r, sel_c]
rays_o = rays_o_tr[sel_b, sel_r, sel_c]
rays_d = rays_d_tr[sel_b, sel_r, sel_c]
viewdirs = viewdirs_tr[sel_b, sel_r, sel_c]
times_o = times_tr[sel_b, sel_r, sel_c]
else:
raise NotImplementedError
if cfg.data.load2gpu_on_the_fly:
target = target.to(device)
target = target.to(device)
rays_o = rays_o.to(device)
rays_d = rays_d.to(device)
viewdirs = viewdirs.to(device)
times_o = times_o.to(device)
# volume rendering
render_result = model(rays_o, rays_d, viewdirs, times_o, global_step=global_step, **render_kwargs)
# gradient descent step
optimizer.zero_grad(set_to_none=True)
rgb_loss0 = F.mse_loss(render_result['rgb_marched'][0], target)
rgb_loss1 = F.mse_loss(render_result['rgb_marched'][-1], target)
if (global_step > cfg_train.lut_from) and cfg_train.lut_field and cfg.weight_main0 > 0:
loss = cfg.weight_main0 * rgb_loss0 + cfg.weight_main1 * rgb_loss1
else:
loss = cfg.weight_main1 * rgb_loss1
psnr0 = utils.mse2psnr(rgb_loss0.detach()).item()
psnr1 = utils.mse2psnr(rgb_loss1.detach()).item()
density_tv_loss = torch.zeros(1)
rgb_tv_loss = torch.zeros(1)
lut_tv_loss = torch.zeros(1)
surface_normal_loss = torch.zeros(1)
if cfg_train.weight_entropy_last > 0:
pout = render_result['alphainv_cum'][..., -1].clamp(1e-6, 1 - 1e-6)
entropy_last_loss = -(pout * torch.log(pout) + (1 - pout) * torch.log(1 - pout)).mean()
loss += cfg_train.weight_entropy_last * entropy_last_loss
if cfg_train.weight_rgbper > 0:
rgbper = (render_result['raw_rgb'] - target.unsqueeze(-2)).pow(2).sum(-1)
rgbper_loss = (rgbper * render_result['weights'].detach()).sum(-1).mean()
loss += cfg_train.weight_rgbper * rgbper_loss
if stage == 'coarse':
# pdb.set_trace()
density_tv_loss = cfg_train.weight_density_tv * model.density_feature_total_variation()
loss += density_tv_loss
loss.backward()
else:
if cfg.cuda_tv:
loss.backward()
dense_tv = 1000000
if cfg_train.weight_density_tv > 0 and global_step > cfg_train.tv_from and global_step % cfg_train.tv_every == 0:
model.density_total_variation_add_grad( cfg_train.weight_density_tv, global_step < dense_tv)
if cfg_train.weight_rgb_tv > 0 and global_step>cfg_train.tv_from and global_step%cfg_train.tv_every==0 and cfg.dual_voxel:
model.k0_total_variation_add_grad(cfg_train.weight_rgb_tv, global_step < dense_tv)
else:
if cfg_train.weight_density_tv > 0 and global_step > cfg_train.tv_from and global_step % cfg_train.tv_every == 0:
density_tv_loss = cfg_train.weight_density_tv * model.density_feature_total_variation()
loss += density_tv_loss
if cfg_train.weight_rgb_tv > 0 and global_step > cfg_train.tv_from and global_step % cfg_train.tv_every == 0 and cfg.dual_voxel:
rgb_tv_loss = cfg_train.weight_rgb_tv * model.rgb_feature_total_variation()
loss += rgb_tv_loss
loss.backward()
optimizer.step()
psnr_lst0.append(psnr0)
psnr_lst1.append(psnr1)
# pdb.set_trace()
if stage == 'coarse':
decay_steps = cfg_train.lrate_decay * 1000
else:
decay_steps = cfg_train.N_iters
decay_factor = 0.1 ** (1 / decay_steps)
density_decay_factor = cfg.tv_decay_factor ** (1 / decay_steps)
for i_opt_g, param_group in enumerate(optimizer.param_groups):
if ('lut' in param_group['name'] or 'LUT' in param_group['name']) and not cfg_train.lut_decay_lr:
pass
else:
param_group['lr'] = param_group['lr'] * decay_factor
if cfg.decay_density_tv:
cfg_train.weight_density_tv = cfg_train.weight_density_tv * density_decay_factor
if cfg.decay_rgb_tv:
cfg_train.weight_rgb_tv = cfg_train.weight_rgb_tv * density_decay_factor
# check log & save
if global_step % args.i_print == 0:
eps_time = time.time() - time0
data_record.write('save_path：{} \n'.format(cfg.save_path))
data_record.write('learning rate：{} \n'.format(param_group['lr']))
eps_time_str = f'{eps_time // 3600:02.0f}:{eps_time // 60 % 60:02.0f}:{eps_time % 60:02.0f}'
# flow = render_result['flow'][0]
lr_temp = param_group['lr']
tqdm.write(f'scene_rep_reconstruction ({stage}): iter {global_step:6d} / '
f'save_path：{cfg.save_path} / '
f'learning rate: {lr_temp:.9f} / '
f'total_loss: {loss.item():.9f} / '
f'rgb_loss0: {rgb_loss0.item():.9f} / PSNR0: {np.mean(psnr_lst0):5.2f} / '
f'rgb_loss1: {rgb_loss1.item():.9f} / PSNR1: {np.mean(psnr_lst1):5.2f} / '
f'surface_normal_loss Loss: {surface_normal_loss.item():.9f} / '
f'density_tv_loss Loss: {density_tv_loss.item():.9f} / '
f'rgb_tv_loss Loss: {rgb_tv_loss.item():.9f} / '
f'lut_tv_loss Loss: {lut_tv_loss.item():.9f} / '
f'Eps: {eps_time_str}')
data_record.write(f'{eps_time // 3600:02.0f}:{eps_time // 60 % 60:02.0f}:{eps_time % 60:02.0f} \n')
data_record.write(f'scene_rep_reconstruction ({stage}): iter {global_step:6d} / '
f'total_loss: {loss.item():.9f} / '
f'rgb_loss0: {rgb_loss0.item():.9f} / PSNR0: {np.mean(psnr_lst0):5.2f} / '
f'rgb_loss1: {rgb_loss1.item():.9f} / PSNR1: {np.mean(psnr_lst1):5.2f} / '
f'surface_normal_loss Loss: {surface_normal_loss.item():.9f} / '
f'density_tv_loss Loss: {density_tv_loss.item():.9f} / '
f'rgb_tv_loss Loss: {rgb_tv_loss.item():.9f} / '
f'lut_tv_loss Loss: {lut_tv_loss.item():.9f} / '
f'Eps: {eps_time_str} \n')
if global_step % args.i_vis_weight == -1:
path = os.path.join(cfg.save_path, f'{stage}_{global_step:06d}.tar')
model.get_voxel_vis(path, global_step)
print(f'scene_rep_reconstruction ({stage}): saved checkpoints at', path)
torch.save({
'global_step': cfg_train.N_iters,
'model_kwargs': model.get_kwargs(),
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, last_ckpt_path)
print(f'scene_rep_reconstruction ({stage}): saved checkpoints at', last_ckpt_path)
return data_record
def train(args, cfg, data_dict):
# init
print('train: start')
eps_time = time.time()
print(os.path.join(cfg.save_path, 'code'))
os.makedirs(os.path.join(cfg.save_path, 'code'), exist_ok=True)
# back up files
source1 = './run.py'
source6 = './configs'
source7 = './lib'
source8 = './utils'
source = [source1]
for i in source:
shutil.copy(i, os.path.join(cfg.save_path, 'code'))
if not os.path.exists(os.path.join(cfg.save_path, 'code' + '/configs')):
shutil.copytree(source6, os.path.join(cfg.save_path, 'code' + '/configs'))
if not os.path.exists(os.path.join(cfg.save_path, 'code' + '/lib')):
shutil.copytree(source7, os.path.join(cfg.save_path, 'code' + '/lib'))
if not os.path.exists(os.path.join(cfg.save_path, 'code' + '/lib')):
shutil.copytree(source8, os.path.join(cfg.save_path, 'code' + '/lib'))
data_record = open(os.path.join(cfg.save_path, 'args.txt'), 'a')
for arg in sorted(vars(args)):
attr = getattr(args, arg)
data_record.write('{} = {}\n'.format(arg, attr))
cfg.dump(os.path.join(cfg.save_path, 'config.py'))
xyz_min_coarse, xyz_max_coarse = 0, 0
# pdb.set_trace()
if cfg.coarse_model_and_render.geometry_search:
# coarse geometry searching
eps_coarse = time.time()
xyz_min_coarse, xyz_max_coarse = compute_bbox_by_cam_frustrm(args=args, cfg=cfg, **data_dict)
data_record = scene_rep_reconstruction(
args=args, cfg=cfg,
cfg_model=cfg.coarse_model_and_render, cfg_train=cfg.coarse_train,
xyz_min=xyz_min_coarse, xyz_max=xyz_max_coarse, xyz_min_coarse=xyz_min_coarse,
xyz_max_coarse=xyz_max_coarse,
data_dict=data_dict, stage='coarse', data_record=data_record)
eps_coarse = time.time() - eps_coarse
eps_time_str = f'{eps_coarse // 3600:02.0f}:{eps_coarse // 60 % 60:02.0f}:{eps_coarse % 60:02.0f}'
print('train: coarse geometry searching in', eps_time_str)
eps_fine = time.time()
coarse_ckpt_path = os.path.join(cfg.save_path, f'coarse_last.tar')
xyz_min_fine, xyz_max_fine = compute_bbox_by_coarse_geo(
model_class=V4D.v4d, model_path=coarse_ckpt_path,
thres=cfg.fine_model_and_render.bbox_thres, time_concat=cfg.fine_model_and_render.time_concat, cfg=cfg)
print('xyz_min_coarse, xyz_max_coarse:', xyz_min_coarse, xyz_max_coarse)
print('xyz_min_fine, xyz_max_fine:', xyz_min_fine, xyz_max_fine)
data_record.write('\n geometry searching \n: xyz_min_coarse = {}, xyz_max_coarse = {} \n '
'xyz_min_fine = {}, xyz_max_fine = {}\n'.format(xyz_min_coarse, xyz_max_coarse, xyz_min_fine,
xyz_max_fine))
elif cfg.data.dataset_type == 'hyper_dataset':
xyz_min_fine, xyz_max_fine = compute_bbox_by_cam_frustrm_hyper(args=args, cfg=cfg, data_class=data_dict['data_class'])
eps_fine = time.time()
else:
if 'lego' in cfg.save_path:
xyz_min_fine = np.array([-0.6651, -1.1022, -0.4391])
xyz_max_fine = np.array([0.5691, 1.0999, 0.7508])
elif 'mutant' in cfg.save_path:
xyz_min_fine = np.array([-0.8582, -0.8581, -0.6661])
xyz_max_fine = np.array([0.8460, 0.1607, 0.8073])
elif 'bouncingballs' in cfg.save_path:
xyz_min_fine = np.array([-1.3542, -1.2514, -0.4963])
xyz_max_fine = np.array([1.2538, 1.3660, 1.0336])
elif 'hellwarrior' in cfg.save_path:
xyz_min_fine = np.array([-0.6724, -0.8591, -1.1523])
xyz_max_fine = np.array([0.6524, 0.8041, 1.0948])
elif 'standup' in cfg.save_path:
xyz_min_fine = np.array([-0.5807, -0.5698, -1.2333])
xyz_max_fine = np.array([0.3268, 0.4537, 1.0893])
elif 'jumpingjacks' in cfg.save_path:
xyz_min_fine = np.array([-0.9271, -0.2787, -1.3461])
xyz_max_fine = np.array([0.9333, 0.3433, 1.0906])
elif 'hook' in cfg.save_path:
xyz_min_fine = np.array([-0.4363, -1.0708, -1.0063])
xyz_max_fine = np.array([0.4688, 0.6876, 1.0903])
elif 'trex' in cfg.save_path:
xyz_min_fine = np.array([-0.6203, -1.1333, -0.3828])
xyz_max_fine = np.array([0.6306, 0.8566, 1.0339])
elif 'static' in cfg.save_path:
xyz_min_fine, xyz_max_fine = np.array([-1.2682, -1.2635, -1.0545]), np.array(
[1.3802, 1.3275, 1.1109])
eps_fine = time.time()