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train.py
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train.py
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import os
from tqdm.auto import tqdm
from opt import config_parser
from renderer import *
from utils import *
import datetime
from dataLoader import dataset_dict
import sys
import time
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
renderer = OctreeRender_trilinear_fast
class SimpleSampler:
def __init__(self, total, batch):
self.total = total
self.batch = batch
self.curr = total
self.ids = None
def nextids(self):
self.curr+=self.batch
if self.curr + self.batch > self.total:
self.ids = torch.LongTensor(np.random.permutation(self.total))
self.curr = 0
return self.ids[self.curr:self.curr+self.batch]
@torch.no_grad()
def render_test(args):
# init dataset
dataset = dataset_dict[args.dataset_name]
test_dataset = dataset(args.datadir, split='test', downsample=args.downsample_train, is_stack=True)
white_bg = test_dataset.white_bg
ndc_ray = args.ndc_ray
if not os.path.exists(args.ckpt):
print('the ckpt path does not exists!!')
return
tensorf = torch.load(args.ckpt, map_location=device)
logfolder = os.path.dirname(args.ckpt)
if args.render_train:
os.makedirs(f'{logfolder}/imgs_train_all', exist_ok=True)
train_dataset = dataset(args.datadir, split='train', downsample=args.downsample_train, is_stack=True)
PSNRs_test = evaluation(train_dataset,tensorf, args, renderer, f'{logfolder}/imgs_train_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
print(f'======> {args.expname} train all psnr: {np.mean(PSNRs_test)} <========================')
if args.render_test:
os.makedirs(f'{logfolder}/imgs_test_all', exist_ok=True)
PSNRs_test = evaluation(test_dataset,tensorf, args, renderer, f'{logfolder}/imgs_test_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
print(f'======> {args.expname} test all psnr: {np.mean(PSNRs_test)} <========================')
if args.render_path:
c2ws = test_dataset.render_path
os.makedirs(f'{logfolder}/imgs_path_all', exist_ok=True)
evaluation_path(test_dataset,tensorf, c2ws, renderer, f'{logfolder}/imgs_path_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
def reconstruction(args):
# init dataset
dataset = dataset_dict[args.dataset_name]
train_dataset = dataset(args.datadir, split='train', downsample=args.downsample_train, is_stack=False)
test_dataset = dataset(args.datadir, split='test', downsample=args.downsample_train, is_stack=True)
white_bg = train_dataset.white_bg
near_far = train_dataset.near_far
ndc_ray = args.ndc_ray
# init resolution
upsamp_list = args.upsamp_list
update_AlphaMask_list = args.update_AlphaMask_list
n_lamb_sigma = args.n_lamb_sigma
n_lamb_sh = args.n_lamb_sh
if args.add_timestamp:
logfolder = f'{args.basedir}/{args.expname}{datetime.datetime.now().strftime("-%Y%m%d-%H%M%S")}'
else:
logfolder = f'{args.basedir}/{args.expname}'
# init log file
os.makedirs(logfolder, exist_ok=True)
os.makedirs(f'{logfolder}/imgs_vis', exist_ok=True)
os.makedirs(f'{logfolder}/imgs_rgba', exist_ok=True)
os.makedirs(f'{logfolder}/rgba', exist_ok=True)
# init parameters
aabb = train_dataset.scene_bbox.to(device)
reso_cur = N_to_reso(args.N_voxel_init, aabb)
nSamples = min(args.nSamples, cal_n_samples(reso_cur,args.step_ratio))
if args.ckpt is not None:
tensorf = torch.load(args.ckpt, map_location=device)
else:
tensorf = eval(args.model_name)(aabb, reso_cur, device,
density_n_comp=n_lamb_sigma, appearance_n_comp=n_lamb_sh, app_dim=args.data_dim_color, near_far=near_far,
shadingMode=args.shadingMode, alphaMask_thres=args.alpha_mask_thre, density_shift=args.density_shift, distance_scale=args.distance_scale,
pos_pe=args.pos_pe, view_pe=args.view_pe, fea_pe=args.fea_pe, featureC=args.featureC, step_ratio=args.step_ratio, fea2denseAct=args.fea2denseAct)
grad_vars = tensorf.get_optparam_groups(args.lr_init, args.lr_basis)
if args.lr_decay_iters > 0:
lr_factor = args.lr_decay_target_ratio**(1/args.lr_decay_iters)
else:
args.lr_decay_iters = args.n_iters
lr_factor = args.lr_decay_target_ratio**(1/args.n_iters)
print("lr decay", args.lr_decay_target_ratio, args.lr_decay_iters)
optimizer = torch.optim.Adam(grad_vars, betas=(0.9,0.99))
#linear in logrithmic space
N_voxel_list = (torch.round(torch.exp(torch.linspace(np.log(args.N_voxel_init), np.log(args.N_voxel_final), len(upsamp_list)+1))).long()).tolist()[1:]
torch.cuda.empty_cache()
PSNRs,PSNRs_test = [],[0]
batch_size = 2048
allrays, allrgbs = train_dataset.all_rays, train_dataset.all_rgbs
if not args.ndc_ray:
allrays, allrgbs = tensorf.filtering_rays(allrays, allrgbs, bbox_only=True)
trainingSampler = SimpleSampler(allrays.shape[0], batch_size)
L1_reg_weight = args.L1_weight_inital
print("initial L1_reg_weight", L1_reg_weight)
pbar = tqdm(range(args.n_iters), miniters=args.progress_refresh_rate, file=sys.stdout)
for iteration in pbar:
ray_idx = trainingSampler.nextids()
rays_train, rgb_train = allrays[ray_idx], allrgbs[ray_idx].to(device)
#rgb_map, alphas_map, depth_map, weights, uncertainty
rgb_map, alphas_map, depth_map, weights, others = renderer(rays_train,
tensorf,
chunk=batch_size,
N_samples=nSamples,
white_bg = white_bg,
ndc_ray=ndc_ray,
device=device,
is_train=True)
mse_loss = torch.mean((rgb_map - rgb_train) ** 2)
total_loss = mse_loss
if others['normals'] is not None:
Ro = torch.sum(others['normals'] * others['valid_viewdirs'], dim=-1)
Ro = F.relu(Ro).pow(2) * others['valid_weights']
Ro = Ro.mean()
total_loss += 0.3 * Ro
if L1_reg_weight > 0:
loss_reg_L1 = tensorf.density_L1()
total_loss += L1_reg_weight*loss_reg_L1
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
mse_loss = mse_loss.detach().item()
PSNRs.append(-10.0 * np.log(mse_loss) / np.log(10.0))
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr'] * lr_factor
# Print the current values of the losses.
if iteration % args.progress_refresh_rate == 0:
pbar.set_description(
f'Iteration {iteration:05d}:'
+ f' train_psnr = {float(np.mean(PSNRs)):.2f}'
+ f' test_psnr = {float(np.mean(PSNRs_test)):.2f}'
+ f' mse = {mse_loss:.6f}'
)
PSNRs = []
if iteration % args.vis_every == args.vis_every - 1 and args.N_vis!=0:
PSNRs_test = evaluation(test_dataset,tensorf,
args,
renderer,
f'{logfolder}/imgs_vis/',
N_vis=args.N_vis,
prtx=f'{iteration:06d}_',
N_samples=nSamples,
white_bg = white_bg,
ndc_ray=ndc_ray,
compute_extra_metrics=False)
if iteration in update_AlphaMask_list:
# reso_cur = N_to_reso(250**3, tensorf.aabb)
if reso_cur[0] * reso_cur[1] * reso_cur[2]<256**3:# update volume resolution
reso_mask = reso_cur
new_aabb = tensorf.updateAlphaMask(tuple(reso_mask))
if iteration == update_AlphaMask_list[0]:
tensorf.shrink(new_aabb)
L1_reg_weight = args.L1_weight_rest
print("continuing L1_reg_weight", L1_reg_weight)
if not args.ndc_ray and iteration == update_AlphaMask_list[1]:
# filter rays outside the bbox
allrays,allrgbs = tensorf.filtering_rays(allrays,allrgbs)
batch_size = args.batch_size
trainingSampler = SimpleSampler(allrgbs.shape[0], batch_size)
print(f'Update batch size to {args.batch_size}')
if iteration in upsamp_list:
n_voxels = N_voxel_list.pop(0)
reso_cur = N_to_reso(n_voxels, tensorf.aabb)
nSamples = min(args.nSamples, cal_n_samples(reso_cur,args.step_ratio))
tensorf.upsample_volume_grid(reso_cur)
if args.lr_upsample_reset:
lr_scale = 1 #0.1 ** (iteration / args.n_iters)
else:
lr_scale = args.lr_decay_target_ratio ** (iteration / args.n_iters)
grad_vars = tensorf.get_optparam_groups(args.lr_init*lr_scale, args.lr_basis*lr_scale)
optimizer = torch.optim.Adam(grad_vars, betas=(0.9, 0.99))
if iteration == upsamp_list[0]:
batch_size = args.batch_size
trainingSampler = SimpleSampler(allrgbs.shape[0], batch_size)
print(f'Update batch size to {batch_size}')
torch.save(tensorf, f'{logfolder}/{args.expname}.pt')
if args.render_train:
os.makedirs(f'{logfolder}/imgs_train_all', exist_ok=True)
train_dataset = dataset(args.datadir, split='train', downsample=args.downsample_train, is_stack=True)
PSNRs_test = evaluation(train_dataset,tensorf, args, renderer, f'{logfolder}/imgs_train_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
print(f'======> {args.expname} test all psnr: {np.mean(PSNRs_test)} <========================')
if args.render_test:
os.makedirs(f'{logfolder}/imgs_test_all', exist_ok=True)
PSNRs_test = evaluation(test_dataset,tensorf, args, renderer, f'{logfolder}/imgs_test_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
print(f'======> {args.expname} test all psnr: {np.mean(PSNRs_test)} <========================')
if args.render_path:
c2ws = test_dataset.render_path
print('========>',c2ws.shape)
os.makedirs(f'{logfolder}/imgs_path_all', exist_ok=True)
evaluation_path(test_dataset,tensorf, c2ws, renderer, f'{logfolder}/imgs_path_all/',
N_vis=-1, N_samples=-1, white_bg = white_bg, ndc_ray=ndc_ray,device=device)
if __name__ == '__main__':
torch.set_default_dtype(torch.float32)
torch.manual_seed(20211202)
np.random.seed(20211202)
args = config_parser()
if args.export_mesh:
export_mesh(args)
if args.render_only and (args.render_test or args.render_path):
render_test(args)
else:
reconstruction(args)