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guanjunwu 2023-10-17 15:39:25 +08:00
parent 4b181e2b34
commit 54f3436156
11 changed files with 217 additions and 19 deletions
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1
arguments/__init__.py
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@ -134,6 +134,7 @@ class OptimizationParams(ParamGroup):
self.opacity_threshold_coarse = 0.005
self.opacity_threshold_fine_init = 0.005
self.opacity_threshold_fine_after = 0.005
self.batch_size=1,
super().__init__(parser, "Optimization Parameters")

14
arguments/dynerf/default.py
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@ -6,22 +6,24 @@ ModelHiddenParams = dict(
'resolution': [64, 64, 64, 150]
},
multires = [1,2,4,8],
defor_depth = 2,
defor_depth = 1,
net_width = 256,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.001,
no_do=False
)
OptimizationParams = dict(
dataloader=True,
iterations = 30_000,
batch_size=4,
coarse_iterations = 3000,
densify_until_iter = 15_000,
opacity_reset_interval = 6000,
densify_until_iter = 20_000,
opacity_reset_interval = 20000,
opacity_threshold_coarse = 0.005,
opacity_threshold_fine_init = 0.005,
opacity_threshold_fine_after = 0.005,
opacity_threshold_coarse = 0.05,
opacity_threshold_fine_init = 0.05,
opacity_threshold_fine_after = 0.05,
# pruning_interval = 2000
)

29
arguments/dynerf_2/default.py Normal file
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@ -0,0 +1,29 @@
ModelHiddenParams = dict(
kplanes_config = {
'grid_dimensions': 2,
'input_coordinate_dim': 4,
'output_coordinate_dim': 16,
'resolution': [64, 64, 64, 150]
},
multires = [1,2,4,8],
defor_depth = 1,
net_width = 256,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.001,
no_do=False
)
OptimizationParams = dict(
dataloader=True,
iterations = 20_000,
batch_size=8,
coarse_iterations = 3000,
densify_until_iter = 20_000,
opacity_reset_interval = 3000,
opacity_threshold_coarse = 0.05,
opacity_threshold_fine_init = 0.05,
opacity_threshold_fine_after = 0.05,
# pruning_interval = 2000
)

29
arguments/dynerf_3/default.py Normal file
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@ -0,0 +1,29 @@
ModelHiddenParams = dict(
kplanes_config = {
'grid_dimensions': 2,
'input_coordinate_dim': 4,
'output_coordinate_dim': 16,
'resolution': [64, 64, 64, 150]
},
multires = [1,2,4,8],
defor_depth = 1,
net_width = 256,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.001,
no_do=False
)
OptimizationParams = dict(
dataloader=True,
iterations = 60_000,
batch_size=8,
coarse_iterations = 3000,
densify_until_iter = 20_000,
opacity_reset_interval = 20000,
opacity_threshold_coarse = 0.05,
opacity_threshold_fine_init = 0.05,
opacity_threshold_fine_after = 0.05,
# pruning_interval = 2000
)

29
arguments/dynerf_4/default.py Normal file
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@ -0,0 +1,29 @@
ModelHiddenParams = dict(
kplanes_config = {
'grid_dimensions': 2,
'input_coordinate_dim': 4,
'output_coordinate_dim': 16,
'resolution': [64, 64, 64, 150]
},
multires = [1,2,4,8],
defor_depth = 1,
net_width = 256,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.001,
no_do=False
)
OptimizationParams = dict(
dataloader=True,
iterations = 60_000,
batch_size=1,
coarse_iterations = 3000,
densify_until_iter = 40_000,
opacity_reset_interval = 20000,
opacity_threshold_coarse = 0.05,
opacity_threshold_fine_init = 0.05,
opacity_threshold_fine_after = 0.05,
# pruning_interval = 2000
)

34
arguments/dynerf_static/default.py Normal file
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@ -0,0 +1,34 @@
ModelHiddenParams = dict(
kplanes_config = {
'grid_dimensions': 2,
'input_coordinate_dim': 4,
'output_coordinate_dim': 16,
'resolution': [64, 64, 64, 150]
},
multires = [1,2,4,8],
defor_depth = 1,
net_width = 256,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.001,
no_do=False
)
OptimizationParams = dict(
dataloader=True,
iterations = 10_000,
batch_size=8,
coarse_iterations = 10000,
densify_until_iter = 20_000,
opacity_reset_interval = 3000,
opacity_threshold_coarse = 0.05,
opacity_threshold_fine_init = 0.05,
opacity_threshold_fine_after = 0.05,
# pruning_interval = 2000
# deformation_lr_init = 0.00016,
# deformation_lr_final = 0.000016,
# deformation_lr_delay_mult = 0.01,
# grid_lr_init = 0.0016,
# grid_lr_final = 0.00016,
)

5
arguments/hypernerf/default.py
View File

@ -15,9 +15,10 @@ ModelHiddenParams = dict(
)
OptimizationParams = dict(
dataloader=False,
iterations = 60_000,
iterations = 30000,
batch_size=1,
coarse_iterations = 3000,
densify_until_iter = 45_000,
densify_until_iter = 20_000,
opacity_reset_interval = 6000,
# position_lr_init = 0.00016,
# position_lr_final = 0.0000016,

63
scene/grid.py Normal file
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@ -0,0 +1,63 @@
import os
import time
import functools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
parent_dir = os.path.dirname(os.path.abspath(__file__))
def create_grid(type, **kwargs):
if type == 'DenseGrid':
return DenseGrid(**kwargs)
elif type == 'TensoRFGrid':
return TensoRFGrid(**kwargs)
else:
raise NotImplementedError
''' Dense 3D grid
'''
class DenseGrid(nn.Module):
def __init__(self, channels, world_size, xyz_min, xyz_max, **kwargs):
super(DenseGrid, self).__init__()
self.channels = channels
self.world_size = world_size
self.register_buffer('xyz_min', torch.Tensor(xyz_min))
self.register_buffer('xyz_max', torch.Tensor(xyz_max))
self.grid = nn.Parameter(torch.zeros([1, channels, *world_size]))
def forward(self, xyz):
'''
xyz: global coordinates to query
'''
shape = xyz.shape[:-1]
xyz = xyz.reshape(1,1,1,-1,3)
ind_norm = ((xyz - self.xyz_min) / (self.xyz_max - self.xyz_min)).flip((-1,)) * 2 - 1
out = F.grid_sample(self.grid, ind_norm, mode='bilinear', align_corners=True)
out = out.reshape(self.channels,-1).T.reshape(*shape,self.channels)
if self.channels == 1:
out = out.squeeze(-1)
return out
def scale_volume_grid(self, new_world_size):
if self.channels == 0:
self.grid = nn.Parameter(torch.zeros([1, self.channels, *new_world_size]))
else:
self.grid = nn.Parameter(
F.interpolate(self.grid.data, size=tuple(new_world_size), mode='trilinear', align_corners=True))
def get_dense_grid(self):
return self.grid
@torch.no_grad()
def __isub__(self, val):
self.grid.data -= val
return self
def extra_repr(self):
return f'channels={self.channels}, world_size={self.world_size.tolist()}'

1
scene/neural_3D_dataset_NDC.py
View File

@ -264,6 +264,7 @@ class Neural3D_NDC_Dataset(Dataset):
self.near_fars = poses_arr[:, -2:]
videos = glob.glob(os.path.join(self.root_dir, "cam*"))
videos = sorted(videos)
breakpoint()
assert len(videos) == poses_arr.shape[0]
H, W, focal = poses[0, :, -1]

27
scripts/train_dynerf.sh
View File

@ -1,18 +1,27 @@
exp_name=$1
export CUDA_VISIBLE_DEVICES=0&&python train.py -s data/hypernerf/misc/split-cookie --port 6068 --expname "$exp_name/split-cookie" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=1&&python train.py -s data/hypernerf/virg/vrig-3dprinter --port 6066 --expname "$exp_name/3dprinter" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=2&&python train.py -s data/hypernerf/interp/chickchicken --port 6069 --expname "$exp_name/interp-chicken" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=3&&python train.py -s data/hypernerf/interp/cut-lemon1 --port 6070 --expname "$exp_name/cut-lemon1" --configs arguments/$exp_name/cut-lemon1.py &
# export CUDA_VISIBLE_DEVICES=3&&python train.py -s data/hypernerf/interp/hand1-dense-v2 --port 6071 --expname "$exp_name/hand1-dense-v2" --configs arguments/$exp_name/hand1-dense-v2.py
export CUDA_VISIBLE_DEVICES=0&&python train.py -s data/dynerf/cut_roasted_beef --port 6068 --expname "$exp_name/cut_roasted_beef" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=1&&python train.py -s data/dynerf/cook_spinach --port 6066 --expname "$exp_name/cook_spinach" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=2&&python train.py -s data/dynerf/sear_steak --port 6069 --expname "$exp_name/sear_steak" --configs arguments/$exp_name/default.py &
wait
export CUDA_VISIBLE_DEVICES=2&&python render.py --model_path output/$exp_name/cut_roasted_beef --configs arguments/$exp_name/default.py --skip_train --skip_test&
export CUDA_VISIBLE_DEVICES=3&&python render.py --model_path output/$exp_name/cook_spinach --configs arguments/$exp_name/default.py --skip_train --skip_test &
export CUDA_VISIBLE_DEVICES=2&&python render.py --model_path output/$exp_name/sear_steak --configs arguments/$exp_name/default.py --skip_train --skip_test&
export CUDA_VISIBLE_DEVICES=0&&python train.py -s data/dynerf/flame_salmon_1 --port 6070 --expname "$exp_name/flame_salmon_1" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=1&&python train.py -s data/dynerf/flame_steak --port 6071 --expname "$exp_name/flame_steak" --configs arguments/$exp_name/default.py &
export CUDA_VISIBLE_DEVICES=2&&python train.py -s data/dynerf/coffee_martini --port 6071 --expname "$exp_name/coffee_martini" --configs arguments/$exp_name/default.py &
wait
export CUDA_VISIBLE_DEVICES=0&&python render.py --model_path output/$exp_name/cut_roasted_beef --configs arguments/$exp_name/default.py --skip_train &
export CUDA_VISIBLE_DEVICES=1&&python render.py --model_path output/$exp_name/cook_spinach --configs arguments/$exp_name/default.py --skip_train &
export CUDA_VISIBLE_DEVICES=2&&python render.py --model_path output/$exp_name/sear_steak --configs arguments/$exp_name/default.py --skip_train &
# export CUDA_VISIBLE_DEVICES=3&&python render.py --model_path output/$exp_name/hand1-dense-v2 --configs arguments/$exp_name/hand1-dense-v2.py --skip_train
wait
export CUDA_VISIBLE_DEVICES=0&&python metrics.py --model_path "output/$exp_name/cut_roasted_beefe/" &
export CUDA_VISIBLE_DEVICES=0&&python render.py --model_path output/$exp_name/flame_salmon_1 --configs arguments/$exp_name/default.py --skip_train &
export CUDA_VISIBLE_DEVICES=1&&python render.py --model_path output/$exp_name/flame_steak --configs arguments/$exp_name/default.py --skip_train &
export CUDA_VISIBLE_DEVICES=2&&python render.py --model_path output/$exp_name/coffee_martini --configs arguments/$exp_name/default.py --skip_train &
wait
export CUDA_VISIBLE_DEVICES=0&&python metrics.py --model_path "output/$exp_name/cut_roasted_beef/" &
export CUDA_VISIBLE_DEVICES=1&&python metrics.py --model_path "output/$exp_name/cook_spinach/" &
export CUDA_VISIBLE_DEVICES=2&&python metrics.py --model_path "output/$exp_name/sear_steak/" &
# export CUDA_VISIBLE_DEVICES=3&&python metrics.py --model_path "output/$exp_name/hand1-dense-v2/"
wait
export CUDA_VISIBLE_DEVICES=0&&python metrics.py --model_path "output/$exp_name/flame_salmon_1/" &
export CUDA_VISIBLE_DEVICES=1&&python metrics.py --model_path "output/$exp_name/flame_steak/" &
export CUDA_VISIBLE_DEVICES=2&&python metrics.py --model_path "output/$exp_name/coffee_martini/" &
echo "Done"

4
train.py
View File

@ -89,7 +89,7 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
# Pick a random Camera
if not viewpoint_stack:
viewpoint_stack = scene.getTrainCameras()
batch_size = 1
batch_size = opt.batch_size
viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,shuffle=True,num_workers=32,collate_fn=list)
loader = iter(viewpoint_stack_loader)
if opt.dataloader:
@ -97,7 +97,7 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
viewpoint_cams = next(loader)
except StopIteration:
print("reset dataloader")
batch_size = 1
batch_size = opt.batch_size
loader = iter(viewpoint_stack_loader)
else:
idx = randint(0, len(viewpoint_stack)-1)