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modify Neu3D hyper params

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This commit is contained in:
guanjunwu 2024-03-04 11:46:17 +08:00
parent 4a9d7517fa
commit 433b593f32
15 changed files with 52 additions and 24 deletions
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29
arguments/dycheck/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],
defor_depth = 1,
net_width = 128,
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.0001,
render_process=True
)
OptimizationParams = dict(
# dataloader=True,
iterations = 60_000,
batch_size=2,
coarse_iterations = 3000,
densify_until_iter = 10_000,
opacity_reset_interval = 300000,
# grid_lr_init = 0.0016,
# grid_lr_final = 16,
# opacity_threshold_coarse = 0.005,
# opacity_threshold_fine_init = 0.005,
# opacity_threshold_fine_after = 0.005,
# pruning_interval = 2000
)

1
arguments/dynerf/cook_spinach.py
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@ -1,4 +1,5 @@
_base_ = './default.py'
OptimizationParams = dict(
batch_size=2,
)

2
arguments/dynerf/cut_roasted_beef.py
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@ -1,4 +1,4 @@
_base_ = './default.py'
OptimizationParams = dict(
batch_size=2,
)

6
arguments/dynerf/default.py
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@ -21,11 +21,11 @@ ModelHiddenParams = dict(
)
OptimizationParams = dict(
dataloader=True,
iterations = 15000,
batch_size=1,
iterations = 14000,
batch_size=4,
coarse_iterations = 3000,
densify_until_iter = 10_000,
# opacity_reset_interval = 60000,
opacity_reset_interval = 60000,
opacity_threshold_coarse = 0.005,
opacity_threshold_fine_init = 0.005,
opacity_threshold_fine_after = 0.005,

1
arguments/dynerf/flame_steak.py
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@ -1,4 +1,5 @@
_base_ = './default.py'
OptimizationParams = dict(
batch_size=2,
)

2
arguments/dynerf/sear_steak.py
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@ -1,4 +1,4 @@
_base_ = './default.py'
OptimizationParams = dict(
batch_size=2,
)

2
arguments/hypernerf/default.py
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@ -11,7 +11,7 @@ ModelHiddenParams = dict(
plane_tv_weight = 0.0002,
time_smoothness_weight = 0.001,
l1_time_planes = 0.0001,
render_process=False
render_process=True
)
OptimizationParams = dict(
# dataloader=True,

2
gaussian_renderer/__init__.py
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@ -77,7 +77,7 @@ def render(viewpoint_camera, pc : GaussianModel, pipe, bg_color : torch.Tensor,
scales = pc._scaling
rotations = pc._rotation
deformation_point = pc._deformation_table
if stage == "coarse" :
if "coarse" in stage:
means3D_final, scales_final, rotations_final, opacity_final, shs_final = means3D, scales, rotations, opacity, shs
else:
# time0 = get_time()

2
scene/deformation.py
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@ -24,7 +24,7 @@ class Deformation(nn.Module):
self.grid_pe = grid_pe
self.no_grid = args.no_grid
self.grid = HexPlaneField(args.bounds, args.kplanes_config, args.multires)
# breakpoint()
self.args = args
# self.args.empty_voxel=True
if self.args.empty_voxel:

9
scene/hyper_loader.py
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@ -103,8 +103,9 @@ class Load_hyper_data(Dataset):
self.image_mask = [f'{datadir}/covisible/{int(2)}x/val/{i}.png' for i in self.all_img_origin]
else:
self.image_mask = None
self.generate_video_path()
# self.generate_video_path()
# self.i_test
def generate_video_path(self):
self.select_video_cams = [item for i, item in enumerate(self.all_cam_params) if i % 1 == 0 ]
@ -120,14 +121,14 @@ class Load_hyper_data(Dataset):
elif self.split == "test":
return self.load_raw(self.i_test[index])
elif self.split == "video":
return self.load_video(index)
return self.load_raw(index)
def __len__(self):
if self.split == "train":
return len(self.i_train)
elif self.split == "test":
return len(self.i_test)
elif self.split == "video":
return len(self.video_path)
return len(self.i_test)
# return len(self.video_v2)
def load_video(self, idx):
if idx in self.map.keys():

2
scripts/process_dnerf.sh
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@ -39,4 +39,4 @@ wait
export CUDA_VISIBLE_DEVICES=2&&python metrics.py --model_path "output/$exp_name1/bouncingballs/" &
export CUDA_VISIBLE_DEVICES=0&&python metrics.py --model_path "output/$exp_name1/lego/"
wait
echo "Done"
echo "Done"

10
scripts/read_all_metrics.py
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@ -1,10 +1,8 @@
import json
import os
# exp_name = ["dnerf_tv_nodx","dnerf_tv_nodr","dnerf_tv_nods","dnerf_tv","dnerf_tv_dshs","dnerf_tv_do",
# "dnerf_tv_2","dnerf_tv_8","dnerf_tv_deepmlp"]
# exp_name= ["dnerf_tv_2_slim"]
exp_name=["dynerf_default","dynerf_9"]
# exp_name = ["hypernerf_3dgs"]
# exp_name = ["hypernerf"]
# exp_name= ["dnerf"]
exp_name=["dynerf"]
scene_name = ["coffee_martini", "cook_spinach", "cut_roasted_beef", "flame_salmon_1", "flame_steak", "sear_steak"]
# scene_name = ["bouncingballs","jumpingjacks","lego","standup","hook","mutant","hellwarrior","trex"]
# scene_name = ["3dprinter","broom2","peel-banana","vrig-chicken"]
@ -18,8 +16,6 @@ for scene in scene_name:
load_path = os.path.join("output",experiment,scene,json_name)
with open(load_path) as f:
js = json.load(f)
# print(js)
# print(scene, experiment, js["ours_20000"])
for res in ["ours_30000","ours_20000","ours_14000","ours_10000","ours_7000","ours_3000"]:
if res in js.keys():
for key, item in js[res].items():

2
scripts/train_dnerf.sh
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@ -12,4 +12,4 @@ wait
export CUDA_VISIBLE_DEVICES=2&&python train.py -s data/dnerf/hook --port 6069 --expname "$exp_name1/hook" --configs arguments/$exp_name1/hook.py &
export CUDA_VISIBLE_DEVICES=3&&python train.py -s data/dnerf/hellwarrior --port 6070 --expname "$exp_name1/hellwarrior" --configs arguments/$exp_name1/hellwarrior.py &
wait
echo "Done"
echo "Done"

2
scripts/train_dycheck.sh
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@ -18,4 +18,4 @@ export CUDA_VISIBLE_DEVICES=2&&python metrics.py --model_path output/$exp_name1/
export CUDA_VISIBLE_DEVICES=3&&python metrics.py --model_path output/$exp_name1/teddy/ &
export CUDA_VISIBLE_DEVICES=2&&python metrics.py --model_path output/$exp_name1/space-out/ &
export CUDA_VISIBLE_DEVICES=3&&python metrics.py --model_path output/$exp_name1/spin/
echo "Done"
echo "Done"

4
train.py
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@ -86,10 +86,10 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
viewpoint_stack = scene.getTrainCameras()
if opt.custom_sampler is not None:
sampler = FineSampler(viewpoint_stack)
viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,sampler=sampler,num_workers=32,collate_fn=list)
viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,sampler=sampler,num_workers=16,collate_fn=list)
random_loader = False
else:
viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,shuffle=True,num_workers=32,collate_fn=list)
viewpoint_stack_loader = DataLoader(viewpoint_stack, batch_size=batch_size,shuffle=True,num_workers=16,collate_fn=list)
random_loader = True
loader = iter(viewpoint_stack_loader)