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Commit 6294701e authored by Nianchen Deng's avatar Nianchen Deng
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sync

parent 2824f796
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.gitignore
View file @ 6294701e
...@@ -108,6 +108,6 @@ output/ ...@@ -108,6 +108,6 @@ output/
outputE/ outputE/
# Data # Data
data/ /data
bin/ bin/
\ No newline at end of file
.vscode/launch.json
View file @ 6294701e
...@@ -23,18 +23,18 @@ ...@@ -23,18 +23,18 @@
"request": "launch", "request": "launch",
"program": "train.py", "program": "train.py",
"args": [ "args": [
//"-c", "-c",
//"snerf_voxels+ls+f32", "_lr_snerf_voxels+ls",
"/data1/dnc/dvs/data/__nerf/room/_nets/train/snerf_voxels+ls+f32/checkpoint_1.tar", //"/home/dengnc/dvs/data/classroom/_nets/pano_t0.8/smnerf_voxels+ls+lbl/checkpoint_35.tar",
"--prune", //"--prune",
"1", //"1",
"--split", //"--split",
"1", //"1",
"-e", //"-e",
"100", //"100",
"--views", //"--views",
"5", //"5",
//"data/__nerf/room/train.json" "data/classroom/lr_view_t0.8_r360x80_train"
], ],
"justMyCode": false, "justMyCode": false,
"console": "integratedTerminal" "console": "integratedTerminal"
...@@ -46,13 +46,13 @@ ...@@ -46,13 +46,13 @@
"program": "test.py", "program": "test.py",
"args": [ "args": [
"-m", "-m",
"/home/dengnc/dvs/data/__new/barbershop_fovea_r360x80_t0.6/_nets/train_t0.3/snerfadv_voxels+ls2/checkpoint_50.tar", "/home/dengnc/dvs/data/classroom/_nets/ms_train_t0.8/_cnerf/checkpoint_50.tar",
"-o", "-o",
"perf", "perf",
"color", "color",
"--output-type", "--output-type",
"image", "image",
"/home/dengnc/dvs/data/__new/barbershop_fovea_r360x80_t0.6/test_t0.3.json", "/home/dengnc/dvs/data/classroom/lr_view_t0.8_r360x80_test.json",
"--views", "--views",
"1" "1"
], ],
......
README.md
View file @ 6294701e
...@@ -40,7 +40,7 @@ $ ffmpeg -y -r 50 -i %04d.png -c:v libx264 -vframes 600 ../classroom_hmd_mono_hi ...@@ -40,7 +40,7 @@ $ ffmpeg -y -r 50 -i %04d.png -c:v libx264 -vframes 600 ../classroom_hmd_mono_hi
## 2. Convert onnx to tensorRT ## 2. Convert onnx to tensorRT
``` ```
$ trtexec --onnx=net@256x256x2.onnx --fp16 --saveEngine=net@256x256x2.trt --workspace=4096 $ trtexec --onnx=in.onnx --fp16 --saveEngine=out.trt --workspace=4096
``` ```
# Install FFMpeg with Extra Codecs: # Install FFMpeg with Extra Codecs:
......
batch_test.sh 0 → 100755
View file @ 6294701e
#!/bin/bash
test_dataset=$1
test_model_dir=$2
for i in "$test_model_dir"*
do
python test.py -m "$(pwd)/$i/checkpoint_50.tar" -o perf color --output-type image "$test_dataset"
done
echo Test Finished
ls $test_model_dir/*/output_50/perf* | awk -F"/" '{print $6, "\t", $8}'
\ No newline at end of file
blender/gen_utils.py
View file @ 6294701e
import bpy import bpy
import math
import json import json
import os import os
import math import math
......
clib/__init__.py
View file @ 6294701e
...@@ -21,7 +21,7 @@ import torch.nn as nn ...@@ -21,7 +21,7 @@ import torch.nn as nn
import sys import sys
import numpy as np import numpy as np
from utils.geometry import discretize_points from utils.geometry import discretize_points
from utils.constants import HUGE_FLOAT from utils import math
try: try:
import builtins import builtins
...@@ -381,7 +381,7 @@ def inverse_cdf_sampling(pts_idx: torch.Tensor, min_depth: torch.Tensor, max_dep ...@@ -381,7 +381,7 @@ def inverse_cdf_sampling(pts_idx: torch.Tensor, min_depth: torch.Tensor, max_dep
def _parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, deterministic=False): def _parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, deterministic=False):
# uniform sampling # uniform sampling
_min_depth = min_depth.min(1)[0] _min_depth = min_depth.min(1)[0]
_max_depth = max_depth.masked_fill(max_depth.eq(HUGE_FLOAT), 0).max(1)[0] _max_depth = max_depth.masked_fill(max_depth.eq(math.huge), 0).max(1)[0]
max_ray_length = (_max_depth - _min_depth).max() max_ray_length = (_max_depth - _min_depth).max()
delta = torch.arange(int(max_ray_length / MARCH_SIZE), delta = torch.arange(int(max_ray_length / MARCH_SIZE),
...@@ -413,9 +413,9 @@ def _parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, determinis ...@@ -413,9 +413,9 @@ def _parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, determinis
sampled_depth.masked_fill_( sampled_depth.masked_fill_(
(max_ids.ne(min_ids)) | (max_ids.ne(min_ids)) |
(sampled_depth > _max_depth[:, None]) | (sampled_depth > _max_depth[:, None]) |
(sampled_dists == 0.0), HUGE_FLOAT) (sampled_dists == 0.0), math.huge)
sampled_depth, ordered_index = sampled_depth.sort(-1) # sort again sampled_depth, ordered_index = sampled_depth.sort(-1) # sort again
sampled_masks = sampled_depth.eq(HUGE_FLOAT) sampled_masks = sampled_depth.eq(math.huge)
num_max_steps = (~sampled_masks).sum(-1).max() num_max_steps = (~sampled_masks).sum(-1).max()
sampled_depth = sampled_depth[:, :num_max_steps] sampled_depth = sampled_depth[:, :num_max_steps]
...@@ -456,7 +456,7 @@ def parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, determinist ...@@ -456,7 +456,7 @@ def parallel_ray_sampling(MARCH_SIZE, pts_idx, min_depth, max_depth, determinist
return xt return xt
sampled_idx = padding_points(sampled_idx, -1) sampled_idx = padding_points(sampled_idx, -1)
sampled_depth = padding_points(sampled_depth, HUGE_FLOAT) sampled_depth = padding_points(sampled_depth, math.huge)
sampled_dists = padding_points(sampled_dists, 0.0) sampled_dists = padding_points(sampled_dists, 0.0)
return sampled_idx, sampled_depth, sampled_dists return sampled_idx, sampled_depth, sampled_dists
......
components/fnr.py
View file @ 6294701e
...@@ -3,7 +3,7 @@ import torch.nn.functional as nn_f ...@@ -3,7 +3,7 @@ import torch.nn.functional as nn_f
from typing import Any, List, Mapping, Tuple from typing import Any, List, Mapping, Tuple
from torch import nn from torch import nn
from utils.view import * from utils.view import *
from utils.constants import * from utils import math
from .post_process import * from .post_process import *
from .foveation import Foveation from .foveation import Foveation
...@@ -52,7 +52,7 @@ class FoveatedNeuralRenderer(object): ...@@ -52,7 +52,7 @@ class FoveatedNeuralRenderer(object):
using_mask=True, using_mask=True,
mono_periph_mode=0, mono_periph_mode=0,
ret_raw=False) -> Union[Mapping[str, torch.Tensor], Tuple[Mapping[str, torch.Tensor]]]: ret_raw=False) -> Union[Mapping[str, torch.Tensor], Tuple[Mapping[str, torch.Tensor]]]:
if stereo_disparity > TINY_FLOAT: if stereo_disparity > math.tiny:
left_view = Trans( left_view = Trans(
view.trans_point(torch.tensor([-stereo_disparity / 2, 0, 0], device=self.device)), view.trans_point(torch.tensor([-stereo_disparity / 2, 0, 0], device=self.device)),
view.r) view.r)
......
components/foveation.py
View file @ 6294701e
import math
import torch import torch
import torch.nn.functional as nn_f import torch.nn.functional as nn_f
from typing import List, Tuple from typing import List, Tuple
from utils import img from utils import img
from utils import view from utils import view
from utils import misc from utils import misc
from utils import math
class Foveation(object): class Foveation(object):
......
components/post_process.py
View file @ 6294701e
...@@ -3,7 +3,6 @@ import torch ...@@ -3,7 +3,6 @@ import torch
import numpy as np import numpy as np
import torch.nn.functional as nn_f import torch.nn.functional as nn_f
from utils import img from utils import img
from utils.constants import *
def constrast_enhance(image, sigma, fe): def constrast_enhance(image, sigma, fe):
......
components/refine.py
View file @ 6294701e
import torch import torch
import torch.nn.functional as nn_f import torch.nn.functional as nn_f
from utils import view from utils import view
from utils.constants import * from utils import math
class GuideRefinement(object): class GuideRefinement(object):
...@@ -15,7 +15,7 @@ class GuideRefinement(object): ...@@ -15,7 +15,7 @@ class GuideRefinement(object):
for i in range(guides_image.size(0)) for i in range(guides_image.size(0))
], 0) ], 0)
self.guides_diff = (guides_image - guides_inferred) / \ self.guides_diff = (guides_image - guides_inferred) / \
(guides_inferred + TINY_FLOAT) (guides_inferred + math.tiny)
self.guides_view = guides_view self.guides_view = guides_view
self.guides_cam = guides_cam self.guides_cam = guides_cam
......
configs/_cnerf.json 0 → 100644
View file @ 6294701e
{
"model": "CNeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"space": "voxels",
"steps": [4, 16, 8],
"interp_on_coarse": false,
"sub_models": [
{
"core_params": {
"nf": 256,
"n_layers": 2
},
"n_samples": 16
},
{
"core_params": {
"nf": 256,
"n_layers": 2,
"f_chns": 256
},
"n_samples": 64
},
{
"core_params": {
"nf": 256,
"n_layers": 2,
"f_chns": 256
},
"n_samples": 128
}
]
},
"train": {
"max_epochs": 150,
"prune_epochs": [30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150],
"split_epochs": [10, 30, 70, 120],
"freeze_epochs": [50, 100],
"checkpoint_interval": 10,
"level_by_level": true,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_cnerf_ioc.json 0 → 100644
View file @ 6294701e
{
"model": "CNeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"space": "voxels",
"steps": [4, 16, 8],
"interp_on_coarse": true,
"sub_models": [
{
"core_params": {
"nf": 256,
"n_layers": 2
},
"n_samples": 16
},
{
"core_params": {
"nf": 256,
"n_layers": 2,
"f_chns": 256
},
"n_samples": 64
},
{
"core_params": {
"nf": 256,
"n_layers": 2,
"f_chns": 256
},
"n_samples": 128
}
]
},
"train": {
"max_epochs": 150,
"prune_epochs": [30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150],
"split_epochs": [10, 30, 70, 120],
"freeze_epochs": [50, 100],
"checkpoint_interval": 10,
"level_by_level": true,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_cnerfadv.json 0 → 100644
View file @ 6294701e
{
"model": "CNeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core": "nerfadv",
"space": "voxels",
"steps": [4, 16, 8],
"interp_on_coarse": false,
"sub_models": [
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined"
},
"n_samples": 16
},
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined",
"f_chns": 256
},
"n_samples": 64
},
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined",
"f_chns": 256
},
"n_samples": 128
}
]
},
"train": {
"max_epochs": 150,
"prune_epochs": [30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150],
"split_epochs": [10, 30, 70, 120],
"freeze_epochs": [50, 100],
"checkpoint_interval": 10,
"level_by_level": true,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_cnerfadv_ioc.json 0 → 100644
View file @ 6294701e
{
"model": "CNeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core": "nerfadv",
"space": "voxels",
"steps": [4, 16, 8],
"interp_on_coarse": true,
"sub_models": [
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined"
},
"n_samples": 16
},
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined",
"f_chns": 256
},
"n_samples": 64
},
{
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined",
"f_chns": 256
},
"n_samples": 128
}
]
},
"train": {
"max_epochs": 150,
"prune_epochs": [30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150],
"split_epochs": [10, 30, 70, 120],
"freeze_epochs": [50, 100],
"checkpoint_interval": 10,
"level_by_level": true,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_hr_snerf.json 0 → 100644
View file @ 6294701e
{
"model": "NeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core_params": {
"nf": 256,
"n_layers": 8,
"skips": [4]
},
"space": "voxels",
"steps": [16, 64, 32],
"n_samples": 64
},
"train": {
"max_epochs": 50,
"prune_epochs": [10],
"split_epochs": [10, 30],
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_hr_snerfadv.json 0 → 100644
View file @ 6294701e
{
"model": "NeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core": "nerfadv",
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 4
},
"color_net": {
"nf": 256,
"n_layers": 3
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined"
},
"space": "voxels",
"steps": [16, 64, 32],
"n_samples": 64
},
"train": {
"max_epochs": 50,
"prune_epochs": [10],
"split_epochs": [10, 30],
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_lr_snerf.json 0 → 100644
View file @ 6294701e
{
"model": "NeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core_params": {
"nf": 256,
"n_layers": 2
},
"space": "voxels",
"steps": [4, 16, 8],
"n_samples": 16
},
"train": {
"max_epochs": 50,
"prune_epochs": [30, 40, 50],
"split_epochs": [10, 30],
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_lr_snerfadv.json 0 → 100644
View file @ 6294701e
{
"model": "SNeRFAdvance",
"args": {
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core": "nerfadv",
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined"
},
"space": "voxels",
"steps": [4, 16, 8],
"n_samples": 16,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
},
"train": {
"max_epochs": 50,
"prune_epochs": [10],
"split_epochs": [10, 30],
"checkpoint_interval": 10
}
}
\ No newline at end of file
configs/_mr_snerf.json 0 → 100644
View file @ 6294701e
{
"model": "NeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core_params": {
"nf": 256,
"n_layers": 4
},
"space": "voxels",
"steps": [4, 16, 8],
"n_samples": 16
},
"train": {
"max_epochs": 50,
"prune_epochs": [30, 40, 50],
"split_epochs": [10, 20, 30],
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
}
}
\ No newline at end of file
configs/_mr_snerfadv.json 0 → 100644
View file @ 6294701e
{
"model": "NeRF",
"args": {
"spherical": true,
"color": "rgb",
"encode_x": 10,
"encode_d": 4,
"core": "nerfadv",
"core_params": {
"density_net": {
"nf": 256,
"n_layers": 2
},
"color_net": {
"nf": 256,
"n_layers": 2
},
"specular_net": {
"nf": 128,
"n_layers": 1
},
"appearance": "combined"
},
"space": "voxels",
"steps": [16, 64, 32],
"n_samples": 64,
"density_regularization_weight": 1e-4,
"density_regularization_scale": 1e4
},
"train": {
"max_epochs": 50,
"prune_epochs": [10],
"split_epochs": [20, -1],
"checkpoint_interval": 10
}
}
\ No newline at end of file
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