NeRF神经辐射场学习笔记（二）——Pytorch版NeRF实现以及代码注释
- 声明
- NeRF-Pytorch源码地址
- 准备工作
* - Win10下Anaconda环境配置
- GPU版Pytorch安装
- Vscode下运行NeRF-Pytorch代码
* - 安装命令
- 数据下载
- 训练数据
- NeRF-Pytorch代码详解
* - Vscode代码调试
- 代码框架概述
- 1.参数设置
- 2.数据加载
– - 3.NeRF网络构建
– - 4.生成rays数据
– - 5.体素渲染
– - 6.loss计算+训练
– - 参考文献和资料
声明
本人书写本系列博客目的是为了记录我学习三维重建领域相关知识的过程和心得，不涉及任何商业意图，欢迎互相交流，批评指正。
NeRF-Pytorch源码地址
NeRF作者所给出的源码是TensorFlow版本的，由于本人的学习需求，所以采用来自yenchen lin的Pytorch版代码进行实现和注释，该版本是基于原版的NeRF所复现的，而且在实现的基础上还要比原版的结果输出时间快了1.3倍；
源码GitHub地址为：https://github.com/yenchenlin/nerf-pytorch.git
准备工作
Win10下Anaconda环境配置
GPU版Pytorch安装
Vscode下运行NeRF-Pytorch代码
安装命令
git clone https://github.com/yenchenlin/nerf-pytorch.git
cd nerf-pytorch
pip install -r requirements.txt
- 下载好源码后需要进入项目所在目录下，并且激活对应的conda环境（conda activate ）；
- 若安装超时，可以参考教程换源后逐个安装；
- 安装的package中pytorch的版本没有硬性要求，但一定要和CUDA版本对应；
数据下载
bash download_example_data.sh
安装好后的数据集文件夹：
训练数据
python run_nerf.py --config configs/lego.txt
若出现如下报错，参考教程进行更改：
在run_nerf.py的第206行添加相应语句即可：
optimizer.param_groups[0]['capturable'] = True
紧接着开始训练，训练完成后查看训练结果：
渲染的结果和相关参数全部存在./log/blender_paper_lego当中，包括测试渲染出的图片以及视频等；
NeRF-Pytorch代码详解
Vscode代码调试
在调试过程中我们所用到的参数采用./config/configs.txt中的参数，所以在调试时，我们要将参数传入调试命令，添加launch.json的具体操作参考教程，我们在配置文件中添加”args”参数，如下图所示：
; 代码框架概述
1.参数设置
def config_parser():
import configargparse
parser = configargparse.ArgumentParser()
parser.add_argument('--config', is_config_file=True,
help='config file path')
parser.add_argument("--expname", type=str,
help='experiment name')
parser.add_argument("--basedir", type=str, default='./logs/',
help='where to store ckpts and logs')
parser.add_argument("--datadir", type=str, default='./data/nerf_llff_data/fern',
help='input data directory')
parser.add_argument("--netdepth", type=int, default=8,
help='layers in network')
parser.add_argument("--netwidth", type=int, default=256,
help='channels per layer')
parser.add_argument("--netdepth_fine", type=int, default=8,
help='layers in fine network')
parser.add_argument("--netwidth_fine", type=int, default=256,
help='channels per layer in fine network')
parser.add_argument("--N_rand", type=int, default=32*32*4,
help='batch size (number of random rays per gradient step)')
parser.add_argument("--lrate", type=float, default=5e-4,
help='learning rate')
parser.add_argument("--lrate_decay", type=int, default=250,
help='exponential learning rate decay (in 1000 steps)')
parser.add_argument("--chunk", type=int, default=1024*32,
help='number of rays processed in parallel, decrease if running out of memory')
parser.add_argument("--netchunk", type=int, default=1024*64,
help='number of pts sent through network in parallel, decrease if running out of memory')
parser.add_argument("--no_batching", action='store_true',
help='only take random rays from 1 image at a time')
parser.add_argument("--no_reload", action='store_true',
help='do not reload weights from saved ckpt')
parser.add_argument("--ft_path", type=str, default=None,
help='specific weights npy file to reload for coarse network')
parser.add_argument("--N_samples", type=int, default=64,
help='number of coarse samples per ray')
parser.add_argument("--N_importance", type=int, default=0,
help='number of additional fine samples per ray')
parser.add_argument("--perturb", type=float, default=1.,
help='set to 0. for no jitter, 1. for jitter')
parser.add_argument("--use_viewdirs", action='store_true',
help='use full 5D input instead of 3D')
parser.add_argument("--i_embed", type=int, default=0,
help='set 0 for default positional encoding, -1 for none')
parser.add_argument("--multires", type=int, default=10,
help='log2 of max freq for positional encoding (3D location)')
parser.add_argument("--multires_views", type=int, default=4,
help='log2 of max freq for positional encoding (2D direction)')
parser.add_argument("--raw_noise_std", type=float, default=0.,
help='std dev of noise added to regularize sigma_a output, 1e0 recommended')
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',
help='render the test set instead of render_poses path')
parser.add_argument("--render_factor", type=int, default=0,
help='downsampling factor to speed up rendering, set 4 or 8 for fast preview')
parser.add_argument("--precrop_iters", type=int, default=0,
help='number of steps to train on central crops')
parser.add_argument("--precrop_frac", type=float,
default=.5, help='fraction of img taken for central crops')
parser.add_argument("--dataset_type", type=str, default='llff',
help='options: llff / blender / deepvoxels')
parser.add_argument("--testskip", type=int, default=8,
help='will load 1/N images from test/val sets, useful for large datasets like deepvoxels')
parser.add_argument("--shape", type=str, default='greek',
help='options : armchair / cube / greek / vase')
parser.add_argument("--white_bkgd", action='store_true',
help='set to render synthetic data on a white bkgd (always use for dvoxels)')
parser.add_argument("--half_res", action='store_true',
help='load blender synthetic data at 400x400 instead of 800x800')
parser.add_argument("--factor", type=int, default=8,
help='downsample factor for LLFF images')
parser.add_argument("--no_ndc", action='store_true',
help='do not use normalized device coordinates (set for non-forward facing scenes)')
parser.add_argument("--lindisp", action='store_true',
help='sampling linearly in disparity rather than depth')
parser.add_argument("--spherify", action='store_true',
help='set for spherical 360 scenes')
parser.add_argument("--llffhold", type=int, default=8,
help='will take every 1/N images as LLFF test set, paper uses 8')
parser.add_argument("--i_print", type=int, default=100,
help='frequency of console printout and metric loggin')
parser.add_argument("--i_img", type=int, default=500,
help='frequency of tensorboard image logging')
parser.add_argument("--i_weights", type=int, default=10000,
help='frequency of weight ckpt saving')
parser.add_argument("--i_testset", type=int, default=50000,
help='frequency of testset saving')
parser.add_argument("--i_video", type=int, default=50000,
help='frequency of render_poses video saving')
return parser
2.数据加载
代码流程图
; load_llff_data()
def load_llff_data(basedir, factor=8, recenter=True, bd_factor=.75, spherify=False, path_zflat=False):
poses, bds, imgs = _load_data(basedir, factor=factor)
print('Loaded', basedir, bds.min(), bds.max())
'''
np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)指的是进行矩阵变换，将poses每个通道的第0行的相反数和第1行互换位置；
紧接着用np.moveaxis(poses, -1, 0).astype(np.float32)将坐标轴的第-1轴换到第0轴；
得到的poses的shape为（20,3,5）
imgs同理，变换完的shape为（20,378,504,3）
bds的shape为（20,2）
'''
poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
poses = np.moveaxis(poses, -1, 0).astype(np.float32)
imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
images = imgs
bds = np.moveaxis(bds, -1, 0).astype(np.float32)
sc = 1. if bd_factor is None else 1./(bds.min() * bd_factor)
poses[:,:3,3] *= sc
bds *= sc
if recenter:
poses = recenter_poses(poses)
if spherify:
poses, render_poses, bds = spherify_poses(poses, bds)
else:
'''[[ 1.0000000e+00 0.0000000e+00 0.0000000e+00 1.4901161e-09]
[ 0.0000000e+00 1.0000000e+00 -1.8730975e-09 -9.6857544e-09]
[-0.0000000e+00 1.8730975e-09 1.0000000e+00 0.0000000e+00]]
'''
c2w = poses_avg(poses)
print('recentered', c2w.shape)
print(c2w[:3,:4])
up = normalize(poses[:, :3, 1].sum(0))
close_depth, inf_depth = bds.min()*.9, bds.max()*5.
dt = .75
mean_dz = 1./(((1.-dt)/close_depth + dt/inf_depth))
focal = mean_dz
shrink_factor = .8
zdelta = close_depth * .2
tt = poses[:,:3,3]
rads = np.percentile(np.abs(tt), 90, 0)
c2w_path = c2w
N_views = 120
N_rots = 2
if path_zflat:
zloc = -close_depth * .1
c2w_path[:3,3] = c2w_path[:3,3] + zloc * c2w_path[:3,2]
rads[2] = 0.
N_rots = 1
N_views/=2
render_poses = render_path_spiral(c2w_path, up, rads, focal, zdelta, zrate=.5, rots=N_rots, N=N_views)
render_poses = np.array(render_poses).astype(np.float32)
c2w = poses_avg(poses)
print('Data:')
print(poses.shape, images.shape, bds.shape)
dists = np.sum(np.square(c2w[:3,3] - poses[:,:3,3]), -1)
i_test = np.argmin(dists)
print('HOLDOUT view is', i_test)
images = images.astype(np.float32)
poses = poses.astype(np.float32)
return images, poses, bds, render_poses, i_test
_load_data()
def _load_data(basedir, factor=None, width=None, height=None, load_imgs=True):
poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))
poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1,2,0])
'''
.npy文件是一个shape为（20，17），dtype为float64的array，20代表数据集的个数（一共有20张图片），17代表位姿参数。
poses_arr[:, :-2]代表取前15列，为一个（20,15）的array，
reshape([-1, 3, 5])代表将（20,15）的array转换为（20,3,5）的array，也就是把15列的一维数据变为3*5的二维数据。
transpose([1,2,0])则是将array的坐标系调换顺序，0换到2, 1、2换到0、1，shape变为（3,5,20）;
最后poses输出的是一个（3,5,20）的array
'''
bds = poses_arr[:, -2:].transpose([1,0])
'''
poses_arr[:, -2:].transpose([1,0])则是先提取poses_arr的后两列数据（20，2），然后将0,1坐标系对调，得到（2,20）shape的array：bds
bds指的是bounds深度范围
'''
img0 = [os.path.join(basedir, 'images', f) for f in sorted(os.listdir(os.path.join(basedir, 'images'))) \
if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')][0]
sh = imageio.imread(img0).shape
sfx = ''
if factor is not None:
sfx = '_{}'.format(factor)
_minify(basedir, factors=[factor])
factor = factor
elif height is not None:
factor = sh[0] / float(height)
width = int(sh[1] / factor)
_minify(basedir, resolutions=[[height, width]])
sfx = '_{}x{}'.format(width, height)
elif width is not None:
factor = sh[1] / float(width)
height = int(sh[0] / factor)
_minify(basedir, resolutions=[[height, width]])
sfx = '_{}x{}'.format(width, height)
else:
factor = 1
imgdir = os.path.join(basedir, 'images' + sfx)
if not os.path.exists(imgdir):
print( imgdir, 'does not exist, returning' )
return
imgfiles = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir)) if f.endswith('JPG') or f.endswith('jpg') or f.endswith('png')]
if poses.shape[-1] != len(imgfiles):
print( 'Mismatch between imgs {} and poses {} !!!!'.format(len(imgfiles), poses.shape[-1]) )
return
sh = imageio.imread(imgfiles[0]).shape
poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
poses[2, 4, :] = poses[2, 4, :] * 1./factor
'''
sh[:2]存的是前两个数据，也就是图片单通道的大小（378,504）；
np.array(sh[:2]).reshape([2, 1])将其先array化后reshape为2*1的大小：array([[378],[504]])
poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])则表示将poses中3*5矩阵的前两行的第5列存放height=378，width=504；
poses[2, 4, :]则表示第三行第5列的存放图像的分辨率f，更新f的值最后为3261/8=407.56579161
另外，3*5矩阵的前3行3列为旋转变换矩阵，第4列为平移变换矩阵，第5列为h、w、f；
'''
if not load_imgs:
return poses, bds
def imread(f):
if f.endswith('png'):
return imageio.imread(f, ignoregamma=True)
else:
return imageio.imread(f)
imgs = imgs = [imread(f)[...,:3]/255. for f in imgfiles]
imgs = np.stack(imgs, -1)
print('Loaded image data', imgs.shape, poses[:,-1,0])
return poses, bds, imgs
_minify()
def _minify(basedir, factors=[], resolutions=[]):
needtoload = False
for r in factors:
imgdir = os.path.join(basedir, 'images_{}'.format(r))
if not os.path.exists(imgdir):
needtoload = True
for r in resolutions:
imgdir = os.path.join(basedir, 'images_{}x{}'.format(r[1], r[0]))
if not os.path.exists(imgdir):
needtoload = True
if not needtoload:
return
from shutil import copy
from subprocess import check_output
imgdir = os.path.join(basedir, 'images')
imgs = [os.path.join(imgdir, f) for f in sorted(os.listdir(imgdir))]
imgs = [f for f in imgs if any([f.endswith(ex) for ex in ['JPG', 'jpg', 'png', 'jpeg', 'PNG']])]
imgdir_orig = imgdir
wd = os.getcwd()
for r in factors + resolutions:
if isinstance(r, int):
name = 'images_{}'.format(r)
resizearg = '{}%'.format(100./r)
else:
name = 'images_{}x{}'.format(r[1], r[0])
resizearg = '{}x{}'.format(r[1], r[0])
imgdir = os.path.join(basedir, name)
if os.path.exists(imgdir):
continue
print('Minifying', r, basedir)
os.makedirs(imgdir)
check_output('cp {}/* {}'.format(imgdir_orig, imgdir), shell=True)
ext = imgs[0].split('.')[-1]
args = ' '.join(['mogrify', '-resize', resizearg, '-format', 'png', '*.{}'.format(ext)])
print(args)
os.chdir(imgdir)
check_output(args, shell=True)
os.chdir(wd)
if ext != 'png':
check_output('rm {}/*.{}'.format(imgdir, ext), shell=True)
print('Removed duplicates')
print('Done')
recenter_poses()
def recenter_poses(poses):
poses_ = poses+0
bottom = np.reshape([0,0,0,1.], [1,4])
c2w = poses_avg(poses)
c2w = np.concatenate([c2w[:3,:4], bottom], -2)
bottom = np.tile(np.reshape(bottom, [1,1,4]), [poses.shape[0],1,1])
poses = np.concatenate([poses[:,:3,:4], bottom], -2)
poses = np.linalg.inv(c2w) @ poses
poses_[:,:3,:4] = poses[:,:3,:4]
poses = poses_
return poses
render_path_spiral()
def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
render_poses = []
rads = np.array(list(rads) + [1.])
hwf = c2w[:,4:5]
for theta in np.linspace(0., 2. * np.pi * rots, N+1)[:-1]:
c = np.dot(c2w[:3,:4], np.array([np.cos(theta), -np.sin(theta), -np.sin(theta*zrate), 1.]) * rads)
z = normalize(c - np.dot(c2w[:3,:4], np.array([0,0,-focal, 1.])))
render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
return render_poses
3.NeRF网络构建
代码流程图
; NeRF网络结构图
create_nerf()
def create_nerf(args):
"""Instantiate NeRF's MLP model.
"""
embed_fn, input_ch = get_embedder(args.multires, args.i_embed)
input_ch_views = 0
embeddirs_fn = None
if args.use_viewdirs:
embeddirs_fn, input_ch_views = get_embedder(args.multires_views, args.i_embed)
output_ch = 5 if args.N_importance > 0 else 4
skips = [4]
model = NeRF(D=args.netdepth, W=args.netwidth,
input_ch=input_ch, output_ch=output_ch, skips=skips,
input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
grad_vars = list(model.parameters())
model_fine = None
if args.N_importance > 0:
model_fine = NeRF(D=args.netdepth_fine, W=args.netwidth_fine,
input_ch=input_ch, output_ch=output_ch, skips=skips,
input_ch_views=input_ch_views, use_viewdirs=args.use_viewdirs).to(device)
grad_vars += list(model_fine.parameters())
network_query_fn = lambda inputs, viewdirs, network_fn : run_network(inputs, viewdirs, network_fn,
embed_fn=embed_fn,
embeddirs_fn=embeddirs_fn,
netchunk=args.netchunk)
optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))
optimizer.param_groups[0]['capturable'] = True
start = 0
basedir = args.basedir
expname = args.expname
if args.ft_path is not None and args.ft_path!='None':
ckpts = [args.ft_path]
else:
ckpts = [os.path.join(basedir, expname, f) for f in sorted(os.listdir(os.path.join(basedir, expname))) if 'tar' in f]
print('Found ckpts', ckpts)
if len(ckpts) > 0 and not args.no_reload:
ckpt_path = ckpts[-1]
print('Reloading from', ckpt_path)
ckpt = torch.load(ckpt_path)
start = ckpt['global_step']
optimizer.load_state_dict(ckpt['optimizer_state_dict'])
model.load_state_dict(ckpt['network_fn_state_dict'])
if model_fine is not None:
model_fine.load_state_dict(ckpt['network_fine_state_dict'])
render_kwargs_train = {
'network_query_fn' : network_query_fn,
'perturb' : args.perturb,
'N_importance' : args.N_importance,
'network_fine' : model_fine,
'N_samples' : args.N_samples,
'network_fn' : model,
'use_viewdirs' : args.use_viewdirs,
'white_bkgd' : args.white_bkgd,
'raw_noise_std' : args.raw_noise_std,
}
if args.dataset_type != 'llff' or args.no_ndc:
print('Not ndc!')
render_kwargs_train['ndc'] = False
render_kwargs_train['lindisp'] = args.lindisp
render_kwargs_test = {k : render_kwargs_train[k] for k in render_kwargs_train}
render_kwargs_test['perturb'] = False
render_kwargs_test['raw_noise_std'] = 0.
return render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer
get_embedder()
def get_embedder(multires, i=0):
if i == -1:
return nn.Identity(), 3
embed_kwargs = {
'include_input' : True,
'input_dims' : 3,
'max_freq_log2' : multires-1,
'num_freqs' : multires,
'log_sampling' : True,
'periodic_fns' : [torch.sin, torch.cos],
}
embedder_obj = Embedder(**embed_kwargs)
embed = lambda x, eo=embedder_obj : eo.embed(x)
return embed, embedder_obj.out_dim
class Embedder
class Embedder:
def __init__(self, **kwargs):
self.kwargs = kwargs
self.create_embedding_fn()
def create_embedding_fn(self):
embed_fns = []
d = self.kwargs['input_dims']
out_dim = 0
if self.kwargs['include_input']:
embed_fns.append(lambda x : x)
out_dim += d
max_freq = self.kwargs['max_freq_log2']
N_freqs = self.kwargs['num_freqs']
if self.kwargs['log_sampling']:
freq_bands = 2.**torch.linspace(0., max_freq, steps=N_freqs)
else:
freq_bands = torch.linspace(2.**0., 2.**max_freq, steps=N_freqs)
for freq in freq_bands:
for p_fn in self.kwargs['periodic_fns']:
embed_fns.append(lambda x, p_fn=p_fn, freq=freq : p_fn(x * freq))
out_dim += d
self.embed_fns = embed_fns
self.out_dim = out_dim
def embed(self, inputs):
return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
class NeRF
class NeRF(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False):
"""
"""
super(NeRF, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.pts_linears = nn.ModuleList(
[nn.Linear(input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + input_ch, W) for i in range(D-1)])
self.views_linears = nn.ModuleList([nn.Linear(input_ch_views + W, W//2)])
if use_viewdirs:
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W//2, 3)
else:
self.output_linear = nn.Linear(W, output_ch)
def forward(self, x):
input_pts, input_views = torch.split(x, [self.input_ch, self.input_ch_views], dim=-1)
h = input_pts
for i, l in enumerate(self.pts_linears):
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
h = torch.cat([input_pts, h], -1)
if self.use_viewdirs:
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views], -1)
for i, l in enumerate(self.views_linears):
h = self.views_linears[i](h)
h = F.relu(h)
rgb = self.rgb_linear(h)
outputs = torch.cat([rgb, alpha], -1)
else:
outputs = self.output_linear(h)
return outputs
run_network()
def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn, netchunk=1024*64):
"""Prepares inputs and applies network 'fn'.
"""
inputs_flat = torch.reshape(inputs, [-1, inputs.shape[-1]])
embedded = embed_fn(inputs_flat)
if viewdirs is not None:
input_dirs = viewdirs[:,None].expand(inputs.shape)
input_dirs_flat = torch.reshape(input_dirs, [-1, input_dirs.shape[-1]])
embedded_dirs = embeddirs_fn(input_dirs_flat)
embedded = torch.cat([embedded, embedded_dirs], -1)
outputs_flat = batchify(fn, netchunk)(embedded)
outputs = torch.reshape(outputs_flat, list(inputs.shape[:-1]) + [outputs_flat.shape[-1]])
return outputs
Optimizer
optimizer = torch.optim.Adam(params=grad_vars, lr=args.lrate, betas=(0.9, 0.999))
optimizer.param_groups[0]['capturable'] = True
4.生成rays数据
代码流程图
; get_rays_np()
def get_rays_np(H, W, K, c2w):
i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')
dirs = np.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -np.ones_like(i)], -1)
rays_d = np.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1)
rays_o = np.broadcast_to(c2w[:3,-1], np.shape(rays_d))
return rays_o, rays_d
5.体素渲染
代码流程图
; render()
def render(H, W, K, chunk=1024*32, rays=None, c2w=None, ndc=True,
near=0., far=1.,
use_viewdirs=False, c2w_staticcam=None,
**kwargs):
"""Render rays
Args:
H: int. Height of image in pixels.图像高度
W: int. Width of image in pixels.图像宽度
focal: float. Focal length of pinhole camera.针孔相机焦距
chunk: int. Maximum number of rays to process simultaneously. Used to
control maximum memory usage. Does not affect final results.同步处理的最大光线数
rays: array of shape [2, batch_size, 3]. Ray origin and direction for
each example in batch. 2表示每个batch的原点和方向；
c2w: array of shape [3, 4]. Camera-to-world transformation matrix.相机到世界的旋转矩阵
ndc: bool. If True, represent ray origin, direction in NDC coordinates.
near: float or array of shape [batch_size]. Nearest distance for a ray.
far: float or array of shape [batch_size]. Farthest distance for a ray.
use_viewdirs: bool. If True, use viewing direction of a point in space in model.
c2w_staticcam: array of shape [3, 4]. If not None, use this transformation matrix for
camera while using other c2w argument for viewing directions.
Returns:
rgb_map: [batch_size, 3]. Predicted RGB values for rays.预测的RGB图
disp_map: [batch_size]. Disparity map. Inverse of depth.视差图
acc_map: [batch_size]. Accumulated opacity (alpha) along a ray.深度图、不透明度、alpha
extras: dict with everything returned by render_rays().
"""
if c2w is not None:
rays_o, rays_d = get_rays(H, W, K, c2w)
else:
rays_o, rays_d = rays
if use_viewdirs:
viewdirs = rays_d
if c2w_staticcam is not None:
rays_o, rays_d = get_rays(H, W, K, c2w_staticcam)
viewdirs = viewdirs / torch.norm(viewdirs, dim=-1, keepdim=True)
viewdirs = torch.reshape(viewdirs, [-1,3]).float()
sh = rays_d.shape
if ndc:
rays_o, rays_d = ndc_rays(H, W, K[0][0], 1., rays_o, rays_d)
rays_o = torch.reshape(rays_o, [-1,3]).float()
rays_d = torch.reshape(rays_d, [-1,3]).float()
near, far = near * torch.ones_like(rays_d[...,:1]), far * torch.ones_like(rays_d[...,:1])
rays = torch.cat([rays_o, rays_d, near, far], -1)
if use_viewdirs:
rays = torch.cat([rays, viewdirs], -1)
all_ret = batchify_rays(rays, chunk, **kwargs)
for k in all_ret:
k_sh = list(sh[:-1]) + list(all_ret[k].shape[1:])
all_ret[k] = torch.reshape(all_ret[k], k_sh)
k_extract = ['rgb_map', 'disp_map', 'acc_map']
ret_list = [all_ret[k] for k in k_extract]
ret_dict = {k : all_ret[k] for k in all_ret if k not in k_extract}
return ret_list + [ret_dict]
batchify_rays()
def batchify_rays(rays_flat, chunk=1024*32, **kwargs):
"""Render rays in smaller minibatches to avoid OOM.
"""
all_ret = {}
for i in range(0, rays_flat.shape[0], chunk):
ret = render_rays(rays_flat[i:i+chunk], **kwargs)
for k in ret:
if k not in all_ret:
all_ret[k] = []
all_ret[k].append(ret[k])
all_ret = {k : torch.cat(all_ret[k], 0) for k in all_ret}
return all_ret
render_rays()
def render_rays(ray_batch,
network_fn,
network_query_fn,
N_samples,
retraw=False,
lindisp=False,
perturb=0.,
N_importance=0,
network_fine=None,
white_bkgd=False,
raw_noise_std=0.,
verbose=False,
pytest=False):
"""Volumetric rendering.体素渲染
Args:
ray_batch: array of shape [batch_size, ...]. All information necessary
for sampling along a ray, including: ray origin, ray direction, min
dist, max dist, and unit-magnitude viewing direction.
用来view_ray采样的所有必需数据：ray原点、ray方向、最大最小距离、方向单位向量；
network_fn: function. Model for predicting RGB and density at each point
in space.
nerf网络，用来预测空间中每个点的RGB和不透明度的函数
network_query_fn: function used for passing queries to network_fn.
将查询传递给network_fn的函数
N_samples: int. Number of different times to sample along each ray.coarse采样点数
retraw: bool. If True, include model's raw, unprocessed predictions.是否压缩数据
lindisp: bool. If True, sample linearly in inverse depth rather than in depth.在深度图上面逆向线性采样；
perturb: float, 0 or 1. If non-zero, each ray is sampled at stratified
random points in time.扰动
N_importance: int. Number of additional times to sample along each ray.
These samples are only passed to network_fine.fine增加的精细采样点数；
network_fine: "fine" network with same spec as network_fn.
white_bkgd: bool. If True, assume a white background.
raw_noise_std: ...
verbose: bool. If True, print more debugging info.
Returns:
rgb_map: [num_rays, 3]. Estimated RGB color of a ray. Comes from fine model.
disp_map: [num_rays]. Disparity map. 1 / depth.
acc_map: [num_rays]. Accumulated opacity along each ray. Comes from fine model.
raw: [num_rays, num_samples, 4]. Raw predictions from model.
rgb0: See rgb_map. Output for coarse model.
disp0: See disp_map. Output for coarse model.
acc0: See acc_map. Output for coarse model.
z_std: [num_rays]. Standard deviation of distances along ray for each
sample.
"""
N_rays = ray_batch.shape[0]
rays_o, rays_d = ray_batch[:,0:3], ray_batch[:,3:6]
viewdirs = ray_batch[:,-3:] if ray_batch.shape[-1] > 8 else None
bounds = torch.reshape(ray_batch[...,6:8], [-1,1,2])
near, far = bounds[...,0], bounds[...,1]
t_vals = torch.linspace(0., 1., steps=N_samples)
if not lindisp:
z_vals = near * (1.-t_vals) + far * (t_vals)
else:
z_vals = 1./(1./near * (1.-t_vals) + 1./far * (t_vals))
z_vals = z_vals.expand([N_rays, N_samples])
if perturb > 0.:
mids = .5 * (z_vals[...,1:] + z_vals[...,:-1])
upper = torch.cat([mids, z_vals[...,-1:]], -1)
lower = torch.cat([z_vals[...,:1], mids], -1)
t_rand = torch.rand(z_vals.shape)
if pytest:
np.random.seed(0)
t_rand = np.random.rand(*list(z_vals.shape))
t_rand = torch.Tensor(t_rand)
z_vals = lower + (upper - lower) * t_rand
pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None]
raw = network_query_fn(pts, viewdirs, network_fn)
rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest)
if N_importance > 0:
rgb_map_0, disp_map_0, acc_map_0 = rgb_map, disp_map, acc_map
z_vals_mid = .5 * (z_vals[...,1:] + z_vals[...,:-1])
z_samples = sample_pdf(z_vals_mid, weights[...,1:-1], N_importance, det=(perturb==0.), pytest=pytest)
z_samples = z_samples.detach()
z_vals, _ = torch.sort(torch.cat([z_vals, z_samples], -1), -1)
pts = rays_o[...,None,:] + rays_d[...,None,:] * z_vals[...,:,None]
run_fn = network_fn if network_fine is None else network_fine
raw = network_query_fn(pts, viewdirs, run_fn)
rgb_map, disp_map, acc_map, weights, depth_map = raw2outputs(raw, z_vals, rays_d, raw_noise_std, white_bkgd, pytest=pytest)
ret = {'rgb_map' : rgb_map, 'disp_map' : disp_map, 'acc_map' : acc_map}
if retraw:
ret['raw'] = raw
if N_importance > 0:
ret['rgb0'] = rgb_map_0
ret['disp0'] = disp_map_0
ret['acc0'] = acc_map_0
ret['z_std'] = torch.std(z_samples, dim=-1, unbiased=False)
for k in ret:
if (torch.isnan(ret[k]).any() or torch.isinf(ret[k]).any()) and DEBUG:
print(f"! [Numerical Error] {k} contains nan or inf.")
return ret
raw2outputs()
def raw2outputs(raw, z_vals, rays_d, raw_noise_std=0, white_bkgd=False, pytest=False):
"""Transforms model's predictions to semantically meaningful values.
Args:
raw: [num_rays, num_samples along ray, 4]. Prediction from model.
z_vals: [num_rays, num_samples along ray]. Integration time.
rays_d: [num_rays, 3]. Direction of each ray.
Returns:
rgb_map: [num_rays, 3]. Estimated RGB color of a ray.
disp_map: [num_rays]. Disparity map. Inverse of depth map.
acc_map: [num_rays]. Sum of weights along each ray.
weights: [num_rays, num_samples]. Weights assigned to each sampled color.
depth_map: [num_rays]. Estimated distance to object.
"""
raw2alpha = lambda raw, dists, act_fn=F.relu: 1.-torch.exp(-act_fn(raw)*dists)
dists = z_vals[...,1:] - z_vals[...,:-1]
dists = torch.cat([dists, torch.Tensor([1e10]).expand(dists[...,:1].shape)], -1)
dists = dists * torch.norm(rays_d[...,None,:], dim=-1)
rgb = torch.sigmoid(raw[...,:3])
noise = 0.
if raw_noise_std > 0.:
noise = torch.randn(raw[...,3].shape) * raw_noise_std
if pytest:
np.random.seed(0)
noise = np.random.rand(*list(raw[...,3].shape)) * raw_noise_std
noise = torch.Tensor(noise)
'''
raw2alpha表示alpha的计算；
weights权重的计算则是w=T*alpha，其中T=exp（-sum（sigma*delta））？
RGB_map则是由w*rgb累加得到，以上三个公式为公式3的全部内容；
深度图depth_map=sum（w*z）
视差图disp_map为深度图取逆；
'''
alpha = raw2alpha(raw[...,3] + noise, dists)
weights = alpha * torch.cumprod(torch.cat([torch.ones((alpha.shape[0], 1)), 1.-alpha + 1e-10], -1), -1)[:, :-1]
rgb_map = torch.sum(weights[...,None] * rgb, -2)
depth_map = torch.sum(weights * z_vals, -1)
disp_map = 1./torch.max(1e-10 * torch.ones_like(depth_map), depth_map / torch.sum(weights, -1))
acc_map = torch.sum(weights, -1)
if white_bkgd:
rgb_map = rgb_map + (1.-acc_map[...,None])
return rgb_map, disp_map, acc_map, weights, depth_map
sample_pdf()
def sample_pdf(bins, weights, N_samples, det=False, pytest=False):
weights = weights + 1e-5
pdf = weights / torch.sum(weights, -1, keepdim=True)
cdf = torch.cumsum(pdf, -1)
cdf = torch.cat([torch.zeros_like(cdf[...,:1]), cdf], -1)
if det:
u = torch.linspace(0., 1., steps=N_samples)
u = u.expand(list(cdf.shape[:-1]) + [N_samples])
else:
u = torch.rand(list(cdf.shape[:-1]) + [N_samples])
if pytest:
np.random.seed(0)
new_shape = list(cdf.shape[:-1]) + [N_samples]
if det:
u = np.linspace(0., 1., N_samples)
u = np.broadcast_to(u, new_shape)
else:
u = np.random.rand(*new_shape)
u = torch.Tensor(u)
u = u.contiguous()
inds = torch.searchsorted(cdf, u, right=True)
below = torch.max(torch.zeros_like(inds-1), inds-1)
above = torch.min((cdf.shape[-1]-1) * torch.ones_like(inds), inds)
inds_g = torch.stack([below, above], -1)
matched_shape = [inds_g.shape[0], inds_g.shape[1], cdf.shape[-1]]
cdf_g = torch.gather(cdf.unsqueeze(1).expand(matched_shape), 2, inds_g)
bins_g = torch.gather(bins.unsqueeze(1).expand(matched_shape), 2, inds_g)
denom = (cdf_g[...,1]-cdf_g[...,0])
denom = torch.where(denom<1e-5, torch.ones_like(denom), denom)
t = (u-cdf_g[...,0])/denom
samples = bins_g[...,0] + t * (bins_g[...,1]-bins_g[...,0])
return samples
6.loss计算+训练
代码流程图
; img2mse()
img2mse = lambda x, y : torch.mean((x - y) ** 2)
img_loss = img2mse(rgb, target_s)
trans = extras['raw'][...,-1]
loss = img_loss
psnr = mse2psnr(img_loss)
train()
def train():
parser = config_parser()
args = parser.parse_args()
K = None
if args.dataset_type == 'llff':
images, poses, bds, render_poses, i_test = load_llff_data(args.datadir, args.factor,
recenter=True, bd_factor=.75,
spherify=args.spherify)
hwf = poses[0,:3,-1]
poses = poses[:,:3,:4]
print('Loaded llff', images.shape, render_poses.shape, hwf, args.datadir)
if not isinstance(i_test, list):
i_test = [i_test]
if args.llffhold > 0:
print('Auto LLFF holdout,', args.llffhold)
i_test = np.arange(images.shape[0])[::args.llffhold]
i_val = i_test
i_train = np.array([i for i in np.arange(int(images.shape[0])) if
(i not in i_test and i not in i_val)])
print('DEFINING BOUNDS')
if args.no_ndc:
near = np.ndarray.min(bds) * .9
far = np.ndarray.max(bds) * 1.
else:
near = 0.
far = 1.
print('NEAR FAR', near, far)
H, W, focal = hwf
H, W = int(H), int(W)
hwf = [H, W, focal]
if K is None:
K = np.array([
[focal, 0, 0.5*W],
[0, focal, 0.5*H],
[0, 0, 1]
])
if args.render_test:
render_poses = np.array(poses[i_test])
basedir = args.basedir
expname = args.expname
os.makedirs(os.path.join(basedir, expname), exist_ok=True)
f = os.path.join(basedir, expname, 'args.txt')
with open(f, 'w') as file:
for arg in sorted(vars(args)):
attr = getattr(args, arg)
file.write('{} = {}\n'.format(arg, attr))
if args.config is not None:
f = os.path.join(basedir, expname, 'config.txt')
with open(f, 'w') as file:
file.write(open(args.config, 'r').read())
render_kwargs_train, render_kwargs_test, start, grad_vars, optimizer = create_nerf(args)
global_step = start
bds_dict = {
'near' : near,
'far' : far,
}
render_kwargs_train.update(bds_dict)
render_kwargs_test.update(bds_dict)
render_poses = torch.Tensor(render_poses).to(device)
if args.render_only:
print('RENDER ONLY')
with torch.no_grad():
if args.render_test:
images = images[i_test]
else:
images = None
testsavedir = os.path.join(basedir, expname, 'renderonly_{}_{:06d}'.format('test' if args.render_test else 'path', start))
os.makedirs(testsavedir, exist_ok=True)
print('test poses shape', render_poses.shape)
rgbs, _ = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test, gt_imgs=images, savedir=testsavedir, render_factor=args.render_factor)
print('Done rendering', testsavedir)
imageio.mimwrite(os.path.join(testsavedir, 'video.mp4'), to8b(rgbs), fps=30, quality=8)
return
N_rand = args.N_rand
use_batching = not args.no_batching
if use_batching:
print('get rays')
rays = np.stack([get_rays_np(H, W, K, p) for p in poses[:,:3,:4]], 0)
print('done, concats')
rays_rgb = np.concatenate([rays, images[:,None]], 1)
rays_rgb = np.transpose(rays_rgb, [0,2,3,1,4])
rays_rgb = np.stack([rays_rgb[i] for i in i_train], 0)
rays_rgb = np.reshape(rays_rgb, [-1,3,3])
rays_rgb = rays_rgb.astype(np.float32)
print('shuffle rays')
np.random.shuffle(rays_rgb)
print('done')
i_batch = 0
if use_batching:
images = torch.Tensor(images).to(device)
poses = torch.Tensor(poses).to(device)
if use_batching:
rays_rgb = torch.Tensor(rays_rgb).to(device)
N_iters = 200000 + 1
print('Begin')
print('TRAIN views are', i_train)
print('TEST views are', i_test)
print('VAL views are', i_val)
start = start + 1
for i in trange(start, N_iters):
time0 = time.time()
if use_batching:
batch = rays_rgb[i_batch:i_batch+N_rand]
batch = torch.transpose(batch, 0, 1)
batch_rays, target_s = batch[:2], batch[2]
i_batch += N_rand
if i_batch >= rays_rgb.shape[0]:
print("Shuffle data after an epoch!")
rand_idx = torch.randperm(rays_rgb.shape[0])
rays_rgb = rays_rgb[rand_idx]
i_batch = 0
else:
img_i = np.random.choice(i_train)
target = images[img_i]
target = torch.Tensor(target).to(device)
pose = poses[img_i, :3,:4]
if N_rand is not None:
rays_o, rays_d = get_rays(H, W, K, torch.Tensor(pose))
if i < args.precrop_iters:
dH = int(H//2 * args.precrop_frac)
dW = int(W//2 * args.precrop_frac)
coords = torch.stack(
torch.meshgrid(
torch.linspace(H//2 - dH, H//2 + dH - 1, 2*dH),
torch.linspace(W//2 - dW, W//2 + dW - 1, 2*dW)
), -1)
if i == start:
print(f"[Config] Center cropping of size {2*dH} x {2*dW} is enabled until iter {args.precrop_iters}")
else:
coords = torch.stack(torch.meshgrid(torch.linspace(0, H-1, H), torch.linspace(0, W-1, W)), -1)
coords = torch.reshape(coords, [-1,2])
select_inds = np.random.choice(coords.shape[0], size=[N_rand], replace=False)
select_coords = coords[select_inds].long()
rays_o = rays_o[select_coords[:, 0], select_coords[:, 1]]
rays_d = rays_d[select_coords[:, 0], select_coords[:, 1]]
batch_rays = torch.stack([rays_o, rays_d], 0)
target_s = target[select_coords[:, 0], select_coords[:, 1]]
rgb, disp, acc, extras = render(H, W, K, chunk=args.chunk, rays=batch_rays,
verbose=i < 10, retraw=True,
**render_kwargs_train)
optimizer.zero_grad()
img_loss = img2mse(rgb, target_s)
trans = extras['raw'][...,-1]
loss = img_loss
psnr = mse2psnr(img_loss)
if 'rgb0' in extras:
img_loss0 = img2mse(extras['rgb0'], target_s)
loss = loss + img_loss0
psnr0 = mse2psnr(img_loss0)
loss.backward()
optimizer.step()
decay_rate = 0.1
decay_steps = args.lrate_decay * 1000
new_lrate = args.lrate * (decay_rate ** (global_step / decay_steps))
for param_group in optimizer.param_groups:
param_group['lr'] = new_lrate
dt = time.time()-time0
if i%args.i_weights==0:
path = os.path.join(basedir, expname, '{:06d}.tar'.format(i))
torch.save({
'global_step': global_step,
'network_fn_state_dict': render_kwargs_train['network_fn'].state_dict(),
'network_fine_state_dict': render_kwargs_train['network_fine'].state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}, path)
print('Saved checkpoints at', path)
if i%args.i_video==0 and i > 0:
with torch.no_grad():
rgbs, disps = render_path(render_poses, hwf, K, args.chunk, render_kwargs_test)
print('Done, saving', rgbs.shape, disps.shape)
moviebase = os.path.join(basedir, expname, '{}_spiral_{:06d}_'.format(expname, i))
imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8)
imageio.mimwrite(moviebase + 'disp.mp4', to8b(disps / np.max(disps)), fps=30, quality=8)
if i%args.i_testset==0 and i > 0:
testsavedir = os.path.join(basedir, expname, 'testset_{:06d}'.format(i))
os.makedirs(testsavedir, exist_ok=True)
print('test poses shape', poses[i_test].shape)
with torch.no_grad():
render_path(torch.Tensor(poses[i_test]).to(device), hwf, K, args.chunk, render_kwargs_test, gt_imgs=images[i_test], savedir=testsavedir)
print('Saved test set')
if i%args.i_print==0:
tqdm.write(f"[TRAIN] Iter: {i} Loss: {loss.item()} PSNR: {psnr.item()}")
global_step += 1
参考文献和资料
Original: https://blog.csdn.net/weixin_44292547/article/details/126249933
Author: 右边的口袋
Title: NeRF神经辐射场学习笔记（二）——Pytorch版NeRF实现以及代码注释
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