"""位置编码

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 = [lambda x : x]

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):

# inputs [B...3] => [B...3(2*L+1)]

# [x, sin(x*freq1), cos(x*freq1), sin(x*freq2), cos....]

# x 可有可无

#? 没乘以PI。可乘可不乘，只要频率有变化即可。

"""构造编码函数 dim(x)->dim(x)*(2L+1)

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)

def __init__(self,

D=8,

W=256,

input_ch=3,

input_ch_views=3,

output_ch=4,

skips=[4],

use_viewdirs=False):

"""NeRF模型

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)])

### Implementation according to the official code release (https://github.com/bmild/nerf/blob/master/run_nerf_helpers.py#L104-L105)

self.views_linears = nn.ModuleList([nn.Linear(input_ch_views + W, W//2)])

### Implementation according to the paper

# self.views_linears = nn.ModuleList(

# [nn.Linear(input_ch_views + W, W//2)] + [nn.Linear(W//2, W//2) for i in range(D//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

def load_weights_from_keras(self, weights):

assert self.use_viewdirs, "Not implemented if use_viewdirs=False"

# Load pts_linears

for i in range(self.D):

idx_pts_linears = 2 * i

self.pts_linears[i].weight.data = torch.from_numpy(np.transpose(weights[idx_pts_linears]))

self.pts_linears[i].bias.data = torch.from_numpy(np.transpose(weights[idx_pts_linears+1]))

# Load feature_linear

idx_feature_linear = 2 * self.D

self.feature_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_feature_linear]))

self.feature_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_feature_linear+1]))

# Load views_linears

idx_views_linears = 2 * self.D + 2

self.views_linears[0].weight.data = torch.from_numpy(np.transpose(weights[idx_views_linears]))

self.views_linears[0].bias.data = torch.from_numpy(np.transpose(weights[idx_views_linears+1]))

# Load rgb_linear

idx_rbg_linear = 2 * self.D + 4

self.rgb_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear]))

self.rgb_linear.bias.data = torch.from_numpy(np.transpose(weights[idx_rbg_linear+1]))

# Load alpha_linear

idx_alpha_linear = 2 * self.D + 6

self.alpha_linear.weight.data = torch.from_numpy(np.transpose(weights[idx_alpha_linear]))

i, j = torch.meshgrid(torch.linspace(0, W-1, W), torch.linspace(0, H-1, H)) # pytorch's meshgrid has indexing='ij'

i = i.t()

j = j.t()

dirs = torch.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -torch.ones_like(i)], -1)

# Rotate ray directions from camera frame to the world frame

rays_d = torch.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1) # dot product, equals to: [c2w.dot(dir) for dir in dirs]

# Translate camera frame's origin to the world frame. It is the origin of all rays.

rays_o = c2w[:3,-1].expand(rays_d.shape)

# i, j 的shapp=(H, W)

i, j = np.meshgrid(np.arange(W, dtype=np.float32), np.arange(H, dtype=np.float32), indexing='xy')

# 计算viewpoint到所有像素点向方向，在用c2w矩阵变换到世界坐标中(将相机摆正)。 左手系

# (i-0.5W)/focal,-(j-0.5H)/focal, -1

# dirs = np.stack([(i-W*0.5)/focal, -(j-H*0.5)/focal, -np.ones_like(i)], -1)

# = np.stack([(i-W*0.5), -(j-H*0.5), -np.ones_like(i)*focal], -1)

dirs = np.stack([(i-K[0][2])/K[0][0], -(j-K[1][2])/K[1][1], -np.ones_like(i)], -1)

# Rotate ray directions from camera frame to the world frame

rays_d = np.sum(dirs[..., np.newaxis, :] * c2w[:3,:3], -1) # dot product, equals to: [c2w.dot(dir) for dir in dirs]

# Translate camera frame's origin to the world frame. It is the origin of all rays.

rays_o = np.broadcast_to(c2w[:3,-1], np.shape(rays_d))

#! rays_d没有归一化

# Shift ray origins to near plane

t = -(near + rays_o[...,2]) / rays_d[...,2]

rays_o = rays_o + t[...,None] * rays_d

# Projection

o0 = -1./(W/(2.*focal)) * rays_o[...,0] / rays_o[...,2]

o1 = -1./(H/(2.*focal)) * rays_o[...,1] / rays_o[...,2]

o2 = 1. + 2. * near / rays_o[...,2]

d0 = -1./(W/(2.*focal)) * (rays_d[...,0]/rays_d[...,2] - rays_o[...,0]/rays_o[...,2])

d1 = -1./(H/(2.*focal)) * (rays_d[...,1]/rays_d[...,2] - rays_o[...,1]/rays_o[...,2])

d2 = -2. * near / rays_o[...,2]

rays_o = torch.stack([o0,o1,o2], -1)

rays_d = torch.stack([d0,d1,d2], -1)

# Get pdf

weights = weights + 1e-5 # prevent nans

pdf = weights / torch.sum(weights, -1, keepdim=True)

cdf = torch.cumsum(pdf, -1)

cdf = torch.cat([torch.zeros_like(cdf[...,:1]), cdf], -1) # (batch, len(bins))

# Take uniform samples

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])

# Pytest, overwrite u with numpy's fixed random numbers

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)

# Invert CDF

u = u.contiguous()

inds = torch.searchsorted(cdf, u, right=True) # [N_rays, N_importance]

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) # (batch, N_samples, 2)

# cdf_g = tf.gather(cdf, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2)

# bins_g = tf.gather(bins, inds_g, axis=-1, batch_dims=len(inds_g.shape)-2)

# [N_rays, N_importance, N_smaples]

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])

# 将denom中小于1e-5的数设为1。

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])