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import os, sys
import imageio
import random
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def run_network(inputs, viewdirs, fn, embed_fn, embeddirs_fn,label, netchunk=1024*64):
"""Prepares inputs and applies network 'fn'.
"""
inputs_flat = torch.reshape(inputs, [inputs.shape[0],-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, [inputs.shape[0],-1, input_dirs.shape[-1]])
embedded_dirs = embeddirs_fn(input_dirs_flat)
#embedded = torch.cat([embedded, embedded_dirs], -1)
input_all=torch.cat([inputs_flat,embedded_dirs],-1)
outputs_flat = fn(input_all,label)
outputs = torch.reshape(outputs_flat, list(inputs.shape[:-1]) + [outputs_flat.shape[-1]])
return outputs
import torch
# torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
# Misc
img2mse = lambda x, y : torch.mean((x - y) ** 2)
mse2psnr = lambda x : -10. * torch.log(x) / torch.log(torch.Tensor([10.]).to(x.device))
to8b = lambda x : (255*np.clip(x,0,1)).astype(np.uint8)
# Positional encoding (section 5.1)
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)
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 Triplane(nn.Module):
def __init__(
self,
):
super().__init__()
self.plane_axis=self.generate_planes()
def generate_planes(self):
"""
Defines planes by the three vectors that form the "axes" of the
plane. Should work with arbitrary number of planes and planes of
arbitrary orientation.
"""
return torch.tensor([[[1, 0, 0],
[0, 1, 0],
[0, 0, 1]],
[[1, 0, 0],
[0, 0, 1],
[0, 1, 0]],
[[0, 0, 1],
[0, 1, 0],
[1, 0, 0]]], dtype=torch.float32)
def project_onto_planes(self,planes, coordinates):
"""
Does a projection of a 3D point onto a batch of 2D planes,
returning 2D plane coordinates.
Takes plane axes of shape n_planes, 3, 3
# Takes coordinates of shape N, M, 3
# returns projections of shape N*n_planes, M, 2
"""
N, M, C = coordinates.shape
n_planes, _, _ = planes.shape
coordinates = coordinates.unsqueeze(1).expand(-1, n_planes, -1, -1).reshape(N*n_planes, M, 3)
inv_planes = torch.linalg.inv(planes).unsqueeze(0).expand(N, -1, -1, -1).reshape(N*n_planes, 3, 3).to(device=coordinates.device)
projections = torch.bmm(coordinates, inv_planes)
return projections[..., :2]
def sample_from_planes(self,plane_axes, plane_features, coordinates, mode='bilinear', padding_mode='zeros', box_warp=None):
assert padding_mode == 'zeros'
N, n_planes, C, H, W = plane_features.shape
_, M, _ = coordinates.shape
plane_features = plane_features.view(N*n_planes, C, H, W)
coordinates = (2/box_warp) * coordinates # TODO: add specific box bounds
#ipdb.set_trace()
coordinates = self.project_onto_planes(plane_axes, coordinates).unsqueeze(1)
output_features = torch.nn.functional.grid_sample(plane_features, coordinates.float(), mode=mode, padding_mode=padding_mode, align_corners=False).permute(0, 3, 2, 1).reshape(N, n_planes, M, C) # xy,xz,zy
return output_features
def forward(self, planes, sample_coordinates,box=1):
#ipdb.set_trace()
return self.sample_from_planes(self.plane_axis, planes, sample_coordinates, padding_mode='zeros', box_warp=box)
def positional_encoding(positions, freqs):
freq_bands = (2**torch.arange(freqs).float()).to(positions.device) # (F,)
pts = (positions[..., None] * freq_bands).reshape(
positions.shape[:-1] + (freqs * positions.shape[-1], )) # (..., DF)
pts = torch.cat([torch.sin(pts), torch.cos(pts)], dim=-1)
return pts
def exists(val):
return val is not None
def resize_image_to(
image,
target_image_size,
clamp_range = None,
mode = 'nearest'
):
orig_image_size = image.shape[-1]
if orig_image_size == target_image_size:
return image
out = F.interpolate(image, target_image_size, mode = mode)
if exists(clamp_range):
out = out.clamp(*clamp_range)
return out
class NeRF(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, size=256,input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False,num_instance=1):
"""
"""
super(NeRF, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch//3
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.hidden_dim=W
self.triplane=Triplane()
#ipdb.set_trace()
self.tri_planes = nn.Parameter(torch.randn(num_instance, input_ch, size, size))
nn.init.normal_(self.tri_planes, mean=0, std=0.1)
#self.weight=nn.Parameter(torch.ones(1,3,1,input_ch))
#ipdb.set_trace()
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.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)])
self.softplus=nn.Softplus()
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W, 3)
# for m in self.children():
# if isinstance(m, nn.Linear):
# nn.init.normal_(m.weight, std=0.01)
def forward(self, x, label):
#ipdb.set_trace()
input_pts, input_views = torch.split(x, [int(x.shape[-1]-self.input_ch_views), self.input_ch_views], dim=-1)
B,N,M=input_views.shape
#ipdb.set_trace()
# eal=resize_image_to(self.tri_planes[label],256)
# eal=resize_image_to(eal,256)
norm=torch.abs(self.tri_planes[label]).max(2)[0].max(2)[0].unsqueeze(-1).unsqueeze(-1)
sample_triplane=(self.tri_planes[label]/norm).view(1,3,self.tri_planes.shape[-3]//3,self.tri_planes.shape[-2],self.tri_planes.shape[-1]).repeat(B,1,1,1,1)
#ipdb.set_trace()
input_pts=(self.triplane(sample_triplane,input_pts,2.5)).mean(1).view(-1,self.tri_planes.shape[-3]//3)
#ipdb.set_trace()
h = input_pts
for i, l in enumerate(self.pts_linears):
#ipdb.set_trace()
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
# ipdb.set_trace()
h = torch.cat([input_pts, h], -1)
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views.view(B*N,M)], -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.view(B,N,3), alpha.view(B,N,1)], -1)
#ipdb.set_trace()
return outputs
class NeRF11(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False,num_instance=1):
"""
"""
super(NeRF11, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch//3
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.hidden_dim=W
self.triplane=Triplane()
self.weight = nn.Parameter(torch.zeros(1, 1, 256))
self.tri_planes = nn.Parameter(torch.randn(num_instance, input_ch, 256, 256))
#nn.init.normal_(self.tri_planes, mean=0, std=0.1)
#self.weight=nn.Parameter(torch.ones(1,3,1,input_ch))
#ipdb.set_trace()
self.label_emb = nn.Embedding(num_instance, W)
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.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)])
self.softplus=nn.Softplus()
#self.label_feature = nn.Linear(W, W)
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W, 3)
def forward(self, x,label):
#ipdb.set_trace()
input_pts, input_views = torch.split(x, [int(x.shape[-1]-self.input_ch_views), self.input_ch_views], dim=-1)
B,N,M=input_views.shape
sample_triplane=self.tri_planes[label].view(B,3,self.tri_planes.shape[-3]//3,self.tri_planes.shape[-2],self.tri_planes.shape[-1])
input_pts=(self.triplane(sample_triplane,input_pts,4)).mean(1).view(B,-1,self.tri_planes.shape[-3]//3)
#ipdb.set_trace()
label_emb=(self.weight*self.label_emb(label).unsqueeze(1)).expand(-1,N,-1)
h = input_pts
for i, l in enumerate(self.pts_linears):
#ipdb.set_trace()
h = self.pts_linears[i](h)
h=h+label_emb
h = F.relu(h)
if i in self.skips:
# ipdb.set_trace()
h = torch.cat([input_pts, h], -1)
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views.view(B,N,M)], -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.view(B,N,3), alpha.view(B,N,1)], -1)
return outputs
class NeRF0(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False,num_instance=1):
"""
"""
super(NeRF0, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch//3
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.hidden_dim=W
self.triplane=Triplane()
self.tri_planes = nn.Parameter(torch.randn(num_instance, input_ch, 256, 256))
#nn.init.normal_(self.tri_planes, mean=0, std=0.1)
#self.weight=nn.Parameter(torch.ones(1,3,1,input_ch))
#ipdb.set_trace()
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.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)])
self.softplus=nn.Softplus()
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W, 3)
def forward(self, x,label):
#ipdb.set_trace()
input_pts, input_views = torch.split(x, [int(x.shape[-1]-self.input_ch_views), self.input_ch_views], dim=-1)
B,N,M=input_views.shape
sample_triplane=self.tri_planes[label].view(B,3,self.tri_planes.shape[-3]//3,self.tri_planes.shape[-2],self.tri_planes.shape[-1])
#ipdb.set_trace()
input_pts=(self.triplane(sample_triplane,input_pts,8)).mean(1).view(-1,self.tri_planes.shape[-3]//3)
h = input_pts
for i, l in enumerate(self.pts_linears):
#ipdb.set_trace()
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
# ipdb.set_trace()
h = torch.cat([input_pts, h], -1)
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views.view(B*N,M)], -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.view(B,N,3), alpha.view(B,N,1)], -1)
return outputs
class NeRF1(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False,num_instance=1):
"""
"""
super(NeRF1, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch//3*9
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.hidden_dim=W
self.triplane=Triplane()
self.tri_planes = nn.Parameter(torch.randn(num_instance, input_ch, 256, 256))
#nn.init.normal_(self.tri_planes, mean=0, std=0.1)
#self.weight=nn.Parameter(torch.ones(1,3,1,input_ch))
#ipdb.set_trace()
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.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)])
self.softplus=nn.Softplus()
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W//2, 3)
def forward(self, x,label):
#ipdb.set_trace()
input_pts, input_views = torch.split(x, [int(x.shape[-1]-self.input_ch_views), self.input_ch_views], dim=-1)
B,N,M=input_views.shape
sample_triplane=self.tri_planes[label].view(B,3,self.tri_planes.shape[-3]//3,self.tri_planes.shape[-2],self.tri_planes.shape[-1])
#ipdb.set_trace()
input_pts=(self.triplane(sample_triplane,input_pts,4)).mean(1).view(-1,self.tri_planes.shape[-3]//3)
h = torch.cat((input_pts,positional_encoding(input_pts,4)),-1)
for i, l in enumerate(self.pts_linears):
#ipdb.set_trace()
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
# ipdb.set_trace()
h = torch.cat([input_pts, h], -1)
alpha = self.alpha_linear(h)
feature = self.feature_linear(h)
h = torch.cat([feature, input_views.view(B*N,M)], -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.view(B,N,3), alpha.view(B,N,1)], -1)
return outputs
class NeRF_dual(nn.Module):
def __init__(self, D=8, W=256, input_ch=3, input_ch_views=3, output_ch=4, skips=[4], use_viewdirs=False,num_instance=1):
"""
"""
super(NeRF_dual, self).__init__()
self.D = D
self.W = W
self.input_ch = input_ch//3*9+input_ch_views
self.input_ch2= input_ch//3*5
self.input_ch_views = input_ch_views
self.skips = skips
self.use_viewdirs = use_viewdirs
self.hidden_dim=W
self.triplane=Triplane()
self.tri_planes1 = nn.Parameter(torch.randn(num_instance, input_ch, 256, 256))
self.tri_planes2 = nn.Parameter(torch.randn(num_instance, input_ch, 256, 256))
#nn.init.normal_(self.tri_planes, mean=0, std=0.1)
#self.weight=nn.Parameter(torch.ones(1,3,1,input_ch))
#ipdb.set_trace()
self.pts_linears = nn.ModuleList(
[nn.Linear(self.input_ch, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.input_ch, W) for i in range(D-1)])
self.pts_linears2 = nn.ModuleList(
[nn.Linear(self.input_ch2, W)] + [nn.Linear(W, W) if i not in self.skips else nn.Linear(W + self.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(W, W//2)])
self.softplus=nn.Softplus()
self.feature_linear = nn.Linear(W, W)
self.alpha_linear = nn.Linear(W, 1)
self.rgb_linear = nn.Linear(W//2, 3)
def forward(self, x,label):
#ipdb.set_trace()
input_pts, input_views = torch.split(x, [int(x.shape[-1]-self.input_ch_views), self.input_ch_views], dim=-1)
B,N,M=input_views.shape
sample_triplane1=self.tri_planes1[label].view(B,3,self.tri_planes1.shape[-3]//3,self.tri_planes1.shape[-2],self.tri_planes1.shape[-1])
#ipdb.set_trace()
input_pts1=(self.triplane(sample_triplane1,input_pts,8)).mean(1).view(B,-1,self.tri_planes1.shape[-3]//3)
sample_triplane2=self.tri_planes2[label].view(B,3,self.tri_planes2.shape[-3]//3,self.tri_planes2.shape[-2],self.tri_planes2.shape[-1])
#ipdb.set_trace()
input_pts2=(self.triplane(sample_triplane2,input_pts,8)).mean(1).view(B,-1,self.tri_planes2.shape[-3]//3)
#ipdb.set_trace()
h = torch.cat((input_pts1,positional_encoding(input_pts1,4),input_views),-1)
for i, l in enumerate(self.pts_linears):
#ipdb.set_trace()
h = self.pts_linears[i](h)
h = F.relu(h)
if i in self.skips:
# ipdb.set_trace()
h = torch.cat([input_pts, h], -1)
h = self.feature_linear(h)
for i, l in enumerate(self.views_linears):
h = self.views_linears[i](h)
h = F.relu(h)
rgb = self.rgb_linear(h)
h = torch.cat((input_pts2,positional_encoding(input_pts2,2)),-1)
for i, l in enumerate(self.pts_linears2):
#ipdb.set_trace()
h = self.pts_linears2[i](h)
h = F.relu(h)
alpha = self.alpha_linear(h)
outputs = torch.cat([rgb.view(B,N,3), alpha.view(B,N,1)], -1)
return outputs
# Ray helpers
def get_rays(H, W, K, c2w):
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)
return rays_o, rays_d
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)
# 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))
return rays_o, rays_d
def ndc_rays(H, W, focal, near, rays_o, rays_d):
# 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)
return rays_o, rays_d
# Hierarchical sampling (section 5.2)
def sample_pdf(bins, weights, N_samples, det=False, pytest=False):
# 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)
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)
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
def render_path1(batch_rays, chunk, render_kwargs, gt_imgs=None, savedir=None,savedir1=None,near=None,far=None,label=None):
rgbs = []
disps = []
t = time.time()
#render(chunk=newargs.chunk, rays=batch_rays,near=near,far=far,label=label, retraw=True, **render_kwargs_train)
rgbs, disps, acc, _ = render(chunk=chunk, rays=batch_rays,near=near,far=far, label=label,**render_kwargs)
#ipdb.set_trace()
reso=int(rgbs.shape[-2]**0.5)
rgbs=rgbs.view(-1,reso,reso,3)
disps=disps.view(-1,reso,reso,1)
acc=acc.view(-1,reso,reso,1)
#ipdb.set_trace()
gt_imgs=gt_imgs.view(-1,reso,reso,3)
mask=(gt_imgs.mean(-1)<0.9999)
#ipdb.set_trace()
if savedir is not None:
for i in range(len(rgbs)):
rgb8 =to8b(rgbs[i].cpu().numpy())#np.fliplr(np.rot90(to8b(rgbs[i]),-1))
filename = os.path.join(savedir)
imageio.imwrite(savedir, rgb8)
imageio.imwrite(savedir1, np.uint8(gt_imgs[i].cpu().numpy()*255))
#ipdb.set_trace()
print('psnr:' ,mse2psnr(img2mse(torch.Tensor(rgb8/255).to(device=gt_imgs.device)[mask[i]],(gt_imgs[i])[mask[i]])))
print('psnr_all:' ,mse2psnr(img2mse(torch.Tensor(rgb8/255).to(device=gt_imgs.device),(gt_imgs[i]))))
psnr_list = []
for i in range(len(rgbs)):
rgb8 = to8b(rgbs[i].cpu().numpy())
psnr = mse2psnr(img2mse(torch.Tensor(rgb8/255).to(device=gt_imgs.device),(gt_imgs[i])))
psnr_list.append(psnr)
#ipdb.set_trace()
return rgbs, disps, acc, psnr_list
def render(chunk=1024*32, rays=None, c2w=None, ndc=True,label=None,
near=0., far=1.,
use_viewdirs=False, c2w_staticcam=None,
**kwargs):
"""Render rays
Args:
H: int. Height of image in pixels.
W: int. Width of image in pixels.
focal: float. Focal length of pinhole camera.
chunk: int. Maximum number of rays to process simultaneously. Used to
control maximum memory usage. Does not affect final results.
rays: array of shape [2, batch_size, 3]. Ray origin and direction for
each example in batch.
c2w: array of shape [3, 4]. Camera-to-world transformation matrix.
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.
disp_map: [batch_size]. Disparity map. Inverse of depth.
acc_map: [batch_size]. Accumulated opacity (alpha) along a ray.
extras: dict with everything returned by render_rays().
"""
#ipdb.set_trace()
rays_o, rays_d = rays[:,0,...], rays[:,1,...]
viewdirs = rays_d
viewdirs = viewdirs / torch.norm(viewdirs, dim=-1, keepdim=True)
viewdirs = torch.reshape(viewdirs, [rays_d.shape[0],-1,3]).float()
sh = rays_d.shape # [..., 3]
# Create ray batch
rays_o = torch.reshape(rays_o, [sh[0],-1,3]).float()
rays_d = torch.reshape(rays_d, [sh[0],-1,3]).float()
#ipdb.set_trace()
near, far = near[:,None,:] * torch.ones_like(rays_d[...,:1]), far[:,None,:] * 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)
#ipdb.set_trace()
# Render and reshape
all_ret = batchify_rays(rays, label,chunk,**kwargs)
for k in all_ret:
k_sh = list(sh[:-1]) + list(all_ret[k].shape[2:])
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]
def batchify_rays(rays_flat,label, chunk=1024*32, **kwargs):
"""Render rays in smaller minibatches to avoid OOM.
"""
all_ret = {}
for i in range(0, rays_flat.shape[1], chunk):
#ipdb.set_trace()
ret = render_rays(rays_flat[:,i:i+chunk],label=label, **kwargs)
for k in ret:
if k not in all_ret:
all_ret[k] = []
all_ret[k].append(ret[k])
#ipdb.set_trace()
all_ret = {k : torch.cat(all_ret[k], 1) for k in all_ret}
return all_ret
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.,
label=None,
verbose=False,
pytest=False):
"""Volumetric rendering.
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.
network_fn: function. Model for predicting RGB and density at each point
in space.
network_query_fn: function used for passing queries to network_fn.
N_samples: int. Number of different times to sample along each ray.
retraw: bool. If True, include model's raw, unprocessed predictions.
lindisp: bool. If True, sample linearly in inverse depth rather than in depth.
perturb: float, 0 or 1. If non-zero, each ray is sampled at stratified
random points in time.
N_importance: int. Number of additional times to sample along each ray.
These samples are only passed to network_fine.
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.
"""
B,N_rays,_ = ray_batch.shape
rays_o, rays_d = ray_batch[:,:,0:3], ray_batch[:,:,3:6] # [N_rays, 3] each
viewdirs = ray_batch[:,:,-3:]
bounds = torch.reshape(ray_batch[...,6:8], [B,-1,1,2])
near, far = bounds[...,0], bounds[...,1] # [-1,1]
t_vals = torch.linspace(0., 1., steps=N_samples).to(near.device)
z_vals = near * (1.-t_vals) + far * (t_vals)
#z_vals = z_vals.expand([N_rays, N_samples])
if perturb > 0.:
# get intervals between samples
mids = .5 * (z_vals[...,1:] + z_vals[...,:-1])
upper = torch.cat([mids, z_vals[...,-1:]], -1)
lower = torch.cat([z_vals[...,:1], mids], -1)
# stratified samples in those intervals
t_rand = torch.rand(z_vals.shape)
# Pytest, overwrite u with numpy's fixed random numbers
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] # [N_rays, N_samples, 3]
# raw = run_network(pts)
raw = network_query_fn(pts, viewdirs, label,network_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) # [N_rays]
for k in ret:
if (torch.isnan(ret[k]).any() or torch.isinf(ret[k]).any()):
print(f"! [Numerical Error] {k} contains nan or inf.")
return ret
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.
"""
#ipdb.set_trace()
act_ff=nn.Softplus()
raw2alpha = lambda raw, dists, act_fn=act_ff: 1.-torch.exp(-act_fn(raw)*dists)
dists = z_vals[...,1:] - z_vals[...,:-1]
dists = torch.cat([dists, torch.Tensor([1e10]).to(dists.device).expand(dists[...,:1].shape)], -1) # [N_rays, N_samples]
dists = dists * torch.norm(rays_d[...,None,:], dim=-1)
rgb = torch.sigmoid(raw[...,:3]) # [N_rays, N_samples, 3]
noise = 0.
if raw_noise_std > 0.:
noise = torch.randn(raw[...,3].shape) * raw_noise_std
# Overwrite randomly sampled data if pytest
if pytest:
np.random.seed(0)
noise = np.random.rand(*list(raw[...,3].shape)) * raw_noise_std
noise = torch.Tensor(noise)
#ipdb.set_trace()
alpha = raw2alpha(raw[...,3] + noise, dists) # [N_rays, N_samples]
#ipdb.set_trace()
weights = alpha * torch.cumprod(torch.cat([torch.ones((alpha.shape[0],alpha.shape[1], 1)).to(alpha.device), 1.-alpha + 1e-10], -1), -1)[:,:, :-1]
rgb_map = torch.sum(weights[...,None] * rgb, -2) # [N_rays, 3]
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
def get_rays(H, W, K, c2w):
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)
return rays_o, rays_d
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