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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_scatter import scatter_mean, scatter_max
from .unet import UNet
from .resnet_block import ResnetBlockFC
import numpy as np
class DiagonalGaussianDistribution(object):
def __init__(self, mean, logvar, deterministic=False):
self.mean = mean
self.logvar = logvar
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean).to(device=self.mean.device)
def sample(self):
x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.mean.device)
return x
def kl(self, other=None):
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=[1, 2,3])
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=[1, 2, 3])
def nll(self, sample, dims=[1,2,3]):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
class ConvPointnet_Encoder(nn.Module):
''' PointNet-based encoder network with ResNet blocks for each point.
Number of input points are fixed.
Args:
c_dim (int): dimension of latent code c
dim (int): input points dimension
hidden_dim (int): hidden dimension of the network
scatter_type (str): feature aggregation when doing local pooling
unet (bool): weather to use U-Net
unet_kwargs (str): U-Net parameters
plane_resolution (int): defined resolution for plane feature
plane_type (str): feature type, 'xz' - 1-plane, ['xz', 'xy', 'yz'] - 3-plane, ['grid'] - 3D grid volume
padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
n_blocks (int): number of blocks ResNetBlockFC layers
'''
def __init__(self, c_dim=128, dim=3, hidden_dim=128,latent_dim=32, scatter_type='max',
unet=False, unet_kwargs=None,
plane_resolution=None, plane_type=['xz', 'xy', 'yz'], padding=0.1, n_blocks=5):
super().__init__()
self.c_dim = c_dim
self.fc_pos = nn.Linear(dim, 2 * hidden_dim)
self.blocks = nn.ModuleList([
ResnetBlockFC(2 * hidden_dim, hidden_dim) for i in range(n_blocks)
])
self.fc_c = nn.Linear(hidden_dim, c_dim)
self.actvn = nn.ReLU()
self.hidden_dim = hidden_dim
if unet:
self.unet = UNet(unet_kwargs['output_dim'], in_channels=c_dim, **unet_kwargs)
else:
self.unet = None
self.reso_plane = plane_resolution
self.plane_type = plane_type
self.padding = padding
if scatter_type == 'max':
self.scatter = scatter_max
elif scatter_type == 'mean':
self.scatter = scatter_mean
self.mean_fc = nn.Conv2d(unet_kwargs['output_dim'], latent_dim,kernel_size=1)
self.logvar_fc = nn.Conv2d(unet_kwargs['output_dim'], latent_dim,kernel_size=1)
# takes in "p": point cloud and "query": sdf_xyz
# sample plane features for unlabeled_query as well
def forward(self, p,point_emb): # , query2):
batch_size, T, D = p.size()
#print('origin',torch.amin(p[0],dim=0),torch.amax(p[0],dim=0))
# acquire the index for each point
coord = {}
index = {}
if 'xz' in self.plane_type:
coord['xz'] = self.normalize_coordinate(p.clone(), plane='xz', padding=self.padding)
index['xz'] = self.coordinate2index(coord['xz'], self.reso_plane)
if 'xy' in self.plane_type:
coord['xy'] = self.normalize_coordinate(p.clone(), plane='xy', padding=self.padding)
index['xy'] = self.coordinate2index(coord['xy'], self.reso_plane)
if 'yz' in self.plane_type:
coord['yz'] = self.normalize_coordinate(p.clone(), plane='yz', padding=self.padding)
index['yz'] = self.coordinate2index(coord['yz'], self.reso_plane)
net = self.fc_pos(point_emb)
net = self.blocks[0](net)
for block in self.blocks[1:]:
pooled = self.pool_local(coord, index, net)
net = torch.cat([net, pooled], dim=2)
net = block(net)
c = self.fc_c(net)
#print(c.shape)
fea = {}
plane_feat_sum = 0
# second_sum = 0
if 'xz' in self.plane_type:
fea['xz'] = self.generate_plane_features(p, c,
plane='xz') # shape: batch, latent size, resolution, resolution (e.g. 16, 256, 64, 64)
if 'xy' in self.plane_type:
fea['xy'] = self.generate_plane_features(p, c, plane='xy')
if 'yz' in self.plane_type:
fea['yz'] = self.generate_plane_features(p, c, plane='yz')
cat_feature = torch.cat([fea['xz'], fea['xy'], fea['yz']],
dim=2) # concat at row dimension
#print(cat_feature.shape)
plane_feat=self.unet(cat_feature)
mean=self.mean_fc(plane_feat)
logvar=self.logvar_fc(plane_feat)
posterior = DiagonalGaussianDistribution(mean, logvar)
x = posterior.sample()
kl = posterior.kl()
return kl, x, mean, logvar
def normalize_coordinate(self, p, padding=0.1, plane='xz'):
''' Normalize coordinate to [0, 1] for unit cube experiments
Args:
p (tensor): point
padding (float): conventional padding paramter of ONet for unit cube, so [-0.5, 0.5] -> [-0.55, 0.55]
plane (str): plane feature type, ['xz', 'xy', 'yz']
'''
if plane == 'xz':
xy = p[:, :, [0, 2]]
elif plane == 'xy':
xy = p[:, :, [0, 1]]
else:
xy = p[:, :, [1, 2]]
#print("origin",torch.amin(xy), torch.amax(xy))
xy=xy/2 #xy is originally -1 ~ 1
xy_new = xy / (1 + padding + 10e-6) # (-0.5, 0.5)
xy_new = xy_new + 0.5 # range (0, 1)
#print("scale",torch.amin(xy_new),torch.amax(xy_new))
# f there are outliers out of the range
if xy_new.max() >= 1:
xy_new[xy_new >= 1] = 1 - 10e-6
if xy_new.min() < 0:
xy_new[xy_new < 0] = 0.0
return xy_new
def coordinate2index(self, x, reso):
''' Normalize coordinate to [0, 1] for unit cube experiments.
Corresponds to our 3D model
Args:
x (tensor): coordinate
reso (int): defined resolution
coord_type (str): coordinate type
'''
x = (x * reso).long()
index = x[:, :, 0] + reso * x[:, :, 1]
index = index[:, None, :]
return index
# xy is the normalized coordinates of the point cloud of each plane
# I'm pretty sure the keys of xy are the same as those of index, so xy isn't needed here as input
def pool_local(self, xy, index, c):
bs, fea_dim = c.size(0), c.size(2)
keys = xy.keys()
c_out = 0
for key in keys:
# scatter plane features from points
fea = self.scatter(c.permute(0, 2, 1), index[key], dim_size=self.reso_plane ** 2)
if self.scatter == scatter_max:
fea = fea[0]
# gather feature back to points
fea = fea.gather(dim=2, index=index[key].expand(-1, fea_dim, -1))
c_out += fea
return c_out.permute(0, 2, 1)
def generate_plane_features(self, p, c, plane='xz'):
# acquire indices of features in plane
xy = self.normalize_coordinate(p.clone(), plane=plane, padding=self.padding) # normalize to the range of (0, 1)
index = self.coordinate2index(xy, self.reso_plane)
# scatter plane features from points
fea_plane = c.new_zeros(p.size(0), self.c_dim, self.reso_plane ** 2)
c = c.permute(0, 2, 1) # B x 512 x T
fea_plane = scatter_mean(c, index, out=fea_plane) # B x 512 x reso^2
fea_plane = fea_plane.reshape(p.size(0), self.c_dim, self.reso_plane,
self.reso_plane) # sparce matrix (B x 512 x reso x reso)
#print(fea_plane.shape)
return fea_plane
# sample_plane_feature function copied from /src/conv_onet/models/decoder.py
# uses values from plane_feature and pixel locations from vgrid to interpolate feature
def sample_plane_feature(self, query, plane_feature, plane):
xy = self.normalize_coordinate(query.clone(), plane=plane, padding=self.padding)
xy = xy[:, :, None].float()
vgrid = 2.0 * xy - 1.0 # normalize to (-1, 1)
sampled_feat = F.grid_sample(plane_feature, vgrid, padding_mode='border', align_corners=True,
mode='bilinear').squeeze(-1)
return sampled_feat
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