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"""
Taken from gensdf
https://github.com/princeton-computational-imaging/gensdf
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
# from dnnlib.util import printarr
try:
from torch_scatter import scatter_mean, scatter_max
except:
pass
# from .unet import UNet
import torch
import torch.nn as nn
import torch.nn.functional as F
# Resnet Blocks
class ResnetBlockFC(nn.Module):
''' Fully connected ResNet Block class.
Args:
size_in (int): input dimension
size_out (int): output dimension
size_h (int): hidden dimension
'''
def __init__(self, size_in, size_out=None, size_h=None):
super().__init__()
# Attributes
if size_out is None:
size_out = size_in
if size_h is None:
size_h = min(size_in, size_out)
self.size_in = size_in
self.size_h = size_h
self.size_out = size_out
# Submodules
self.fc_0 = nn.Linear(size_in, size_h)
self.fc_1 = nn.Linear(size_h, size_out)
self.actvn = nn.ReLU()
if size_in == size_out:
self.shortcut = None
else:
self.shortcut = nn.Linear(size_in, size_out, bias=False)
# Initialization
nn.init.zeros_(self.fc_1.weight)
def forward(self, x):
net = self.fc_0(self.actvn(x))
dx = self.fc_1(self.actvn(net))
if self.shortcut is not None:
x_s = self.shortcut(x)
else:
x_s = x
return x_s + dx
class ConvPointnet(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, 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(c_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
# takes in "p": point cloud and "query": sdf_xyz
# sample plane features for unlabeled_query as well
def forward(self, p):#, query2):
batch_size, T, D = p.size()
# 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(p)
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)
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)
# plane_feat_sum += self.sample_plane_feature(query, fea['xz'], 'xz')
#second_sum += self.sample_plane_feature(query2, fea['xz'], 'xz')
if 'xy' in self.plane_type:
fea['xy'] = self.generate_plane_features(p, c, plane='xy')
# plane_feat_sum += self.sample_plane_feature(query, fea['xy'], 'xy')
#second_sum += self.sample_plane_feature(query2, fea['xy'], 'xy')
if 'yz' in self.plane_type:
fea['yz'] = self.generate_plane_features(p, c, plane='yz')
# plane_feat_sum += self.sample_plane_feature(query, fea['yz'], 'yz')
#second_sum += self.sample_plane_feature(query2, fea['yz'], 'yz')
return fea
# return plane_feat_sum.transpose(2,1)#, second_sum.transpose(2,1)
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]]
xy_new = xy / (1 + padding + 10e-6) # (-0.5, 0.5)
xy_new = xy_new + 0.5 # range (0, 1)
# 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)
# printarr(fea_plane, c, p, xy, index)
# import pdb; pdb.set_trace()
# process the plane features with UNet
# if self.unet is not None:
# fea_plane = self.unet(fea_plane)
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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