63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class TetTexNet(nn.Module):
|
|
def __init__(self, plane_reso=64, padding=0.1, fea_concat=True):
|
|
super().__init__()
|
|
# self.c_dim = c_dim
|
|
self.plane_reso = plane_reso
|
|
self.padding = padding
|
|
self.fea_concat = fea_concat
|
|
|
|
def forward(self, rolled_out_feature, query):
|
|
# rolled_out_feature: rolled-out triplane feature
|
|
# query: queried xyz coordinates (should be scaled consistently to ptr cloud)
|
|
|
|
plane_reso = self.plane_reso
|
|
|
|
triplane_feature = dict()
|
|
triplane_feature['xy'] = rolled_out_feature[:, :, :, 0: plane_reso]
|
|
triplane_feature['yz'] = rolled_out_feature[:, :, :, plane_reso: 2 * plane_reso]
|
|
triplane_feature['zx'] = rolled_out_feature[:, :, :, 2 * plane_reso:]
|
|
|
|
query_feature_xy = self.sample_plane_feature(query, triplane_feature['xy'], 'xy')
|
|
query_feature_yz = self.sample_plane_feature(query, triplane_feature['yz'], 'yz')
|
|
query_feature_zx = self.sample_plane_feature(query, triplane_feature['zx'], 'zx')
|
|
|
|
if self.fea_concat:
|
|
query_feature = torch.cat((query_feature_xy, query_feature_yz, query_feature_zx), dim=1)
|
|
else:
|
|
query_feature = query_feature_xy + query_feature_yz + query_feature_zx
|
|
|
|
output = query_feature.permute(0, 2, 1)
|
|
|
|
return output
|
|
|
|
# uses values from plane_feature and pixel locations from vgrid to interpolate feature
|
|
def sample_plane_feature(self, query, plane_feature, plane):
|
|
# CYF note:
|
|
# for pretraining, query are uniformly sampled positions w.i. [-scale, scale]
|
|
# for training, query are essentially tetrahedra grid vertices, which are
|
|
# also within [-scale, scale] in the current version!
|
|
# xy range [-scale, scale]
|
|
if plane == 'xy':
|
|
xy = query[:, :, [0, 1]]
|
|
elif plane == 'yz':
|
|
xy = query[:, :, [1, 2]]
|
|
elif plane == 'zx':
|
|
xy = query[:, :, [2, 0]]
|
|
else:
|
|
raise ValueError("Error! Invalid plane type!")
|
|
|
|
xy = xy[:, :, None].float()
|
|
# not seem necessary to rescale the grid, because from
|
|
# https://pytorch.org/docs/stable/generated/torch.nn.functional.grid_sample.html,
|
|
# it specifies sampling locations normalized by plane_feature's spatial dimension,
|
|
# which is within [-scale, scale] as specified by encoder's calling of coordinate2index()
|
|
vgrid = 1.0 * xy
|
|
sampled_feat = F.grid_sample(plane_feature, vgrid, padding_mode='border', align_corners=True, mode='bilinear').squeeze(-1)
|
|
|
|
return sampled_feat
|