def forward(self, z):
# x: SparseTensor z: PointTensor
x0 = initial_voxelize(z, self.pres, self.vres)
x0 = self.stem(x0)
z0 = voxel_to_point(x0, z, nearest=False)
z0.F = z0.F
x1 = point_to_voxel(x0, z0)
x1 = self.stage1(x1)
x2 = self.stage2(x1)
z1 = voxel_to_point(x2, z0)
z1.F = z1.F + self.point_transforms[0](z0.F)
y3 = point_to_voxel(x2, z1)
if self.dropout:
y3.F = self.dropout(y3.F)
y3 = self.up1[0](y3)
y3 = torchsparse.cat([y3, x1])
y3 = self.up1[1](y3)
y4 = self.up2[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.up2[1](y4)
z3 = voxel_to_point(y4, z1)
z3.F = z3.F + self.point_transforms[1](z1.F)
return z3.F
def forward(self, x):
x0 = self.stem(x)
x1 = self.stage1(x0)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
y1 = self.up1[0](x4)
y1 = torchsparse.cat([y1, x3])
y1 = self.up1[1](y1)
y2 = self.up2[0](y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.up2[1](y2)
y3 = self.up3[0](y2)
y3 = torchsparse.cat([y3, x1])
y3 = self.up3[1](y3)
y4 = self.up4[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.up4[1](y4)
out = self.classifier(y4.F)
return out
def forward(self, x):
# x: SparseTensor z: PointTensor
z = PointTensor(x.F, x.C.float())
#x0 = initial_voxelize(z, self.pres, self.vres)
x0 = point_to_voxel(x, z)
x0 = self.stem(x0)
z0 = voxel_to_point(x0, z)
z0.F = z0.F #+ self.point_transforms[0](z.F)
x1 = point_to_voxel(x0, z0)
x1 = self.downsample[0](x1)
x2 = self.downsample[1](x1)
x3 = self.downsample[2](x2)
x4 = self.downsample[3](x3)
# point transform 32 to 256
z1 = voxel_to_point(x4, z0)
z1.F = z1.F + self.point_transforms[0](z0.F)
y1 = point_to_voxel(x4, z1)
y1.F = self.dropout(y1.F)
y1 = self.upsample[0].transition(y1)
y1 = torchsparse.cat([y1, x3])
y1 = self.upsample[0].feature(y1)
#print('y1', y1.C)
y2 = self.upsample[1].transition(y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.upsample[1].feature(y2)
# point transform 256 to 128
z2 = voxel_to_point(y2, z1)
z2.F = z2.F + self.point_transforms[1](z1.F)
y3 = point_to_voxel(y2, z2)
y3.F = self.dropout(y3.F)
y3 = self.upsample[2].transition(y3)
y3 = torchsparse.cat([y3, x1])
y3 = self.upsample[2].feature(y3)
y4 = self.upsample[3].transition(y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.upsample[3].feature(y4)
z3 = voxel_to_point(y4, z2)
z3.F = z3.F + self.point_transforms[2](z2.F)
self.classifier.set_in_channel(z3.F.shape[-1])
out = self.classifier(z3.F)
return out
def embedder(self, y3, x0, z2):
y4 = self.e_up4[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.e_up4[1](y4)
z3 = voxel_to_point(y4, z2)
z3.F = z3.F + self.e_point_transform(z2.F)
return self.e_lin(z3.F)
def forward(self, x):
# x: SparseTensor z: PointTensor
z = PointTensor(x.F, x.C.float())
x0 = initial_voxelize(z, self.hparams.model.pres,
self.hparams.model.vres)
x0 = self.stem(x0)
z0 = voxel_to_point(x0, z, nearest=False)
z0.F = z0.F
x1 = point_to_voxel(x0, z0)
x1 = self.stage1(x1)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
z1 = voxel_to_point(x4, z0)
z1.F = z1.F + self.point_transforms[0](z0.F)
y1 = point_to_voxel(x4, z1)
y1.F = self.dropout(y1.F)
y1 = self.up1[0](y1)
y1 = torchsparse.cat([y1, x3])
y1 = self.up1[1](y1)
y2 = self.up2[0](y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.up2[1](y2)
z2 = voxel_to_point(y2, z1)
z2.F = z2.F + self.point_transforms[1](z1.F)
y3 = point_to_voxel(y2, z2)
y3.F = self.dropout(y3.F)
y3 = self.up3[0](y3)
y3 = torchsparse.cat([y3, x1])
y3 = self.up3[1](y3)
task = self.hparams.task
if task == 'semantic':
out = self.classifier(y3, x0, z2)
elif task == 'instance':
out = self.embedder(y3, x0, z2)
elif task == 'panoptic':
out = (self.classifier(y3, x0, z2), self.embedder(y3, x0, z2))
else:
raise RuntimeError("invalid task!")
return out
def forward(self, x):
# x: SparseTensor z: PointTensor
z = PointTensor(x.F, x.C.float())
x0 = initial_voxelize(z, self.hparams.model.pres,
self.hparams.model.vres)
x0 = self.stem(x0)
z0 = voxel_to_point(x0, z, nearest=False)
z0.F = z0.F
x1 = point_to_voxel(x0, z0)
x1 = self.stage1(x1)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
z1 = voxel_to_point(x4, z0)
z1.F = z1.F + self.point_transforms[0](z0.F)
y1 = point_to_voxel(x4, z1)
y1.F = self.dropout(y1.F)
y1 = self.up1[0](y1)
y1 = torchsparse.cat([y1, x3])
y1 = self.up1[1](y1)
y2 = self.up2[0](y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.up2[1](y2)
z2 = voxel_to_point(y2, z1)
z2.F = z2.F + self.point_transforms[1](z1.F)
y3 = point_to_voxel(y2, z2)
y3.F = self.dropout(y3.F)
y3 = self.up3[0](y3)
y3 = torchsparse.cat([y3, x1])
y3 = self.up3[1](y3)
task = self.hparams.task
out = {}
if task == 'semantic' or task == 'panoptic':
out['pred_semantic_scores'] = self.classifier(y3, x0, z2)
out['pred_semantic_labels'] = out['pred_semantic_scores'].argmax(
dim=1)
if task == 'instance' or task == 'panoptic':
out['pred_offsets'] = self.embedder(y3, x0, z2)
return out
def forward(self, x):
# x: SparseTensor z: PointTensor
z = PointTensor(x.F, x.C.float())
x0 = initial_voxelize(z, 1.0, self.vres)
x0 = self.stem(x0)
z0 = voxel_to_point(x0, z, nearest=False)
z0.F = z0.F
x1 = point_to_voxel(x0, z0)
x1 = self.stage1(x1)
x2 = self.stage2(x1)
x3 = self.stage3(x2)
x4 = self.stage4(x3)
z1 = voxel_to_point(x4, z0)
z1.F = z1.F + self.point_transforms[0](z0.F)
y1 = point_to_voxel(x4, z1)
y1.F = self.dropout(y1.F)
y1 = self.up1[0](y1)
y1 = torchsparse.cat([y1, x3])
y1 = self.up1[1](y1)
y2 = self.up2[0](y1)
y2 = torchsparse.cat([y2, x2])
y2 = self.up2[1](y2)
z2 = voxel_to_point(y2, z1)
z2.F = z2.F + self.point_transforms[1](z1.F)
y3 = point_to_voxel(y2, z2)
y3.F = self.dropout(y3.F)
y3 = self.up3[0](y3)
y3 = torchsparse.cat([y3, x1])
y3 = self.up3[1](y3)
y4 = self.up4[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.up4[1](y4)
z3 = voxel_to_point(y4, z2)
z3.F = z3.F + self.point_transforms[2](z2.F)
self.output = self.classifier(z3.F)
return self.output
def embedder(self, y3, x0, z2):
y4 = self.e_up4[0](y3)
y4 = torchsparse.cat([y4, x0])
y4 = self.e_up4[1](y4)
z3 = voxel_to_point(y4, z2)
z3.F = z3.F + self.e_point_transform(z2.F)
out = self.e_lin(z3.F)
if 'tanh_scale' in self.hparams.model and self.hparams.model.tanh_scale is not None:
out = torch.tanh(out) * self.hparams.model.tanh_scale
return out
def cat(*args, dim=1):
return TS.cat(args, dim)
def cat(*args):
return TS.cat(args)